U.S. patent application number 13/827746 was filed with the patent office on 2014-05-08 for systems and methods for expert systems for underbalanced drilling operations using bayesian decision networks.
The applicant listed for this patent is Saudi Arabian Oil Company. Invention is credited to Abdullah Saleh Hussain Al-Yami, Jerome Schubert.
Application Number | 20140124265 13/827746 |
Document ID | / |
Family ID | 50621319 |
Filed Date | 2014-05-08 |
United States Patent
Application |
20140124265 |
Kind Code |
A1 |
Al-Yami; Abdullah Saleh Hussain ;
et al. |
May 8, 2014 |
SYSTEMS AND METHODS FOR EXPERT SYSTEMS FOR UNDERBALANCED DRILLING
OPERATIONS USING BAYESIAN DECISION NETWORKS
Abstract
Systems and methods are provided for an underbalanced drilling
(UBD) expert system that provides underbalanced drilling
recommendations, such as best practices. The UBD expert system may
include one or more Bayesian decision network (BDN) model that
receive inputs and output recommendations based on Bayesian
probability determinations. The BDN models may include: a general
UBD BDN model, a flow UBD BDN model, a gaseated (i.e., aerated) UBD
BDN model, a foam UBD BDN model, a gas (e.g., air or other gases)
UBD BDN model, a mud cap UBD BDN model, an underbalanced liner
drilling (UBLD) BDN model, an underbalanced coil tube (UBCT) BDN
model, and a snubbing and stripping BDN model.
Inventors: |
Al-Yami; Abdullah Saleh
Hussain; (Dhahran, SA) ; Schubert; Jerome;
(College Station, TX) |
|
Applicant: |
Name |
City |
State |
Country |
Type |
Saudi Arabian Oil Company |
Dhahran |
|
SA |
|
|
Family ID: |
50621319 |
Appl. No.: |
13/827746 |
Filed: |
March 14, 2013 |
Related U.S. Patent Documents
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Application
Number |
Filing Date |
Patent Number |
|
|
61722027 |
Nov 2, 2012 |
|
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Current U.S.
Class: |
175/24 |
Current CPC
Class: |
G06N 7/005 20130101;
E21B 44/00 20130101 |
Class at
Publication: |
175/24 |
International
Class: |
E21B 44/00 20060101
E21B044/00 |
Claims
1. A system, comprising: one or more processors; a non-transitory
tangible computer-readable memory, the memory comprising: an
underbalanced drilling expert system executable by the one or more
processors and configured to provide one or more underbalanced
drilling recommendations based on one or more inputs, the
underbalanced drilling expert system comprising an underbalanced
drilling Bayesian decision network (BDN) model, the underbalanced
drilling BDN model comprising: a first section, comprising: a
formation indicators uncertainty node configured to receive one or
more formation indicators from the one or more inputs; a formation
considerations decision node configured to receive one or more
formation considerations from the one or more inputs; and a first
consequences node dependent on the formation indicators uncertainty
node and the formation considerations decision node and configured
to output the one or more underbalanced drilling recommendations
based on one or more Bayesian probabilities calculated from one or
more formation indicators and the one or more formation
considerations; a second section, comprising: a planning phases
uncertainty node configured to receive one or more planning phases
from the one or more inputs; a planning phases recommendations
decision node configured to receive one or more planning phases
recommendations from the one or more inputs; and a second
consequences node dependent on the planning phases uncertainty node
and the planning phases recommendations decision node and
configured to output the one or more underbalanced drilling
recommendations based on one or more Bayesian probabilities
calculated from one or more planning phases and the one or more
planning phases recommendations; and a third section, comprising:
an equipment requirements uncertainty node configured to receive
one or more equipment requirements from the one or more inputs; an
equipment recommendations decision node configured to receive one
or more equipment recommendations from the one or more inputs; and
a third consequences node dependent on the equipment requirements
uncertainty node and the equipment recommendations decision node
and configured to output the one or more underbalanced drilling
recommendations based on one or more Bayesian probabilities
calculated from the one or more equipment requirements and the one
or more equipment recommendations.
2. The system of claim 1, wherein the UBD BDN model comprises a
final consequences node dependent on the first consequences node,
the second consequences, and the third consequences node.
3. The system of claim 1, comprising a user interface configured to
display the UBD BDN model and receive user selections of the one or
more inputs.
4. The system of claim 1, wherein the one or more formation
indicators, the one or more planning phases, and the one or more
equipment requirements are each associated with a respective
plurality of probabilities.
5. A computer-implemented method for an underbalanced drilling
expert system having an underbalanced drilling Bayesian decision
network (BDN) model, the method comprising: receiving one or more
inputs; providing the one or more nodes of a first section of the
underbalanced drilling BDN model, the one or more nodes comprising:
a formation indicators uncertainty node; a formation considerations
decision node; determining one or more underbalanced drilling
recommendations at a consequences node of the first section of the
underbalanced drilling BDN model, the determination comprising a
calculation of one or more Bayesian probabilities based on the one
or more inputs; providing the one or more underbalanced drilling
recommendations to a user.
6. The method of claim 5, wherein providing the one or more
underbalanced drilling recommendations to a user comprises
displaying the one or more underbalanced drilling recommendations
in a user interface element of a user interface configured to
display the UBD BDN model.
7. The computer-implemented method of claim 5, comprising:
providing the one or more inputs to one or more nodes of a second
section of the underbalanced drilling BDN model, the one or more
nodes comprising: a planning phases uncertainty node configured to
receive one or more planning phases; a planning phases
recommendations decision node configured to receive one or more
planning phases recommendations; determining the one or more
underbalanced drilling recommendations at a second consequences
node of the second section of the underbalanced drilling BDN model,
the determination comprising a calculation of one or more Bayesian
probabilities based on the one or more inputs.
8. The computer-implemented method of claim 7, comprising:
providing the one or more inputs to one or more nodes of a third
section of the underbalanced drilling BDN model, the one or more
nodes comprising: an equipment requirements uncertainty node
configured to receive one or more equipment requirements; an
equipment recommendations decision node configured to receive one
or more equipment recommendations; determining the one or more
underbalanced drilling recommendations at a third consequences node
of the third section of the underbalanced drilling BDN model, the
determination comprising a calculation of one or more Bayesian
probabilities based on the one or more inputs.
9. A system, comprising: one or more processors; a non-transitory
tangible computer-readable memory, the memory comprising: an
underbalanced drilling expert system executable by the one or more
processors and configured to provide one or more flow underbalanced
drilling recommendations based on one or more inputs, the flow
underbalanced drilling expert system comprising a flow
underbalanced drilling Bayesian decision network (BDN) model, the
flow underbalanced drilling BDN model comprising: a first section,
comprising: a tripping types uncertainty node configured to receive
one or more tripping types from the one or more inputs; a
permeability level uncertainty node configured to receive one or
more permeability levels from the one or more inputs; a tripping
options decision node configured to receive one or more tripping
options from the one or more inputs; a first consequences node
dependent on the tripping uncertainty node, the permeability level
uncertainty node, and the tripping options decision node and
configured to output the one or more flow underbalanced drilling
recommendations based on one or more Bayesian probabilities
calculated from the one or more tripping types, the one or more
permeability levels, and the one or more tripping options; a second
section, comprising: a connection types uncertainty node configured
to receive one or more connection types from the one or more
inputs; a connection options decision node configured to receive
one or more connection options from the one or more inputs; a
second consequences node dependent on the connection uncertainty
node and the connection options decision node and configured to
output the one or more flow underbalanced drilling recommendations
based on one or more Bayesian probabilities calculated from the one
or more connection types and the one or more connection options; a
third section, comprising: a flow drilling types uncertainty node
configured to receive one or more flow drilling types from the one
or more inputs; a flow drilling options decision node configured to
receive one or more flow drilling options from the one or more
inputs; a third consequences node dependent on the flow drilling
uncertainty node and the flow drilling options decision node and
configured to output the one or more flow underbalanced drilling
recommendations based on one or more Bayesian probabilities
calculated from the one or more flow drilling types and the one or
more flow drilling options.
10. The system of claim 9, wherein the flow UBD BDN model comprises
a final consequences node dependent on the first consequences node,
the second consequences, and the third consequences node.
11. The system of claim 9, comprising a user interface configured
to display the flow UBD BDN model and receive user selections of
the one or more inputs.
12. The system of claim 9, wherein the one or more tripping types,
the one or more permeability levels, the one or connection types,
and the one or more flow drilling types are each associated with a
respective plurality of probabilities.
13. A computer-implemented method for an underbalanced drilling
expert system having a flow underbalanced drilling (UBD) Bayesian
decision network (BDN) model, the method comprising: receiving one
or more inputs; providing the one or more inputs to one or more
nodes of a first section of the flow underbalanced drilling BDN
model, the one or more nodes comprising: a tripping uncertainty
node configured to receive one or more tripping types; a
permeability level uncertainty node configured to receive one or
more permeability levels; and a tripping options decision node a
tripping options decision node configured to receive one or more
tripping options; determining one or more underbalanced drilling
recommendations at a consequences node of the first section of the
flow underbalanced drilling BDN model, the determination comprising
a calculation of one or more Bayesian probabilities based on the
one or more inputs; providing the one or more underbalanced
drilling recommendations to a user.
14. The computer-implemented method of claim 13, wherein providing
the one or more underbalanced drilling recommendations to a user
comprises displaying the one or more underbalanced drilling
recommendations in a user interface element of a user interface
configured to display the flow UBD BDN model.
15. The computer-implemented method of claim 13, comprising:
providing the one or more inputs to one or more nodes of a second
section of the flow UBD BDN model, the one or more nodes
comprising: a connection types uncertainty node configured to
receive one or more connection types; a connection options decision
node configured to receive one or more connection options;
determining the one or more underbalanced drilling recommendations
at a second consequences node of the second section of the
underbalanced drilling BDN model, the determination comprising a
calculation of one or more Bayesian probabilities based on the one
or more inputs.
16. The computer-implemented method of claim 15, comprising:
providing the one or more inputs to one or more nodes of a third
section of the flow UBD BDN model, the one or more nodes
comprising: a flow drilling types uncertainty node configured to
receive one or more flow drilling types; a flow drilling options
decision node configured to receive one or more flow drilling
options; determining the one or more underbalanced drilling
recommendations at a third consequences node of the third section
of the flow UBD BDN model, the determination comprising a
calculation of one or more Bayesian probabilities based on the one
or more inputs.
17. A system, comprising: one or more processors; a non-transitory
tangible computer-readable memory accessible by the one or more
processors, the memory comprising: an underbalanced drilling expert
system executable by the one or more processors and configured to
provide one or more underbalanced drilling recommendations based on
one or more inputs, the underbalanced drilling expert system
comprising a gaseated underbalanced drilling Bayesian decision
network (BDN) model, the gaseated underbalanced drilling BDN model
comprising: a first section, comprising: a gas injection process
uncertainty node configured to receive one or more gas injection
process types from the one or more inputs; a gas injection
processes characteristics decision node configured to receive one
or more gas injection process characteristics from the one or more
inputs; a first consequences node dependent on the gas injection
process uncertainty node and the gas infection processes
considerations decision node and configured to output the one or
more gaseated underbalanced drilling recommendations based on one
or more Bayesian probabilities calculated from the one or more gas
injection process types and the one or more gas injection process
characteristics; a second section, comprising: a fluid volume
limits uncertainty node configured to receive one or more fluid
volume limits from the one or more inputs; a fluid volume limits
requirements decision node configured to receive one or more fluid
volume limits requirements from the one or more inputs; a second
consequences node dependent on the fluid volume limits uncertainty
node and the fluid volume limits requirements decision node and
configured to output the one or more gaseated underbalanced
drilling recommendations based on one or more Bayesian
probabilities calculated from the one or more fluid volume limits
and the one or more fluid volume limits requirements; a third
section, comprising: a kick type uncertainty node configured to
receive one or more kick types from the one or more inputs; a kicks
recommendations decision node configured to receive one or more
kicks recommendations from the one or more inputs; a third
consequences node dependent on the kick type uncertainty node and
the kicks recommendations decision node and configured to output
the one or more gaseated underbalanced drilling recommendations
based on one or more Bayesian probabilities calculated from the one
or more kick types and the one or more kicks recommendations; and a
fourth section, comprising: an operational considerations
uncertainty node configured to receive one or more operational
considerations from the one or more inputs; an operational
recommendations decision node configured to receive one or more
operational recommendations from the one or more inputs; and a
fourth consequences node dependent on the operational
considerations uncertainty node and the operational recommendations
decision node and configured to output the one or more gaseated
underbalanced drilling recommendations based on one or more
Bayesian probabilities calculated from the one or more operational
recommendations and the one or more operational
recommendations.
18. The system of claim 17, wherein the gaseated UBD BDN model
comprises a final consequences node dependent on the first
consequences node, the second consequences, the third consequences
node, and the fourth consequences node.
19. The system of claim 17, comprising a user interface configured
to display the gaseated UBD BDN model and receive user selections
of the one or more inputs.
20. The system of claim 17, wherein the one or more gas injection
process types, the one or more fluid volume limits, the one or kick
types, and the one or more operational considerations are each
associated with a respective plurality of probabilities
21. A computer-implemented method for an underbalanced drilling
expert system having a gaseated underbalanced drilling Bayesian
decision network (BDN) model, the method comprising: receiving one
or more inputs; providing the one or more inputs to one or more
nodes of a first section of the gaseated underbalanced drilling
(UBD) BDN model, the one or more nodes comprising: a gas injection
process uncertainty node configured to receive one or more gas
injection process types; a gas injection processes characteristics
decision node configured to receive one or more gas injection
process characteristics; determining one or more underbalanced
drilling recommendations at a consequences node of the first
section of the gaseated UBD BDN model, the determination comprising
a calculation of one or more Bayesian probabilities based on the
one or more inputs; providing the one or more underbalanced
drilling recommendations to a user.
22. The computer-implemented method of claim 21, wherein providing
the one or more underbalanced drilling recommendations to a user
comprises displaying the one or more underbalanced drilling
recommendations in a user interface element of a user interface
configured to display the gaseated UBD BDN model.
23. The computer-implemented method of claim 21, comprising:
providing the one or more inputs to one or more nodes of a second
section of the gaseated UBD BDN model, the one or more nodes
comprising: a fluid volume limits uncertainty node configured to
receive one or more fluid volume limits from the one or more
inputs; a fluid volume limits requirements decision node configured
to receive one or more fluid volume limits requirements;
determining the one or more underbalanced drilling recommendations
at a second consequences node of the second section of the gaseated
UBD BDN model, the determination comprising a calculation of one or
more Bayesian probabilities based on the one or more inputs.
24. The computer-implemented method of claim 23, comprising:
providing the one or more inputs to one or more nodes of a third
section of the gaseated UBD BDN model, the one or more nodes
comprising: a kick type uncertainty node configured to receive one
or more kick types; a kicks recommendations decision node
configured to receive one or more kicks recommendations;
determining the one or more underbalanced drilling recommendations
at a third consequences node of the third section of the gaseated
UBD BDN model, the determination comprising a calculation of one or
more Bayesian probabilities based on the one or more inputs.
25. The computer-implemented method of claim 24, comprising:
providing the one or more inputs to one or more nodes of a fourth
section of the gaseated UBD BDN model, the one or more nodes
comprising: an operational considerations uncertainty node
configured to receive one or more operational considerations; an
operational recommendations decision node configured to receive one
or more operational recommendations; determining the one or more
underbalanced drilling recommendations at a fourth consequences
node of the third section of the gaseated UBD BDN model, the
determination comprising a calculation of one or more Bayesian
probabilities based on the one or more inputs.
26. A system, comprising: one or more processors; a non-transitory
tangible computer-readable memory accessible by the one or more
processors, the memory comprising: an underbalanced drilling expert
system executable by the one or more processors and configured to
provide one or more underbalanced drilling recommendations based on
one or more inputs, the underbalanced drilling expert system
comprising a foam underbalanced drilling (UBD) Bayesian decision
network (BDN) model, the foam UBD BDN model comprising: a first
section, comprising: a foam systems considerations uncertainty node
configured to receive one or more foam systems considerations from
the one or more inputs; a foam systems recommendations decision
node configured to receive one or more foam systems recommendations
from the one or more inputs; and a first consequences node
dependent on the foam systems considerations uncertainty node and
the foam systems recommendations decision node and configured to
output the one or more underbalanced drilling recommendations based
on one or more Bayesian probabilities calculated from the one or
more foam systems considerations and the one or more foam systems
recommendations; and a second section, comprising: a foam systems
designs uncertainty node configured to receive one or more foam
system designs from the one or more inputs; a foam system designs
recommendations decision node configured to receive one or more
foam system designs recommendations from the one or more inputs;
and a second consequences node dependent on the foam systems
designs uncertainty node and the foam system designs
recommendations decision node and configured to output the one or
more underbalanced drilling recommendations based on one or more
Bayesian probabilities calculated from the one or more foam system
designs and the one or more foam system designs
recommendations.
27. The system of claim 26, wherein the foam UBD BDN model
comprises a final consequences node dependent on the first
consequences node and the second consequences node.
28. The system of claim 26, comprising a user interface configured
to display the foam UBD BDN model and receive user selections of
the one or more inputs.
29. The system of claim 26, wherein the one or more foam systems
considerations and the one or more foam systems designs are each
associated with a respective plurality of probabilities.
30. A computer-implemented method for an underbalanced drilling
expert system having a foam underbalanced drilling (UBD) Bayesian
decision network (BDN) model, the method comprising: receiving one
or more inputs; providing the one or more inputs to one or more
nodes of a first section of the foam UBD BDN model, the one or more
nodes comprising: a foam systems considerations uncertainty node
configured to receive one or more foam systems considerations; and
a foam systems recommendations decision node configured to receive
one or more foam systems recommendations; determining one or more
underbalanced drilling recommendations at a consequences node of
the first section of the foam UBD BDN model, the determination
comprising a calculation of one or more Bayesian probabilities
based on the one or more inputs; and providing the one or more
underbalanced drilling recommendations to a user.
31. The computer-implemented method of claim 30, wherein providing
the one or more underbalanced drilling recommendations to a user
comprises displaying the one or more underbalanced drilling
recommendations in a user interface element of a user interface
configured to display the foam UBD BDN model.
32. The computer-implemented method of claim 30, comprising:
providing the one or more inputs to one or more nodes of a second
section of the foam UBD BDN model, the one or more nodes
comprising: a foam systems designs uncertainty node configured to
receive one or more foam system designs; a foam system designs
recommendations decision node configured to receive one or more
foam system designs recommendations; determining the one or more
underbalanced drilling recommendations at a second consequences
node of the second section of the foam UBD BDN model, the
determination comprising a calculation of one or more Bayesian
probabilities based on the one or more inputs.
33. A system, comprising: one or more processors; a non-transitory
tangible computer-readable memory, the memory comprising: an
underbalanced drilling expert system executable by the one or more
processors and configured to provide one or more underbalanced
drilling recommendations based on one or more inputs, the
underbalanced drilling expert system comprising a gas underbalanced
drilling (UBD) Bayesian decision network (BDN) model, the gas
underbalanced drilling BDN model comprising: a first section,
comprising: a rotary and hammer drilling uncertainty node
configured to receive one or more rotary and hammer drilling types
from the one or more inputs; a rotary and hammer drilling
recommendations decision node configured to receive one or more
rotary and hammer drilling recommendations from the one or more
inputs; and a first consequences node dependent on the rotary and
hammer drilling uncertainty node and the rotary and hammer drilling
recommendations decision node and configured to output the one or
more air and gas underbalanced drilling recommendations based on
one or more Bayesian probabilities calculated from the one or more
rotary and hammer drilling types and the one or more rotary and
hammer drilling recommendations; a second section, comprising: a
gas drilling considerations uncertainty node configured to receive
one or more gas drilling considerations from the one or more
inputs; a gas drilling considerations recommendations decision node
configured to receive gas drilling considerations recommendations
from the one or more inputs; and a second consequences node
dependent on the gas drilling considerations uncertainty node and
the gas drilling considerations recommendations decision node and
configured to output the one or more air and gas underbalanced
drilling recommendations based on one or more Bayesian
probabilities calculated from the one or more gas drilling
considerations and the one or more gas drilling considerations
recommendations; a third section, comprising: a gas drilling
operations uncertainty node configured to receive one or more gas
drilling operations from the one or more inputs; a gas drilling
operations recommendations decision node configured to receive gas
drilling operations recommendations from the one or more inputs;
and a third consequences node dependent on the gas drilling
operations uncertainty node and the gas drilling operations
recommendations decision node and configured to output the one or
more air and gas underbalanced drilling recommendations based on
one or more Bayesian probabilities calculated from the one or more
gas drilling operations and the one or more gas drilling operations
recommendations; and a fourth section, comprising: a gas drilling
rig equipment uncertainty node configured to receive one or more
gas drilling rig equipment from the one or more inputs; a gas
drilling rig equipment recommendations decision node configured to
receive gas drilling rig equipment recommendations from the one or
more inputs; and a fourth consequences node dependent on the gas
drilling rig equipment uncertainty node and the gas drilling rig
equipment recommendations decision node and configured to output
the one or more air and gas underbalanced drilling recommendations
based on one or more Bayesian probabilities calculated from the one
or more gas drilling rig equipment and the one or more gas drilling
rig equipment recommendations.
34. The system of claim 33, wherein the gas UBD BDN model comprises
a final consequences node dependent on the first consequences node,
the second consequences node, the third consequences node, and the
fourth consequences node.
35. The system of claim 33, comprising a user interface configured
to display the gas UBD BDN model and receive user selections of the
one or more inputs.
36. The system of claim 33, wherein the one or more rotary and
hammer drilling types, the one or more gas drilling considerations,
the one or more gas drilling operations, and the one or more gas
drilling rig equipment are each associated with a respective
plurality of probabilities.
37. A computer-implemented method for an underbalanced drilling
expert system having a gas underbalanced drilling (UBD) Bayesian
decision network (BDN) model, the method comprising: receiving one
or more inputs; providing the one or more inputs to one or more
nodes of a first section of the gas underbalanced drilling BDN
model, the one or more nodes comprising: a rotary and hammer
drilling uncertainty node; a rotary and hammer recommendations
decision node; determining one or more underbalanced drilling
recommendations at a consequences node of the first section of the
gas underbalanced drilling BDN model, the determination comprising
a calculation of one or more Bayesian probabilities based on the
one or more inputs; providing the one or more underbalanced
drilling recommendations to a user.
38. The computer-implemented method of claim 37, wherein providing
the one or more underbalanced drilling recommendations to a user
comprises displaying the one or more underbalanced drilling
recommendations in a user interface element of a user interface
configured to display the gas UBD BDN model.
39. The computer-implemented method of claim 37, comprising:
providing the one or more inputs to one or more nodes of a second
section of the gas UBD BDN model, the one or more nodes comprising:
a gas drilling considerations uncertainty node configured to
receive one or more gas drilling considerations; a gas drilling
considerations recommendations decision node configured to receive
gas drilling considerations recommendations; determining the one or
more underbalanced drilling recommendations at a second
consequences node of the second section of the gas UBD BDN model,
the determination comprising a calculation of one or more Bayesian
probabilities based on the one or more inputs.
40. The computer-implemented method of claim 39, comprising:
providing the one or more inputs to one or more nodes of a third
section of the gas UBD BDN model, the one or more nodes comprising:
a gas drilling operations uncertainty node configured to receive
one or more gas drilling operations; a gas drilling operations
recommendations decision node configured to receive gas drilling
operations recommendations; determining the one or more
underbalanced drilling recommendations at a second consequences
node of the third section of the gas UBD BDN model, the
determination comprising a calculation of one or more Bayesian
probabilities based on the one or more inputs.
41. The computer-implemented method of claim 40, comprising:
providing the one or more inputs to one or more nodes of a fourth
section of the gas UBD BDN model, the one or more nodes comprising:
a gas drilling rig equipment uncertainty node configured to receive
one or more gas drilling rig equipment from the one or more inputs;
a gas drilling rig equipment recommendations decision node
configured to receive gas drilling rig equipment recommendations;
determining the one or more underbalanced drilling recommendations
at a fourth consequences node of the fourth section of the gas UBD
BDN model, the determination comprising a calculation of one or
more Bayesian probabilities based on the one or more inputs.
42. A system, comprising, one or more processors; a non-transitory
tangible computer-readable memory, the memory comprising: a mud cap
underbalanced drilling expert system executable by the one or more
processors and configured to provide one or more mud cap
underbalanced drilling recommendations based on one or more inputs,
the mud cap underbalanced drilling expert system comprising a mud
cap underbalanced drilling Bayesian decision network (BDN) model,
the mud cap underbalanced drilling BDN model comprising: a first
section, comprising: a mud cap drilling types uncertainty node
configured to receive one or more mud cap drilling types from the
one or more inputs; a mud cap drilling types recommendations
decision node configured to receive one or more mud cap drilling
types recommendations from the one or more inputs; and a first
consequences node dependent on the mud cap drilling types
uncertainty node and the mud cap drilling types recommendations
decision node and configured to output the one or more mud cap
underbalanced drilling recommendations based on one or more
Bayesian probabilities calculated from the one or more mud cap
drilling types and the one or more mud cap drilling types
recommendations; a second section, comprising: a mud cap drilling
problems uncertainty node configured to receive one or more mud cap
drilling problems from the one or more inputs; a mud cap drilling
problems recommendations decision node configured to receive one or
more mud cap drilling problems recommendations from the one or more
inputs; and a second consequences node dependent on the mud cap
drilling problems uncertainty node and the mud cap drilling
problems recommendations decision node and configured to output the
one or more mud cap underbalanced drilling recommendations based on
one or more Bayesian probabilities calculated from the one or more
mud cap drilling problems and the one or more mud cap drilling
problems recommendations; and a third section, comprising: a
floating mud cap drilling considerations uncertainty node
configured to receive one or more floating mud cap drilling
considerations types from the one or more inputs; a floating mud
cap drilling recommendations decision node configured to receive
one or more floating mud cap drilling recommendations from the one
or more inputs; and a third consequences node dependent on the
floating mud cap drilling considerations uncertainty node and the
floating mud cap drilling recommendations decision node and
configured to output the one or more mud cap underbalanced drilling
recommendations based on one or more Bayesian probabilities
calculated from the one or more floating mud cap drilling
considerations types and the one or more floating mud cap drilling
recommendations.
43. The system of claim 42, wherein the mud cap UBD BDN model
comprises a final consequences node dependent on the first
consequences node, the second consequences node, and the third
consequences node.
44. The system of claim 42, comprising a user interface configured
to display the mud cap UBD BDN model and receive user selections of
the one or more inputs.
45. The system of claim 42, wherein the one or more mud cap
drilling types, the one or more mud cap drilling problems, and the
one or more floating mud cap considerations are each associated
with a respective plurality of probabilities.
46. A computer-implemented method for an underbalanced drilling
expert system having a mud cap underbalanced drilling (UBD)
Bayesian decision network (BDN) model, the method comprising:
receiving one or more inputs; providing the one or more inputs to
one or more nodes of a first section of the mud cap UBD BDN model,
the one or more nodes comprising: a mud cap drilling types
uncertainty node configured to receive one or more mud cap drilling
types; a mud cap drilling types recommendations decision node
configured to receive one or more mud cap drilling types
recommendations; determining one or more underbalanced drilling
recommendations at a consequences node of the first section of the
mud cap UBD BDN model, the determination comprising a calculation
of one or more Bayesian probabilities based on the one or more
inputs; and providing the one or more underbalanced drilling
recommendations to a user.
47. The computer-implemented method of claim 46, wherein providing
the one or more underbalanced drilling recommendations to a user
comprises displaying the one or more underbalanced drilling
recommendations in a user interface element of a user interface
configured to display the mud cap UBD BDN model.
48. The computer-implemented method of claim 46, comprising:
providing the one or more inputs to one or more nodes of a second
section of the mud cap UBD BDN model, the one or more nodes
comprising: a mud cap drilling problems uncertainty node configured
to receive one or more mud cap drilling problems; a mud cap
drilling problems recommendations decision node configured to
receive one or more mud cap drilling problems recommendations;
determining the one or more underbalanced drilling recommendations
at a second consequences node of the second section of the gas UBD
BDN model, the determination comprising a calculation of one or
more Bayesian probabilities based on the one or more inputs.
49. The computer-implemented method of claim 48, comprising:
providing the one or more inputs to one or more nodes of a third
section of the mud cap UBD BDN model, the one or more nodes
comprising: a floating mud cap drilling considerations uncertainty
node configured to receive one or more floating mud cap drilling
considerations; a floating mud cap drilling recommendations
decision node configured to receive one or more floating mud cap
drilling recommendations; determining the one or more underbalanced
drilling recommendations at a third consequences node of the third
section of the mud cap UBD BDN model, the determination comprising
a calculation of one or more Bayesian probabilities based on the
one or more inputs.
50. A system, comprising, one or more processors; a non-transitory
tangible computer-readable memory, the memory comprising: a
underbalanced drilling expert system executable by the one or more
processors and configured to provide one or more underbalanced
drilling recommendations based on one or more inputs, the
underbalanced expert system comprising an underbalanced liner
drilling (UBLD) Bayesian decision network (BDN) model, the UBLD BDN
model comprising: a first section, comprising: a UBLD plans
uncertainty node configured to receive one or more UBLD plans from
the one or more inputs; a UBLD plans recommendations decision node
configured to receive one or more UBLD plans recommendations from
the one or more inputs; and a first consequences node dependent on
the UBLD planning uncertainty node and the UBLD planning
recommendations decision node and configured to output the one or
more underbalanced liner drilling recommendations based on one or
more Bayesian probabilities calculated from the one or more UBLD
plans and the one or more UBLD plans recommendations; a second
section, comprising: a UBLD solvable problems uncertainty node
configured to receive one or more UBLD solvable problems from the
one or more inputs; a UBLD advantages decision node configured to
receive one or more UBLD advantages from the one or more inputs;
and a second consequences node dependent on the UBLD problems
uncertainty node and the UBLD advantages decision node and
configured to output the one or more underbalanced liner drilling
recommendations based on one or more Bayesian probabilities
calculated from the one or more UBLD solvable problems and the one
or more UBLD advantages; and a third section, comprising: a UBLD
considerations uncertainty node configured to receive one or more
UBLD considerations from the one or more inputs; a UBLD
considerations recommendations decision node configured to receive
one or more UBLD considerations recommendations from the one or
more inputs; and a third consequences node dependent on the UBLD
considerations uncertainty node and the UBLD recommendations
decision node and configured to output the one or more
underbalanced liner drilling recommendations based on one or more
Bayesian probabilities calculated from the one or more UBLD
considerations and the one or more UBLD considerations
recommendations.
51. The system of claim 50, wherein the UBLD BDN model comprises a
final consequences node dependent on the first consequences node,
the second consequences node, and the third consequences node.
52. The system of claim 50, comprising a user interface configured
to display the UBLD BDN model and receive user selections of the
one or more inputs.
53. The system of claim 50, wherein the one or more UBLD plans, the
one or more UBLD solvable problems, and the one or more UBLD
considerations are each associated with a respective plurality of
probabilities.
54. A computer-implemented method for an underbalanced drilling
expert system having an underbalanced liner drilling (UBLD)
Bayesian decision network (BDN) model, the method comprising:
receiving one or more inputs; providing the one or more inputs to
one or more nodes of a first section of the UBLD BDN model, the one
or more nodes comprising: a UBLD plans uncertainty node configured
to receive one or more UBLD plans; a UBLD plans recommendations
decision node configured to receive one or more UBLD plans
recommendations; determining one or more underbalanced drilling
recommendations at a consequences node of the first section of the
UBLD BDN model, the determination comprising a calculation of one
or more Bayesian probabilities based on the one or more inputs;
providing the one or more underbalanced drilling recommendations to
a user.
55. The computer-implemented method of claim 54, wherein providing
the one or more underbalanced drilling recommendations to a user
comprises displaying the one or more underbalanced drilling
recommendations in a user interface element of a user interface
configured to display the UBLD BDN model.
56. The computer-implemented method of claim 54, comprising:
providing the one or more inputs to one or more nodes of a second
section of the UBLD BDN model, the one or more nodes comprising: a
UBLD solvable problems uncertainty node configured to receive one
or more UBLD solvable problems; a UBLD advantages decision node
configured to receive one or more UBLD advantages; determining the
one or more underbalanced drilling recommendations at a second
consequences node of the second section of the UBLD BDN model, the
determination comprising a calculation of one or more Bayesian
probabilities based on the one or more inputs.
57. The computer-implemented method of claim 56, comprising:
providing the one or more inputs to one or more nodes of a third
section of the UBLD BDN model, the one or more nodes comprising: a
UBLD considerations uncertainty node configured to receive one or
more UBLD considerations; a UBLD considerations recommendations
decision node configured to receive one or more UBLD considerations
recommendations; determining the one or more underbalanced drilling
recommendations at a third consequences node of the third section
of the UBLD BDN model, the determination comprising a calculation
of one or more Bayesian probabilities based on the one or more
inputs.
58. A system, comprising: one or more processors; a non-transitory
tangible computer-readable memory, the memory comprising: an
underbalanced drilling (UBD) expert system executable by the one or
more processors and configured to provide one or more UBD
recommendations based on one or more inputs, the UBD expert system
comprising an underbalanced coil tube (UBCT) Bayesian decision
network (BDN) model, the UBCT BDN model comprising: a first
section, comprising: a UBCT preplanning uncertainty node configured
to receive one or more UBCT preplans from the one or more inputs; a
UBCT preplanning requirements decision node configured to receive
one or more UBCT preplan requirements from the one or more inputs;
and a first consequences node dependent on the UBCT preplanning
uncertainty node and the UBCT preplanning recommendations decision
node and configured to output the one or more UBCT drilling
requirements based on one or more Bayesian probabilities calculated
from the one or more UBCT preplans and the one or more UBCT preplan
requirements; and a second section, comprising: a UBCT
considerations uncertainty node configured to receive one or more
UBCT considerations from the one or more inputs; a UBCT
recommendations decision node configured to receive one or more
UBCT recommendations from the one or more inputs; and a second
consequences node dependent on the UBCT considerations uncertainty
node and the UBCT recommendations decision node and configured to
output the one or more underbalanced drilling recommendations based
on one or more Bayesian probabilities calculated from the one or
more UBCT considerations and the one or more UBCT
recommendations.
59. The system of claim 58, wherein the UBCT BDN model comprises a
final consequences node dependent on the first consequences node
and the second consequences node.
60. The system of claim 58, comprising a user interface configured
to display the UBCT BDN model and receive user selections of the
one or more inputs.
61. The system of claim 58, wherein the one or more UBCT preplans
and the one or more UBCT considerations are each associated with a
respective plurality of probabilities.
62. A computer-implemented method for an underbalanced drilling
expert system having an underbalanced coil tube (UBCT) drilling
Bayesian decision network (BDN) model, the method comprising:
receiving one or more inputs; providing the one or more inputs to
one or more nodes of a first section of the UBCT BDN model, the one
or more nodes comprising: a UBCT preplanning uncertainty node
configured to receive one or more UBCT preplans; a UBCT preplanning
requirements decision node configured to receive one or more UBCT
preplan requirements; determining one or more underbalanced
drilling recommendations at a consequences node of the first
section of the UBCT BDN model, the determination comprising a
calculation of one or more Bayesian probabilities based on the one
or more inputs; providing the one or more underbalanced drilling
recommendations to a user.
63. The computer-implemented method of claim 62, wherein providing
the one or more underbalanced drilling recommendations to a user
comprises displaying the one or more underbalanced drilling
recommendations in a user interface element of a user interface
configured to display the UBCT BDN model.
64. The computer-implemented method of claim 62, comprising:
providing the one or more inputs to one or more nodes of a second
section of the UBCT BDN model, the one or more nodes comprising: a
UBCT considerations uncertainty node configured to receive one or
more UBCT considerations; a UBCT recommendations decision node
configured to receive one or more UBCT recommendations; determining
the one or more underbalanced drilling recommendations at a second
consequences node of the second section of the UBCT BDN model, the
determination comprising a calculation of one or more Bayesian
probabilities based on the one or more inputs.
65. A system, comprising: one or more processors; a non-transitory
tangible computer-readable memory, the memory comprising: an
underbalanced drilling expert system executable by the one or more
processors and configured to provide one or more underbalanced
drilling recommendations based on one or more inputs, the
underbalanced drilling expert system comprising a snubbing and
stripping Bayesian decision network (BDN) model, the snubbing and
stripping BDN model comprising a first section, comprising: a
snubbing types uncertainty node configured to receive one or more
snubbing types from the one or more inputs; a snubbing types
recommendations decision node configured to receive one or more
snubbing types recommendations from the one or more inputs; and a
first consequences node dependent on the snubbing types uncertainty
node and the snubbing types recommendations decision node and
configured to output the one or more underbalanced recommendations
based on one or more Bayesian probabilities calculated from the one
or more snubbing types and the one or more snubbing types
recommendations; and a second section, comprising: a snubbing units
uncertainty node configured to receive one or more snubbing units
from the one or more inputs; a snubbing units recommendations
decision node configured to receive one or more snubbing units
recommendations from the one or more inputs; and a second
consequences node dependent on the snubbing units uncertainty node
and the snubbing units recommendations decision node and configured
to output the one or more stripping and snubbing recommendations
based on one or more Bayesian probabilities calculated from the one
or more snubbing units types and the one or more snubbing units
recommendations; and a third section, comprising: a snubbing
operations uncertainty node configured to receive one or more
snubbing operations from the one or more inputs; a snubbing
operations recommendations decision node configured to receive one
or more snubbing operations recommendations from the one or more
inputs; and a third consequences node dependent on the snubbing
operations uncertainty node and the snubbing operations
recommendations decision node and configured to output the one or
more stripping and snubbing recommendations based on one or more
Bayesian probabilities calculated from the one or more snubbing
operations and the one or more snubbing operations recommendations;
and a fourth section, comprising: a stripping procedures
uncertainty node configured to receive one or more stripping
procedures from the one or more inputs; a stripping procedures
recommendations decision node configured to receive one or more
stripping procedures recommendations from the one or more inputs;
and a fourth consequences node dependent on the stripping
procedures uncertainty node and the stripping procedures
recommendations decision node and configured to output the one or
more stripping and snubbing recommendations based on one or more
Bayesian probabilities calculated from the one or more stripping
procedures and the one or more stripping procedures
recommendations.
66. The system of claim 65, wherein the snubbing and stripping BDN
model comprises a final consequences node dependent on the first
consequences node and the second consequences node.
67. The system of claim 65, comprising a user interface configured
to display the snubbing and stripping BDN model and receive user
selections of the one or more inputs.
68. The system of claim 65, wherein the one or more snubbing types,
the one or more snubbing units, the one or more snubbing
operations, and the one or more stripping procedures are each
associated with a respective plurality of probabilities.
69. A computer-implemented method for an underbalanced drilling
expert system having a snubbing and stripping Bayesian decision
network (BDN) model, the method comprising: receiving one or more
inputs; providing the one or more inputs to one or more nodes of a
first section of the snubbing and stripping BDN model, the one or
more nodes comprising: a snubbing types uncertainty node configured
to receive one or more snubbing types; a snubbing types
recommendations decision node configured to receive one or more
snubbing types recommendations; determining one or more
underbalanced drilling recommendations at a consequences node of
the first section of the snubbing and stripping BDN model, the
determination comprising a calculation of one or more Bayesian
probabilities based on the one or more inputs; and providing the
one or more underbalanced drilling recommendations to a user.
70. The computer-implemented method of claim 69, wherein providing
the one or more underbalanced drilling recommendations to a user
comprises displaying the one or more underbalanced drilling
recommendations in a user interface element of a user interface
configured to display the snubbing and stripping BDN model.
71. The computer-implemented method of claim 69, comprising:
providing the one or more inputs to one or more nodes of a second
section of the snubbing and stripping BDN model, the one or more
nodes comprising: a snubbing units uncertainty node configured to
receive one or more snubbing units; a snubbing units
recommendations decision node configured to receive one or more
snubbing units recommendations; determining the one or more
underbalanced drilling recommendations at a second consequences
node of the second section of the snubbing and stripping BDN model,
the determination comprising a calculation of one or more Bayesian
probabilities based on the one or more inputs.
72. The computer-implemented method of claim 71, comprising:
providing the one or more inputs to one or more nodes of a third
section of the snubbing and stripping BDN model, the one or more
nodes comprising: a snubbing operations uncertainty node configured
to receive one or more snubbing operations; a snubbing operations
recommendations decision node configured to receive one or more
snubbing operations recommendations; determining the one or more
underbalanced drilling recommendations at a third consequences node
of the third section of the snubbing and stripping BDN model, the
determination comprising a calculation of one or more Bayesian
probabilities based on the one or more inputs.
73. The computer-implemented method of claim 72, comprising:
providing the one or more inputs to one or more nodes of a fourth
section of the snubbing and stripping BDN model, the one or more
nodes comprising: a stripping procedures uncertainty node
configured to receive one or more stripping procedures; a stripping
procedures recommendations decision node configured to receive one
or more stripping procedures recommendations; determining the one
or more underbalanced drilling recommendations at a fourth
consequences node of the fourth section of the snubbing and
stripping BDN model, the determination comprising a calculation of
one or more Bayesian probabilities based on the one or more inputs.
Description
PRIORITY CLAIM
[0001] This application claims priority to U.S. Provisional Patent
Application No. 61/722,027 filed on Nov. 2, 2012, entitled "Systems
and Methods for Expert Systems for Underbalanced Drilling
Operations Using Bayesian Decision Networks," the disclosure of
which is hereby incorporated by reference in its entirety.
BACKGROUND OF THE INVENTION
[0002] 1. Field of the Invention
[0003] This invention relates generally to the drilling and
extraction of oil, natural gas, and other resources, and more
particularly to evaluation and selection of underbalanced drilling
systems.
[0004] 2. Description of the Related Art
[0005] Oil, gas, and other natural resources are used for numerous
energy and material purposes. The search for extraction of oil,
natural gas, and other subterranean resources from the earth may
cost significant amounts of time and money. Once a resource is
located, drilling systems may be used to access the resources, such
as by drilling into various geological formations to access
deposits of such resources. The drilling systems rely on numerous
components and operational techniques to reduce cost and time and
maximize effectiveness. For example, drill strings, drill bits,
drilling fluids, and other components may be selected to achieve
maximum effectiveness for a formation and other parameters that
affect the drilling system. Typically, many years of field
experience and laboratory work are used to develop and select the
appropriate components and operational practices for a drilling
system. However, these techniques may be time-consuming and
expensive. Moreover, such techniques may produce inconsistent
results and may not incorporate recent changes in practices and
opinions regarding the drilling systems.
SUMMARY OF THE INVENTION
[0006] Various embodiments of methods and systems for expert
systems for determining underbalanced drilling operations using
Bayesian decision networks are provided herein. In some
embodiments, a system is provided that includes one or more
processors and a non-transitory tangible computer-readable memory.
The non-transitory tangible computer-readable memory includes an
underbalanced drilling expert system executable by the one or more
processors and configured to provide one or more underbalanced
drilling recommendations based on one or more inputs. The
underbalanced drilling expert system includes an underbalanced
drilling Bayesian decision network (BDN) model. The underbalanced
drilling BDN model includes a first section having a formation
indicators uncertainty node configured to receive one or more
formation indicators from the one or more inputs, a formation
considerations decision node configured to receive one or more
formation considerations from the one or more inputs, and a first
consequences node dependent on the formation indicators uncertainty
node and the formation considerations decision node and configured
to output the one or more underbalanced drilling recommendations
based on one or more Bayesian probabilities calculated from one or
more formation indicators and the one or more formation
considerations. The underbalanced BDN model includes a second
section having a planning phases uncertainty node configured to
receive one or more planning phases from the one or more inputs, a
planning phases recommendations decision node configured to receive
one or more planning phases recommendations from the one or more
inputs, and a second consequences node dependent on the planning
phases uncertainty node and the planning phases recommendations
decision node and configured to output the one or more
underbalanced drilling recommendations based on one or more
Bayesian probabilities calculated from one or more planning phases
and the one or more planning phases recommendations. Finally, the
underbalanced drilling BDN model also includes a third section
having a an equipment requirements uncertainty node configured to
receive one or more equipment requirements from the one or more
inputs, an equipment recommendations decision node configured to
receive one or more equipment recommendations from the one or more
inputs, and a third consequences node dependent on the equipment
requirements uncertainty node and the equipment recommendations
decision node and configured to output the one or more
underbalanced drilling recommendations based on one or more
Bayesian probabilities calculated from the one or more equipment
requirements and the one or more equipment recommendations.
[0007] In some embodiments, a computer-implemented method for an
underbalanced drilling expert system having an underbalanced
drilling Bayesian decision network (BDN) model is provided. The
method includes receiving one or more inputs and providing the one
or more nodes of a first section of the underbalanced drilling BDN
model. The one or more nodes include a formation indicators
uncertainty node and a formation considerations decision node.
Additionally, the method further includes determining one or more
underbalanced drilling recommendations at a consequences node of
the first section of the underbalanced drilling BDN model, the
determination comprising a calculation of one or more Bayesian
probabilities based on the one or more inputs and providing the one
or more underbalanced drilling recommendations to a user.
[0008] Additionally, in some embodiments, a system having one or
more processors and a non-transitory tangible computer-readable
memory is provided. The memory the memory includes an underbalanced
drilling expert system executable by the one or more processors and
configured to provide one or more underbalanced drilling
recommendations based on one or more inputs. The underbalanced
drilling expert system includes a flow underbalanced drilling
Bayesian decision network (BDN) model. The flow underbalanced
drilling BDN model includes a first section having a tripping types
uncertainty node configured to receive one or more tripping types
from the one or more inputs, a permeability level uncertainty node
configured to receive one or more permeability levels from the one
or more inputs, a tripping options decision node configured to
receive one or more tripping options from the one or more inputs,
and a first consequences node dependent on the tripping uncertainty
node, the permeability level uncertainty node, and the tripping
options decision node and configured to output the one or more flow
underbalanced drilling recommendations based on one or more
Bayesian probabilities calculated from the one or more tripping
types, the one or more permeability levels, and the one or more
tripping options. The flow underbalanced drilling BDN model also
includes a second section having a connection types uncertainty
node configured to receive one or more connection types from the
one or more inputs, a connection options decision node configured
to receive one or more connection options from the one or more
inputs, and a second consequences node dependent on the connection
uncertainty node and the connection options decision node and
configured to output the one or more flow underbalanced drilling
recommendations based on one or more Bayesian probabilities
calculated from the one or more connection types and the one or
more connection options. Finally, the foam underbalanced drilling
BDN model includes a third section having a flow drilling types
uncertainty node configured to receive one or more flow drilling
types from the one or more inputs, a flow drilling options decision
node configured to receive one or more flow drilling options from
the one or more inputs, and a third consequences node dependent on
the flow drilling uncertainty node and the flow drilling options
decision node and configured to output the one or more flow
underbalanced drilling recommendations based on one or more
Bayesian probabilities calculated from the one or more flow
drilling types and the one or more flow drilling options.
[0009] Further, in some embodiments a computer-implemented method
for an underbalanced drilling expert system having a flow
underbalanced drilling (UBD) Bayesian decision network (BDN) model
is provided. The method includes receiving one or more inputs and
providing the one or more inputs to one or more nodes of a first
section of the flow underbalanced drilling BDN model. The one or
more nodes include a tripping uncertainty node configured to
receive one or more tripping types, a permeability level
uncertainty node configured to receive one or more permeability
levels, and a tripping options decision node a tripping options
decision node configured to receive one or more tripping options.
The method further includes determining one or more underbalanced
drilling recommendations at a consequences node of the first
section of the flow underbalanced drilling BDN model by calculating
one or more Bayesian probabilities based on the one or more inputs
and providing the one or more underbalanced drilling
recommendations to a user.
[0010] Additionally, in some embodiments a system is provided
having one or more processors and a non-transitory tangible
computer-readable memory accessible by the one or more processors.
The memory includes an underbalanced drilling expert system
executable by the one or more processors and configured to provide
one or more underbalanced drilling recommendations based on one or
more inputs. The underbalanced drilling expert system includes a
gaseated underbalanced drilling Bayesian decision network (BDN)
model. The gaseated underbalanced drilling BDN model includes a
first section having a gas injection process uncertainty node
configured to receive one or more gas injection process types from
the one or more inputs, a gas injection processes characteristics
decision node configured to receive one or more gas injection
process characteristics from the one or more inputs, and a first
consequences node dependent on the gas injection process
uncertainty node and the gas infection processes considerations
decision node and configured to output the one or more gaseated
underbalanced drilling recommendations based on one or more
Bayesian probabilities calculated from the one or more gas
injection process types and the one or more gas injection process
characteristics. The gaseated underbalanced drilling BDN model also
includes a second section having a fluid volume limits uncertainty
node configured to receive one or more fluid volume limits from the
one or more inputs, a fluid volume limits requirements decision
node configured to receive one or more fluid volume limits
requirements from the one or more inputs, and a second consequences
node dependent on the fluid volume limits uncertainty node and the
fluid volume limits requirements decision node and configured to
output the one or more gaseated underbalanced drilling
recommendations based on one or more Bayesian probabilities
calculated from the one or more fluid volume limits and the one or
more fluid volume limits requirements. Additionally, the gaseated
underbalanced drilling BDN model includes a third section having a
kick type uncertainty node configured to receive one or more kick
types from the one or more inputs, a kicks recommendations decision
node configured to receive one or more kicks recommendations from
the one or more inputs, and a third consequences node dependent on
the kick type uncertainty node and the kicks recommendations
decision node and configured to output the one or more gaseated
underbalanced drilling recommendations based on one or more
Bayesian probabilities calculated from the one or more kick types
and the one or more kicks recommendations. Finally, the gaseated
underbalanced drilling BDN model includes a fourth section having
an operational considerations uncertainty node configured to
receive one or more operational considerations from the one or more
inputs, an operational recommendations decision node configured to
receive one or more operational recommendations from the one or
more inputs, and a fourth consequences node dependent on the
operational considerations uncertainty node and the operational
recommendations decision node and configured to output the one or
more gaseated underbalanced drilling recommendations based on one
or more Bayesian probabilities calculated from the one or more
operational recommendations and the one or more operational
recommendations.
[0011] In some embodiments, a computer-implemented method for an
underbalanced drilling expert system having a gaseated
underbalanced drilling Bayesian decision network (BDN) model is
provided. The method includes receiving one or more inputs and
providing the one or more inputs to one or more nodes of a first
section of the gaseated underbalanced drilling (UBD) BDN model. The
one or more nodes include a gas injection process uncertainty node
configured to receive one or more gas injection process types, and
a gas injection processes characteristics decision node configured
to receive one or more gas injection process characteristics.
Additionally, the method includes determining one or more
underbalanced drilling recommendations at a consequences node of
the first section of the gaseated UBD BDN model by calculating one
or more Bayesian probabilities based on the one or more inputs and
providing the one or more underbalanced drilling recommendations to
a user.
[0012] Moreover, in some embodiments a system is provided having
one or more processors and a non-transitory tangible
computer-readable memory accessible by the one or more processors.
The memory includes an underbalanced drilling expert system
executable by the one or more processors and configured to provide
one or more underbalanced drilling recommendations based on one or
more inputs. The underbalanced drilling expert system includes a
foam underbalanced drilling (UBD) Bayesian decision network (BDN)
model. The foam underbalanced drilling BDN model includes a first
section having a foam systems considerations uncertainty node
configured to receive one or more foam systems considerations from
the one or more inputs, a foam systems recommendations decision
node configured to receive one or more foam systems recommendations
from the one or more inputs and a first consequences node dependent
on the foam systems considerations uncertainty node and the foam
systems recommendations decision node and configured to output the
one or more underbalanced drilling recommendations based on one or
more Bayesian probabilities calculated from the one or more foam
systems considerations and the one or more foam systems
recommendations. The foam underbalanced drilling BDN model also
includes a second section having a foam systems designs uncertainty
node configured to receive one or more foam system designs from the
one or more inputs, a foam system designs recommendations decision
node configured to receive one or more foam system designs
recommendations from the one or more inputs, and a second
consequences node dependent on the foam systems designs uncertainty
node and the foam system designs recommendations decision node and
configured to output the one or more underbalanced drilling
recommendations based on one or more Bayesian probabilities
calculated from the one or more foam system designs and the one or
more foam system designs recommendations.
[0013] In some embodiments, a computer-implemented method for an
underbalanced drilling expert system having a foam underbalanced
drilling (UBD) Bayesian decision network (BDN) model is provided.
The method includes receiving one or more inputs and providing the
one or more inputs to one or more nodes of a first section of the
foam UBD BDN model. The one or more nodes include a foam systems
considerations uncertainty node configured to receive one or more
foam systems considerations and a foam systems recommendations
decision node configured to receive one or more foam systems
recommendations. Additionally, the method includes determining one
or more underbalanced drilling recommendations at a consequences
node of the first section of the foam UBD BDN model, by calculating
one or more Bayesian probabilities based on the one or more inputs
and providing the one or more underbalanced drilling
recommendations to a user.
[0014] Additionally, in some embodiments a system is provided
having one or more processors and a non-transitory tangible
computer-readable memory accessible by the one or more processors.
The memory includes an underbalanced drilling expert system
executable by the one or more processors and configured to provide
one or more underbalanced drilling recommendations based on one or
more inputs. The underbalanced drilling expert system includes a
gas underbalanced drilling (UBD) Bayesian decision network (BDN)
model. The gas underbalanced drilling BDN model includes a first
section having a rotary and hammer drilling uncertainty node
configured to receive one or more rotary and hammer drilling types
from the one or more inputs, a rotary and hammer drilling
recommendations decision node configured to receive one or more
rotary and hammer drilling recommendations from the one or more
inputs, and a first consequences node dependent on the rotary and
hammer drilling uncertainty node and the rotary and hammer drilling
recommendations decision node and configured to output the one or
more air and gas underbalanced drilling recommendations based on
one or more Bayesian probabilities calculated from the one or more
rotary and hammer drilling types and the one or more rotary and
hammer drilling recommendations. The gas underbalanced drilling BDN
model includes a second section a gas drilling considerations
uncertainty node configured to receive one or more gas drilling
considerations from the one or more inputs, a gas drilling
considerations recommendations decision node configured to receive
gas drilling considerations recommendations from the one or more
inputs, and a second consequences node dependent on the gas
drilling considerations uncertainty node and the gas drilling
considerations recommendations decision node and configured to
output the one or more air and gas underbalanced drilling
recommendations based on one or more Bayesian probabilities
calculated from the one or more gas drilling considerations and the
one or more gas drilling considerations recommendations.
Additionally, the gas underbalanced drilling BDN model includes a
third section having a gas drilling operations uncertainty node
configured to receive one or more gas drilling operations from the
one or more inputs, a gas drilling operations recommendations
decision node configured to receive gas drilling operations
recommendations from the one or more inputs, and a third
consequences node dependent on the gas drilling operations
uncertainty node and the gas drilling operations recommendations
decision node and configured to output the one or more air and gas
underbalanced drilling recommendations based on one or more
Bayesian probabilities calculated from the one or more gas drilling
operations and the one or more gas drilling operations
recommendations. Finally, the gas underbalanced drilling BDN model
includes a fourth section having a gas drilling rig equipment
uncertainty node configured to receive one or more gas drilling rig
equipment from the one or more inputs, a gas drilling rig equipment
recommendations decision node configured to receive gas drilling
rig equipment recommendations from the one or more inputs, and a
fourth consequences node dependent on the gas drilling rig
equipment uncertainty node and the gas drilling rig equipment
recommendations decision node and configured to output the one or
more air and gas underbalanced drilling recommendations based on
one or more Bayesian probabilities calculated from the one or more
gas drilling rig equipment and the one or more gas drilling rig
equipment recommendations.
[0015] Further, in some embodiments a computer-implemented method
is provided for an underbalanced drilling expert system having a
gas underbalanced drilling (UBD) Bayesian decision network (BDN)
model. The method includes receiving one or more inputs and
providing the one or more inputs to one or more nodes of a first
section of the gas underbalanced drilling BDN model. The one or
more nodes include a rotary and hammer drilling uncertainty node
and a rotary and hammer recommendations decision node.
Additionally, the method includes determining one or more
underbalanced drilling recommendations at a consequences node of
the first section of the gas underbalanced drilling BDN model, by
calculating one or more Bayesian probabilities based on the one or
more inputs and providing the one or more underbalanced drilling
recommendations to a user.
[0016] In some embodiments, a system is provided having one or more
processors and a non-transitory tangible computer-readable memory
accessible by the one or more processors. The memory includes an
underbalanced drilling expert system executable by the one or more
processors and configured to provide one or more underbalanced
drilling recommendations based on one or more inputs. The
underbalanced drilling expert system includes a mud cap
underbalanced drilling (UBD) Bayesian decision network (BDN) model.
The mud cap underbalanced drilling BDN model includes a first
section having a mud cap drilling types uncertainty node configured
to receive one or more mud cap drilling types from the one or more
inputs, a mud cap drilling types recommendations decision node
configured to receive one or more mud cap drilling types
recommendations from the one or more inputs, and a first
consequences node dependent on the mud cap drilling types
uncertainty node and the mud cap drilling types recommendations
decision node and configured to output the one or more
underbalanced drilling recommendations based on one or more
Bayesian probabilities calculated from the one or more mud cap
drilling types and the one or more mud cap drilling types
recommendations.
[0017] In some embodiments a computer-implemented method is
provided for an underbalanced drilling expert system having a mud
cap underbalanced drilling (UBD) Bayesian decision network (BDN)
model. The method includes receiving one or more inputs and
providing the one or more inputs to one or more nodes of a first
section of the mud cap UBD BDN model. The one or more nodes include
mud cap drilling types uncertainty node configured to receive one
or more mud cap drilling types a mud cap drilling types
recommendations decision node configured to receive one or more mud
cap drilling types recommendations. The method further includes
determining one or more underbalanced drilling recommendations at a
consequences node of the first section of the mud cap UBD BDN model
by calculating one or more Bayesian probabilities based on the one
or more inputs and providing the one or more underbalanced drilling
recommendations to a user.
[0018] Moreover, in some embodiments a system is provided having
one or more processors and a non-transitory tangible
computer-readable memory accessible by the one or more processors.
The memory includes an underbalanced drilling expert system
executable by the one or more processors and configured to provide
one or more underbalanced drilling recommendations based on one or
more inputs. The underbalanced drilling expert system includes an
underbalanced liner drilling (UBLD) Bayesian decision network (BDN)
model. The UBLD BDN model includes a first section having a UBLD
plans uncertainty node configured to receive one or more UBLD plans
from the one or more inputs, a UBLD plans recommendations decision
node configured to receive one or more UBLD plans recommendations
from the one or more inputs, and a first consequences node
dependent on the UBLD planning uncertainty node and the UBLD
planning recommendations decision node and configured to output the
one or more underbalanced liner drilling recommendations based on
one or more Bayesian probabilities calculated from the one or more
UBLD plans and the one or more UBLD plans recommendations. The UBLD
BDN model also includes a second section having a UBLD solvable
problems uncertainty node configured to receive one or more UBLD
solvable problems from the one or more inputs, a UBLD advantages
decision node configured to receive one or more UBLD advantages
from the one or more inputs, and a second consequences node
dependent on the UBLD problems uncertainty node and the UBLD
advantages decision node and configured to output the one or more
underbalanced drilling recommendations based on one or more
Bayesian probabilities calculated from the one or more UBLD
solvable problems and the one or more UBLD advantages.
Additionally, the UBLD BDN model includes a third section having a
UBLD considerations uncertainty node configured to receive one or
more UBLD considerations from the one or more inputs, a UBLD
considerations recommendations decision node configured to receive
one or more UBLD considerations recommendations from the one or
more inputs, and a third consequences node dependent on the UBLD
considerations uncertainty node and the UBLD recommendations
decision node and configured to output the one or more
underbalanced liner drilling recommendations based on one or more
Bayesian probabilities calculated from the one or more UBLD
considerations and the one or more UBLD considerations
recommendations.
[0019] In some embodiments a computer-implemented method is
provided for an underbalanced drilling expert system having an
underbalanced drilling liner (UBLD) Bayesian decision network (BDN)
model. The method includes receiving one or more inputs and
providing the one or more inputs to one or more nodes of a first
section of the UBLD BDN model. The one or more nodes include a UBLD
plans uncertainty node configured to receive one or more UBLD plans
and a UBLD plans recommendations decision node configured to
receive one or more UBLD plans recommendations. The method also
includes determining one or more underbalanced drilling
recommendations at a consequences node of the first section of the
UBLD BDN model by calculating one or more Bayesian probabilities
based on the one or more inputs and providing the one or more
underbalanced drilling recommendations to a user.
[0020] Moreover, in some embodiments a system is provided having
one or more processors and a non-transitory tangible
computer-readable memory accessible by the one or more processors.
The memory includes an underbalanced drilling expert system
executable by the one or more processors and configured to provide
one or more underbalanced drilling recommendations based on one or
more inputs. The underbalanced drilling expert system includes an
underbalanced coil tube (UBCT) Bayesian decision network (BDN)
model. The UBCT BDN model includes a first section having a UBCT
preplanning uncertainty node configured to receive one or more UBCT
preplans from the one or more inputs, a UBCT preplanning
requirements decision node configured to receive one or more UBCT
preplan requirements from the one or more inputs, a first
consequences node dependent on the UBCT preplanning uncertainty
node and the UBCT preplanning recommendations decision node and
configured to output the one or more UBCT drilling requirements
based on one or more Bayesian probabilities calculated from the one
or more UBCT preplans and the one or more UBCT preplan
requirements. The UBCT BDN model also includes a second section
having a UBCT considerations uncertainty node configured to receive
one or more UBCT considerations from the one or more inputs, a UBCT
recommendations decision node configured to receive one or more
UBCT recommendations from the one or more inputs, and a second
consequences node dependent on the UBCT considerations uncertainty
node and the UBCT recommendations decision node and configured to
output the one or more underbalanced drilling recommendations based
on one or more Bayesian probabilities calculated from the one or
more UBCT considerations and the one or more UBCT
recommendations.
[0021] In some embodiments a computer-implemented method is
provided for an underbalanced drilling expert system having an
underbalanced coil tube (UBCT) Bayesian decision network (BDN)
model. The method includes receiving one or more inputs and
providing the one or more inputs to one or more nodes of a first
section of the UBCT BDN model. The one or more nodes include a UBCT
preplanning uncertainty node configured to receive one or more UBCT
preplans and a UBCT preplanning requirements decision node
configured to receive one or more UBCT preplan requirements. The
method also includes determining one or more underbalanced drilling
recommendations at a consequences node of the first section of the
UBCT BDN model, the determination comprising a calculation of one
or more Bayesian probabilities based on the one or more inputs and
providing the one or more underbalanced drilling recommendations to
a user.
[0022] Further, in some embodiments another system is provided
having one or more processors and a non-transitory tangible
computer-readable memory accessible by the one or more processors.
The memory includes an underbalanced drilling expert system
executable by the one or more processors and configured to provide
one or more underbalanced drilling recommendations based on one or
more inputs. The underbalanced drilling expert system includes a
snubbing and stripping Bayesian decision network (BDN) model. The
snubbing and stripping BDN model includes a first section having a
snubbing types uncertainty node configured to receive one or more
snubbing types from the one or more inputs and a snubbing types
recommendations decision node configured to receive one or more
snubbing types recommendations from the one or more inputs, and a
first consequences node dependent on the snubbing types uncertainty
node and the snubbing types recommendations decision node and
configured to output the one or more underbalanced recommendations
based on one or more Bayesian probabilities calculated from the one
or more snubbing types and the one or more snubbing types
recommendations. The snubbing and stripping BDN model also includes
a second section having a snubbing units uncertainty node
configured to receive one or more snubbing units from the one or
more inputs, a snubbing units recommendations decision node
configured to receive one or more snubbing units recommendations
from the one or more inputs, and a second consequences node
dependent on the snubbing units uncertainty node and the snubbing
units recommendations decision node and configured to output the
one or more stripping and snubbing recommendations based on one or
more Bayesian probabilities calculated from the one or more
snubbing units types and the one or more snubbing units
recommendations. Additionally, the snubbing and stripping BDN model
includes a third section having a snubbing operations uncertainty
node configured to receive one or more snubbing operations from the
one or more inputs, a snubbing operations recommendations decision
node configured to receive one or more snubbing operations
recommendations from the one or more inputs, and a third
consequences node dependent on the snubbing operations uncertainty
node and the snubbing operations recommendations decision node and
configured to output the one or more stripping and snubbing
recommendations based on one or more Bayesian probabilities
calculated from the one or more snubbing operations and the one or
more snubbing operations recommendations. Finally, the snubbing and
stripping BDN model also includes a fourth section having a
stripping procedures uncertainty node configured to receive one or
more stripping procedures from the one or more inputs, a stripping
procedures recommendations decision node configured to receive one
or more stripping procedures recommendations from the one or more
inputs, and a fourth consequences node dependent on the stripping
procedures uncertainty node and the stripping procedures
recommendations decision node and configured to output the one or
more stripping and snubbing recommendations based on one or more
Bayesian probabilities calculated from the one or more stripping
procedures and the one or more stripping procedures
recommendations.
[0023] Finally, in some embodiments another computer-implemented
method is provided for an underbalanced drilling expert system
having a snubbing and stripping Bayesian decision network (BDN)
model. The method includes receiving one or more inputs and
providing the one or more inputs to one or more nodes of a first
section of the snubbing and stripping BDN model. The one or more
nodes include snubbing types uncertainty node configured to receive
one or more snubbing types and a snubbing types recommendations
decision node configured to receive one or more snubbing types
recommendations. The method also includes determining one or more
underbalanced drilling recommendations at a consequences node of
the first section of the snubbing and stripping BDN model, the
determination comprising a calculation of one or more Bayesian
probabilities based on the one or more inputs and providing the one
or more underbalanced drilling recommendations to a user.
BRIEF DESCRIPTION OF THE DRAWINGS
[0024] FIG. 1 is a block diagram that illustrates a system in
accordance with an embodiment of the present invention;
[0025] FIG. 2 is a schematic diagram of a computer and an
underbalanced drilling expert system in accordance with an
embodiment of the present invention;
[0026] FIGS. 3A-3I are block diagrams of processes of an
underbalanced drilling expert system in accordance with an
embodiment of the present invention;
[0027] FIG. 4 is a schematic diagram of an example of a Bayesian
decision network model for the selection of a swelling packer in
accordance with an embodiment of the present invention;
[0028] FIGS. 5-8 are tables of the probability states associated
with the nodes of the Bayesian decision network model of FIG.
4;
[0029] FIG. 9 is a table of input utility values assigned to a
consequences node of the Bayesian decision network model of FIG.
4;
[0030] FIG. 10 is a table of total probability calculations for
drilling fluid types of the Bayesian decision network model of FIG.
4;
[0031] FIG. 11 is a table of Bayesian probability determinations
for the Bayesian decision network model of FIG. 4;
[0032] FIG. 12 is a table of consequences based on the Bayesian
probability determinations depicted in FIG. 11;
[0033] FIG. 13 is a table of expected utilities based on the
consequences depicted in FIG. 12;
[0034] FIG. 14 is a table of consequences based on the probability
states depicted in FIG. 8;
[0035] FIG. 15 is a table of expected utilities based on the
consequences depicted in FIG. 14;
[0036] FIGS. 16A-16I are schematic diagrams that depict a general
UBD BDN model and inputs to the general UBD BDN model in accordance
with an embodiment of the present invention;
[0037] FIG. 17 is a schematic diagram that depicts a selected input
to the general UBD BDN model of FIG. 16A;
[0038] FIG. 18 is a table that depicts the output from the general
UBD BDN model of FIG. 16A;
[0039] FIGS. 19A-19H are schematic diagrams that depict a flow UBD
BDN model and inputs to the flow UBD BDN model in accordance with
an embodiment of the present invention;
[0040] FIGS. 20A and 20B are schematic diagrams that depict
selected inputs to the flow UBD BDN model of FIG. 19A;
[0041] FIG. 21. Is a table that depicts the output from the flow
UBD BDN model of FIG. 19A;
[0042] FIGS. 22A-22I are schematic diagrams that depict a gaseated
UBD BDN model and inputs to the gaseated UBD BDN model in
accordance with an embodiment of the present invention;
[0043] FIGS. 23A and 23B are schematic diagrams that depict a
selected input to and an output from the gaseated UBD BDN model of
FIG. 22A;
[0044] FIGS. 24A-24E are schematic diagrams that depict a foam UBD
BDN model and inputs to the foam UBD BDN model in accordance with
an embodiment of the present invention;
[0045] FIGS. 25A and 25B are schematic diagrams that depict a
selected input to and an output from the foam UBD BDN model of FIG.
24A;
[0046] FIGS. 26A-26I are schematic diagrams that depict an air and
gas UBD BDN model and inputs to the air and gas UBD BDN model in
accordance with an embodiment of the present invention;
[0047] FIGS. 27A and 27B are schematic diagrams that depict a
selected input to and an output from the air and gas UBD BDN model
of FIG. 26A;
[0048] FIGS. 28A and 28B are schematic diagrams that depict another
selected input to and an output from the air and gas UBD BDN model
of FIG. 26A;
[0049] FIGS. 29A-29G are schematic diagrams that depict a mud cap
UBD BDN model and inputs to the mud cap UBD BDN model in accordance
with an embodiment of the present invention;
[0050] FIGS. 30A and 30B are schematic diagrams that depict a
selected input to and an output from the mud cap UBD BDN model of
FIG. 29A;
[0051] FIGS. 31A and 31B are schematic diagrams that depict another
selected input to and an output from the mud cap UBD BDN model of
FIG. 29A;
[0052] FIGS. 32A-32G are schematic diagrams that depict a UBLD BDN
model and inputs to the UBLD BDN model in accordance with an
embodiment of the present invention;
[0053] FIGS. 33A and 33B are schematic diagrams that depict a
selected input to and an output from the UBLD BDN model of FIG.
32A;
[0054] FIGS. 34A and 34B are schematic diagrams that depict another
selected input to and an output from the UBLD BDN model of FIG.
32A;
[0055] FIGS. 35A-35E are schematic diagrams that depict a UBCTD BDN
model and inputs to the UBCTD BDN model in accordance with an
embodiment of the present invention;
[0056] FIGS. 36A and 36B are schematic diagrams that depict a
selected input to and an output from the UBCTD BDN model of FIG.
35A;
[0057] FIGS. 37A-37I are schematic diagrams that depict a snubbing
and stripping BDN model and inputs to the snubbing and stripping
BDN model in accordance with an embodiment of the present
invention;
[0058] FIGS. 38A and 38B are schematic diagrams that depict a
selected input to and an output from the snubbing and stripping BDN
model of FIG. 37A;
[0059] FIGS. 39A and 39B are schematic diagrams that depict a
selected input to and an output from the snubbing and stripping BDN
model of FIG. 37A;
[0060] FIG. 40 is a block diagram that depicts a process for
constructing a BDN model in accordance with an embodiment of the
present invention; and
[0061] FIG. 41 is a block diagram of a computer in accordance with
an embodiment of the present invention.
[0062] While the invention is susceptible to various modifications
and alternative forms, specific embodiments thereof are shown by
way of example in the drawings and will herein be described in
detail. The drawings may not be to scale. It should be understood,
however, that the drawings and detailed description thereto are not
intended to limit the invention to the particular form disclosed,
but to the contrary, the intention is to cover all modifications,
equivalents, and alternatives falling within the spirit and scope
of the present invention as defined by the appended claims.
DETAILED DESCRIPTION
[0063] As discussed in more detail below, provided in some
embodiments are systems, methods, and computer-readable media for
an underbalanced drilling (UBD) expert system based on Bayesian
decision network (BDN) models. In some embodiments, the UBD expert
system includes a user interface and incorporates probability data
based on expert opinions. The UBD expert system may include
multiple BDN models, such as a general UBD model, a flow UBD
drilling model, a gaseated (i.e., aerated) UBD drilling model, a
foam UBD model, a gas (e.g., air or other gases) UBD model, a mud
cap UBD model, an underbalanced liner drilling (UBLD) model, an
underbalanced coil tube (UBCT) drilling model, and a snubbing and
stripping model. Each model may include multiple sections and may
receive inputs and provide outputs, such as recommendations, based
on the inputs. The inputs to an uncertainty node of a BDN model may
include probabilities associated with each input, or a user may
select a specific input for the uncertainty node. Based on these
inputs, and the inputs to a decision node, a model may put
recommendations from a consequences node.
[0064] FIG. 1 is a block diagram that illustrates a system 100 in
accordance with an embodiment of the present invention. The system
100 includes a formation 102, a well 104, and an underbalanced
drilling (UBD) system 106. The system 100 also includes an
underbalanced drilling expert system 108 for use with the
underbalanced drilling system 106. As described further below, the
underbalanced drilling expert system 108 may be implemented on a
computer and may include one or more Bayesian decision networks to
evaluate inputs and output recommended UBD operations for use with
the underbalanced drilling system 106. As will be appreciated, the
well 104 may be formed on the formation 102 to provide for
extraction of various resources, such as hydrocarbons (e.g., oil
and/or natural gas), from the formation 102. In some embodiments,
the well 104 is land-based (e.g., a surface system) or subsea
(e.g., a subsea system).
[0065] The underbalanced drilling system 106 may develop the well
104 by drilling a hole into the formation 102 using a drill bit,
e.g., a roller cone bits, drag bits, etc. The underbalanced
drilling system 106 may generally include, for example, a wellhead,
pipes, bodies, valves, seals and so on that enable drilling of the
well 104, provide for regulating pressure in the well 16, and
provide for the injection of chemicals into the well 104. As used
herein, the term underbalanced drilling refers to a drilling
operation in which the wellbore pressure is purposely maintained at
a lower pressure than the fluid pressure in the formation 102.
Accordingly, the UBD drilling system 106 may include, for example,
dry air systems, mist systems, aerated mud systems, gaseated
systems, foam systems (e.g., stable foam systems) and other
suitable systems. During operation, various UBD-specific scenarios
may occur that require adjustments to different parameters of the
UDB drilling system 106, such as different equipment, different
operations, different tripping, different flow, different
connections, different gas injections, different gas and fluid
volumes, well kicks, different foams, different air and gas
systems, different mud caps, different underbalanced liners,
different underbalanced coil tubes, and snubbing and stripping. In
some embodiments, the well 104, underbalanced drilling system 106
and other components may include sensors, such as temperature
sensors, pressure sensors, and the like, to monitor the drilling
process and enable a user to gather information about well
conditions.
[0066] The underbalanced drilling system 106, well 104, and
formation 102 may provide a basis for various inputs 112 to the
underbalanced drilling expert system 108. For example, as described
below, temperature ranges, the formation 102, and potential hole
problems may be provided as inputs 112 to the underbalanced
drilling expert system 108. The underbalanced drilling expert
system 108 may access an expert data repository 114 that includes
expert data, such as probability data used by the underbalanced
drilling expert system 108. The expert data may be derived from
best practices, expert opinions, research papers, and the like. As
described further below, based on the inputs 112, the underbalanced
drilling expert system 108 may output recommendations for the
underbalanced drilling system 106. For example, the underbalanced
drilling expert system 108 may provide the optimal equipment, UBD
operations, tripping, connections, flow drilling operations, gas
injection processes, air and gas operations, and so on as described
further below. Based on these recommendations, different practices
may be selected and used in the UBD drilling system 106
[0067] FIG. 2 depicts a computer 200 implementing an underbalanced
drilling expert system 202 in accordance with an embodiment of the
present invention. As shown in FIG. 2, a user 204 may interact with
the computer 200 and the underbalanced drilling expert system 202.
In some embodiments, as shown in FIG. 2, the underbalanced drilling
expert system 202 may be implemented in a single computer 200.
However, in other embodiments, the underbalanced drilling expert
system 202 may be implemented on multiple computers in
communication with each other over a network. Such embodiments may
include, for example, a client/server arrangement of computer, a
peer-to-peer arrangement of computers, or any other suitable
arrangement that enables execution of the underbalanced drilling
expert system 202. In some embodiments, the underbalanced drilling
expert system 202 may implemented as a computer program stored on a
memory of the computer 200 and executed by a process of the
computer 200.
[0068] In some embodiments, the underbalanced drilling expert
system 202 may include a user interface 206 and an expert data
repository 208. The user interface 206 may be implemented using any
suitable elements, such as windows, menus, buttons, web pages, and
so on. As described in detail below, the underbalanced drilling
expert system 202 may include one or more Bayesian decision network
(BDN) models 210 that implemented Bayesian probability logic 212.
The BDN models 210 may evaluate selections of inputs and associated
probabilities 214 and output a decision 216 from the BDN model. In
the embodiments described herein, the BDN model 210 may include
nine different BDN models related to UDB drilling: a general
approach to UBD model, a flow UBD drilling model, a gaseated (i.e.,
aerated) UBD drilling model, a foam UBD model, a gas (e.g., air or
other gases) UBD model, a mud cap UBD model, an underbalanced liner
drilling (UBLD) model, an underbalanced coil tube (UBCT) drilling
model, and a snubbing and stripping model. Each model may include
multiple sections and is described in further detail below. The UBD
expert system 202 may include any one or combination of the models
mentioned above. The BDN models 210 may then calculate Bayesian
probabilities for the consequences resulting from the selected
inputs, and then output recommended operations. For each BDN model,
the output may include a table of probabilities for various
recommendations, a single recommendation based on the highest
Bayesian probability, or expected utility values for each BDN model
to enable to user to evaluate and select the operation having the
optimal expected utility for the selected inputs.
[0069] As described below, a user 204 may use the user interface
206 to enter selections 210 of inputs for the BDN model 210. The
associated probabilities for the inputs may be obtained from the
expert data repository 208. Based on the inputs 210, a user 204 may
receive the outputs 212 from the BDN model 210, such as recommended
UBD operations and expected utility values. The output 212 may be
provided for viewing in the user interface 206. Further, as
explained below, a user may return to the underbalanced drilling
expert system 202 to add or change the inputs 214. The BDN model
210 may recalculate the outputs 216 based on the added or changed
inputs 214 and the Bayesian probability logic 212. The recalculated
outputs 216 may then provide additional or changed recommended
underbalanced drilling practices and expected utility values. Here
again, the outputs 216 may be provided to the user in the user
interface 206. The user 204 may use a single BDN model of the UBD
expert system 202, or may use multiple models of the UBD expert
system 202, such as two, three, four, five, six, seven, eight, or
nine models of the UDB expert system 202.
[0070] FIGS. 3A-3I each depict a process corresponding to a BDN
model that may be implemented in a UBD expert system in accordance
with an embodiment of the present invention. As explained below, a
UBD expert system may include any one or combination of the BDN
models described below, and thus may executed any one or
combination of the processes described in FIGS. 3A-3I. FIG. 3A
depicts a process 300 of the operation of a general UBD BDN model
of a UBD expert system in accordance with an embodiment of the
present invention. Initially, a user interface for an underbalanced
drilling expert system may be provided to a user (block 302). From
the user interface, various selections of inputs may be received.
For example, selections of formation indicators may be received
(block 304) by the underbalanced drilling expert system. As
explained below, a user may enter a selection of one or more
possible formation indicators into the underbalanced drilling
expert system. Additionally, selections of UBD planning phases may
be received (block 306) by the underbalanced drilling expert
system. As explained below, inputs may be provided at any node of a
BDN model of the underbalanced drilling expert system.
Additionally, in some embodiments, equipment requirements may also
be selected and received by the underbalanced drilling expert
system (block 308). Finally planned operations a UBD system may be
selected and received by the model (block 310). As mentioned above,
any one of or combination of these selections may be received. As
described below, the BDN model enables a user to enter inputs at
any node of the BDN model.
[0071] Next, the received selections may be provided as inputs to
uncertainty nodes of a general UBD BDN model of the UBD expert
system (block 310), and the selected inputs may include associated
probability states, as determined from expert data 312. Next, the
data from the uncertainly nodes may be combined (i.e., propagated
to) a consequence node of the general UBD BDN model based on the
expert systems data (block 312). The propagation and determination
of consequences is based on the Bayesian logic described below in
FIGS. 4-15 and implemented in the UBD BDN model described below and
illustrated in FIGS. 16A-161. Next, general recommendations and
expected utility values may be calculated by the general UBD BDN
model (block 316). Finally, the recommendations and expected
utility values may be output in a user interface of the UBD expert
system (block 318).
[0072] FIG. 3B depicts a process 312 of the operation of flow UBD
BDN model of an UBD expert system in accordance with an embodiment
of the present invention. The process 312 illustrates inputs and
flow recommendations of the underbalanced drilling expert system,
as illustrated further below. Initially, a user interface for an
underbalanced drilling expert system may be provided to a user
(block 313). From the user interface, various selections of inputs
may be received. For example, selections of tripping types may be
received (block 314) by the underbalanced drilling expert system.
As explained below, a user may enter a selection of one or more
possible tripping types into the underbalanced drilling expert
system. Additionally, selections of connection types may be
received (block 315) by the underbalanced drilling expert system.
Finally, in some embodiments, flow drilling types may also be
selected by a user and received by the underbalanced drilling
expert system (block 316). As explained above, any one of or
combination of the selections described above may be input by a
user and received by the UBD expert system.
[0073] Next, the received selections may be provided as inputs to
uncertainty nodes of a flow UBD BDN model of the UBD expert system
(block 317), and the selected inputs may include associated
probability states, as determined from expert data 318. Next, the
data from the uncertainly nodes may be combined (i.e., propagated
to) a consequence node of the flow UBD BDN model based on the
expert systems data (block 319), as based on the Bayesian logic
described below in FIGS. 4-15 and implemented in the flow UBD BDN
model described below and illustrated in FIGS. 19A-19H. Next,
recommendations and expected utility values may be calculated by
the BDN model (block 320). Finally, the recommendations and
expected utility values may be output in a user interface of the
UBD expert system (block 321).
[0074] FIG. 3C depicts a process 324 of the operation of another
model of an underbalanced drilling expert system in accordance with
an embodiment of the present invention. The process 324 illustrates
a gaseated (i.e., aerated) UBD BDN model of the underbalanced
drilling expert system, as illustrated further below. Again, a user
interface for an underbalanced drilling expert system may be
provided to a user (block 325). From the user interface, various
selections of inputs may be received. For example, selections of a
gas injection process may be received (block 326) by the
underbalanced drilling expert system. As explained below, a user
may enter a selection of one or more gas injection processes into
the underbalanced drilling expert system. Additionally, selections
of gas and fluid volume limits may be received (block 327) by the
underbalanced drilling expert system. In some embodiments, kick
types of a UBD system may also be selected by a user and received
by the underbalanced drilling expert system (block 328). Finally, a
user selection of operational considerations of a gaseated UBD
system may also be received by the underbalanced drilling expert
system (block 329). Any one of or combination of these selections
may be received, as the gaseated UBD BDN model enables a user to
enter inputs at any node of the BDN model.
[0075] Next, the received selections may be provided as inputs to
uncertainty nodes of a gaseated UBD BDN model of the underbalanced
drilling expert system (block 330), and the selected inputs may
include associated probability states, as determined from expert
data 332. Next, the data from the uncertainly nodes may be combined
(i.e., propagated to) a consequence node of the gaseated UBD BDN
model (block 333) based on the Bayesian logic described below in
FIGS. 4-15 and the gaseated UBD BDN model described below and
illustrated in FIGS. 22A-22I. Next, recommendations and expected
utility values may be calculated by the BDN model (block 334).
Finally, the recommended circulation processes and expected utility
values may be output in a user interface of the underbalanced
drilling expert system (block 335).
[0076] FIG. 3D depicts a process 338 of the operation of a UBD
expert system and a fourth BDN model in accordance with an
embodiment of the present invention. The fourth BDN model may
include a foam UBD BDN model. Initially, a user interface for an
underbalanced drilling expert system may be provided to a user
(block 339). Here again, the user interface may provide for various
selections of inputs may be received. For example, selections of
foam systems considerations (e.g., challenges and technical limits)
may be received (block 340) by the underbalanced drilling expert
system. As explained below, a user may enter a selection of one or
more considerations into the UBD drilling expert system.
Additionally, selections of foam UBD system designs may be received
(block 341) by the underbalanced drilling expert system. As
explained below, inputs may be provided at any node of a BDN model
of the underbalanced drilling expert system and, as mentioned
above, any one of or combination of these selections may be
received.
[0077] Next, the received selections may be provided as inputs to
uncertainty nodes of a general UBD BDN model of the UBD expert
system (block 342), and the selected inputs may include associated
probability states, as determined from expert data 343. Next, the
data from the uncertainly nodes may be combined (i.e., propagated
to) a consequence node of the general UBD BDN model based on the
expert systems data (block 344) based on the Bayesian logic
described below in FIGS. 4-15 and implemented in the foam UBD BDN
model described below and illustrated in FIGS. 24A-24E. Next,
recommendations of foam systems and expected utility values may be
calculated by the foam UBD BDN model (block 345). Finally, the
recommendations and expected utility values may be output in a user
interface of the UBD expert system (block 346).
[0078] FIG. 3E depicts a process 348 of the operation of an
underbalanced drilling expert system implementing a fifth BDN model
in accordance with an embodiment of the present invention. The
process 348 depicts use of an air and gas UBD BDN model of the
underbalanced drilling expert system, as illustrated further below.
Here again, a user interface for an underbalanced drilling expert
system may be provided to a user (block 349). From the user
interface, various selections of inputs may be received. For
example, selections of rotary or hammer drilling may be received
(block 350) by the underbalanced drilling expert system. As
explained below, a user may enter a selection of one or more rotary
or hammer drilling types into the underbalanced drilling expert
system. Additionally, selections of considerations (e.g., limits,
extremes, challenges, and the like) for air and gas drilling may be
received (block 351) by the underbalanced drilling expert system.
In some embodiments, gas drilling operation types of a UBD system
may also be selected by a user and received by the underbalanced
drilling expert system (block 352). Finally, a selection of gas
drilling rig equipment for an air and gas UBD system may also be
received by the underbalanced drilling expert system (block 353).
Any one of or combination of these selections may be received, as
the air and gas UBD BDN model enables a user to enter inputs at any
node of the BDN model.
[0079] Next, the received selections may be provided as inputs to
uncertainty nodes of the air and gas UBD BDN model of the
underbalanced drilling expert system (block 354), and the selected
inputs may include associated probability states, as determined
from expert data 355. Next, the data from the uncertainly nodes may
be combined (i.e., propagated to) a consequence node of the air and
gas UBD BDN model (block 356) based on the Bayesian logic described
below in FIGS. 4-15 and the gas BDN model described below and
illustrated in FIGS. 26A-26I. Using the Bayesian logic described
below, recommendations and expected utility values may then be
calculated by the air and gas BDN model (block 357). Finally,
recommendations and expected utility values may be output in a user
interface of the underbalanced drilling expert system (block
358).
[0080] FIG. 3F depicts a process 360 of the operation of a UBD
expert system and a sixth BDN model in accordance with an
embodiment of the present invention. The process 360 illustrates
the inputs to a mud cap BDN model of the underbalanced drilling
expert system, as illustrated further below. Initially, a user
interface for an underbalanced drilling expert system may be
provided to a user (block 361). From the user interface, various
selections of inputs may be received. For example, selections of
drilling problems may be received (block 362) by the underbalanced
drilling expert system. As explained below, a user may enter a
selection of one or more possible drilling problems into the
underbalanced drilling expert system. Selections of mud cap
drilling types may also be received (block 363) by the
underbalanced drilling expert system. Finally, in some embodiments,
floating mud cap drilling considerations may also be selected by a
user and received by the underbalanced drilling expert system
(block 364). As explained above, any one of or combination of the
selections described above may be input by a user and received by
the UBD expert system.
[0081] Next, the received selections may be provided as inputs to
uncertainty nodes of a flow UBD BDN model of the UBD expert system
(block 365), and the selected inputs may include associated
probability states, as determined from expert data 366. Next, the
data from the uncertainly nodes may be combined (i.e., propagated
to) a consequence node of the flow UBD BDN model based on the
expert systems data (block 367), as based on the Bayesian logic
described below in FIGS. 4-15 and implemented in the mud cap BDN
model described below and illustrated in FIG. 29A-29G. Next,
recommendations and expected utility values may be calculated by
the mud cap BDN model (block 368) and output in a user interface of
the UBD expert system (block 369).
[0082] FIG. 3G depicts a process 370 of the operation of a UBD
expert system and a seventh BDN model in accordance with an
embodiment of the present invention. As shown in FIG. 3G, the
process 370 illustrates the inputs to an underbalanced liner
drilling (UBLD) model of the underbalanced drilling expert system,
as illustrated further below. Initially, a user interface for an
underbalanced drilling expert system may be provided to a user
(block 371). From the user interface, various selections of inputs
may be received. For example, selections of UBLD solvable problems
may be received (block 372) by the underbalanced drilling expert
system. As explained below, a user may enter a selection of one or
more problems that may be solved by UBLD drilling into the
underbalanced drilling expert system. Additionally, selections of
UBLD plans may also be received (block 373) by the underbalanced
drilling expert system. Moreover, in some embodiments, UBLD
considerations, such as limits and challenges, may also be selected
by a user and received by the underbalanced drilling expert system
(block 374). As explained above, any one of or combination of the
selections described above may be input by a user and received by
the UBD expert system.
[0083] Next, the received selections may be provided as inputs to
uncertainty nodes of a UBLD BDN model of the UBD expert system
(block 375), and the selected inputs may include associated
probability states, as determined from expert data 376. The data
from the uncertainly nodes may then be combined (i.e., propagated
to) a consequence node of the UBLD BDN model based on the expert
systems data (block 378), as based on the Bayesian logic described
below in FIGS. 4-15 and implemented in the UBLD BDN model described
below and illustrated in FIGS. 32A-32G. Next, recommendations and
expected utility values may be calculated by the UBLD BDN model
(block 379) and output in a user interface of the UBD expert system
(block 380).
[0084] FIG. 3H depicts a process 382 of the operation of a UBD
expert system and an eighth BDN model in accordance with an
embodiment of the present invention. The eighth BDN model may
include an underbalanced coil tube UBCT BDN model. Initially, a
user interface for an underbalanced drilling expert system may be
provided to a user (block 383). Here again, the user interface may
provide for various selections of inputs may be received. For
example, selections of pre-planning types of a UBCT system may be
received (block 384) by the underbalanced drilling expert system.
As explained below, a user may enter a selection of one or more
preplans (i.e., preparation plans) for a UBCT drilling system into
the UBD drilling expert system. Additionally, selections of UBCT
drilling considerations (e.g., challenges) may be received (block
385) by the underbalanced drilling expert system. As explained
below, inputs may be provided at any node of a BDN model of the
underbalanced drilling expert system and, as mentioned above, any
one of or combination of these selections may be received.
[0085] Next, the received selections may be provided as inputs to
uncertainty nodes of a UBCT BDN model of the UBD expert system
(block 386), and the selected inputs may include associated
probability states, as determined from expert data 387. Next, the
data from the uncertainly nodes may be combined (i.e., propagated
to) a consequence node of the UBCT UBD BDN model based on the
expert systems data (block 388) based on the Bayesian logic
described below in FIGS. 4-15 and implemented in the UBCT UBD BDN
model described below and illustrated in FIGS. 35A-35E. Next,
recommendations for UBCT systems and expected utility values may be
calculated by the foam UBD BDN model (block 389). Finally, the
recommendations and expected utility values may be output in a user
interface of the UBD expert system (block 390).
[0086] Finally, FIG. 3I depicts a process 392 of the operation of a
snubbing and stripping BDN model of a UBD expert system in
accordance with an embodiment of the present invention. Initially,
a user interface for an underbalanced drilling expert system may be
provided to a user (block 393). From the user interface, various
selections of inputs may be received. For example, selections of
snubbing types may be received (block 394) by the underbalanced
drilling expert system. As explained below, a user may enter a
selection of one or more snubbing types into the underbalanced
drilling expert system. Additionally, selections of snubbing units
may be received (block 395) by the underbalanced drilling expert
system. Additionally, in some embodiments, snubbing operations may
also be selected and received by the underbalanced drilling expert
system (block 396). Finally, general stripping procedures for a UBD
system may be selected and received by the model (block 397). As
mentioned above, any one of or combination of these selections may
be received, and as described below, the BDN model enables a user
to enter inputs at any node of the BDN model.
[0087] Next, the received selections may be provided as inputs to
uncertainty nodes of a scrubbing and stripping BDN model of the UBD
expert system (block 398), and the selected inputs may include
associated probability states, as determined from expert data 399.
Next, the data from the uncertainly nodes may be combined (i.e.,
propagated to) a consequence node of the scrubbing and stripping
BDN model based on the expert systems data (block 400). The
propagation and determination of consequences is based on the
Bayesian logic described below in FIGS. 4-15 and implemented in the
scrubbing and stripping BDN model described below and illustrated
in FIGS. 37A-37I. Next, general recommendations and expected
utility values may be calculated by the scrubbing and stripping BDN
model (block 401). Finally, the recommendations and expected
utility values may be output in a user interface of the UBD expert
system (block 402).
[0088] FIGS. 4-15 depict an example of a BDN model simulating the
decision-making process of the selection of a swelling packer. The
model described below in FIGS. 4-15 is illustrative of the
application of a Bayesian decision network to the selection of a
swelling packer for use in a drilling system. Based on the
techniques illustrated in FIGS. 4-15 and described below, various
BDN model associated with an UBD expert system, such as that
described above in FIGS. 1 and 2 may be implemented. These BDN
models are illustrated in detail in FIGS. 16-39 and described
below. Thus, the techniques and implementation described in FIGS.
4-15 may be applied to the more detailed BDN models illustrated in
FIGS. 16-39.
[0089] FIG. 4 depicts a BDN model 400 for the selection of a
swelling packer in accordance with an embodiment of the present
invention. The BDN model 400 depicted in FIG. 4 includes a swelling
packer decision node 402, a treating fluid uncertainty node 404, a
drilling fluid type uncertainty node 406, a consequences node 408,
and a completion expert system value node 410. As will be
appreciated, the selection of a swelling packer may be relevant in
the completion of a well to production status. In the illustrated
BDN model 400, the various connection lines 412 indicate direct
dependencies between the different nodes. Accordingly, the
consequences node may be influenced by the inputs to the
uncertainty nodes 404 and 406 and the decision node 402. Similarly,
the treating fluid uncertainty node 404 may be influenced by the
swelling packer decision node 402.
[0090] After defining the BDN model 400, the probability states
associated with each node may be defined. FIGS. 5-7 depict various
tables illustrating the states, such as probability states,
associated with each node of the BDN model 400. The probability
distributions may be defined based on expert data entered in the
BDN model 400. FIG. 5 depicts a table 500 illustrating the states
associated with the swelling packer decision node 402. As shown in
table 500, the swelling packer decision node 402 may have a first
probability state 502 of "water swelling packer" and a second
probability state 504 of "oil swelling packer." Next, FIG. 6
depicts a table 600 illustrating the probability states associated
with the treating fluid uncertainty node 404. The probability
states associated with the treating fluid uncertainty node 404 are
influenced by the dependency on the swelling packer decision node
402. As shown in table 600, the probability states for two treating
fluids 602 ("Lactic acid") and 604 ("HCl acid") are shown. For
example, for a lactic acid treating fluid 602, the probability
state for a water swelling packer 606 is 0.9 and the probability
state for an oil swelling packer 608 is 0.5. Similarly, for an HCl
acid treating fluid 604, the probability state for the water
swelling packer 606 is 0.1 and the probability state for the oil
swelling packer 608 is 0.5.
[0091] FIG. 7 depicts a table 700 illustrating the probability
states associated with the drilling fluid type uncertainty node
406. As shown in the BDN model 400 depicted in FIG. 4, the drilling
fluid type uncertainty node 406 is influenced by the dependency on
the treating fluid uncertainty node 404 and the swelling packer
decision node 406. In the table 700, the probably states associated
with two drilling fluid types 702 ("Formate drilling fluid") and
704 ("CaCO.sub.3 drilling fluid") are depicted for combinations of
a water swelling packer 706, an oil swelling packer 708, a lactic
acid treating fluid 710, and an HCl acid treating fluid 712. For
example, as shown in FIG. 7, for the formate drilling fluid type
702, the probability state for the water swelling packer 706 and
lactic acid treating fluid 710 is 0.8 and the probability state for
the water swelling packer 706 and HCl acid treating fluid 712 is
0.2. Similarly, for the CaCO.sub.3 drilling fluid type 704, the
probability state for the water swelling packer 706 and lactic acid
treating fluid 710 is 0.2 and the probability state for the water
swelling packer 706 and HCl acid treating fluid 712 is 0.8. In a
similar manner, the table 700 of FIG. 7 depicts the probability
states for the oil swelling packer 708 and the various combinations
of lactic acid treating fluid 710 and the HCl acid treating fluid
712, and the formate drilling fluid type 702 and the CaCO.sub.3
drilling fluid type 704.
[0092] FIG. 8 depicts a table 800 illustrating the probability
states of the consequences node 408. The consequences node 408 is
influenced by its dependency on the swelling packer decision node
402, treating fluid uncertainty node 404, and the drilling fluid
type uncertainty node 406. As shown in table 800, the probability
states associated with two consequences 802 ("Recommended") and 804
("Not recommended") are depicted for various combinations of a
water swelling packer 806 or an oil swelling packer 808, a formate
drilling fluid type 810 or a CaCO.sub.3 drilling fluid type 812,
and a lactic acid treating fluid 814 or an HCl acid treating fluid
816. For example, for the Recommended consequence 802, the
probability state for the combination of water swelling packer 806,
the formate drilling fluid 810, and lactic acid treating fluid 814
is 0 and the probability state for the combination of the water
swelling packer 806, the formate drilling fluid 810, and HCl acid
treating fluid 816 is 1. In another example, as shown in table 800,
for the Not recommended consequence 804, the probability state for
combination of the water swelling packer 806, the formate drilling
fluid 810, and lactic acid treating fluid 814 is 1 and the
probability state for the combination of the water swelling packer
806, the formate drilling fluid 810, and HCl acid treating fluid
816 is 0.
[0093] In the BDN model 400, the consequences associated with the
consequences utility node 408 may be assigned input utility values.
FIG. 9 depicts a table 900 illustrating the input utility values
assigned to the consequences from the consequences utility node
408. As shown in table 900, a value 902 may be assigned to each
consequence of the consequence node 408. For a consequence 904 of
Recommended, an input utility value of 1 may be assigned.
Similarly, for a consequence 906 of Not Recommended, an input
utility value of 0 may be assigned. As described below, after the
probability states for the consequences are determined in the BDN
model 400, the input utility values assigned to each consequence
may be
[0094] Using the model and probabilities described above, the
functionality of the BDN model 400 will be described. After
receiving inputs to the model 400, the model 400 may simulate the
uncertainty propagation based on the evidence, e.g., the
probability states, at each node, using Bayesian probability
determinations. A Bayesian probability may be determined according
to Equation 1:
p ( hypothesis evidence ) = ( p ( evidence hypothesis ) p (
hypothesis ) p ( evidence ) ) ( 1 ) ##EQU00001##
Where:
[0095] p(hypothesis|evidence) is the probability of a hypothesis
conditioned upon evidence; p(evidence|hypothesis) is the
probability the evidence is plausible based on the hypothesis;
p(hypothesis) is the degree of certainty of the hypothesis; and
p(evidence) is the degree of certainty of the evidence.
[0096] Referring again to the BDN model 400 discussed above, the
model 400 illustrates that a selection of drilling fluid affects
the treating fluid and the swelling packer, as illustrated by the
dependencies in the model 400. First, the total probability for a
drilling fluid type may be calculated based on the evidence from
the uncertainty nodes by Equation 2:
i = 1 m P ( B A i ) P ( A i ) ( 2 ) ##EQU00002##
Where:
[0097] P(B|A.sub.i) is the probability based on B in view of
A.sub.i; P(A.sub.i) is the probability of A.sub.i; and m is the
total number of evidence items.
[0098] Using Equation 2, the total probability for a drilling fluid
type and lactic acid treating fluid may be calculated according to
Equation 3:
i = 1 m p ( formatedrillingfluid lacticacid i ) P ( lacticacid i )
( 3 ) ##EQU00003##
For example, using the probability data illustrated in FIGS. 6 and
7, the total probability for a formate drilling fluid type may be
calculated as the sum of 0.9 (probability for a lactic acid
treating fluid and water swelling packer) multiplied by 0.8
(probability for a formate drilling fluid type, lactic acid
treating fluid, and water swelling packer) and 0.1 (probability for
a lactic acid treating fluid and water swelling packer) multiplied
by 0.2 (probability for a lactic acid treating fluid and water
swelling packer).
[0099] The results of the total probability calculations for
drilling fluid types are illustrated in table 1000 depicted in FIG.
10. Table 1000 depicts the total probabilities for various
combinations of drilling fluids 1002 ("Formate drilling fluid) and
1004 ("CaCO3 drilling fluid") and a water swelling packer 1006 and
an oil swelling packer 1008. As explained above, the total
probabilities at the drilling fluid uncertainty node are dependent
on the evidence from the treating fluid uncertainty node and the
swelling packer decision node. As shown in table 1000 of FIG. 10,
the total probability for a formate drilling fluid 1002 and the
water swelling packer 1006 is 0.74, and the total probability for a
formate drilling fluid 1002 and the oil swelling packer 1008 is
0.5. Similarly, total probabilities for the CaCO.sub.3 drilling
fluid type 1004 are also depicted in table 1000.
[0100] Using the total probabilities determined above, the Bayesian
probability determination of Equation 1 may be used to calculate
the Bayesian probability of a treating fluid used with a specific
drilling fluid type and a particular swelling packer. Accordingly,
a Bayesian probability may be derived by combining the Bayesian
probability of Equation 1 with the total probability calculation of
Equation 2, resulting in Equation 4:
P ( A j B ) = p ( B A j ) P ( A j ) i = 1 m P ( B A i ) ( P ( A i )
( 4 ) ##EQU00004##
[0101] Thus, based on Equation 4, the Bayesian probability
determination for a lactic acid treating fluid and a formate
drilling fluid type for a water swelling packer may be determined
according to Equation 5, using the total probabilities depicted in
the table 700 of FIG. 7 and the table 1000 of FIG. 10:
P ( lacticacid formate = ( P ( formate lacticacid ) P ( lacticacid
) P ( formate ) ) = 0.8 .times. 0.9 0.74 = 0.9729 ( 5 )
##EQU00005##
[0102] As depicted above in FIG. 7, the probability associated with
a formate drilling fluid type conditioned on lactic acid treating
fluid is 0.8 and the probability of lactic acid for a water
swelling packer is 0.9. Additionally, as calculated above in FIG.
10, the total probability associated with a formate drilling fluid
and a water swelling packer is 0.74. Using these probabilities, the
Bayesian probability for a lactic acid treating fluid and a formate
drilling fluid type may be calculated as shown in Equation 5.
Similarly, Equation 6 depicts the Bayesian probability
determination for an HCl treating fluid and a formate drilling
fluid type, as shown below:
P ( HClacid formate = ( P ( formate HClacid ) P ( HClacid ) P (
formate ) ) = 0.2 .times. 0.1 0.74 = 0.0270 ( 6 ) ##EQU00006##
[0103] As noted above, the values for the probabilities depicted in
Equation 6 may be obtained from the probability states depicted in
tables 600 and 700 of FIGS. 6 and 7 and the total probability
calculations depicted in table 1000 of FIG. 10. In a similar
manner, Equations 7 and 8 depict the Bayesian probability
determinations for a CaCO.sub.3 drilling fluid type:
P ( lacticacid CaCo 3 = ( P ( CaCo 3 lacticacid ) P ( lacticacid )
P ( CaCo 3 ) ) = 0.2 .times. 0.9 0.26 = 0.6923 ( 7 ) P ( HClacid
CaCo 3 = ( P ( CaCo 3 HClacid ) P ( HClacid ) P ( CaCo 3 ) ) = 0.8
.times. 0.1 0.26 = 0.3076 ( 8 ) ##EQU00007##
[0104] The Bayesian probability determinations may also be
performed for an oil swelling packer for the various combinations
of treating fluid and drilling fluid types. Using the probability
states depicted in tables 600 and 700 of FIGS. 6 and 7 and the
total probability calculations depicted in table 1000 of FIG. 10,
these Bayesian probability determinations are shown below in
Equations 9-12:
P ( lacticacid formate = ( P ( formate lacticacid ) P ( lacticacid
) P ( formate ) ) = 0.8 .times. 0.5 0.5 = 0.8 ( 9 ) P ( HClacid
formate = ( P ( formate HClacid ) P ( formate ) ) = 0.2 .times. 0.5
0.5 = 0.02 ( 10 ) P ( lacticacid CaCo 3 = ( P ( CaCo 3 lacticacid )
P ( lacticacid ) P ( CaCo 3 ) ) = 0.8 .times. 0.5 0.5 = 0.8 ( 11 )
P ( HClacid CaCo 3 = ( P ( CaCo 3 HClacid ) P ( HClacid ) P ( CaCo
3 ) ) = 0.2 .times. 0.5 0.5 = 0.2 ( 12 ) ##EQU00008##
[0105] The results of the calculations shown above in Equations
5-12 are depicted in table 1100 in FIG. 11. Table 1100 depicts the
Bayesian probability determinations for treating fluids 1102
("Lactic acid") and 1104 ("HCl acid") and swelling packers 1106
("water swelling packer") and 1108 ("oil swelling packer"). The
Bayesian probability determinations are shown for both a formate
drilling fluid type 1110 and CaCO.sub.3 drilling fluid type
1112.
[0106] After determining the Bayesian probabilities described
above, the BDN model 400 may be used to select a swelling packer
based on the inputs provided to the uncertainty nodes of the model
400. For example, the BDN model 400 may be used with two different
interpretations of the output to provide the optimal swelling
packer for the inputs provided to the model 400. In one
interpretation, the model 400 may receive a user selection of an
input for one uncertainty node, and an optimal swelling packer may
be determined based on the possible inputs to the other uncertainty
node. Thus, as shown table 1100 and FIG. 11, the drilling types
1110 and 1112 may be "Selected by user." By specifying a type of
drilling fluid, the respective Bayesian probability determinations
may be read from the table 1100.
[0107] FIG. 12 depicts a table 1200 illustrating the consequences
for a user selection of a CaCO.sub.3 drilling fluid type based on
the Bayesian probability determinations depicted in FIG. 11. For
example, if a CaCO.sub.3 drilling fluid type is used to drill a
well, the consequences of using a water swelling packer 1202 or an
oil swelling packer 1204 are depicted in table 1200. The
consequences illustrated in table 1200 may include a "Recommended"
consequence 1206 and a "Not Recommended" consequence 1208.
Accordingly, for a user selection of a CaCO.sub.3 drilling fluid
type, the Bayesian probabilities read from table 1100 for a water
swelling packer are 0.6923 for a lactic acid and 0.3076 for an HCl
acid treating fluid. Similarly, values for a user selection of a
CaCO.sub.3 drilling fluid type and an oil swelling packer are 0.8
and 0.2. As shown in FIG. 12, the Bayesian probability
determinations greater than 50% (0.5) may be provided as
Recommended consequences 1206 and the Bayesian probability
determinations less than 50% (0.5) may be included as Non
Recommended consequences 1208.
[0108] As mentioned above, table 900 of FIG. 9 depicts input
utility values associated with Recommended and Not Recommended
consequences. As shown in this table, a Recommended consequence has
an input utility value of 1 and a Not Recommended consequence has
an input utility value of 0. By combining the input utility values
and the Bayesian probabilities depicted in FIG. 12, the expected
utility may be calculated according to Equation 13:
Expectedutiilty = i = 1 n consequenceresult .times.
inpututilityvalue ( 13 ) ##EQU00009##
Where:
[0109] Expectedutility is the expected utility value; Consequence
result is the Bayesian probability value associated with a
consequence; Inpututilityvalue is the input utility value
associated with a consequence; and n is the total number of
consequences.
[0110] Accordingly, based on the input utility values depicted in
FIG. 9 and the Bayesian probabilities depicted in FIG. 12, the
expected utility value may be calculated using Equation 13. For
example, for a user selection of a CaCO.sub.3 drilling fluid type,
the Bayesian probability associated with the Recommended
consequence is 0.6923 (table 1100 in FIG. 11) and the input utility
value associated with the Recommended consequence is 1 (table 900
in FIG. 9). Similarly, for a user selection of a CaCO.sub.3
drilling fluid type, the Bayesian probability associated with the
Recommended consequence is 0.3076 (table 1100 in FIG. 11) and the
input utility value associated with the Recommended consequence is
0 (table 900 in FIG. 9). The calculation of the expected utility
for a water swelling packer and a user selection of a CaCO.sub.3
drilling fluid type is illustrated below in Equation 14:
Expectedutiilty = i = 1 n consequenceresult .times.
inpututilityvalue = 0.6923 .times. 1 + 0.3076 .times. 0 = 0.6923 (
14 ) ##EQU00010##
[0111] The calculation the expected utility of the expected utility
for an oil swelling packer and a user selection of a CaCO.sub.3
drilling fluid type is illustrated below in Equation 15:
Expectedutiilty = i = 1 n consequenceresult .times.
inpututilityvalue = 0.8 .times. 1 + 0.2 .times. 0 = 0.8 ( 15 )
##EQU00011##
[0112] The results of the calculations performed in Equations 14
and 15 are summarized in FIG. 13. FIG. 13 depicts a table 1300
showing the expected utility 1302 calculated above. As shown in
this figure, the expected utility 1302 for a water swelling packer
1304 is 0.6293 (Equation 14), and the expected utility 1302 for an
oil swelling packer 1306 is 0.8 (Equation 15). Thus, after
inputting a drilling fluid type in the drilling fluid uncertainty
node 406 in the BDN model 400, the BDN model 400 may output these
expected utility values for the swelling packers associated with
the swelling packer decision node 402. Based on these expected
utility values, a user may select an optimal swelling packer for
use with the selected drilling fluid type. For example, a user may
select the swelling packer with the higher expected utility value,
i.e., the oil swelling packer. That is, as shown in table 1300 of
FIG. 13, the expected utility value of 0.8 associated with the oil
swelling packer is greater than the expected utility value of
0.6923 associated with the water swelling packer.
[0113] In other interpretations, a user may input values for all of
the uncertainty nodes of the BDN model 400 to determine the optimal
selection of a swelling packer. In such instances, the consequences
may be determined directly from the consequences node 408 of the
BDN model 400, as depicted above in table 800 of FIG. 8. For
example, a user may select inputs for the treating fluid
uncertainty node 404 and the drilling fluid type uncertainty node
406 of the BDN model 400. Accordingly, FIG. 14 depicts a table 1400
showing the consequences for different swelling packers based on a
user selection of a formate drilling fluid type and a lactic acid
treating fluid. As shown in FIG. 14, the consequences may include a
"Recommended" consequence 1402 and a "Not Recommended" consequence
1404 for both a water swelling packer 1406 and an oil swelling
packer 1408. For a user selection of a formate drilling fluid type
and a lactic acid treating fluid, table 800 of FIG. 8 shows a
Recommended consequence value of 0 Not Recommended consequence
value of 1 for a water swelling packer. Accordingly, the table 1400
shows that the water swelling packer 1406 has a Recommended
consequence value of 0 and a Not Recommended consequence value of
1. Similarly, for a user selection of a formate drilling fluid type
and a lactic acid treating fluid, table 800 of FIG. 8 shows a
Recommended consequence value of 1 and a Not Recommended
consequence value of 0 for an oil swelling packer. Thus, the table
1400 shows that the oil swelling packer 1408 has a Recommended
consequence value of 1 and a Not Recommended consequence value of
0.
[0114] Based on the consequences described above, the expected
utility for the different swelling packers may be determined using
Equation 13 described above. For example, based on table 1400 of
FIG. 14, the calculation of the expected utility for a water
swelling packer is illustrated below in Equation 16:
Expectedutiilty = i = 1 n consequenceresult .times.
inpututilityvalue = 0 .times. 1 + 1 .times. 0 = 0 ( 16 )
##EQU00012##
[0115] Similarly, the calculation of the expected utility for an
oil swelling packer, using the values for consequences shown in
table 1400 of FIG. 14, is illustrated below in Equation 17:
Expectedutiilty = i = 1 n consequenceresult .times.
inpututilityvalue = 1 .times. 1 + 0 .times. 0 = 0 ( 17 )
##EQU00013##
[0116] FIG. 15 depicts a table 1500 illustrated the results of the
calculations performed above in Equations 16 and 17. An expected
utility 1502 for a water swelling packer 1504 and an oil swelling
packer 1506 is illustrated in table 1500. Based on a user selection
of a formate drilling fluid type and a lactic acid treating fluid,
an expected utility value for the water swelling packer 1504 is 0
and the expected utility value for the oil swelling packer 1506 is
1. Based on these values, a user may select a swelling packer for
use based on the BDN model 400. For example, a user may select the
swelling packer with the higher expected utility value in table
1500, i.e., an oil swelling packer. Here again, a user may select
an optimal swelling packer for use with the inputs, i.e., a
selected treating fluid and drilling fluid type, provided to the
BDN model 400. For example, a user may select the swelling packer
with the higher expected utility value, i.e., the oil swelling
packer. That is, as shown in table 1500 of FIG. 15, the expected
utility value of 1 associated with the oil swelling packer is
greater than the expected utility value of 0 associated with the
water swelling packer.
[0117] With the above concepts in mind, the BDN modeling techniques
described above may be applied to more complicated models for an a
UBD system. Such models may serve as a training tool or a guide to
aid engineers, scientists, or other users in selecting and
executing operations of an UBD system. FIGS. 16-39 describe various
BDN models related to UDB systems that may be implemented in a UBD
expert system. As mentioned above, a UBD expert system may
implement multiple models, such as one of or any combination of the
BDN models described further below.
[0118] FIG. 16A depicts an example of a general UDB BDN model 1600
in accordance with an embodiment of the present invention. The
general UBD BDN model 1600 may be divided into four sections: a
formation section 1602, a planning phase section 1604, an equipment
section 1606, and an operations types section 1608. The nodes of
each section of the general UBD BDN model 1600 are described
further below. As shown in FIG. 16, the connection lines 1609
indicate the dependencies between each node of the model 1600. The
formation section 1602 of the general UBD BDN model 1600 includes a
formation indicators uncertainty node 1610, a formation
characteristics decision node 1612, and a formation consequences
node 1614. The formation consequences node 1614 is dependent on the
inputs to the formation indicators uncertainty node 1610 and the
formation characteristics decision node 1612.
[0119] The planning phase section 1604 of the general UBD BDN model
1600 may include a planning phases uncertainty node 1616, a
planning phases recommendations decision node 1618, and a planning
phases consequences node 1620. As shown in the general UBD BDN
model 1600, the planning phases consequences node 1620 is dependent
on the inputs to the planning phases uncertainty node 1616 and the
planning phases recommendations decision node 1618. The equipment
section 1606 of the general UBD BDN model 1600 may include an
equipment requirements uncertainty node 1622, an equipment
recommendations decision node 1624, and an equipment consequences
node 1626. As shown in FIG. 16A, the equipment consequences node
1626 is dependent on the inputs to the equipment requirements
uncertainty node 1622 and the equipment recommendations decision
node 1624.
[0120] Finally, the operation planning section 1608 of the general
UBD BDN model 1600 includes an operations types uncertainty node
1628, an operations decision node 1630, and an operations
consequences node 1632 that is dependent on the inputs to the nodes
1628 and 1630. The output from each section 1602, 1604, 1606, and
1608 of the general UBD BDN model 1600 is propagated to a final
consequences node 1634 and a general UBD expert node 1636. Thus,
the final consequences node 1634 is dependent on the consequences
nodes 1614, 1620, 1626, and 1632 of each section of the general UBD
BDN model 1600.
[0121] In some embodiments, the BDN model 1600 may be implemented
in a user interface similar to the depiction of the model 1600 in
FIG. 16A. In such embodiments, for example, each node of the model
1600 may include a button 1638 that enables a user to select a
value for the node or see the determinations performed by a node.
For example, as described below, a user may select (e.g., click)
the button 1638A to select a formation indicator input for the
model 1600, select the button 1638D to select a planning phases
input for the model 1600, and so on. The inputs may be displayed in
a dialog box or other user interface element.
[0122] FIGS. 16B-16I depict the selectable inputs for each node of
the UBD BDN model 1600 in accordance with an embodiment of the
present invention. FIGS. 16B and 16C depict the selectable inputs
for the formation section 1602 of the BDN model 1600. FIG. 16B
depicts inputs 1640 for the formation indicators node 1610. As
shown in FIG. 16B, the inputs 1640 may include selectable formation
indicators and may include N number of inputs from
"formation_indicator.sub.--1" to "formation_indicator_N." As will
be appreciated, in some embodiments the inputs 1640 may include
associated probabilities, such as probabilities p.sub.--1 through
p_N. In some embodiments, a user may select one the inputs 1640
instead of using probabilities associated with the inputs 1640. The
inputs 1640 may correspond to possible formation indicators
corresponding to formations drilled by underbalanced drilling
system. For example, in some embodiments the formation indicators
may include the following: "Depeleted_reservoirs",
"Naturally_fractured_and_vugular_formation", "Hard_rock_formation",
"Highly_permeable_formations," and
"Formations_susceptible_to_formation_damage_to_fluid_invasion".
[0123] FIG. 16C depicts inputs for the formation characteristics
decision node 1612 in accordance with an embodiment of the present
invention. As shown in FIG. 16C, the inputs 1642 may include
characteristics of different formations and may have N number of
inputs from "characteristic.sub.--1" to "characteristic N." The
inputs 1642 may correspond to characteristics of different
formations when used with an underbalanced drilling system. For
example, in some embodiments, the inputs 1642 may include the
following:
"Typically_exhibit_lost_circulation_and_differential_sticking_problems_an-
d_a_consolidated_formation_is_an_excellent_UBD_candidate",
"Usually_exhibit_huge_losses_which_can_increase_the_chance_of_well_contro-
l_problems_or_lead.sub.--to_differential_or_mechanical_sticking_making_thi-
s_type_of_formation_a_good_candidate_for_UBD",
"Usually_consolidated_and_therefore_can_sustain_UBD_and_UBD_will_provide_-
an_improve ment_in_ROP_and_bit_life_in_hard_rock",
"Typically_exhibit_lost_circulation_and_differential_sticking_problems_an-
d_a_consolidated_formation_makes_an_excellent_UBD_candidate",_and_"Fluid_i-
nvasion_can_be_minimized_or_even_eliminated_with_UBD." As shown in
the UBD BDN model 1600, the inputs for the formation indicators
node 1610 and the considerations node 1612 are propagated to the
consequences node 1614.
[0124] FIGS. 16D and 16E depict inputs for the planning phases
section 1604 of the UBD BDN model 1600. FIG. 16D depicts the inputs
for the planning phases uncertainty node 1616 in accordance with an
embodiment of the present invention. The planning phases
uncertainty node 1616 may provide for the input of planning phases
for implementation of an UBD system. As shown in FIG. 16D, inputs
1644 may be planning phases and may include N number of inputs
"phase.sub.--1" through "phase_N." As will be appreciated, in some
embodiments the inputs 1644 to the uncertainty node 1616 may
include associated probabilities, such as probabilities p.sub.--1
to p_N associated with each input phase.sub.--1 to phase N. In some
embodiments, a user may select one the inputs 1644 instead of
relying on the probability distribution associated with the inputs
1644. For example, in some embodiments the inputs 1644 may include
"Phase.sub.--1", "Phase.sub.--2", "Phase.sub.--3", and
Phase.sub.--4".
[0125] Additionally, FIG. 16E depicts inputs 1646 for the planning
phases recommendations decision node 1618 in accordance with an
embodiment of the present invention. The inputs 1646 may include
planning phases recommendations and may have N number of inputs
from "phase_recommendation.sub.--1" through
"phase_recommendation_N." For example, in some embodiments, the
inputs 1646 may include:
"Planning_involves_preliminary_data_gathering_candidate_screening_and_fea-
sibility_studies_to_result_in_a_quick_look_at_the_project_and_allow_for_bu-
dgetary_proposals_including_basic_equipment_and_personnel_set_up",
"Planning_involves_detailed_hydraulic_modeling_write_up_of_the_well_plan_-
to_be_added_to_the_client_drilling_plan_detailed_equipment_setup_and_drawi-
ngs_and_operating_procedures_and_personnel_selection",
"Execution_may_or_may_not_involve_engineering_at_site_as_a_minimum_it_wil-
l_require_UBD_supervision_at_the_rig_location", and
"Close_out_will_involve_issuance_of_an_end_of_well_report_closure_of_any_-
service_qualify_issue_outstanding_and_archiving_data_gathered_during_the_w-
ell".
[0126] FIGS. 16F and 16G depict inputs for the equipment section
1606 of the general UBD BDN model 1600. Accordingly, FIG. 16F
depicts inputs 1648 for the equipment requirements uncertainty node
1622 in accordance with an embodiment of the present invention. As
shown in FIG. 16F, the inputs 1648 may include equipment
requirements and may have N number of inputs from
"Equipment.sub.--1" to "Equipment_N." As will be appreciated, in
some embodiments the inputs 1648 may include associated
probabilities, such as respective probabilities p.sub.--1 to p_N.
associated with each input, and a user may select one the inputs
1648 instead of relying on the probability distribution associated
with the inputs 1648. In some embodiments, for example, the inputs
1648 may include: "Drill_string_requirement_and_BHA_components",
"Rotating_control_device", "Four-phase_separation_system",
"ESD_valve", "Secondary_flow_line", and "Geologic_sampler."
Additionally, FIG. 16G depicts inputs 1650 for the equipment
recommendations decision node 1624 in accordance with an embodiment
of the present invention. As shown in FIG. 16G, the inputs 1650 may
be equipment recommendations and may include N number of inputs
from "Equipment_Rec.sub.--1" to "Equipment_Rec_N." The inputs 1650
may include detailed recommendations for various equipment, such as
the equipment input to the uncertainty node 1622, used in an UBD
system.
[0127] Next, FIGS. 16H and 16I depict the inputs for the operations
section 1608 of the general UBD BDN model 1600. FIG. 16H depicts
inputs 1652 for the operations types uncertainty node 1628 in
accordance with an embodiment of the present invention. The inputs
may include operation types for operating a UBD system and may
include N number of inputs from "operation_type.sub.--1" to
"operation_type_N." In some embodiments, the inputs 1652 may
include "Low_pressure_well", and "High_pressure_well." Similarly,
FIG. 16I depicts inputs 1654 for the operations types
recommendations decision node 1630 in accordance with an embodiment
of the present invention. The inputs 1654 to the decision node 1630
may include operations type recommendations and may have N number
of inputs from "Operation_rec.sub.--1" to "Operation_rec_N." The
inputs 1654 may be detailed recommendations for operation types for
operating a UBD system.
[0128] After selecting one or more inputs for the nodes of the
different sections 1602, 1604, 1606, and 1610 of the UBD BDN model
1600, the inputs may be propagated to the various consequence nodes
1614, 1620, 1626, and 1632 of each section, and then to the final
consequences node 1634. The UBD BDN model 1600 may propagate the
inputs using the Bayesian probability determinations described
above in Equations 1, 2, and 4. By using the probabilities
associated with the inputs, the UBD BDN model 1600 may then provide
recommendations or expected utilities at each consequence node
1614, 1620, 1626, and 1632 for each section. Additionally, the UBD
BDN model 1600 may provide recommendations or expected utilities at
the final consequence node 1632 based on the propagated outputs
from the consequence nodes 1614, 1620, 1626, and 1632. In some
embodiments, the uncertainty nodes of the UBD BDN model 1600 may
have inputs with associated probabilities. A user may select an
input for one or more uncertainty nodes and view the
recommendations based on the propagation of the selected input. For
example, a user may select an input for the formation indicators
node 1610 and receive a recommended formation consideration at the
consequences node 1614. For example, a user may also select an
input for the planning phases uncertainty node 1616 and receive a
recommended planning phase recommendation (based on the inputs to
the planning phases recommendations decision node 1618) from the
consequences node 1620. One or more of the sections 1602, 1604,
1606, and 1608 of the UBD BDN model 1600 may be used; thus, a user
may use one or more sections of the UBD BDN model 1600 but not use
the remaining sections of the UDB BDN model 1600.
[0129] FIGS. 17 and 18 depict selection of inputs and a
corresponding output of the formation section 1602 of the UBD BDN
model 1600 in accordance with an embodiment of the present
invention. As shown in FIG. 17, a user may select an input for the
formation indicator uncertainty node 1610, such as indicator based
on a formation to be drilled via a UBD system. As shown in FIG. 17,
for example, a user may select
"Naturally_fractured_and_singular_formation" as an input 1700 for
the formation indicator uncertainty node 1610. In some embodiments,
the input 1700 may be displayed, such as in a dialog box or other
user interface element, to indicate the input to the uncertainty
node 1610. After entering an input for the formation indicators
uncertainty node 1610, the user may select the consequences node
1614 to view the recommendations based on the selected input and
the inputs to the decision node 1612.
[0130] FIG. 18 depicts an example of the output from the
consequences node 1614 of the formation section 1602 based on the
input described above in FIG. 17 and in accordance with an
embodiment of the present invention. As shown in FIG. 18, in some
embodiments the output may be provided as a table 1800 displaying
probability states 1802 for formation characteristics 1804 (as
input to the formation characteristics decision node 1612). As
shown in table 1800, for example, the formation consideration
"Formation_consideration.sub.--2" has a probability state of 0.1.
The other considerations shown in table 1800 have probability
states of 0. Thus, a user may decide to base a UBD system
implementation on the consideration having the highest expected
probability state in the table 1800. In other embodiments, the
output may be provided as a dialog box displaying the
recommendation having the highest expected probability state (such
that other recommendations having lower probability states are not
displayed).
[0131] As mentioned above, in some embodiments a UBD expert system
may also include a flow UBD BDN model. FIGS. 19A-19H depicts a flow
UBD BDN model 1900 in accordance with an embodiment of the present
invention. The flow UBD BDN model 1900 may include three sections:
a tripping section 1902, a connection section 1904, and a flow
drilling section 1906. The nodes of each section are described
further below. As shown in FIG. 19, the connection lines 1907
indicate the dependencies between each node of the flow UDB BDN
model 1900. The tripping section 1902 of the flow UBD BDN model
1900 includes a tripping types uncertainty node 1908, a
permeability level uncertainty node 1910, a tripping options
decision node 1912, and a tripping recommendation consequences node
1914. The tripping recommendation consequences node 1914 is
dependent on the inputs to the uncertainty nodes 1908 and 1910 and
the decision node 1912. The inputs to these nodes will be discussed
further below.
[0132] The connection section 1904 of the flow UBD BDN model 1900
includes a connection types uncertainty node 1916, a connection
options decision node 1918, and a connection recommendations
consequence node 1920. The connection recommendations consequence
node 1920 is dependent on inputs to the uncertainty node 1916 and
the decision node 1918. Finally, the flow drilling section 1906
includes a flow drilling types uncertainty node 1922, a flow
drilling options decision node 1924, and a flow drilling
recommendations consequence node 1926. As shown in FIG. 19, the
flow drilling recommendations consequence node 1926 is dependent on
the uncertainty node 1922 and the decision node 1924. The output
from each consequence node 1914, 1920, and 1926 of each section
1902, 1904, and 1906 may be propagated to a final consequences node
1928 and a flow UBD expert node 1930. Thus, the final consequences
node 1928 is dependent on the consequences nodes 1914, 1920, and
1926.
[0133] In some embodiments, the flow BDN model 1900 may be
implemented in a user interface similar to the depiction of the
model 1600 in FIG. 16A. In such embodiments, for example, each node
of the model 1900 may include a button 1932 that enables a user to
select a value for the node or see the determinations performed by
a node. For example, as described below, a user may select (e.g.,
click) the button 1932A to select a tripping input for the model
1600, select the button 1932E to select a connection input for the
model 1600, and so on.
[0134] FIGS. 19B-19H depict the inputs for each node of the flow
UBD BDN model 1600 in accordance with an embodiment of the present
invention. In these figures, the various section delineations and
associated reference numbers may be omitted for clarity. FIGS.
19B-19D depict the inputs for the nodes of the tripping section
1902. For example, FIG. 19B depicts inputs 1934 for the tripping
types uncertainty node 1908 in accordance with an embodiment of the
present invention. As shown in FIG. 19B, the inputs 1934 may types
of tripping operations for a UBD system and may have N inputs from
"tripping.sub.--1" through "tripping_N." In some embodiments, for
example, the inputs 1934 may include "run_into_the_hole_(RIH)" and
"pull_out_of_hole_(POH)". As will be appreciated, in some
embodiments the inputs 1934 may include associated probabilities,
such as respective probabilities p.sub.--1 to p_N associated with
each input. Alternatively a user may select one the inputs 1934 to
view recommendations for a specific input.
[0135] FIG. 19C depicts inputs 1936 for the permeability level
uncertainty node 1910 of the flow UBD BDN model 1900 in accordance
with an embodiment of the present invention. The inputs 1936 may
include permeability levels and may have N number of inputs from
"perm_level.sub.--1" to "perm_level_N." For example, in some
embodiments the selectable permeability levels 1936 may include
"Low" and "High". As will be appreciated, in some embodiments the
inputs 1936 may include associated probabilities, such as
respective probabilities p.sub.--1 to p_N. associated with each
input, and a user may select one the inputs 1936 instead of using
the probability distribution associated with the inputs 1936.
[0136] Next, FIG. 19D depicts inputs 1938 for the tripping options
decision node 1912 in accordance with an embodiment of the present
invention. As shown in FIG. 19D, the inputs 1938 may include
tripping options 1938 and may have N number of inputs from
"tripping_option.sub.--1" to "tripping_option_N." In some
embodiments, for example, the inputs 1938 may include "RCD engaged
surface pressure needs to be constant by releasing fluid through
choke", "Turn_off_pump_and_close_choke_and_do_not_fill_hole",
"Use_mud_cap", and
"Turn_off_pump_and_close_choke_and_fill_holes_displacement_volume."
[0137] FIGS. 19E and 19F depict inputs for the connection section
1904 of the flow UBD BDN model 1600. FIG. 19E depicts inputs 1940
for the connection types uncertainty node 1916 in accordance with
an embodiment of the present invention. As shown in FIG. 19E, the
inputs 1940 may include connection types and may have N number of
inputs from "connection.sub.--1" to "connection_N." For example, in
some embodiments, the selectable connections 1940 may include
"On_connection" and "After_connection". As will be appreciated, in
some embodiments the inputs 1940 may include associated
probabilities, such as respective probabilities p.sub.--1 to p_N
associated with each input, and a user may select one the inputs
1940 instead of using the probability distribution associated with
the inputs 1940. Additionally, FIG. 19F depicts inputs 1942 for the
connection options decision node 1918. The inputs 1940 may include
options for connections in a UBD system and may have N number of
inputs from "connection_option.sub.--1" to "connection_option_N."
In some embodiments, the inputs 1940 may include
"Shutin_RCD_and_choke_system_and_keep_pump_on" and
"Start_pump_and_open_choke_slowly_and_lower_pipe." The inputs
described above may be propagated to the connection recommendation
consequences node 1920.
[0138] Finally FIGS. 19G and 19H depict inputs for the flow
drilling section 1906 of the flow UBD BDN model 1900. FIG. 19G
depicts inputs 1944 for the flow drilling types uncertainty node
1922 in accordance with an embodiment of the present invention. The
inputs 1944 include flow drilling types and may have N number of
inputs from "Flow_drilling.sub.--1" to "Flow_drilling_N." The
inputs 1944 may include different types of flow drilling used in a
UBD system. In some embodiments, the inputs 1944 may include
"Flow_drilling_with_normal_returns",
"Flow_drilling_with_formation_gas_or_fluid_returns",
"Flow_drilling_with_no_returns",
"Flow_drilling_with_no_returns_and_with_gas_rising_to_the_surface",
"Drilling_in_a_long_horizontal_or_high_angle_well",
"Circulating_density_changes", and
"Gel_strength_and_inertial_forces." As will be appreciated, in some
embodiments the inputs 1944 may include associated probabilities,
such as respective probabilities p.sub.--1 to p_N. associated with
each input. Alternatively a user may select one the inputs 1944 to
view recommendations for a specific input. Additionally, FIG. 19H
depicts inputs 1946 for the flow drilling options decision node
1924 in accordance with an embodiment of the present invention. The
inputs 1946 for the decision node 1924 may be flow drilling options
and may have N number of inputs from "flow_drilling_option.sub.--1"
to "flow drilling_option_N." The inputs 1946 options may include
options for various types of flow drilling.
[0139] After selecting one or more inputs for the nodes of the
different sections 1902, 1904, and 1906 of the flow UBD BDN model
1900, the inputs may be propagated to the various consequence nodes
1914, 1920, and 1926 of each section, and then to the final
consequences node 1928. The flow UBD BDN model 1900 may propagate
the inputs using the Bayesian probability determinations described
above in Equations 1, 2, and 4. By using the probably states
associate with the inputs, the flow UBD BDN model 1900 may then
provide recommendations or expected utilities at each consequence
node 1914, 1920, and 1926. Additionally, the flow UBD BDN model
1900 may provide recommendations or expected utilities at the final
consequence node 1928 based on the propagated outputs from the
consequence nodes 1914, 1920, and 1926. In some embodiments, the
uncertainty nodes of the UBD BDN model 1900 may have inputs with
associated probability distributions. In some embodiments, a user
may select an input for one or more uncertainty nodes and view the
recommendations based on the propagation of the selected input. For
example, a user may select an input for the tripping types
uncertainty node 1908, the permeability level uncertainty node
1910, or both, and receive a recommendation (based on the inputs to
the tripping options decision node 1912) at the consequences node
1914. Similarly, a user may select an input for the connection
types uncertainty node 1916 and receive a planning phase
recommendation (based on the inputs to the connection options
decision node 1918) from the consequences node 1920. One or more
sections 1902, 1904, and 1906 of the flow UBD BDN model 1900 may be
used; consequently, a user may use one or more sections of the flow
UBD BDN model 1900 and not use the remaining sections of the UDB
BDN model 1900.
[0140] FIGS. 20A-20B and 21 depict selections of inputs and a
corresponding output for the tripping section 1902 of the flow UBD
BDN model 1900. For example, as shown in FIG. 20A, a user may
select an input for the tripping types uncertainty node 1908 based
on a tripping operation used or to be implemented and in accordance
with an embodiment of the present invention. As shown in FIG. 20A,
for example, a user may select "run_in_hole_RIH" as an input 2000
for the node 1908. The selected input 2000 may be displayed, such
as in a dialog box or other user interface element, to indicate the
input to the uncertainty node 1908. Additionally, a user may select
inputs for other nodes, such as the permeability uncertainty node
1910. FIG. 20B depicts selection of an input 2002 for the
permeability uncertainty node 1910 in accordance with an embodiment
of the present invention. For example, as shown in FIG. 20B, a user
may select "High" as the input 2002 for the permeability
uncertainty node 1910. The selected input 2002 may be displayed,
such as in a dialog box or other user interface element, to
indicate the input for the node 1910.
[0141] After entering inputs for the tripping section 1902 of the
flow UBD BDN model 1900, a user may select the tripping
recommendation consequences node 1914 to view the recommendations
determined by the flow UBD BDN model 1900. As shown in FIG. 21, in
some embodiments the output from the tripping recommendation
consequences node 1914 may be provided as a table 2100 displaying
expected utilities 2102 (e.g., "Recommended" and "Not Recommended")
for tripping options 2104, i.e., the inputs to the tripping options
decision node 1912. Each of the tripping options may include an
expected utility value, such as 1 or 0, calculated according to
Equation 13 discussed above. For example, table 2100 depicts a the
tripping option "Use_mud_cap" as having a Recommended expected
utility value of 1 and a Not Recommended expected utility value of
0. The other tripping options depicted in FIG. 21 may have a
Recommended expected utility value of 0 and a Not Recommended
expected utility value of 1. Thus, a user may decide to implement
the tripping option having the highest expected utility depicted in
table 2100, such as the "Use_mud_cap" tripping option in the
present example. In other embodiments, the output may be provided
as a table displaying probability states for recommendations or a
dialog box displaying the recommendation having the highest
expected probability state (such that other recommendations having
lower probability states are not displayed).
[0142] In some embodiments, a UBD expert system may also include a
gaseated UBD BDN model. FIGS. 22A-22I depict a gaseated UBD BDN
model 2200 in accordance with an embodiment of the present
invention. As shown in FIG. 22A, the gaseated UBD BDN model 2200
may include four sections: a gas injection section 2202, a gas and
fluid volume section 2204, a kick section 2206, and an operational
section 2208. The nodes of each of these sections are described
further below. As also shown in FIG. 22, connection lines 2209
indicate the dependencies between each node of the gaseated UBD BDN
model 2200. The gas injection process section 2202 includes a gas
injection uncertainty node 2210, a gas injection process
characteristics decision node 2212, and a consequences node 2214.
As shown in the model 2200, the consequences node 2214 is dependent
on the uncertainty node 2210 and the decision node 2212.
[0143] Next, the gas and fluid volume section 2204 includes a gas
and fluid volume limits uncertainty node 2216, a requirements for
gas and fluid volume limits decision node 2218, and a consequences
node 2220 that is dependent on the uncertainty node 2216 and the
decision node 2218. Additionally, the kicks section 1606 includes a
kick type uncertainty node 2222, a well kicks recommendation
decision node 2224, and a consequences node 2226 dependent on the
uncertainty node 2222 and the well kicks recommendation node 2224.
Finally, the operational section 1618 includes an operational
considerations uncertainty node 2228, an operational
recommendations decision node 2230, and a consequences node 2232.
The consequences node 2232 is dependent on the uncertainty node
2228 and the operational recommendations node 2230. The output from
each consequence node 2214, 2220, 2226, and 2232 of each section
2202, 2204, 2206, and 2208 may be propagated to a final
consequences node 2234 and a flow UBD expert node 2234. Thus, the
final consequences node 2234 is dependent on the consequences nodes
2214, 2220, 2226, and 2232.
[0144] As described above with regard to the other BDN models, in
some embodiments the flow BDN model 2200 may be implemented in a
user interface similar to the depiction of the model 2200 in FIG.
22. In such embodiments, for example, each node of the model 2200
may include a button 2236 that enables a user to select a value for
the node or see the determinations performed by a node. For
example, as described below, a user may select (e.g., click) the
button 2236A to select a gas injection process input for the model
1600, select the button 2236D to select a gas and fluid volume
limit input for the model 2200, and so on.
[0145] FIGS. 22B-22I depict the inputs for each node of the
gaseated UBD BDN model 2200 in accordance with an embodiment of the
present invention. In some figures, the section delineations and
some reference numbers may be omitted for clarity. For example,
FIGS. 22B and 22C depict the inputs for the gas injection section
2202 of the gaseated UBD BDN model 2200. Accordingly, FIG. 22B
depicts inputs 2238 for the gas injection process uncertainty node
2210 in accordance with an embodiment of the present invention. The
inputs 2238 may include gas injection processes and may include N
number of inputs from "gas_injection.sub.--1" to "gas_injection_N."
As will be appreciated, in some embodiments the inputs 2238 may
include associated probabilities, such as probabilities p.sub.--1
through p_N. The inputs 2238 may include different gas injection
processes used in a UBD system. For example, in some embodiments
the inputs 2238 may include "Drill_pipe_injection",
"Drill_pipe_jet_sub", "Parasite_tubing_tubing_string", and
"Concentric_casing_string_or_dual_casing_string." Additionally,
FIG. 22C depicts inputs 2240 for the gas injection process
characteristics decision node 2212 in accordance with an embodiment
of the present invention. The inputs may include characteristics of
different gas injection processes for gaseated UBD systems and may
include N number of inputs from "Characteristic.sub.--1" to
"Characteristic_N." In some embodiments, the characteristics may
include benefits and challenges of using a particular gas injection
process.
[0146] FIGS. 22D and 22E depict the inputs for nodes of the gas and
fluid volume section 2204. For example, FIG. 22D depicts inputs
2242 for the gas and fluid volume limits node 2216 in accordance
with an embodiment of the present invention. The inputs 2242 may be
gas and fluid volume limits and may include N number of inputs from
"volume_limit.sub.--1" to "volume_limit.sub.--2." As will be
appreciated, in some embodiments the inputs 2242 may include
associated probabilities, such as probabilities p.sub.--1 through
p_N. The inputs 2242 may include types and definitions of limits on
gas and fluid volumes for a gaseated UBD system. For example, in
some embodiments the inputs 2242 may include "Gas_limit",
"Liquid_limit", "Back_pressure", and "Motor_constraints."
Additionally, FIG. 22E depicts inputs 2244 for the requirements for
gas and fluid volume limits decision node 2218 in accordance with
an embodiment of the present invention. As shown in FIG. 22E, the
inputs 2244 may be gas and fluid volume limit requirements and may
include N number of inputs from "Volume_limit_req.sub.--1" to
"Volume_limit_req.sub.--2." The inputs 2244 may correspond to
requirements for implementing specific gas and fluid volume limits
for UBD systems.
[0147] FIGS. 22F and 22G depict inputs for nodes of the kick
section 1606 of the gaseated UBD BDN model 2200. Accordingly, FIG.
22F depicts inputs 2246 for the kick type uncertainty node 2222 in
accordance with an embodiment of the present invention. The inputs
to the uncertainty node 2222 may include types of well kicks, and
the inputs 2246 may include N number of inputs from
"kick_type.sub.--1" to "kick_type_N. As will be appreciated, in
some embodiments the inputs 2246 may include associated
probabilities, such as probabilities p.sub.--1 through p_N. The
inputs 2246 may correspond to types of kicks that may be
experienced in a gaseated UBD BDN system. For example, in some
embodiments the inputs 2246 may include "Gas_flow" `and
"Water_oil_flow." Additionally, FIG. 22G depicts inputs 2248 for
the well kicks recommendation decision node 2224 in accordance with
an embodiment of the present invention. The inputs 2248 may include
recommendations for responding to well kicks that may be
encountered in a UBD system. As shown in FIG. 22G, the inputs 2248
may include N number of inputs from "well kick_rec.sub.--1" to
"well_kick_rec_N."
[0148] Finally, FIGS. 22H and 221 depict inputs for the nodes of
the operational section 1608 of the gaseated UBD BDN model 2200.
FIG. 22H depicts inputs 2250 for the operational considerations
uncertainty node 2228 in accordance with an embodiment of the
present invention. As shown in this figure, the inputs may be
operational considerations 2250 and may include N number of inputs
from "Operational_considerations.sub.--1" to
"Operational_considerations_N." As will be appreciated, in some
embodiments the inputs 2250 may include associated probabilities,
such as probabilities p.sub.--1 through p_N. The inputs 2250 may
include various considerations related to operation of a gaseated
UBD system. For example, in some embodiments the inputs 2250 may
include: "Cost", "Pressure_surges", "Unloading_the_casing",
"Connections", "Stripping_in_unbalanced_operation", "Snubbing",
"Inhibition", and "Periodic_kill". In some embodiments, the
considerations may include concerns, challenges, and other
considerations in operating a gaseated UBD system.
[0149] FIG. 22I depicts inputs 2252 for the operational
recommendations decision node 2230 in accordance with an embodiment
of the present invention. The inputs 2252 to the node 2230 may be
operational recommendations 2252 and may include N number of inputs
from "Operation_rec.sub.--1" and "Operational_rec_N". The inputs
2252 may include recommendations for operating a gaseated UBD
system in view of the considerations entered into the model
2200.
[0150] Here again, after selecting one or more inputs for the nodes
of the different sections 2202, 2204, 2206, and 2208 of the
gaseated UBD BDN model 1900, the inputs may be propagated to the
various consequence nodes 2214, 2220, 2226, and 2232 of each
section, and then to the final consequences node 2234, using the
Bayesian probability determinations described above in Equations 1,
2, and 4. By using the probably states associate with the inputs,
the gaseated UBD BDN model 2200 may then provide recommendations or
expected utilities at each consequence node 2214, 2220, 2226, and
2232. Additionally, the gaseated UBD BDN model 2200 may provide
recommendations or expected utilities at the final consequence node
2234 based on the propagated outputs from the consequence nodes
2214, 2220, 2226, and 2232. In some embodiments, the uncertainty
nodes of the UBD BDN model 2200 may have inputs with associated
probability distributions. In some embodiments, a user may select
an input for one or more uncertainty nodes and view the
recommendations based on the propagation of the selected input. For
example, a user may select an input for the gas injection process
uncertainty node 2210 and receive a recommendation (based on the
inputs to the gas injection processes characteristics decision node
2212) at the consequences node 2214. Similarly, a user may select
an input for the kick type uncertainty node 2222 and receive a
recommendation (based on the inputs to the well kicks
recommendations decision node 2224) from the consequences node
2226. One or more sections 2202, 2204, 2206, and 2208 of the
gaseated UBD BDN model 2200 may be used; consequently, a user may
use one or more sections of the gaseated UBD BDN model 2200 and not
use the remaining sections of the gaseated UDB BDN model 2200.
[0151] FIGS. 23A and 23B depict selections of inputs and a
corresponding output for the gas injection section 2202 of the
gaseated UBD BDN model 2200. As shown in FIG. 23A, a user may
select an input 2300 for the gas injection process uncertainty node
2210, such as a gas injection process used in a UBD system, in
accordance with an embodiment of the present invention. As shown in
FIG. 23A, for example, a user may select
"Concentric_casing_string_or_dual_casing_string" as the input 2300
for the node 2210. The selected input 2300 may be displayed, such
as in a dialog box or other user interface element, to indicate the
input to the node 2210.
[0152] After entering inputs for the gas injection section 2202 of
the gaseated UBD BDN model 2200, a user may select the consequences
node 2214 to view the recommendations determined by the gaseated
UBD BDN model 2200. FIG. 23B depicts an example of an output 2302
from the consequences node 2214 based on the input described above
in FIG. 23A and in accordance with an embodiment of the present
invention. As shown in FIG. 23B, in some embodiments the output
2302 may be presented as a dialog box or other user interface
element displaying the recommended output from the consequences
node 2214. For example, the output 2302 may be based on a
determination of a recommendation with the highest Bayesian
probability state as determined from the inputs received from the
uncertainty node 2310 and the decision node 2312. As shown in FIG.
23B, in some embodiments output 2302 may provide text describing
the characteristics (e.g., "Characteristic text), such as benefits
and challenges, for the gas injection process input into the gas
injection process uncertainty node 2210. Thus, a user may view
various recommendations for various inputs to aid in implementation
of a gaseated UBD system. In other embodiments, as described above,
the output from a BDN model may be provided as a table of expected
utility values, a table of Bayesian probability states for each
recommendation, or other suitable outputs.
[0153] Additionally, in some embodiments, a UBD expert system may
also include a foam UBD BDN model for use in determining optimal
operations for a foam UBD system. FIGS. 24A-24E depict a foam UBD
BDN model 2400 in accordance with an embodiment of the present
invention. The UDB BDN model 2400 may include two sections: a foam
systems considerations section 2402 and a foam system design
section 2404. Each of these sections is described further below.
Additionally, the connections lines 2405 depicted in FIG. 24A
illustrate the dependencies between the nodes of the foam UBD BDN
model 2400.
[0154] The foam systems considerations section 2402 includes a foam
systems considerations uncertainty node 2406, a foam systems
considerations decision node 2408, and a consequences node 2410
that is dependent on the uncertainty node 2406 and the decision
node 2408. The foam system design section 2404 includes a foam
system designs uncertainty node 2412, a foam systems designs
recommendations decision node 2414, and a consequences node 2416
that is dependent on the uncertainty node 2412 and the decision
node 2414. The output from each consequence node 2410 and 2416 may
be propagated to a final consequences node 2418 and a UBD expert
node 2420.
[0155] As described above with regard to the other BDN models, in
some embodiments, the foam UBD BDN model 2400 may be implemented in
a user interface similar to the depiction of the model 2400 in FIG.
24A. In such embodiments, for example, each node of the foam UBD
BDN model 2400 may include a button 2422 that enables a user to
select an input for the node or see the determinations performed at
a node. For example, as described below, a user may select (e.g.,
click) the button 2244A to select a consideration input for the
model 2400, select the button 2244D to select a foam systems design
input for the model 2400, and so on.
[0156] FIGS. 24B-24E depict the inputs for nodes of the foam UBD
BDN model 2400 in accordance with an embodiment of the present
invention. In some of these figures, the section delineations and
some reference numbers may be omitted for clarity. FIGS. 24B and
24C depict the inputs for the foam systems considerations section
2402 of the foam UBD BDN model 2400. FIG. 24B depicts inputs 2424
for the foam systems considerations node 2406 in accordance with an
embodiment of the present invention. The inputs 2424 may include N
number of inputs from "Foam_consideration.sub.--1" to
"Foam_consideration_N." As will be appreciated, in some embodiments
the inputs 2424 may include associated probabilities, such as
probabilities p.sub.--1 through p_N. The inputs 2424 may include
considerations, such as challenges, technical limits, and so on,
for implementing a foam UBD system. For example, in some
embodiments the inputs 2424 may include "Cost", "Hot_holes",
"Foam_breakdown", and "One_pass_system_or_disposable_foam".
[0157] FIG. 24C depicts inputs 2426 for the foam systems
considerations recommendations decision node 2408 in accordance
with an embodiment of the present invention. The inputs for the
decision node 2408 may be foam systems recommendations and may
include N number of inputs from
"Foam_system_consideration_rec.sub.--1" to
"Foam_system_consideration_rec_N". The inputs 2426 may include
recommendations for implementing foam UBD systems.
[0158] Next, FIGS. 24D and 24E depict the inputs for the foam
systems design section 2402 in accordance with an embodiment of the
present invention. FIG. 24D depicts the inputs 2428 for the foam
systems designs uncertainty node 2412 in accordance with an
embodiment of the present invention. The inputs 2428 may include N
number of inputs from "foam_systems_design.sub.--1" to
"foam_systems_design_N." As will be appreciated, in some
embodiments the inputs 2428 may include associated probabilities,
such as probabilities p.sub.--1 through p_N. The inputs 2428 may
include design considerations for designing a foam UBD system. For
example, the selectable foam systems designs 2428 may include:
"General_operational_ideas", "Bottom_hole_pressure_reduction",
"The_effect_of_fluid_and_gas_volumes_on_hole_cleaning_and_motor_operation-
s", "Making_a_connection", and "Making_trips". FIG. 22E depicts
inputs 2430 for the foam systems designs recommendations decision
node 2414 in accordance with an embodiment of the present
invention. The inputs 2430 to the node 2414 may be foam systems
designs recommendations and may include N number of inputs from
"foam_systems_designs_details.sub.--1" to
"foam_systems_designs_details_N". The selectable inputs 2430 may
include detailed recommendations for of various foam system designs
input into the foam UBD BDN model 2400.
[0159] As described above, after selecting one or more inputs for
the nodes of the different sections 2402 and 2402 of the flow UBD
BDN model 2400, the inputs may be propagated to the consequence
nodes 2410 and 2416 of each section, and then to the final
consequences node 2418, using the Bayesian probability
determinations described above in Equations 1, 2, and 4. By using
the probably states associate with the inputs, the flow UBD BDN
model 2400 may then provide recommendations or expected utilities
at each consequence node 2410 and 2416\. Additionally, the flow UBD
BDN model 2400 may provide recommendations or expected utilities at
the final consequence node 2418 based on the propagated outputs
from the consequence nodes 2410 and 2416. In some embodiments, the
uncertainty nodes of the flow UBD BDN model 2400 may have inputs
with associated probability distributions. In some embodiments, a
user may select an input for one or more uncertainty nodes and view
the recommendations based on the propagation of the selected input.
For example, a user may select an input for the foam systems
considerations uncertainty node 2406 and receive a recommendation
(based on the inputs to the foam systems considerations decision
node 2408) at the consequences node 2410. Similarly, a user may
select an input for the foam systems design uncertainty node 2412
and receive a recommendation (based on the inputs to the foam
systems decision node 2414) from the consequences node 2416. One or
both sections 2402 and 2404 of the flow UBD BDN model 2400 may be
used; consequently, a user may use one or both sections of the flow
UBD BDN model 2400 and not use the remaining sections of the flow
UDB BDN model 2400.
[0160] FIGS. 25A and 25B depict selections of inputs and
corresponding outputs for the foam UBD BDN model 2400 in accordance
with an embodiment of the present invention. As depicted in FIG.
25A, a user may select an input 2500 for the foam systems
considerations uncertainty node 2406, such as specific
considerations expected in a current or prospective foam UBD
system. As shown in FIG. 25A, a user may select "hot_holes" as the
input 2500 for the uncertainty node 2406. The selected input 2500
may be displayed, such as in a dialog box or other user interface
element, to indicate the input to the node 2406.
[0161] FIG. 25B depicts an example of an output 2502 from the
consequences node 2408 based on the input described above in FIG.
25A and in accordance with an embodiment of the present invention.
As shown in FIG. 25B, in some embodiments the output 2502 may be
presented as a dialog box displaying the recommendation from the
consequences node 2410. For example, the output 2502 may be based
on a determination of a recommendation with the highest Bayesian
probability state as determined from inputs received from the
uncertainty node 2406 and the decision node 2408. As shown in FIG.
25B, in some embodiments the output may include text describing the
recommendation (e.g., "Recommendations text") for the selected
consideration input into the model 2400. Accordingly, a user may
view various recommendations for various inputs to aid in
implementation of a flow UBD system. In other embodiments, as
described above, the output from a BDN model may be provided as a
table of expected utility values, a table of Bayesian probability
states for each recommendation, or other suitable outputs. As will
be appreciated, the other sections of the flow UBD BDN model 2400
may receive inputs and provide outputs in a similar manner as
described above and illustrated FIGS. 25A and 25B.
[0162] As mentioned above, the UBD expert system may also include
an air and gas UBD BDN model for providing optimal operations for
an air and gas UBD system. FIGS. 26A-26I depict an air and gas UBD
BDN model 2600 and associated inputs in accordance with an
embodiment of the present invention. The air and gas UBD BDN model
2600 may include four sections, the nodes of which are described
further below: a rotary and hammer drilling section 2602, a
considerations section 2604, a gas drilling operations section
2606, and a rig equipment section 2608. The air and gas UBD BDN
model 2600 depicted in FIG. 26A also includes connection lines 2609
that indicate dependencies between the nodes of the air and gas UBD
BDN model 2600.
[0163] The rotary and hammer drilling section 2602 of the air and
gas UBD BDN model 2600 may include a rotary and hammer drilling
uncertainty node 2610, a rotary and hammer drilling recommendations
decision node 2612, and a consequences node 2614 that is dependent
on the uncertainty node 2610 and the decision node 2612. The
considerations section 2604 may include a gas drilling
considerations uncertainty node 2616, a gas drilling considerations
decision node 2618, and a consequences node 2620 that is dependent
on the uncertainty node 2616 and the decision node 2618.
Additionally, the gas drilling operations section 2606 includes a
gas drilling operations uncertainty node 2622, a gas drilling
recommendations decision node 2624, and a consequences note 2626
that is dependent on the uncertainty node 2622 and the decision
node 2624. Finally, as also shown in FIG. 26A, the rig equipment
section 2610 includes a gas drilling rig equipment uncertainty node
2628, a gas drilling rig equipment recommendations decision node
2630, and a consequences node 2632 that is dependent on the
uncertainty node 2628 and the decision node 2630. The output from
each of the consequences nodes 2614, 2620, 2626, and 2632 of each
section may be propagated to a final consequences node 2634 and an
air and gas UBD expert system node 2636. Accordingly, the final
consequences node 2634 is dependent on the consequences nodes 2614,
2620, 2626, and 2632, as illustrated by the connection lines
2609.
[0164] In some embodiments, as described with regard to the other
BDN models, the air and gas BDN model 2600 may be implemented in a
user interface similar to the depiction of the model 2200 in FIG.
22. In such embodiments, for example, each node of the model 2600
may include a button 2638 that enables a user to select a value for
the node or see the determinations performed by a node. For
example, as described below, a user may select (e.g., click) the
button 2638A to select a rotary and hammer drilling input for the
model 2600. Similarly, a user may select (e.g., click) the button
2638D to select a considerations input for the model 2600. As noted
above, evidence (inputs) may be introduced at any nodes of the
model 2600 and propagated throughout the air and gas BDN model 2600
using the Bayesian logic described above.
[0165] FIGS. 26B-26I depict the inputs for each node of the air and
gas UBD BDN model 2600 in accordance with an embodiment of the
present invention. In some figures, the section delineations and
some reference numbers may be omitted for clarity. FIGS. 26B and
26C depict the inputs for the rotary and hammer drilling section
2602 of the air and gas UBD BDN model 2600. Accordingly, FIG. 26B
depicts inputs 2640 for the rotary and hammer drilling uncertainty
node 2610 in accordance with an embodiment of the present
invention. The inputs 2640 to the uncertainty node 2610 may include
rotary and hammer drilling types and may include N number of inputs
from "rotary_and_hammer.sub.--1" to "rotary_and_hammer_N." As will
be appreciated, in some embodiments the inputs 2640 may include
associated probabilities, such as probabilities p.sub.--1 through
p_N. The inputs 2640 may include different types of rotary
drilling, hammer drilling, or both that may be used in an air and
gas UBD system. For example, in some embodiments, the inputs 2640
may include: "Rotary_drilling", "Hammer_drilling",
"Horizontal_drilling_with_air_hammers", and "Dual_drill_pipe".
Similarly, FIG. 26C depicts inputs 2642 for the rotary and hammer
drilling recommendations decision node 2612 in accordance with an
embodiment of the present invention. The inputs to the decision
node 2612 may be rotary and hammer drilling recommendations and may
include N number of inputs from "rotary_and_hammer_rec.sub.--1" to
"rotary_and_hammer_rec_N." The inputs 2642 may include recommended
practices for different rotary and hammer drilling types input into
the model 2600.
[0166] Next, FIGS. 26D and 26E depict inputs for the gas drilling
considerations section 2604 of the air and gas UBD BDN model 2600
in accordance with an embodiment of the present invention. FIG. 26D
depicts inputs 2644 for the gas drilling considerations uncertainty
node 2616 in accordance with an embodiment of the present
invention. As shown in FIG. 26D, the inputs 2644 may include N
number of inputs ranging from "Gas_drilling_considerations.sub.--1"
to "Gas_drilling_considerations_N." As will be appreciated, in some
embodiments the inputs 2644 may include associated probabilities,
such as probabilities p.sub.--1 through p_N. The inputs 2644 may
include different considerations or combinations thereof related to
gas drilling implementations in a UBD drilling system. In some
embodiments, the considerations may include limits, extremes,
challenges, and the like associated with gas drilling. As such, in
some embodiments, the inputs 2644 may include, for example:
"Water_or_wet_holes", "Hole_enlargment", "Depth_limits",
"Floating_bed", "Fishing_operations",
"Flashback_fire_in_the_blooie_line_from_the_flare",
"Downhole_fire", and "Air_drilling_and_hydrogen_sulfide_gas". FIG.
26E depicts inputs 2646 for the gas drilling considerations
recommendations decision node 2618 in accordance with an embodiment
of the present invention. The inputs 2646 may include N number of
recommendations ranging from
"gas_drilling_considerations_rec.sub.--1" to
"gas_drilling_considerations_rec_N". The inputs 2646 may include
recommendations, such as recommended practices, for gas drilling in
an UBD system or other recommendations for the considerations input
in to the model 2600.
[0167] Next, FIGS. 26F and 26G depict inputs for the gas drilling
operations section 2606. Accordingly, FIG. 26F depicts inputs 2648
for the gas drilling operations uncertainty node 2622 in accordance
with an embodiment of the present invention. The inputs 2448 for
the node 2622 may include gas drilling operations having N number
of inputs from "Gas_drilling_operation.sub.--1" to
"Gas_drilling_operation_N." As will be appreciated, in some
embodiments the inputs 2448 may include associated probabilities,
such as probabilities p.sub.--1 through p_N. The inputs 2648 may
include different types of gas drilling operations that may be used
in an air and gas UBD drilling system. For example, in some
embodiments, the inputs 2648 may include:
"Gas_drilling_volume_requirements", "Unloading_the_hole",
"Drying_the_hole", "Connections", "Hole_cleaning",
"Well_kicks_detection_and_solution", "Drilling_with_air",
"Drilling_with_cyrogenic_or_membrance_nitrogen",
"Drilling_with_natural_gas", and "Mist_drilling". Additionally,
FIG. 26G depicts inputs 2650 for the gas drilling operations
recommendations decision node 2624 in accordance with an embodiment
of the present invention. The inputs 2650 to the decision node 2624
may include gas drilling operation recommendations and may include
N number of inputs from "Gas_drilling_operation_rec.sub.--1" to
"Gas_drilling_operation_rec_N." The inputs 2650 may include
recommendations, such as recommended practices, for various gas
drilling operations, such the gas drilling operations input into
the air and gas UBD BDN model 2600.
[0168] Finally, FIGS. 26H and 261 depict inputs for the gas
drilling rig equipment section 2610 of the model 2600. As shown in
FIG. 26H, inputs 2652 to the gas drilling rig equipment uncertainty
node 2628 may be gas drilling rig equipment and may include N
number of inputs from "Rig_equipment.sub.--1" to "Rig_equipment_N."
As will be appreciated, in some embodiments the inputs 2652 may
include associated probabilities, such as probabilities p.sub.--1
through p_N. The inputs 2652 may include various rig equipment for
use in an air and gas UBD system. For example, in some embodiments
the inputs 2652 may include "Rotating_head",
"Bit_float_and_string_float", "Fire_float_and_fire_stop_float",
"Blooie_line", "Seperators_dedusters_and_mufflers",
"Injected_air_or_gas", and "Mist_pumps". FIG. 26I depicts inputs
2654 for the gas drilling rig equipment recommendations decision
node 2630 in accordance with an embodiment of the present
invention. The inputs 2654 for the decision node 2630 may be gas
drilling rig equipment recommendations and may include N number of
inputs from "Rig_equipment_rec.sub.--1" to "Rig_equipment_rec_N."
The inputs 2654 may include recommendations for gas drilling rig
equipment, such as recommended practices, recommended types of
equipment, and so on.
[0169] As described above, after selecting one or more inputs for
the nodes of the different sections 2602, 2604, 2606, and 2608 of
the air and gas UBD BDN model 2600, the inputs may be propagated to
the consequence nodes 2614, 2620, 2626, and 2632 of each section,
and then to the final consequences node 2634, using the Bayesian
probability determinations described above in Equations 1, 2, and
4. By using the Bayesian probabilities associate with the inputs,
the air and gas UBD BDN model 2600 may provide recommendations or
expected utilities at each consequence node. Additionally, the air
and gas UBD BDN model 2600 may provide recommendations or expected
utilities at the final consequence node 2634 based on the
propagated outputs from the consequence nodes 2614, 2620, 2626, and
2632. In some embodiments, the uncertainty nodes of the air and gas
UBD BDN model 2600 may have inputs with associated probability
distributions. In some embodiments, a user may select an input for
one or more uncertainty nodes and view the recommendations based on
the propagation of the selected input. For example, a user may
select an input for the rotary and hammer drilling uncertainty node
2610 and receive a recommendation (based on the inputs to the
rotary and hammer drilling recommendations decision node 2612) at
the consequences node 2614. Similarly, a user may select an input
for the gas drilling operations uncertainty node 2622 and receive a
recommendation (based on the inputs to the gas drilling operations
recommendations decision node 2624) from the consequences node
2626. One or multiple sections 2602, 2604, 2606, and 2608 of the
air and gas UBD BDN model 2600 may be used; consequently, a user
may use one or both sections of the air and gas UBD BDN model 2600
and not use the remaining sections of the air and gas UBD BDN model
2600.
[0170] FIGS. 27A and 27B depict selections of inputs and
corresponding outputs for the air and gas UBD BDN model 2600 in
accordance with an embodiment of the present invention. As depicted
in FIG. 27A, a user may select an input for the rotary and hammer
drilling uncertainty node 2610, such as a specific rotary or hammer
drilling type. As shown in FIG. 27A, a user may select
"Horizontal_drilling_with_air_hammers" as the input 2700 for the
uncertainty node 2610. The selected input 2700 may be displayed,
such as in a dialog box or other user interface element, to
indicate the input to the node 2610.
[0171] FIG. 27B depicts an example of an output 2702 from the
consequences node 2614 based on the input described above in FIG.
27A and in accordance with an embodiment of the present invention.
In some embodiments, as shown in FIG. 27B, the output 2702 may be
presented as a dialog box displaying the recommendation from the
consequences node 2614. For example, the output 2702 from the
consequences node 2614 may be based on a determination of a
recommendation with the highest Bayesian probability state as
determined from inputs received from the decision node 2612 and the
selected input 2700 to the uncertainty node 2610. As shown in FIG.
27B, the output 2702 may include text describing details of the
recommendation (e.g., "Recommendations text") for the selected
rotary and hammer drilling type input into the model 2600.
Accordingly, a user may view recommendations for various inputs to
aid in implementation of an air and gas UBD system. In other
embodiments, as described above, the output from a BDN model may be
provided as a table of expected utility values, a table of Bayesian
probability states for each recommendation, or other suitable
outputs.
[0172] Similarly, FIGS. 28A and 28B depict another selection of
inputs and corresponding outputs for the air and gas UBD BDN model
2600 in accordance with an embodiment of the present invention. As
depicted in FIG. 28A, a user may select an input 2800 for the gas
drilling considerations uncertainty node 2616, such as a specific
limit, extreme, challenge or combination thereof encountered in an
air and gas UBD system. As shown in FIG. 28A, a user may select
"Water_or_wet_holes" as the input 2800 for the uncertainty node
2616. Here again, the selected input 2800 may be displayed, such as
in a dialog box or other user interface element, to indicate the
input to the node 2616.
[0173] As described above, an output from the model 2600 may be
provided from a consequences node of the model 2600. FIG. 28B
depicts an example of an output 2802 from the consequences node
2620 based on the input described above in FIG. 28A and in
accordance with an embodiment of the present invention. The output
2802 may be presented in a dialog box displaying the recommendation
from the consequences node 2620. As noted above, the output 2802
may be based on a determination of a recommendation with the
highest Bayesian probability state as determined from inputs
received from the uncertainty node 2616 and the decision node 2618.
As shown in FIG. 28B, the output 2802 may include text describing
details of the recommendations (e.g., "Recommendations text") based
on the inputs to the model 2600. Again, a user may view various
recommendations for various inputs to aid in implementation of an
air and gas UBD system. In other embodiments, as described above,
the output from a BDN model may be provided as a table of expected
utility values, a table of Bayesian probability states for each
recommendation, or other suitable outputs. As will be appreciated,
the other sections of the air and gas UBD BDN model 2600 may
receive inputs and provide outputs in a similar manner as described
above and illustrated FIGS. 27 and 28.
[0174] As described above, the UBD expert system may also include a
mud cap drilling BDN model for use in determining optimal
operations for a mud cap UBD system. FIGS. 29A-29H depict a mud cap
drilling BDN model 2900 and associated inputs in accordance with an
embodiment of the present invention. As shown in FIG. 29A, the mud
cap drilling model 2900 may include three sections: a mud cap
drilling types section 2902, a drilling problems section 2904, and
a floating mud cap drilling section 2906. The nodes of the various
sections are described further below. FIG. 29A also includes
connection lines 2908 that indicate the dependencies between the
nodes of the mud cap drilling model 2900.
[0175] The mud cap drilling types section 2602 includes a mud cap
drilling types uncertainty node 2610, a mud cap drilling types
recommendations decision node 2612, and a consequences node 2914
that is dependent on the uncertainty node 2610 and the decision
node 2612. The drilling problems section 2904 includes a drilling
problems uncertainty node 2916, a drilling problems recommendations
decision node 2918, and a consequences node 2920 that is dependent
on the uncertainty node 2916 and the decision node 2918. Finally,
the floating mud cap drilling section 2906 includes a floating mud
cap drilling considerations uncertainty node 2922, a floating mud
cap drilling recommendations decision node 2924, and a consequences
node 2926 that is dependent on the uncertainty node 2922 and the
decision node 2924. The output from each of the consequences nodes
2914, 2920, and 2926 may be propagated to a final consequences node
2928 and a UBD expert node 2630. Thus, the final consequences node
is dependent on the consequences nodes 2914, 2920, and 2926 of each
section of the mud cap UDB BDN model 2900.
[0176] In some embodiments, as described above with regard to the
other BDN models discussed herein, the mud cap UDB BDN model 2900
may be implemented in a user interface similar to the depiction of
the model 2900 in FIG. 29A. In such embodiments, for example, each
node of the model 2900 may include a button 2932 that enables a
user to select a value for the node or see the determinations
performed by a node. For example, as described below, a user may
select (e.g., click) the button 2932A to select a mud cap drilling
types input for the model 2900. Similarly, a user may select the
button 2932D to select an input for the drilling problems
uncertainty node 2916, the button 2932G to select an input for the
floating mud cap drilling considerations uncertainty node 2922, and
so on. As noted above, evidence (inputs) may be introduced at any
nodes of the model 2900 and propagated throughout the model 2900
using the Bayesian logic described above.
[0177] FIGS. 29B-29H depict the inputs for each node of the mud cap
UBD BDN model 2900 in accordance with an embodiment of the present
invention. In some of these figures, the section delineations and
some reference numerals may be omitted for clarity. FIGS. 29B and
29C depict the inputs for the mud cap drilling types section 2902
of the mud cap UBD BDN model 2900. FIG. 29B depicts inputs 2934 for
the mud cap drilling types uncertainty node 2910 in accordance with
an embodiment of the present invention. The inputs 2934 to the
uncertainty node 2910 may include N number of inputs from
"mud_cap_drilling.sub.--1" to "mud_cap_drilling_N." As will be
appreciated, in some embodiments the inputs 2934 may include
associated probabilities, such as probabilities p.sub.--1 through
p_N. The input 2934 may include different mud cap drilling types
that may be used in a UBD system and may include, for example:
"Mud_cap_construction", "Pressure_mud_cap",
"PMCD_total_losses_applicability_test", "Connections_with_PCMD",
and "Trips_with_pressurized_mud_caps".
[0178] FIG. 29C depicts inputs 2936 for the mud cap drilling types
recommendations decision node 2912 in accordance with an embodiment
of the present invention. The inputs 2936 to the decision node 2912
may include recommendations and may include N number of inputs from
"Mud_cap_drilling_rec.sub.--1" to "Mud_cap_drilling_rec_N." Such
inputs may include recommendations, such as recommended practices,
for types of mud cap drilling operations.
[0179] Next, FIGS. 29D and 29E depict inputs for the nodes of the
drilling problems section 2904. FIG. 29D depicts inputs 2938 for
the drilling problems uncertainty node 2916 in accordance with an
embodiment of the present invention. The inputs 2938 to the
drilling problems uncertainty node 2916 may be drilling problems
2938 and may include N number of inputs from
"drilling_problem.sub.--1" to "drilling_problem_N." As will be
appreciated, in some embodiments the inputs 2938 may include
associated probabilities, such as probabilities p.sub.--1 through
p_N. The inputs 2938 may include various problems that may be
encountered in a mud cap UBD system. For example, in some
embodiments the inputs 2938 may include
"Drilling_with_a_static_overbalanced",
"Drilling_ahead_with_mud_losses", "Constant_bottom_hole_pressure",
and "Horizontal_wells". FIG. 29E depicts inputs 2940 for the
drilling problems recommendations decision node 2918 in accordance
with an embodiment of the present invention. As shown in FIG. 29E,
the inputs 2940 may be drilling problem recommendations 2940 and
may include N number of inputs from "drilling_problem_rec.sub.--1"
to "drilling_problem_rec_N." The inputs 2940 may include
recommendations, such as recommended practices, for addressing the
drilling problems of a mud cap UBD system.
[0180] FIGS. 29F and 29G depict inputs for nodes of the floating
mud cap drilling section 2906. FIG. 29F depicts inputs 2942 for the
uncertainty node 2922 of the floating mud cap drilling section 2906
in accordance with an embodiment of the present invention. The
inputs 2942 for the floating mud cap drilling considerations
uncertainty node 2922 may be floating mud cap drilling types and
may include N number of inputs from "floating_mud_cap.sub.--1" to
"floating_mud_cap_N." As will be appreciated, in some embodiments
the inputs 2942 may include associated probabilities, such as
probabilities p.sub.--1 through p_N. These inputs may include
considerations for floating mud cap operations. In some
embodiments, for example, the inputs 2942 may include:
"Water_availability", "Fluid_level_measurement",
"Continuous_annular_injection_FMCD", and
"Water_sensitive_formations_exposed." Finally, FIG. 29G depicts
inputs 2944 for the decision node 2924 of the floating mud cap
drilling section 2906 in accordance with an embodiment of the
present invention. As shown in FIG. 29G, the inputs 2944 may be
input into the model 2900 and may include N number of inputs from
"floating_mud_cap_rec.sub.--1" to "floating_mud_cap_rec_N." The
inputs 2944 may include recommendations, such as recommended
practices, for operating a floating mud cap UBD drilling system,
such as in a depleted formation.
[0181] As described above, after selecting one or more inputs for
the nodes of the different sections 2902, 2904, and 2906 of the mud
cap UBD BDN model 2900, the inputs may be propagated to the
consequence nodes 2914, 2920, and 2926 of each section, and then to
the final consequences node 2928, using the Bayesian probability
determinations described above in Equations 1, 2, and 4. By using
the probability associate with the inputs, the mud cap UDB BDN
model 2900 may then provide recommendations or expected utilities
at each consequence node 2914, 2920, and 2926. Additionally, the
mud cap UDB BDN model 2900 may provide recommendations or expected
utilities at the final consequence node 2928 based on the
propagated outputs from the consequence nodes 2914, 2920, and 2926.
In some embodiments, the uncertainty nodes of the mud cap UDB BDN
model 2900 may have inputs with associated probability
distributions. In some embodiments, a user may select an input for
one or more uncertainty nodes and view the recommendations based on
the propagation of the selected input. For example, a user may
select an input for the mud cap background uncertainty node 2910
and receive a recommendation (based on the inputs to the mud cap
background recommendations decision node 2912) at the consequences
node 2914. Similarly, a user may select an input for the drilling
problems uncertainty node 2916 and receive a recommendation (based
on the inputs to the drilling problems recommendations decision
node 2918) from the consequences node 2920. Here again, one or
multiple sections 2902, 2904, and 2906 of the mud cap UDB BDN model
2900 may be used; consequently, a user may use one or both sections
of the mud cap UDB BDN model 2900 and not use the remaining
sections of the mud cap UDB BDN model 2900.
[0182] FIGS. 30A and 30B depict selections of inputs and
corresponding outputs for the mud cap UBD BDN model 2900. As
described above, a user may select inputs for nodes of the mud cap
UBD BDN model 2900 and receive outputs from the consequences nodes
based on propagation of the inputs in the model 2900. For example,
as depicted in FIG. 30A, a user may select an input 3000 for the
mud cap drilling types uncertainty node 2910 and in accordance with
an embodiment of the present invention. As shown in FIG. 30A, a
user may select "Trips_with_pressurized_mud_caps" for the
uncertainty node 2910 and the input 3000 may be displayed, such as
in a dialog box or other user interface element.
[0183] FIG. 30B depicts an example of the output from the
consequences node 2914 of the mud cap drilling types section 2902
in accordance with an embodiment of the present invention. As shown
in FIG. 30B, in some embodiments an output 3002 may be provided as
a dialog box displaying the recommendation from the consequences
node 2914. For example, the output 2902 may be based on a
determination of a recommendation with the highest Bayesian
probability state as determined from inputs received from the
uncertainty node 2910 and the decision node 2912. In some
embodiments, as shown in FIG. 30B, the output 3002 may be provided
as a dialog box and may include text describing details of the
recommendations (e.g., "Recommendations text"). Accordingly, a user
may view recommendations for various inputs to aid in
implementation of a mud cap UBD system. In other embodiments, as
described above, the output from a BDN model may be provided as a
table of expected utility values, a table of Bayesian probability
states for each recommendation, or other suitable outputs.
[0184] Additionally, FIGS. 31A and 31B depict another selection of
inputs and corresponding outputs for mud cap UBD BDN model 2900 in
accordance with an embodiment of the present invention. As shown in
FIG. 31A, a user may select an input 3100 for the drilling problems
uncertainty node 2916 of the drilling problems section 2904, such a
specific drilling problem encountered in a mud cap UBD BDN model
2900. As shown this figure, a user may select
"Drilling_ahead_with_mud_losses" as the input 3100 for the
uncertainty node 2916.
[0185] FIG. 31B depicts an example of an output 3102 from the
consequences node 2920 based on the input described above and
depicted in FIG. 31A, and in accordance with an embodiment of the
present invention. Here again, the output 3102 may be provided in a
dialog box that displays the recommendation from the consequences
node 2920. Here again, the output 3102 may be based on a
determination of a recommendation with the highest Bayesian
probability state as determined from inputs received from the
uncertainty node 2916 and the decision node 2918. For example, as
shown in FIG. 31B, the output 3102 may include text describing
details of the drilling problems recommendations (e.g.,
"Recommendations text") for the selected input 3100 entered into
the model 2900. In other embodiments, as described above, the
output from a BDN model may be provided as a table of expected
utility values, a table of Bayesian probability states for each
recommendation, or other suitable outputs. As will be appreciated,
the other sections of the flow UBD BDN model 2900 may receive
inputs and provide outputs in a similar manner as described above
and illustrated FIGS. 30 and 31.
[0186] Further, the UBD expert system may also include an
underbalanced liner drilling (UBLD) BDN model for use in
determining optimal operations in a UBLD system. FIGS. 32A-32G
depict a UBLD BDN model 3200 and associated inputs in accordance
with an embodiment of the present invention. The UBLD BDN model
3200 may include three sections, the nodes of which are described
below: a UBLD plans section 3202, a UBLD advantages and problems
3204, and a UBLD considerations section 3206. FIG. 32A also
includes connection lines 3208 that indicate the dependencies
between the nodes of the UBLD BDN model 3200.
[0187] Each section of the UBLD BDN model 3200 is described further
below. The UBLD plans section 3202 includes a UBLD plans
uncertainty node 3210, a UBLD plans recommendations decision node
3212, and a consequences node 3214 that is dependent on the
uncertainty node 3210 and the decision node 3212. The UBLD problems
and advantages section 3204 includes a UBLD solvable problems
uncertainty node 3216, a UBLD advantages decision node 3218, and a
consequences node 3220 that is dependent on the uncertainty node
3216 and the decision node 3218. Additionally, the considerations
section 3206 includes a UBLD considerations uncertainty node 3222,
a UBLD considerations recommendations decision node 3224, and a
consequences node 3226 that is dependent on the uncertainty node
3222 and the decision node 3224. The output from each of the
consequences nodes 3214, 3220, and 3226 may be propagated to a
final consequences node 3228 and a UBD expert system node 3220.
Accordingly, the final consequences node 3228 is dependent on the
consequences nodes 3214, 3220, and 3226 for each section model
3200.
[0188] In some embodiments, as described above in the other BDN
models discussed herein, the UBLD BDN model 3200 may be implemented
in a user interface similar to the depiction of the model 3200 in
FIG. 32A. In such embodiments, for example, each node of the model
3200 may include a button 3232 that enables a user to select a
value for the node or see the determinations performed by a node.
For example, as described below, a user may select (e.g., click)
the button 3232A to select a UBLD plans input for the model 3200.
Similarly, a user may select the button 3232D to select an input
for the UBLD solvable problems uncertainty node 3216, and so on. As
noted above, evidence (inputs) may be introduced at any nodes of
the model 3200 and propagated throughout the model 3200 using the
Bayesian logic described above.
[0189] FIGS. 32B-32G depict the inputs for each node of the UBLD
BDN model 3200 in accordance with an embodiment of the present
invention. In some of these figures, the section delineations and
some reference numerals may be omitted for clarity. FIGS. 32B and
32C depict the inputs for the UBLD plans section 3202 of the model.
Accordingly, FIG. 32B depicts inputs 3234 for the UBLD plans
uncertainty node 3210 in accordance with an embodiment of the
present invention. The inputs 3234 to the uncertainty node 3210 may
include N number of inputs from "UBLD_plan.sub.--1" to
"UBLD_plan_N." As will be appreciated, in some embodiments the
inputs 3243 may include associated probabilities, such as
probabilities p.sub.--1 through p_N. The inputs 3243 may include
plans for implementing a UBLD system or any other plans associated
with a UBLD system. For example, in some embodiments the selectable
UBLD plans may include: "The_bit", "Hydraulic_design", and
"Torsional_limits". Additionally, FIG. 32C illustrates inputs 3236
for the UBLD plans recommendations decision node 3212 in accordance
with an embodiment of the present invention. The inputs 3236 to the
decision node 3212 may include plan recommendations and may include
N number of inputs, as shown by inputs
"UBLD_basic_plan_rec.sub.--1" to "UBLD_basic_plan_rec_N." The
inputs 3236 may include recommendations, such as recommended
practices, for various UBLD plans.
[0190] Additionally, FIGS. 32D and 32E depict the inputs for the
problems and advantages section 3204 of the UBLD BDN model 3200.
FIG. 32D depicts inputs 3238 for the UBLD solvable problems
uncertainty node 3216 in accordance with an embodiment of the
present invention. The inputs to the uncertainty node 3216 may
include N number of problems from "UBLD_problem.sub.--1" to
"UBLD_problem_N." As will be appreciated, in some embodiments the
inputs 3238 may include associated probabilities, such as
probabilities p.sub.--1 through p_N. The inputs 3238 may include
problems that are solvable via the implementation of a UBLD system.
For example, such problems may include "Dual_pressure_zones",
"Differential_sticking", "Wellbore_ballooning",
"Cementing_the_liner", "No_obvious_depth", "Hole_size_limits", and
"Casing_centralization"and_stabilization". Next, FIG. 32E depicts
the inputs for the UBLD advantages decision node 3218 in accordance
with an embodiment of the present invention. As shown in FIG. 32E,
the inputs 3240 may include N number of inputs from
"UBLD_advantage.sub.--1" to "UBLD_advantage_N." The inputs 3240 may
include various advantages of a UBLD system, such as advantages of
a UBLD system over a conventional UBD system.
[0191] Finally, FIGS. 32F and 32G depict the inputs for the UBLD
considerations section 3206 of the UBLD BDN model 3200. Thus, FIG.
32F depicts inputs 3242 for the UBLD considerations uncertainty
node 3222 in accordance with an embodiment of the present
invention. The inputs 3242 to this node may include N number of
inputs, e.g., "UBLD_considerations.sub.--1" to "UBLD
considerations_N." As will be appreciated, in some embodiments the
inputs 3242 may include associated probabilities, such as
probabilities p.sub.--1 through p_N. The UBLD inputs 3242 may
include considerations, such as limits, challenges, and the like,
to implementing and operating a UBLD system. For example, in some
embodiments such considerations may include, for example,
"Bit_requirements", "Liner_availability", "Liner_hanger",
"Well_control_considerations", and "Drilling_fluid_considerations".
Similarly, FIG. 32G depicts inputs 3244 for the UBLD considerations
requirements decision node 3224 in accordance with an embodiment of
the present invention. The inputs 3244 to the decision node 3224
may be requirements for a UBLD system to address the considerations
of a UBLD system, such as the considerations input for the
uncertainty node 3222. Accordingly, the inputs 3244 may include N
number of inputs from "UBLD_rec.sub.--1" to "UBLD_rec_N."
[0192] As described above, after selecting one or more inputs for
the nodes of the different sections 3202, 3204, and 3206 of the
UBLD BDN model 3200, the inputs may be propagated to the
consequence nodes 3214, 3220, and 3226 of each section, and then to
the final consequences node 3228, using the Bayesian probability
determinations described above in Equations 1, 2, and 4. By using
the Bayesian probabilities associate with the inputs, the UBLD BDN
model 3200 may provide recommendations or expected utilities at
each consequence node. Additionally, the UBLD BDN model 3200 may
provide recommendations or expected utilities at the final
consequence node 3228 based on the propagated outputs from the
consequence nodes 3214, 3220, and 3226. In some embodiments, the
uncertainty nodes of the UBLD BDN model 3200 may have inputs with
associated probability distributions. In some embodiments, a user
may select an input for one or more uncertainty nodes and view the
recommendations based on the propagation of the selected input. For
example, a user may select an input for the UBLD plans uncertainty
node 3210 and receive a recommendation (based on the inputs to the
UBLD plans recommendations decision node 3212) at the consequences
node 3214. Similarly, a user may select an input for the UBLD
problems uncertainty node 3216 and receive a recommendation (based
on the inputs to the UBLD advantages decision node 3218) from the
consequences node 3220. One or multiple sections 3202, 3204, and
3206 of the UBLD BDN model 3200 may be used; thus, a user may use
one or multiple sections of the UBLD BDN model 3200 and not use the
remaining sections of the UBLD BDN model 3200.
[0193] FIGS. 33A and 33B depict selections of inputs and
corresponding outputs for the UBLD BDN model 3200 in accordance
with an embodiment of the present invention. As described above, a
user may select inputs for nodes of the UBLD BDN model 3200 and
receive outputs from the consequences nodes based on propagation of
the inputs in the model 3200. As depicted in FIG. 33A, for example,
a user may select an input 3300 for the UBLD plans uncertainty node
3210. As shown in FIG. 32A, a user may select "The_bit" as the
input for the uncertainty node 3210 and the inputs 3300 may be
displayed, such as in a dialog box or other user interface
element.
[0194] FIG. 33B depicts an example of an output 3202 from the
consequences node 3214 of the UBLD BDN model 3200 in accordance
with an embodiment of the present invention. As shown in FIG. 33B,
the output 3202 may be provided as a dialog box that displays the
recommendation from the consequences node 3214. For example, the
output 3202 from the consequences node 3214 may be based on a
determination of a recommendation with the highest Bayesian
probability state as determined from inputs received from the
decision node 3212 and the selected input 3200 to the uncertainty
node 3210. The output 3202 may include text describing the
recommendation (e.g., "Recommendations text) for the selected input
3000. Thus, a user may view recommendations for various inputs to
aid in implementation of a UBLD system. In other embodiments, as
described above, the output from a BDN model may be provided as a
table of expected utility values, a table of Bayesian probability
states for each recommendation, or other suitable outputs.
[0195] FIGS. 34A and 34B depict another example of another selected
input and corresponding outputs from the UBLD BDN model 3200 in
accordance with an embodiment of the present invention. As shown in
FIG. 34A, a user may select an input 3400 for the UBLD solvable
problems uncertainty node 3216. Here again, in some embodiments the
input may be displayed in a dialog box or other user interface
element. As shown in FIG. 34A, the selected input 3400 for the
uncertainty node 3216 may be "Wellbore_ballooning". Next, FIG. 34B
depicts an output 3402 from the consequences node 3220 in
accordance with an embodiment of the present invention. The output
3402 may be provided in a dialog box or other user interface
element that displays the recommendation from the consequences node
3220. As noted above, the output 3202 may be based on a
determination of a recommendation with the highest Bayesian
probability state as determined from inputs received from the
uncertainty node 3216 and the decision node 3218. As shown in FIG.
34B, the output may include text describing the UBLD advantages
(e.g., advantages text) for the selected problem solved by UBLD
input to the uncertainty node 3216. In other embodiments, as
described above, the output from a BDN model may be provided as a
table of expected utility values, a table of Bayesian probability
states for each recommendation, or other suitable outputs. As will
be appreciated, the other sections of the UBLD BDN model 3200 may
receive inputs and provide outputs in a similar manner as described
above and illustrated FIGS. 33 and 34.
[0196] In some embodiments, a UBD expert system may include a BDN
model for an underbalanced coil tube (UBCT) system for use in
determining optimal operations for a UBCT drilling system. FIGS.
35A-35E depict a UBCT BDN model 3500 and inputs for the various
nodes of the model 3500 in accordance with an embodiment of the
present invention. As shown in FIG. 35A, the UBCT BDN model 3500
may include a preplanning section 3502 and a UBCT considerations
section 3504. The nodes of the sections 3502 and 3504 are described
further below. FIG. 35A also depicts connection lines 3505 that
indicate the dependencies between the nodes of the UBCT model
3500.
[0197] In some embodiments, as described above in the other BDN
models discussed herein, the UBCT drilling BDN model 3500 may be
implemented in a user interface similar to the depiction of the
model 3500 in FIG. 35A. In such embodiments, for example, each node
of the model 3500 may include a control 3522 that enables a user to
select a value for the node or see the determinations performed by
a node. For example, as described below, a user may select (e.g.,
click) the control 3522A to select a preplanning input for the
preplanning uncertainty node 3506. Similarly, a user may select the
control 3522D to select an input for the UBCT drilling
considerations uncertainty node 3512, and so on. As noted above,
evidence (inputs) may be introduced at any nodes of the model 3500
and propagated throughout the model 3500 using the Bayesian logic
described above.
[0198] The preplanning section 3502 includes a preplanning
uncertainty node 3506, preplanning requirements decision node 3508,
and a consequences node 3510 dependent on the uncertainty node 3506
and the decision node 3508. Additionally, the drilling
considerations section 3504 includes a UBCT drilling considerations
uncertainty node 3512, a UBCT drilling considerations solutions
decision node 3514, and a consequences node 3516 dependent on the
uncertainty node 3512 and the decision node 3514. The output from
the consequences nodes 3510 and 3516 may be propagated to a final
consequences node 3518 and a UBD expert system node 3520. Thus, the
final consequences node 3518 is dependent on the consequences nodes
3510 and 3516.
[0199] FIGS. 35B-35E depict the inputs for each node of the UBCT
BDN model 3500 in accordance with an embodiment of the present
invention. In some of these figures, the section delineations and
some reference numerals may be omitted for clarity. FIGS. 35B and
35C depict the inputs for the preplanning section 3502. FIG. 35B
depicts inputs 3524 for the preplanning uncertainty node 3506 in
accordance with an embodiment of the present invention. The inputs
3524 to the uncertainty node 3506 may be UBCT preplans and may
include N number of inputs from "UCTCD_preplan.sub.--1" to
"UCTCD_preplan.sub.--2." As will be appreciated, in some
embodiments the inputs 3524 may include associated probabilities,
such as probabilities p.sub.--1 through p_N. The inputs 3524 may
include plans for preparation (i.e., preplans) of a UBCT drilling
system. For example, in some embodiments, the inputs 3524 may
include" "Candidate_selection",
"Pressure_categories_and_BOP_stack_requirement" and
"Coiled_tubing_mechanical_considerations." Next, FIG. 35C depicts
inputs 3526 for the preplanning requirements decision node 3508 in
accordance with an embodiment of the present invention. The inputs
3526 to the decision node 3508 may be preplanning requirements and
may include N number of inputs from "UBCT_preplan_req.sub.--1" to
"UBCT_preplan_req_N." The inputs 3526 may include detailed
requirements for different preplanning considerations input into
the UBCT BDN model 3500.
[0200] FIGS. 35D and 25E depict the inputs for the UBCT drilling
considerations section 3504. For example, FIG. 35D depicts inputs
3528 for the UBCT drilling considerations uncertainty node 3512 in
accordance with an embodiment of the present invention. The inputs
3528 may be UBCT drilling considerations and may include N number
of inputs from "UBCT_consideration.sub.--1" to
"UBCT_consideration_N." As will be appreciated, in some embodiments
the inputs 3512 may include associated probabilities, such as
probabilities p.sub.--1 through p_N. In some embodiments, the UBCT
drilling considerations may include challenges, limits, and so on
associated with UBCT drilling systems. The inputs 3528 may include
considerations that may be encountered when implementing and
operating a UBCT drilling system. For example, in some embodiments
the inputs 3528 may include "ROP_reduction",
"Wellhead_or_downhole_annulus_pressure",
"Surface_injection_pressure", "Downhole_tubing_pressure", and
"Drill_into_fracture."
[0201] FIG. 35E depicts inputs 3530 for the UBCT drilling
considerations solutions decision node 3514 in accordance with an
embodiment of the present invention. The inputs to the decision
node 3514 may include N number of inputs from
"UBCT_solution.sub.--1" to UBCT_solution_N." The inputs 3530 may
include solutions to considerations that may be encountered in a
UBCT drilling system, such as the considerations input into the
model 3500.
[0202] As described above, after selecting one or more inputs for
the nodes of the different sections 3502 and 3504 of the UBCTD BDN
model 3500, the inputs may be propagated to the consequence nodes
3510 and 3516 of each section, and then to the final consequences
node 3518, using the Bayesian probability determinations described
above in Equations 1, 2, and 4. By using the probably states
associate with the inputs, the UBCTD BDN model 3500 may then
provide recommendations or expected utilities at each consequence
node 3510 and 3516. Additionally, the UBCTD BDN model 3500 may
provide recommendations or expected utilities at the final
consequence node 3518 based on the propagated outputs from the
consequence nodes 3510 and 3516. In some embodiments, the
uncertainty nodes of the UBCTD BDN model 3500 may have inputs with
associated probability distributions. In some embodiments, a user
may select an input for one or more uncertainty nodes and view the
recommendations based on the propagation of the selected input. For
example, a user may select an input for the preplanning uncertainty
node 3506 and receive a recommendation (based on the inputs to the
preplanning requirements decision node 3508) at the consequences
node 3510. Similarly, a user may select an input for the foam
systems design uncertainty node 3512 and receive a recommendation
(based on the inputs to the UBCT drilling considerations decision
node 3514) from the consequences node 3516. One or both sections
3502 and 3504 of the UBCTD BDN model 3500 may be used;
consequently, a user may use one or both sections of the UBCTD BDN
model 3500 and not use the remaining sections of the flow UDB BDN
model 2400
[0203] FIGS. 36A and 36B depict the selection of inputs and the
corresponding outputs for the UBCTD BDN model 3500 in accordance
with an embodiment of the present invention. As mentioned above, a
user may select inputs for one or nodes of the UBCTD BDN model 3500
and receive output from the consequences node as the inputs are
propagated in the model 3500. For example, as shown in FIG. 36A, a
user may select an input 3600 for the preplanning uncertainty node
3506. As depicted in FIG. 36A, a user may select
"Pressure_categories_and_BOP_stack_requirements" as the input 3600
for the uncertainty node 3506. In some embodiments, the input 3600
may be displayed in a dialog box or other user interface element to
indicate the input to a selected node 3506.
[0204] FIG. 36B thus depicts an output 3602 from the consequences
node 3510 of the UBCTD model 3500 based on the input described
above and in accordance with an embodiment of the present
invention. For example, the output 3602 may be provided in a dialog
box or other user interface element that displays the
recommendation from the consequences node 3510. The output 3502 may
be based on a determination of a recommendation with the highest
Bayesian probability state as determined from inputs received from
the uncertainty node 3506 and the decision node 3508. As shown in
FIG. 36B, in some embodiments the output 3602 may include text
describing the recommendation (e.g., Recommendations text) output
from the consequences node 3510. Accordingly, a user may view
recommendations for various inputs to aid in implementation of a
UBCT drilling system. In other embodiments, as described above, the
output 3602 from a BDN model may be provided as a table of expected
utility values, a table of Bayesian probability states for each
recommendation, or other suitable outputs. As will be appreciated,
the other section of the UBCTD BDN model 3500 may receive inputs
and provide outputs in a similar manner as described above and
illustrated FIGS. 36A and 36B.
[0205] Finally, in some embodiments the UBD expert system may
include a snubbing and stripping BDN model for use in determining
optimal snubbing and stripping operations for a UBD system. FIGS.
37A-37I depict a snubbing and stripping BDN model 3700 in
accordance with an embodiment of the present invention. The
snubbing and stripping BDN model 3700 includes four sections: a
snubbing section 3702, a snubbing units section 3704, a snubbing
operations procedure section 3706, and a stripping procedures
section 3708. The nodes of each section are described further
below. FIG. 37A also depicts connection lines 3709 that indicate
the dependencies between the nodes of the model 3700.
[0206] Each section of the snubbing and stripping BDN model 3700 is
described in detail below. The snubbing section 3702 includes a
snubbing types uncertainty node 3710, a snubbing types
recommendations decision node 3712, and a consequences node 3714
that depends on the uncertainty node 3710 and the decision node
3712. Additionally, the snubbing units section 3704 includes a
snubbing units uncertainty node 3716, a snubbing units
recommendations decision node 3718, and a consequences node 3720
that depends on the uncertainty node 3716 and the decision node
3718. The snubbing operations section 3706 includes a snubbing
operations uncertainty node 3722, a snubbing operations
recommendations decision node 3724, and a consequences node 3726
that depends on the uncertainty node 3722 and the decision node
3724. Finally, the stripping procedures section 3708 includes a
general stripping procedures uncertainty node 3728, a general
stripping procedures recommendations decision node 3730, and a
consequences node 3732 that depends on the uncertainty node 3728
and the decision node 3730. The output from each of the
consequences nodes 3714, 3720, 3726, and 3732 may be propagated to
a final consequences node 3734 and a UBD expert system node 3736.
Accordingly, the final consequences node 3734 is dependent on the
consequences nodes 3714, 3720, 3726, and 3732 for each section of
the model 3700.
[0207] In some embodiments, as described above in the other BDN
models discussed herein, the snubbing and stripping BDN model 3700
may be implemented in a user interface similar to the depiction of
the model 3700 in FIG. 37A. In such embodiments, for example, each
node of the model 3700 may include a control 3738 that enables a
user to select a value for the node or see the determinations
performed by a node. For example, as described below, a user may
select (e.g., click) the control 3738A to select an input for the
snubbing types uncertainty node 3710. Similarly, a user may select
the control 3738D to select an input for the snubbing units
uncertainty node 3716, and so on. As noted above, evidence (inputs)
may be introduced at any nodes of the model 3700 and propagated
throughout the model 3700 using the Bayesian logic described
above.
[0208] FIGS. 37B-37I depict the inputs for each node of the
snubbing and stripping model 3700 in accordance with an embodiment
of the present invention. In some of these figures, the section
delineations and some reference numerals may be omitted for
clarity. FIGS. 37B and 37C depict the inputs for the snubbing
section 3702. Accordingly, FIG. 37B depicts inputs 3740 for the
snubbing types uncertainty node 3710 in accordance with an
embodiment of the present invention. The inputs may be snubbing
types and may include N number of inputs from "Snubbing.sub.--1" to
"Snubbing_N." As will be appreciated, in some embodiments the
inputs 3740 may include associated probabilities, such as
probabilities p.sub.--1 through p_N. The inputs 3740 may include
basic considerations for implementing a snubbing operation. For
example, in some embodiments the inputs 3740 may include:
"Hydraulic_system", "Stripping_ram_to_ram",
"Stripping_with_annular_preventer_or_stripping_rubber",
"Pipe_light", "Pipe_heavy", and "Fluid_flow".
[0209] FIG. 37C depicts inputs 3742 for the snubbing types
recommendations decision node 3712 in accordance with an embodiment
of the present invention. The inputs 3742 may be selectable
snubbing recommendations and may include N number of inputs from
"basic_snubbing_rec.sub.--1" to "basic_snubbing_rec_N." The inputs
3742 may include detailed recommendations for various snubbing
types, such as the considerations input into the uncertainty node
3710.
[0210] Next, FIGS. 37D and 37E depicts the inputs for the snubbing
units section 3704. Accordingly, FIG. 37D depicts inputs 3744 for
the snubbing units uncertainty node 3716 in accordance with an
embodiment of the present invention. The inputs 3744 to the
uncertainty node 3716 may be snubbing units and may include N
number of inputs from "snubbing_unit.sub.--1" to "snubbing_unit_N."
As will be appreciated, in some embodiments the inputs 3744 may
include associated probabilities, such as probabilities p.sub.--1
through p_N. The inputs 3744 may include the units used in a
snubbing operation and, in some embodiments, may include the
following: "Basic_snubbing_unit", "Work_string_and_components",
"Well_control", and "Auxiliary_equipment". Additionally, FIG. 37E
depicts inputs 3746 for the snubbing units recommendations decision
node 3718 in accordance with an embodiment of the present
invention. As shown in FIG. 37E, the inputs 3746 may be snubbing
units recommendations and may include N number of inputs from
"snubbing_unit_rec.sub.--1" to "snubbing_unit_rec_N." The inputs
3746 may include detailed recommendations for using a particular
snubbing unit, such as the units input into the uncertainty node
3716.
[0211] Further, FIGS. 37F and 37G depict inputs for the snubbing
operations section 3706. FIG. 37F thus depicts inputs 3748 for the
snubbing operations uncertainty node 3712 in accordance with an
embodiment of the present invention. As shown in FIG. 37F, the
inputs 3748 may be snubbing operations and may include N number of
inputs from "snubbing_operation.sub.--1" to "snubbing_operation_N."
As will be appreciated, in some embodiments the inputs 3748 may
include associated probabilities, such as probabilities p.sub.--1
through p_N. The inputs 3748 may be different types of snubbing
operations and may include, for example:
"Temporary_securing_of_the_well", "Lubrication", and
"Shear_or_connect_disconnect_in_BOP_stack_lubrication."
Additionally, FIG. 37G depicts inputs 3750 for the snubbing
operations recommendations decision node 3714 in accordance with an
embodiment of the present invention. The inputs 3750 may be
snubbing operation recommendations and may include N number of
inputs from "snubbing_operation_rec.sub.--1" to
"snubbing_operation_N." The inputs 3750 may include detailed
recommendations for different snubbing operations, such as the
snubbing operations input for the snubbing units uncertainty node
3716.
[0212] Finally, FIGS. 37H and 371 depict the inputs for the general
stripping procedures section 3708 of the snubbing and stripping BDN
model 3700. FIG. 37H depicts the inputs for the general stripping
procedures uncertainty node 3718 in accordance with an embodiment
of the present invention. As shown in FIG. 37H, inputs 3752 to the
uncertainty node 3718 may be stripping procedures and may include N
number of inputs from "stripping_proc.sub.--1" to
"stripping_proc_N." As will be appreciated, in some embodiments the
inputs 3752 may include associated probabilities, such as
probabilities p.sub.--1 through p_N. The inputs 3752 may include
general stripping procedures for use in UBD system. For example, in
some embodiments the inputs 3752 may include:
"Packoff_stripper_rubber", "Packoff_annular_BOP", "Annular_to_ram",
and "Ram_to_ram." Next, FIG. 37I depicts inputs 3754 for the
stripping procedures recommendations decision node 3730 in
accordance with an embodiment of the present invention. The inputs
3754 to the decision node 3730 may be stripping procedure
recommendations and may include N number of inputs from
"Stripping_proc_rec.sub.--1" to "Stripping_proc_rec_N." The inputs
3754 may include detailed recommendations for various stripping
procedures, such as the stripping procedures input for the general
stripping procedures uncertainty node 3718.
[0213] As described above, after selecting one or more inputs for
the nodes of the different sections 3702, 3704, 3706, and 3708 of
the snubbing and stripping BDN model 3700, the inputs may be
propagated to the consequence nodes 3714, 3720, 3726, and 2632 of
each section, and then to the final consequences node 3734, using
the Bayesian probability determinations described above in
Equations 1, 2, and 4. By using the Bayesian probabilities
associate with the inputs, the snubbing and stripping BDN model
3700 may provide recommendations or expected utilities at each
consequence node. Additionally, the snubbing and stripping BDN
model 3700 may provide recommendations or expected utilities at the
final consequence node 3734 based on the propagated outputs from
the consequence nodes 3714, 3720, 3726, and 2632. In some
embodiments, the uncertainty nodes of the snubbing and stripping
BDN model 3700 may have inputs with associated probability
distributions. In some embodiments, a user may select an input for
one or more uncertainty nodes and view the recommendations based on
the propagation of the selected input. For example, a user may
select an input for the rotary and hammer drilling uncertainty node
3710 and receive a recommendation (based on the inputs to the
rotary and hammer drilling recommendations decision node 3712) at
the consequences node 3714. Similarly, a user may select an input
for the gas drilling operations uncertainty node 3722 and receive a
recommendation (based on the inputs to the gas drilling operations
recommendations decision node 3724) from the consequences node
3726. One or multiple sections 3702, 3704, 3706, and 3708 of the
snubbing and stripping BDN model 3700 may be used; consequently, a
user may use one or both sections of the snubbing and stripping BDN
model 3700 and not use the remaining sections of the snubbing and
stripping BDN model 3700.
[0214] FIGS. 38A and 38B depict selections of inputs and
corresponding outputs for the snubbing and stripping BDN model 3700
in accordance with an embodiment of the present invention. As
depicted in FIG. 37A, a user may select an input for the snubbing
types uncertainty node 3710, such as a snubbing type. As shown in
FIG. 38A, a user may select
"Stripping_with_annular_preventer_or_stripping_rubber" as the input
3800 for the uncertainty node 3810. The selected input 3800 may be
displayed, such as in a dialog box or other user interface element,
to indicate the input to the node 3810.
[0215] FIG. 38B depicts an example of an output 3802 from the
consequences node 3714 based on the input described above in FIG.
38A and in accordance with an embodiment of the present invention.
In some embodiments, as shown in FIG. 38B, the output 3802 may be
presented as a dialog box displaying the recommendation from the
consequences node 3714. For example, as described above, the output
3802 from the consequences node 3714 may be based on a
determination of a recommendation with the highest Bayesian
probability state as determined from inputs received from the
decision node 3712 and the selected input 3800 to the uncertainty
node 3710. As shown in FIG. 38B, the output 3802 may include text
describing details of the recommendation (e.g., "Recommendations
text") for the selected rotary and hammer drilling type input into
the model 3700. Accordingly, a user may view recommendations for
various inputs to aid in implementation of an air and gas UBD
system. In other embodiments, as described above, the output from a
BDN model may be provided as a table of expected utility values, a
table of Bayesian probability states for each recommendation, or
other suitable outputs.
[0216] Similarly, FIGS. 39A and 39B depict another selection of
inputs and corresponding outputs for the snubbing and stripping BDN
model 3700 in accordance with an embodiment of the present
invention. As depicted in FIG. 39A, a user may select an input 3900
for the snubbing units uncertainty node 3716, such as a snubbing
units used in snubbing operation in a UBD system. As shown in FIG.
39A, a user may select "Auxiliary_equipment" as the input 3900 for
the uncertainty node 3716. Here again, the selected input 3900 may
be displayed, such as in a dialog box or other user interface
element, to indicate the input to the node 37161
[0217] As described above, an output from the model 3700 may be
provided from a consequences node of the model 3700. FIG. 39B
depicts an example of an output 3902 from the consequences node
3720 based on the input described above in FIG. 39B and in
accordance with an embodiment of the present invention. As noted
above, the output 3902 may be presented in a dialog box displaying
the recommendation from the consequences node 3720. As noted above,
the output 3902 may be based on a determination of a recommendation
with the highest Bayesian probability state as determined from
inputs received from the uncertainty node 3716 and the decision
node 3718. As shown in FIG. 39B, the output 3902 may include text
describing details of the recommendations (e.g., "Recommendations
text") based on the inputs to the model 3700. Again, a user may
view various recommendations for various inputs to aid in
implementation of an air and gas UBD system. In other embodiments,
as described above, the output from a BDN model may be provided as
a table of expected utility values, a table of Bayesian probability
states for each recommendation, or other suitable outputs. As will
be appreciated, the other sections of the air and gas UBD BDN model
3700 may receive inputs and provide outputs in a similar manner as
described above and illustrated FIGS. 38 and 39.
[0218] The various BDN models described above may be constructed
based on the inputs for the uncertainty nodes, decision nodes, and
the associated probabilities. The construction of a section of the
various BDN models is illustrated in FIG. 40. FIG. 40 depicts a
process 4000 illustrating the construction of a section of a BDN
model in accordance with an embodiment of the present invention.
The process 4000 depicts the construction of a section having an
uncertainty node, a decision node, and a consequences node,
arranged in the manner described above. For example, the inputs to
an uncertainty node may be determined (block 4002). The inputs for
an uncertainty node of a particular section of a specific BDN model
may determined from expert data 4004. For example, in some
embodiments expert data may be obtained from various sources, such
as consultations with experts, scientific literature, expert
reports, and the like. The determine inputs may be entered in the
uncertainty node of the appropriate BDN model (block 4006).
[0219] Additionally, inputs for a decision node of a section of a
specific BDN model may be determined (block 4008). Here again, the
inputs may be determined from the expert data 4004. As described
above, in some embodiments, the expert data 4004 may be used to
generate probability data stored in a database. The determined
inputs and associated probability states may then be entered into a
decision node of the appropriate BDN model. (block 4010).
[0220] Finally the consequence probabilities may be determined
based on the Bayesian logic described above in Equations 1, 2, and
4 (block 4012). Here again, the determination of various
probabilities may be determined from expert data 4004. For example,
various combinations of inputs to the uncertainty node and decision
node may result in different probability states as determined from
the expert data 4004. The consequence probabilities may then be
entered into the consequences node of the appropriate BDN model
(block 4014). Next the section of the BDN model may be completed
and additional sections may be constructed in the manner described
above.
[0221] In some embodiments, after completing a section of a BDN
model or all sections of a BDN model, the BDN model may be tested
(block 4016). For example, inputs to the uncertainty nodes of the
BDN model may be selected and the outputs may be tested against
manual determinations based on the expert data 4004. Finally, if
the model is complete and tested, the UBD expert system
incorporating the BDN model may be provided (block 4018).
[0222] Advantageously, in the case of new and changed practices,
expert opinions, and the like, a BDN model may be updated by
changing the probability states for the appropriate nodes. For
example, practices, expert opinions, and the like may be reviewed
to determine if there are changes (decision block 4020). If there
are new or changed practices, expert opinions, or other sources of
expert data (line 4022), then additional expert data may be
obtained (block 4024) and used to determine inputs to the
uncertainty node and decision node of the appropriate section of a
BDN model. Any new and changed determinations may be entered into
the appropriate nodes and an updated BDN model may be completed
(block 4026).
[0223] FIG. 41 depicts a computer 4100 in accordance with an
embodiment of the present invention. Various portions or sections
of systems and methods described herein include or are executed on
one or more computers similar to computer 4100 and programmed as
special-purpose machines executing some or all steps of methods
described above as executable computer code. Further, processes and
modules described herein may be executed by one or more processing
systems similar to that of computer 4100. For example, the UBD
expert system 108 described may be implemented on one or more
computers similar to computer 4100 and programmed to execute one or
more of the various Bayesian decision models described above.
[0224] As will be understood by those skilled in the art, the
computer 4100 may include various internal and external components
that contribute to the function of the device and which may allow
the computer 4100 to function in accordance with the techniques
discussed herein. As will be appreciated, various components of
computer 4100 may be provided as internal or integral components of
the computer 4100 or may be provided as external or connectable
components. It should further be noted that FIG. 41 depicts merely
one example of a particular implementation and is intended to
illustrate the types of components and functionalities that may be
present in computer 4100. As shown in FIG. 41, the computer 4100
may include one or more processors (e.g., processors 4102a-4102n)
coupled to a memory 4104, a display 4106, I/O ports 4108 and a
network interface 4110, via an interface 4114.
[0225] Computer 4100 may include any combination of devices or
software that may perform or otherwise provide for the performance
of the techniques described herein. For example, the computer 4100
may be representative of the client computer 200 or a server
implementing some or all portions of the UBD expert system 108 or
other components of the systems described above. Accordingly, the
computer 4100 may include or be a combination of a cloud-computing
system, a data center, a server rack or other server enclosure, a
server, a virtual server, a desktop computer, a laptop computer, a
tablet computer, a mobile telephone, a personal digital assistant
(PDA), a media player, a game console, a vehicle-mounted computer,
or the like. The computer 4100 may be a unified device providing
any one of or a combination of the functionality of a media player,
a cellular phone, a personal data organizer, a game console, and so
forth. Computer 4100 may also be connected to other devices that
are not illustrated, or may operate as a stand-alone system. In
addition, the functionality provided by the illustrated components
may in some embodiments be combined in fewer components or
distributed in additional components. Similarly, in some
embodiments, the functionality of some of the illustrated
components may not be provided or other additional functionality
may be available.
[0226] In addition, the computer 4100 may allow a user to connect
to and communicate through a network 4116 (e.g., the Internet, a
local area network, a wide area network, etc.) and to acquire data
from a satellite-based positioning system (e.g., GPS). For example,
the computer 4100 may allow a user to communicate using the World
Wide Web (WWW), e-mail, text messaging, instant messaging, or using
other forms of electronic communication, and may allow a user to
obtain the location of the device from the satellite-based
positioning system, such as the location on an interactive map.
[0227] In one embodiment, the display 4106 may include a liquid
crystal display (LCD) or an organic light emitting diode (OLED)
display, although other display technologies may be used in other
embodiments. The display 4106 may display a user interface (e.g., a
graphical user interface), such a user interface for a Bayesian
decision network. In accordance with some embodiments, the display
4106 may include or be provided in conjunction with touch sensitive
elements through which a user may interact with the user interface.
Such a touch-sensitive display may be referred to as a "touch
screen" and may also be known as or called a touch-sensitive
display system.
[0228] The processor 4102 may provide the processing capability
required to execute the operating system, programs, user interface,
and any functions of the computer 4100. The processor 4102 may
receive instructions and data from a memory (e.g., system memory
4104). The processor 4102 may include one or more processors, such
as "general-purpose" microprocessors, and special purpose
microprocessors, such as ASICs. For example, the processor 4102 may
include one or more reduced instruction set (RISC) processors, such
as those implementing the Advanced RISC Machine (ARM) instruction
set. Additionally, the processor 4102 may include single-core
processors and multicore processors and may include graphics
processors, video processors, and related chip sets. Accordingly,
computer 4100 may be a uni-processor system including one processor
(e.g., processor 4102a), or a multi-processor system including any
number of suitable processors (e.g., 4102a-4102n). Multiple
processors may be employed to provide for parallel or sequential
execution of one or more sections of the techniques described
herein. Processes, such as logic flows, described herein may be
performed by one or more programmable processors executing one or
more computer programs to perform functions by operating on input
data and generating corresponding output.
[0229] As will be understood by those skilled in the art, the
memory 4104 (which may include one or more tangible non-transitory
computer readable storage medium) may include volatile memory, such
as random access memory (RAM), and non-volatile memory, such as
ROM, flash memory, a hard drive, any other suitable optical,
magnetic, or solid-state storage medium, or a combination thereof.
The memory 4104 may be accessible by the processor 4102 and other
components of the computer 4100. The memory 4104 may store a
variety of information and may be used for a variety of purposes.
The memory 4104 may store executable computer code, such as the
firmware for the computer 4100, an operating system for the
computer 4100, and any other programs or other executable code
necessary for the computer 4100 to function. The executable
computer code may include program instructions 4118 executable by a
processor (e.g., one or more of processors 4102a-4102n) to
implement one or more embodiments of the present invention.
Instructions 4118 may include modules of computer program
instructions for implementing one or more techniques described.
Program instructions 4118 may define a computer program (which in
certain forms is known as a program, software, software
application, script, or code). A computer program may be written in
a programming language, including compiled or interpreted
languages, or declarative or procedural languages. A computer
program may include a unit suitable for use in a computing
environment, including as a stand-alone program, a module, a
component, a subroutine. A computer program may or may not
correspond to a file in a file system. A program may be stored in a
section of a file that holds other programs or data (e.g., one or
more scripts stored in a markup language document), in a single
file dedicated to the program in question, or in multiple
coordinated files (e.g., files that store one or more modules, sub
programs, or sections of code). A computer program may be deployed
to be executed on one or more computer processors located locally
at one site or distributed across multiple remote sites and
interconnected by a communication network. In addition, the memory
4104 may be used for buffering or caching during operation of the
computer 4100. The memory 4104 may also store data files such as
media (e.g., music and video files), software (e.g., for
implementing functions on computer 4100), preference information
(e.g., media playback preferences), wireless connection information
(e.g., information that may enable media device to establish a
wireless connection), telephone information (e.g., telephone
numbers), and any other suitable data.
[0230] As mentioned above, the memory 4104 may include volatile
memory, such as random access memory (RAM). The memory 4104 may
also include non-volatile memory, such as ROM, flash memory, a hard
drive, any other suitable optical, magnetic, or solid-state storage
medium, or a combination thereof. The interface 4114 may include
multiple interfaces and may couple various components of the
computer 4100 to the processor 4102 and memory 4104. In some
embodiments, the interface 4114, the processor 4102, memory 4104,
and one or more other components of the computer 4100 may be
implemented on a single chip, such as a system-on-a-chip (SOC). In
other embodiments, these components, their functionalities, or both
may be implemented on separate chips. The interface 4114 may be
configured to coordinate I/O traffic between processors
4102a-4102n, system memory 4104, network interface 1410, I/O
devices 1412, other peripheral devices, or a combination thereof.
The interface 4114 may perform protocol, timing or other data
transformations to convert data signals from one component (e.g.,
system memory 4104) into a format suitable for use by another
component (e.g., processors 4102a-4102n). The interface 4114 may
include support for devices attached through various types of
peripheral buses, such as a variant of the Peripheral Component
Interconnect (PCI) bus standard or the Universal Serial Bus (USB)
standard.
[0231] The computer 4100 may also include an input and output port
4108 to allow connection of additional devices, such as I/O devices
4112. Embodiments of the present invention may include any number
of input and output ports 4108, including headphone and headset
jacks, universal serial bus (USB) ports, Firewire or IEEE-1394
ports, and AC and DC power connectors. Further, the computer 4100
may use the input and output ports to connect to and send or
receive data with any other device, such as other portable
computers, personal computers, printers, etc.
[0232] The computer 4100 depicted in FIG. 41 also includes a
network interface 4110, such as a wired network interface card
(NIC), wireless (e.g., radio frequency) receivers, etc. For
example, the network interface 4110 may receive and send
electromagnetic signals and communicate with communications
networks and other communications devices via the electromagnetic
signals. The network interface 4110 may include known circuitry for
performing these functions, including an antenna system, an RF
transceiver, one or more amplifiers, a tuner, one or more
oscillators, a digital signal processor, a CODEC chipset, a
subscriber identity module (SIM) card, memory, and so forth. The
network interface 1410 may communicate with networks (e.g., network
4116), such as the Internet, an intranet, a cellular telephone
network, a wireless local area network (LAN), a metropolitan area
network (MAN), or other devices by wireless communication. The
communication may use any suitable communications standard,
protocol and technology, including Ethernet, Global System for
Mobile Communications (GSM), Enhanced Data GSM Environment (EDGE),
a 3G network (e.g., based upon the IMT-2000 standard), high-speed
downlink packet access (HSDPA), wideband code division multiple
access (W-CDMA), code division multiple access (CDMA), time
division multiple access (TDMA), a 4G network (e.g., IMT Advanced,
Long-Term Evolution Advanced (LTE Advanced), etc.), Bluetooth,
Wireless Fidelity (Wi-Fi) (e.g., IEEE 802.11a, IEEE 802.11b, IEEE
802.11g or IEEE 802.11n), voice over Internet Protocol (VoIP),
Wi-MAX, a protocol for email (e.g., Internet message access
protocol (IMAP), or any other suitable communication protocol.
[0233] Various embodiments may further include receiving, sending
or storing instructions and/or data implemented in accordance with
the foregoing description upon a computer-accessible medium.
Generally speaking, a computer-accessible/readable storage medium
may include a non-transitory storage media such as magnetic or
optical media, (e.g., disk or DVD/CD-ROM), volatile or non-volatile
media such as RAM (e.g. SDRAM, DDR, RDRAM, SRAM, etc.), ROM, etc.,
as well as transmission media or signals such as electrical,
electromagnetic, or digital signals, conveyed via a communication
medium such as network and/or a wireless link.
[0234] Further modifications and alternative embodiments of various
aspects of the invention will be apparent to those skilled in the
art in view of this description. Accordingly, this description is
to be construed as illustrative only and is for the purpose of
teaching those skilled in the art the general manner of carrying
out the invention. It is to be understood that the forms of the
invention shown and described herein are to be taken as examples of
embodiments. Elements and materials may be substituted for those
illustrated and described herein, parts and processes may be
reversed or omitted, and certain features of the invention may be
utilized independently, all as would be apparent to one skilled in
the art after having the benefit of this description of the
invention. Changes may be made in the elements described herein
without departing from the spirit and scope of the invention as
described in the following claims. Headings used herein are for
organizational purposes only and are not meant to be used to limit
the scope of the description.
[0235] As used throughout this application, the word "may" is used
in a permissive sense (i.e., meaning having the potential to),
rather than the mandatory sense (i.e., meaning must). The words
"include", "including", and "includes" mean including, but not
limited to. As used throughout this application, the singular forms
"a", "an" and "the" include plural referents unless the content
clearly indicates otherwise. Thus, for example, reference to "an
element" includes a combination of two or more elements. Unless
specifically stated otherwise, as apparent from the discussion, it
is appreciated that throughout this specification discussions
utilizing terms such as "processing", "computing", "calculating",
"determining" or the like refer to actions or processes of a
specific apparatus, such as a special purpose computer or a similar
special purpose electronic processing/computing device. In the
context of this specification, a special purpose computer or a
similar special purpose electronic processing/computing device is
capable of manipulating or transforming signals, typically
represented as physical electronic or magnetic quantities within
memories, registers, or other information storage devices,
transmission devices, or display devices of the special purpose
computer or similar special purpose electronic processing/computing
device.
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