U.S. patent application number 15/846661 was filed with the patent office on 2018-06-21 for drillstring sticking management framework.
The applicant listed for this patent is Schlumberger Technology Corporation. Invention is credited to Sophie Androvandi, Maurice Ringer.
Application Number | 20180171774 15/846661 |
Document ID | / |
Family ID | 62556811 |
Filed Date | 2018-06-21 |
United States Patent
Application |
20180171774 |
Kind Code |
A1 |
Ringer; Maurice ; et
al. |
June 21, 2018 |
DRILLSTRING STICKING MANAGEMENT FRAMEWORK
Abstract
A method includes receiving information during a drilling
operation for a drillstring disposed in a bore in a formation;
estimating uncertainty associated with the information; analyzing
at least a portion of the information using a physics-based model
to generate a result; computing, via a Bayesian network, a risk
probability of the drilling string sticking in the bore in the
formation based at least in part on the result and the estimated
uncertainty; and, based at least in part on the risk probability,
issuing a signal.
Inventors: |
Ringer; Maurice;
(Roissy-en-France, FR) ; Androvandi; Sophie;
(Roissy-en-France, FR) |
|
Applicant: |
Name |
City |
State |
Country |
Type |
Schlumberger Technology Corporation |
Sugar Land |
TX |
US |
|
|
Family ID: |
62556811 |
Appl. No.: |
15/846661 |
Filed: |
December 19, 2017 |
Related U.S. Patent Documents
|
|
|
|
|
|
Application
Number |
Filing Date |
Patent Number |
|
|
62437619 |
Dec 21, 2016 |
|
|
|
Current U.S.
Class: |
1/1 |
Current CPC
Class: |
E21B 47/024 20130101;
E21B 41/0092 20130101; E21B 44/00 20130101; E21B 47/002 20200501;
E21B 47/18 20130101; E21B 7/04 20130101; E21B 49/003 20130101 |
International
Class: |
E21B 44/00 20060101
E21B044/00; E21B 41/00 20060101 E21B041/00; E21B 47/024 20060101
E21B047/024; E21B 47/00 20060101 E21B047/00 |
Claims
1. A method comprising: receiving information during a drilling
operation for a drillstring disposed in a bore in a formation;
estimating uncertainty associated with the information; analyzing
at least a portion of the information using a physics-based model
to generate a result; computing, via a Bayesian network, a risk
probability of the drilling string sticking in the bore in the
formation based at least in part on the result and the estimated
uncertainty; and based at least in part on the risk probability,
issuing a signal.
2. The method of claim 1 wherein the signal comprises a control
signal that controls drilling equipment of the drilling
operation.
3. The method of claim 1 wherein the signal comprises an alert.
4. The method of claim 1 comprising, responsive to comparing the
risk probability to a threshold, identifying a primary contributing
factor to the risk probability.
5. The method of claim 4 wherein the identifying comprises
progressing backwards through the Bayesian network to identify an
input to the Bayesian network.
6. The method of claim 1 wherein the Bayesian network comprises a
risk of differential sticking component and a risk of pack-off
sticking component.
7. The method of claim 6 wherein the Bayesian network comprises a
potential for differential sticking component.
8. The method of claim 7 comprising determining potential for
differential sticking via the differential sticking component prior
to performing the drilling operation.
9. The method of claim 1 wherein the computing occurs during the
drilling operation.
10. The method of claim 1 wherein the physics-based model comprises
a torque and drag model.
11. The method of claim 1 wherein the physics-based model comprises
a hydraulics model.
12. The method of claim 1 wherein the physics-based model comprises
a geomechanics model.
13. The method of claim 1 wherein the information comprises
drilling fluid information.
14. The method of claim 1 wherein the computing comprises
determining at least one cause of the risk probability.
15. The method of claim 1 wherein the computing comprises
determining at least one action to mitigate the risk
probability.
16. The method of claim 15 wherein the at least one action
comprises an action that aims to prevent cuttings from packing off
around the drillstring.
17. The method of claim 16 wherein the action comprises at least
one of increasing rotation rate and increasing circulation rate to
clean the bore.
18. The method of claim 17 wherein the action comprises associated
parameters that decrease likelihood of creating a hole or
washout.
19. A system comprising: a processor; memory accessible by the
processor; processor-executable instructions stored in the memory
and executable to instruct the system to: receive information
during a drilling operation for a drillstring disposed in a bore in
a formation; estimate uncertainty associated with the information;
analyze at least a portion of the information using a physics-based
model to generate a result; compute, via a Bayesian network, a risk
probability of the drilling string sticking in the bore in the
formation based at least in part on the result and the estimated
uncertainty; and based at least in part on the risk probability,
issue a signal.
20. One or more computer-readable storage media comprising
processor-executable instructions to instruct a computing system
to: receive information during a drilling operation for a
drillstring disposed in a bore in a formation; estimate uncertainty
associated with the information; analyze at least a portion of the
information using a physics-based model to generate a result;
compute, via a Bayesian network, a risk probability of the drilling
string sticking in the bore in the formation based at least in part
on the result and the estimated uncertainty; and based at least in
part on the risk probability, issue a signal.
Description
RELATED APPLICATIONS
[0001] This application claims the benefit of and priority to a US
Provisional Application having Ser. No. 62/437,619, filed 21 Dec.
2016, which is incorporated by reference herein.
BACKGROUND
[0002] A resource field can be an accumulation, pool or group of
pools of one or more resources (e.g., oil, gas, oil and gas) in a
subsurface environment. A resource field can include at least one
reservoir. A reservoir may be shaped in a manner that can trap
hydrocarbons and may be covered by an impermeable or sealing rock.
A bore can be drilled into an environment where the bore may be
utilized to form a well that can be utilized in producing
hydrocarbons from a reservoir.
[0003] A rig can be a system of components that can be operated to
form a bore in an environment, to transport equipment into and out
of a bore in an environment, etc. As an example, a rig can include
a system that can be used to drill a bore and to acquire
information about an environment, about drilling, etc. A resource
field may be an onshore field, an offshore field or an on- and
offshore field. A rig can include components for performing
operations onshore and/or offshore. A rig may be, for example,
vessel-based, offshore platform-based, onshore, etc.
[0004] Field planning can occur over one or more phases, which can
include an exploration phase that aims to identify and assess an
environment (e.g., a prospect, a play, etc.), which may include
drilling of one or more bores (e.g., one or more exploratory wells,
etc.). Other phases can include appraisal, development and
production phases.
SUMMARY
[0005] A method can include receiving information during a drilling
operation for a drillstring disposed in a bore in a formation;
estimating uncertainty associated with the information; analyzing
at least a portion of the information using a physics-based model
to generate a result; computing, via a Bayesian network, a risk
probability of the drilling string sticking in the bore in the
formation based at least in part on the result and the estimated
uncertainty; and, based at least in part on the risk probability,
issuing a signal. A system can include a processor; memory
accessible by the processor; processor-executable instructions
stored in the memory and executable to instruct the system to:
receive information during a drilling operation for a drillstring
disposed in a bore in a formation; estimate uncertainty associated
with the information; analyze at least a portion of the information
using a physics-based model to generate a result; compute, via a
Bayesian network, a risk probability of the drilling string
sticking in the bore in the formation based at least in part on the
result and the estimated uncertainty; and, based at least in part
on the risk probability, issue a signal. One or more
computer-readable storage media can include processor-executable
instructions to instruct a computing system to: receive information
during a drilling operation for a drillstring disposed in a bore in
a formation; estimate uncertainty associated with the information;
analyze at least a portion of the information using a physics-based
model to generate a result; compute, via a Bayesian network, a risk
probability of the drilling string sticking in the bore in the
formation based at least in part on the result and the estimated
uncertainty; and, based at least in part on the risk probability,
issue a signal. Various other apparatuses, systems, methods, etc.,
are also disclosed.
[0006] This summary is provided to introduce a selection of
concepts that are further described below in the detailed
description. This summary is not intended to identify key or
essential features of the claimed subject matter, nor is it
intended to be used as an aid in limiting the scope of the claimed
subject matter.
BRIEF DESCRIPTION OF THE DRAWINGS
[0007] Features and advantages of the described implementations can
be more readily understood by reference to the following
description taken in conjunction with the accompanying
drawings.
[0008] FIG. 1 illustrates examples of equipment in a geologic
environment;
[0009] FIG. 2 illustrates examples of equipment and examples of
hole types;
[0010] FIG. 3 illustrates an example of a system;
[0011] FIG. 4 illustrates an example of a wellsite system and an
example of a computing system;
[0012] FIG. 5 illustrates an example of a graphical user
interface;
[0013] FIG. 6 illustrates an example of a framework and an example
of a workflow;
[0014] FIG. 7 illustrates an example of a method and an example of
a system;
[0015] FIG. 8 illustrates examples of events;
[0016] FIG. 9 illustrates examples of events;
[0017] FIG. 10 illustrates examples of events;
[0018] FIG. 11 illustrates an example of a method;
[0019] FIG. 12 illustrates an example of a graphical user
interface;
[0020] FIG. 13 illustrates an example of a graphical user
interface;
[0021] FIG. 14 illustrates an example of a graphical user
interface;
[0022] FIG. 15 illustrates an example of a system and example
workflows;
[0023] FIG. 16 illustrates an example of a method and examples of
graphical user interfaces;
[0024] FIG. 17 illustrates examples of graphical user
interfaces;
[0025] FIG. 18 illustrates examples of graphical user
interfaces;
[0026] FIG. 19 illustrates an example of a graphical user
interface;
[0027] FIG. 20 illustrates an example of a system and an example of
a scenario;
[0028] FIG. 21 illustrates an example of a system;
[0029] FIG. 22 illustrates an example of computing system; and
[0030] FIG. 23 illustrates example components of a system and a
networked system.
DETAILED DESCRIPTION
[0031] The following description includes the best mode presently
contemplated for practicing the described implementations. This
description is not to be taken in a limiting sense, but rather is
made merely for the purpose of describing the general principles of
the implementations. The scope of the described implementations
should be ascertained with reference to the issued claims.
[0032] FIG. 1 shows an example of a geologic environment 120. In
FIG. 1, the geologic environment 120 may be a sedimentary basin
that includes layers (e.g., stratification) that include a
reservoir 121 and that may be, for example, intersected by a fault
123 (e.g., or faults). As an example, the geologic environment 120
may be outfitted with any of a variety of sensors, detectors,
actuators, etc. For example, equipment 122 may include
communication circuitry to receive and to transmit information with
respect to one or more networks 125. Such information may include
information associated with downhole equipment 124, which may be
equipment to acquire information, to assist with resource recovery,
etc. Other equipment 126 may be located remote from a well site and
include sensing, detecting, emitting or other circuitry. Such
equipment may include storage and communication circuitry to store
and to communicate data, instructions, etc. As an example, one or
more pieces of equipment may provide for measurement, collection,
communication, storage, analysis, etc. of data (e.g., for one or
more produced resources, etc.). As an example, one or more
satellites may be provided for purposes of communications, data
acquisition, etc. For example, FIG. 1 shows a satellite in
communication with the network 125 that may be configured for
communications, noting that the satellite may additionally or
alternatively include circuitry for imagery (e.g., spatial,
spectral, temporal, radiometric, etc.).
[0033] FIG. 1 also shows the geologic environment 120 as optionally
including equipment 127 and 128 associated with a well that
includes a substantially horizontal portion that may intersect with
one or more fractures 129. For example, consider a well in a shale
formation that may include natural fractures, artificial fractures
(e.g., hydraulic fractures) or a combination of natural and
artificial fractures. As an example, a well may be drilled for a
reservoir that is laterally extensive. In such an example, lateral
variations in properties, stresses, etc. may exist where an
assessment of such variations may assist with planning, operations,
etc. to develop the reservoir (e.g., via fracturing, injecting,
extracting, etc.). As an example, the equipment 127 and/or 128 may
include components, a system, systems, etc. for fracturing, seismic
sensing, analysis of seismic data, assessment of one or more
fractures, injection, production, etc. As an example, the equipment
127 and/or 128 may provide for measurement, collection,
communication, storage, analysis, etc. of data such as, for
example, production data (e.g., for one or more produced
resources). As an example, one or more satellites may be provided
for purposes of communications, data acquisition, etc.
[0034] FIG. 1 also shows an example of equipment 170 and an example
of equipment 180. Such equipment, which may be systems of
components, may be suitable for use in the geologic environment
120. While the equipment 170 and 180 are illustrated as land-based,
various components may be suitable for use in an offshore
system.
[0035] The equipment 170 includes a platform 171, a derrick 172, a
crown block 173, a line 174, a traveling block assembly 175,
drawworks 176 and a landing 177 (e.g., a monkeyboard). As an
example, the line 174 may be controlled at least in part via the
drawworks 176 such that the traveling block assembly 175 travels in
a vertical direction with respect to the platform 171. For example,
by drawing the line 174 in, the drawworks 176 may cause the line
174 to run through the crown block 173 and lift the traveling block
assembly 175 skyward away from the platform 171; whereas, by
allowing the line 174 out, the drawworks 176 may cause the line 174
to run through the crown block 173 and lower the traveling block
assembly 175 toward the platform 171. Where the traveling block
assembly 175 carries pipe (e.g., casing, etc.), tracking of
movement of the traveling block 175 may provide an indication as to
how much pipe has been deployed.
[0036] A derrick can be a structure used to support a crown block
and a traveling block operatively coupled to the crown block at
least in part via line. A derrick may be pyramidal in shape and
offer a suitable strength-to-weight ratio. A derrick may be movable
as a unit or in a piece by piece manner (e.g., to be assembled and
disassembled).
[0037] As an example, drawworks may include a spool, brakes, a
power source and assorted auxiliary devices. Drawworks may
controllably reel out and reel in line. Line may be reeled over a
crown block and coupled to a traveling block to gain mechanical
advantage in a "block and tackle" or "pulley" fashion. Reeling out
and in of line can cause a traveling block (e.g., and whatever may
be hanging underneath it), to be lowered into or raised out of a
bore. Reeling out of line may be powered by gravity and reeling in
by a motor, an engine, etc. (e.g., an electric motor, a diesel
engine, etc.).
[0038] As an example, a crown block can include a set of pulleys
(e.g., sheaves) that can be located at or near a top of a derrick
or a mast, over which line is threaded. A traveling block can
include a set of sheaves that can be moved up and down in a derrick
or a mast via line threaded in the set of sheaves of the traveling
block and in the set of sheaves of a crown block. A crown block, a
traveling block and a line can form a pulley system of a derrick or
a mast, which may enable handling of heavy loads (e.g.,
drillstring, pipe, casing, liners, etc.) to be lifted out of or
lowered into a bore. As an example, line may be about a centimeter
to about five centimeters in diameter as, for example, steel cable.
Through use of a set of sheaves, such line may carry loads heavier
than the line could support as a single strand.
[0039] As an example, a derrickman may be a rig crew member that
works on a platform attached to a derrick or a mast. A derrick can
include a landing on which a derrickman may stand. As an example,
such a landing may be about 10 meters or more above a rig floor. In
an operation referred to as trip out of the hole (TOH), a
derrickman may wear a safety harness that enables leaning out from
the work landing (e.g., monkeyboard) to reach pipe in located at or
near the center of a derrick or a mast and to throw a line around
the pipe and pull it back into its storage location (e.g.,
fingerboards), for example, until it a time at which it may be
desirable to run the pipe back into the bore. As an example, a rig
may include automated pipe-handling equipment such that the
derrickman controls the machinery rather than physically handling
the pipe.
[0040] As an example, a trip may refer to the act of pulling
equipment from a bore and/or placing equipment in a bore. As an
example, equipment may include a drillstring that can be pulled out
of a hole and/or placed or replaced in a hole. As an example, a
pipe trip may be performed where a drill bit has dulled or has
otherwise ceased to drill efficiently and is to be replaced.
[0041] FIG. 2 shows an example of a wellsite system 200 (e.g., at a
wellsite that may be onshore or offshore). As shown, the wellsite
system 200 can include a mud tank 201 for holding mud and other
material (e.g., where mud can be a drilling fluid), a suction line
203 that serves as an inlet to a mud pump 204 for pumping mud from
the mud tank 201 such that mud flows to a vibrating hose 206, a
drawworks 207 for winching drill line or drill lines 212, a
standpipe 208 that receives mud from the vibrating hose 206, a
kelly hose 209 that receives mud from the standpipe 208, a
gooseneck or goosenecks 210, a traveling block 211, a crown block
213 for carrying the traveling block 211 via the drill line or
drill lines 212 (see, e.g., the crown block 173 of FIG. 1), a
derrick 214 (see, e.g., the derrick 172 of FIG. 1), a kelly 218 or
a top drive 240, a kelly drive bushing 219, a rotary table 220, a
drill floor 221, a bell nipple 222, one or more blowout preventors
(BOPs) 223, a drillstring 225, a drill bit 226, a casing head 227
and a flow pipe 228 that carries mud and other material to, for
example, the mud tank 201.
[0042] In the example system of FIG. 2, a borehole 232 is formed in
subsurface formations 230 by rotary drilling; noting that various
example embodiments may also use directional drilling.
[0043] As shown in the example of FIG. 2, the drillstring 225 is
suspended within the borehole 232 and has a drillstring assembly
250 that includes the drill bit 226 at its lower end. As an
example, the drillstring assembly 250 may be a bottom hole assembly
(BHA).
[0044] The wellsite system 200 can provide for operation of the
drillstring 225 and other operations. As shown, the wellsite system
200 includes the platform and the derrick 214 positioned over the
borehole 232. As mentioned, the wellsite system 200 can include the
rotary table 220 where the drillstring 225 pass through an opening
in the rotary table 220.
[0045] As shown in the example of FIG. 2, the wellsite system 200
can include the kelly 218 and associated components, etc., or a top
drive 240 and associated components. As to a kelly example, the
kelly 218 may be a square or hexagonal metal/alloy bar with a hole
drilled therein that serves as a mud flow path. The kelly 218 can
be used to transmit rotary motion from the rotary table 220 via the
kelly drive bushing 219 to the drillstring 225, while allowing the
drillstring 225 to be lowered or raised during rotation. The kelly
218 can pass through the kelly drive bushing 219, which can be
driven by the rotary table 220. As an example, the rotary table 220
can include a master bushing that operatively couples to the kelly
drive bushing 219 such that rotation of the rotary table 220 can
turn the kelly drive bushing 219 and hence the kelly 218. The kelly
drive bushing 219 can include an inside profile matching an outside
profile (e.g., square, hexagonal, etc.) of the kelly 218; however,
with slightly larger dimensions so that the kelly 218 can freely
move up and down inside the kelly drive bushing 219.
[0046] As to a top drive example, the top drive 240 can provide
functions performed by a kelly and a rotary table. The top drive
240 can turn the drillstring 225. As an example, the top drive 240
can include one or more motors (e.g., electric and/or hydraulic)
connected with appropriate gearing to a short section of pipe
called a quill, that in turn may be screwed into a saver sub or the
drillstring 225 itself. The top drive 240 can be suspended from the
traveling block 211, so the rotary mechanism is free to travel up
and down the derrick 214. As an example, a top drive 240 may allow
for drilling to be performed with more joint stands than a
kelly/rotary table approach.
[0047] In the example of FIG. 2, the mud tank 201 can hold mud,
which can be one or more types of drilling fluids. As an example, a
wellbore may be drilled to produce fluid, inject fluid or both
(e.g., hydrocarbons, minerals, water, etc.).
[0048] In the example of FIG. 2, the drillstring 225 (e.g.,
including one or more downhole tools) may be composed of a series
of pipes threadably connected together to form a long tube with the
drill bit 226 at the lower end thereof. As the drillstring 225 is
advanced into a wellbore for drilling, at some point in time prior
to or coincident with drilling, the mud may be pumped by the pump
204 from the mud tank 201 (e.g., or other source) via a the lines
206, 208 and 209 to a port of the kelly 218 or, for example, to a
port of the top drive 240. The mud can then flow via a passage
(e.g., or passages) in the drillstring 225 and out of ports located
on the drill bit 226 (see, e.g., a directional arrow). As the mud
exits the drillstring 225 via ports in the drill bit 226, it can
then circulate upwardly through an annular region between an outer
surface(s) of the drillstring 225 and surrounding wall(s) (e.g.,
open borehole, casing, etc.), as indicated by directional arrows.
In such a manner, the mud lubricates the drill bit 226 and carries
heat energy (e.g., frictional or other energy) and formation
cuttings to the surface where the mud (e.g., and cuttings) may be
returned to the mud tank 201, for example, for recirculation (e.g.,
with processing to remove cuttings, etc.).
[0049] The mud pumped by the pump 204 into the drillstring 225 may,
after exiting the drillstring 225, form a mudcake that lines the
wellbore which, among other functions, may reduce friction between
the drillstring 225 and surrounding wall(s) (e.g., borehole,
casing, etc.). A reduction in friction may facilitate advancing or
retracting the drillstring 225. During a drilling operation, the
entire drill string 225 may be pulled from a wellbore and
optionally replaced, for example, with a new or sharpened drill
bit, a smaller diameter drill string, etc. As mentioned, the act of
pulling a drill string out of a hole or replacing it in a hole is
referred to as tripping. A trip may be referred to as an upward
trip or an outward trip or as a downward trip or an inward trip
depending on trip direction.
[0050] As an example, consider a downward trip where upon arrival
of the drill bit 226 of the drill string 225 at a bottom of a
wellbore, pumping of the mud commences to lubricate the drill bit
226 for purposes of drilling to enlarge the wellbore. As mentioned,
the mud can be pumped by the pump 204 into a passage of the
drillstring 225 and, upon filling of the passage, the mud may be
used as a transmission medium to transmit energy, for example,
energy that may encode information as in mud-pulse telemetry.
[0051] As an example, mud-pulse telemetry equipment may include a
downhole device configured to effect changes in pressure in the mud
to create an acoustic wave or waves upon which information may
modulated. In such an example, information from downhole equipment
(e.g., one or more modules of the drillstring 225) may be
transmitted uphole to an uphole device, which may relay such
information to other equipment for processing, control, etc.
[0052] As an example, telemetry equipment may operate via
transmission of energy via the drillstring 225 itself. For example,
consider a signal generator that imparts coded energy signals to
the drillstring 225 and repeaters that may receive such energy and
repeat it to further transmit the coded energy signals (e.g.,
information, etc.).
[0053] As an example, the drillstring 225 may be fitted with
telemetry equipment 252 that includes a rotatable drive shaft, a
turbine impeller mechanically coupled to the drive shaft such that
the mud can cause the turbine impeller to rotate, a modulator rotor
mechanically coupled to the drive shaft such that rotation of the
turbine impeller causes said modulator rotor to rotate, a modulator
stator mounted adjacent to or proximate to the modulator rotor such
that rotation of the modulator rotor relative to the modulator
stator creates pressure pulses in the mud, and a controllable brake
for selectively braking rotation of the modulator rotor to modulate
pressure pulses. In such example, an alternator may be coupled to
the aforementioned drive shaft where the alternator includes at
least one stator winding electrically coupled to a control circuit
to selectively short the at least one stator winding to
electromagnetically brake the alternator and thereby selectively
brake rotation of the modulator rotor to modulate the pressure
pulses in the mud.
[0054] In the example of FIG. 2, an uphole control and/or data
acquisition system 262 may include circuitry to sense pressure
pulses generated by telemetry equipment 252 and, for example,
communicate sensed pressure pulses or information derived therefrom
for process, control, etc.
[0055] The assembly 250 of the illustrated example includes a
logging-while-drilling (LWD) module 254, a measuring-while-drilling
(MWD) module 256, an optional module 258, a roto-steerable system
and motor 260, and the drill bit 226. Such components or modules
may be referred to as tools where a drillstring can include a
plurality of tools.
[0056] The LWD module 254 may be housed in a suitable type of drill
collar and can contain one or a plurality of selected types of
logging tools. It will also be understood that more than one LWD
and/or MWD module can be employed, for example, as represented at
by the module 256 of the drillstring assembly 250. Where the
position of an LWD module is mentioned, as an example, it may refer
to a module at the position of the LWD module 254, the module 256,
etc. An LWD module can include capabilities for measuring,
processing, and storing information, as well as for communicating
with the surface equipment. In the illustrated example, the LWD
module 254 may include a seismic measuring device.
[0057] The MWD module 256 may be housed in a suitable type of drill
collar and can contain one or more devices for measuring
characteristics of the drillstring 225 and the drill bit 226. As an
example, the MWD tool 254 may include equipment for generating
electrical power, for example, to power various components of the
drillstring 225. As an example, the MWD tool 254 may include the
telemetry equipment 252, for example, where the turbine impeller
can generate power by flow of the mud; it being understood that
other power and/or battery systems may be employed for purposes of
powering various components. As an example, the MWD module 256 may
include one or more of the following types of measuring devices: a
weight-on-bit measuring device, a torque measuring device, a
vibration measuring device, a shock measuring device, a stick slip
measuring device, a direction measuring device, and an inclination
measuring device.
[0058] FIG. 2 also shows some examples of types of holes that may
be drilled. For example, consider a slant hole 272, an S-shaped
hole 274, a deep inclined hole 276 and a horizontal hole 278.
[0059] As an example, a drilling operation can include directional
drilling where, for example, at least a portion of a well includes
a curved axis. For example, consider a radius that defines
curvature where an inclination with regard to the vertical may vary
until reaching an angle between about 30 degrees and about 60
degrees or, for example, an angle to about 90 degrees or possibly
greater than about 90 degrees.
[0060] As an example, a directional well can include several shapes
where each of the shapes may aim to meet particular operational
demands. As an example, a drilling process may be performed on the
basis of information as and when it is relayed to a drilling
engineer. As an example, inclination and/or direction may be
modified based on information received during a drilling
process.
[0061] As an example, deviation of a bore may be accomplished in
part by use of a downhole motor and/or a turbine. As to a motor,
for example, a drillstring can include a positive displacement
motor (PDM).
[0062] As an example, a system may be a steerable system and
include equipment to perform a method such as geosteering. As an
example, a steerable system can include a PDM or a turbine on a
lower part of a drillstring which, just above a drill bit, a bent
sub can be mounted. As an example, above a PDM, MWD equipment that
provides real time or near real time data of interest (e.g.,
inclination, direction, pressure, temperature, real weight on the
drill bit, torque stress, etc.) and/or LWD equipment may be
installed. As to the latter, LWD equipment can make it possible to
send to the surface various types of data of interest, including
for example, geological data (e.g., gamma ray log, resistivity,
density and sonic logs, etc.).
[0063] The coupling of sensors providing information on the course
of a well trajectory, in real time or near real time, with, for
example, one or more logs characterizing the formations from a
geological viewpoint, can allow for implementing a geosteering
method. Such a method can include navigating a subsurface
environment, for example, to follow a desired route to reach a
desired target or targets.
[0064] As an example, a drillstring can include an azimuthal
density neutron (ADN) tool for measuring density and porosity; a
MWD tool for measuring inclination (INCL), azimuth (AZI) and
shocks; a compensated dual resistivity (CDR) tool for measuring
resistivity and gamma ray related phenomena; one or more variable
gauge stabilizers; one or more bend joints; and a geosteering tool,
which may include a motor and optionally equipment for measuring
and/or responding to one or more of inclination, resistivity and
gamma ray related phenomena.
[0065] As an example, geosteering can include intentional
directional control of a wellbore based on results of downhole
geological logging measurements in a manner that aims to keep a
directional wellbore within a desired region, zone (e.g., a pay
zone), etc. As an example, geosteering may include directing a
wellbore to keep the wellbore in a particular section of a
reservoir, for example, to minimize gas and/or water breakthrough
and, for example, to maximize economic production from a well that
includes the wellbore.
[0066] Referring again to FIG. 2, the wellsite system 200 can
include one or more sensors 264 that are operatively coupled to the
control and/or data acquisition system 262. As an example, a sensor
or sensors may be at surface locations. As an example, a sensor or
sensors may be at downhole locations. As an example, a sensor or
sensors may be at one or more remote locations that are not within
a distance of the order of about one hundred meters from the
wellsite system 200. As an example, a sensor or sensor may be at an
offset wellsite where the wellsite system 200 and the offset
wellsite are in a common field (e.g., oil and/or gas field).
[0067] As an example, one or more of the sensors 264 can be
provided for tracking pipe, tracking movement of at least a portion
of a drillstring, etc.
[0068] As an example, the system 200 can include one or more
sensors 266 that can sense and/or transmit signals to a fluid
conduit such as a drilling fluid conduit (e.g., a drilling mud
conduit). For example, in the system 200, the one or more sensors
266 can be operatively coupled to portions of the standpipe 208
through which mud flows. As an example, a downhole tool can
generate pulses that can travel through the mud and be sensed by
one or more of the one or more sensors 266. In such an example, the
downhole tool can include associated circuitry such as, for
example, encoding circuitry that can encode signals, for example,
to reduce demands as to transmission. As an example, circuitry at
the surface may include decoding circuitry to decode encoded
information transmitted at least in part via mud-pulse telemetry.
As an example, circuitry at the surface may include encoder
circuitry and/or decoder circuitry and circuitry downhole may
include encoder circuitry and/or decoder circuitry. As an example,
the system 200 can include a transmitter that can generate signals
that can be transmitted downhole via mud (e.g., drilling fluid) as
a transmission medium.
[0069] As an example, one or more portions of a drillstring may
become stuck. The term stuck can refer to one or more of varying
degrees of inability to move or remove a drillstring from a bore.
As an example, in a stuck condition, it might be possible to rotate
pipe or lower it back into a bore or, for example, in a stuck
condition, there may be an inability to move the drillstring
axially in the bore, though some amount of rotation may be
possible. As an example, in a stuck condition, there may be an
inability to move at least a portion of the drillstring axially and
rotationally.
[0070] As to the term "stuck pipe", this can refer to a portion of
a drillstring that cannot be rotated or moved axially. As an
example, a condition referred to as "differential sticking" can be
a condition whereby the drillstring cannot be moved (e.g., rotated
or reciprocated) along the axis of the bore. Differential sticking
may occur when high-contact forces caused by low reservoir
pressures, high wellbore pressures, or both, are exerted over a
sufficiently large area of the drillstring. Differential sticking
can have time and financial cost.
[0071] As an example, a sticking force can be a product of the
differential pressure between the wellbore and the reservoir and
the area that the differential pressure is acting upon. This means
that a relatively low differential pressure (delta p) applied over
a large working area can be just as effective in sticking pipe as
can a high differential pressure applied over a small area.
[0072] As an example, a condition referred to as "mechanical
sticking" can be a condition where limiting or prevention of motion
of the drillstring by a mechanism other than differential pressure
sticking occurs. Mechanical sticking can be caused, for example, by
one or more of junk in the hole, wellbore geometry anomalies,
cement, keyseats or a buildup of cuttings in the annulus.
[0073] FIG. 3 shows an example of a system 300 that includes
various equipment for evaluation 310, planning 320, engineering 330
and operations 340. For example, a drilling workflow framework 301,
a seismic-to-simulation framework 302, a technical data framework
303 and a drilling framework 304 may be implemented to perform one
or more processes such as a evaluating a formation 314, evaluating
a process 318, generating a trajectory 324, validating a trajectory
328, formulating constraints 334, designing equipment and/or
processes based at least in part on constraints 338, performing
drilling 344 and evaluating drilling and/or formation 348.
[0074] In the example of FIG. 3, the seismic-to-simulation
framework 302 can be, for example, the PETREL.RTM. framework
(Schlumberger Limited, Houston, Tex.) and the technical data
framework 303 can be, for example, the TECHLOG.RTM. framework
(Schlumberger Limited, Houston, Tex.).
[0075] As an example, a framework can include entities that may
include earth entities, geological objects or other objects such as
wells, surfaces, reservoirs, etc. Entities can include virtual
representations of actual physical entities that are reconstructed
for purposes of one or more of evaluation, planning, engineering,
operations, etc.
[0076] Entities may include entities based on data acquired via
sensing, observation, etc. (e.g., seismic data and/or other
information). An entity may be characterized by one or more
properties (e.g., a geometrical pillar grid entity of an earth
model may be characterized by a porosity property). Such properties
may represent one or more measurements (e.g., acquired data),
calculations, etc.
[0077] A framework may be an object-based framework. In such a
framework, entities may include entities based on pre-defined
classes, for example, to facilitate modeling, analysis, simulation,
etc. A commercially available example of an object-based framework
is the MICROSOFT.TM. .NET.TM. framework (Redmond, Wash.), which
provides a set of extensible object classes. In the .NET.TM.
framework, an object class encapsulates a module of reusable code
and associated data structures. Object classes can be used to
instantiate object instances for use in by a program, script, etc.
For example, borehole classes may define objects for representing
boreholes based on well data.
[0078] As an example, a framework can include an analysis component
that may allow for interaction with a model or model-based results
(e.g., simulation results, etc.). As to simulation, a framework may
operatively link to or include a simulator such as the ECLIPSE.RTM.
reservoir simulator (Schlumberger Limited, Houston Tex.), the
INTERSECT.RTM. reservoir simulator (Schlumberger Limited, Houston
Tex.), etc.
[0079] The aforementioned PETREL.RTM. framework provides components
that allow for optimization of exploration and development
operations. The PETREL.RTM. framework includes seismic to
simulation software components that can output information for use
in increasing reservoir performance, for example, by improving
asset team productivity. Through use of such a framework, various
professionals (e.g., geophysicists, geologists, well engineers,
reservoir engineers, etc.) can develop collaborative workflows and
integrate operations to streamline processes. Such a framework may
be considered an application and may be considered a data-driven
application (e.g., where data is input for purposes of modeling,
simulating, etc.).
[0080] As an example, one or more frameworks may be interoperative
and/or run upon one or another. As an example, consider the
commercially available framework environment marketed as the
OCEAN.RTM. framework environment (Schlumberger Limited, Houston,
Tex.), which allows for integration of add-ons (or plug-ins) into a
PETREL.RTM. framework workflow. The OCEAN.RTM. framework
environment leverages .NET.TM. tools (Microsoft Corporation,
Redmond, Wash.) and offers stable, user-friendly interfaces for
efficient development. In an example embodiment, various components
may be implemented as add-ons (or plug-ins) that conform to and
operate according to specifications of a framework environment
(e.g., according to application programming interface (API)
specifications, etc.).
[0081] As an example, a framework can include a model simulation
layer along with a framework services layer, a framework core layer
and a modules layer. The framework may include the commercially
available OCEAN.RTM. framework where the model simulation layer can
include or operatively link to the commercially available
PETREL.RTM. model-centric software package that hosts OCEAN.RTM.
framework applications. In an example embodiment, the PETREL.RTM.
software may be considered a data-driven application. The
PETREL.RTM. software can include a framework for model building and
visualization. Such a model may include one or more grids.
[0082] As an example, the model simulation layer may provide domain
objects, act as a data source, provide for rendering and provide
for various user interfaces. Rendering may provide a graphical
environment in which applications can display their data while the
user interfaces may provide a common look and feel for application
user interface components.
[0083] As an example, domain objects can include entity objects,
property objects and optionally other objects. Entity objects may
be used to geometrically represent wells, surfaces, reservoirs,
etc., while property objects may be used to provide property values
as well as data versions and display parameters. For example, an
entity object may represent a well where a property object provides
log information as well as version information and display
information (e.g., to display the well as part of a model).
[0084] As an example, data may be stored in one or more data
sources (or data stores, generally physical data storage devices),
which may be at the same or different physical sites and accessible
via one or more networks. As an example, a model simulation layer
may be configured to model projects. As such, a particular project
may be stored where stored project information may include inputs,
models, results and cases. Thus, upon completion of a modeling
session, a user may store a project. At a later time, the project
can be accessed and restored using the model simulation layer,
which can recreate instances of the relevant domain objects.
[0085] As an example, the system 300 may be used to perform one or
more workflows. A workflow may be a process that includes a number
of worksteps. A workstep may operate on data, for example, to
create new data, to update existing data, etc. As an example, a
workflow may operate on one or more inputs and create one or more
results, for example, based on one or more algorithms. As an
example, a system may include a workflow editor for creation,
editing, executing, etc. of a workflow. In such an example, the
workflow editor may provide for selection of one or more
pre-defined worksteps, one or more customized worksteps, etc. As an
example, a workflow may be a workflow implementable at least in
part in the PETREL.RTM. software, for example, that operates on
seismic data, seismic attribute(s), etc.
[0086] As an example, seismic data can be data acquired via a
seismic survey where sources and receivers are positioned in a
geologic environment to emit and receive seismic energy where at
least a portion of such energy can reflect off subsurface
structures. As an example, a seismic data analysis framework or
frameworks (e.g., consider the OMEGA.RTM. framework, marketed by
Schlumberger Limited, Houston, Tex.) may be utilized to determine
depth, extent, properties, etc. of subsurface structures. As an
example, seismic data analysis can include forward modeling and/or
inversion, for example, to iteratively build a model of a
subsurface region of a geologic environment. As an example, a
seismic data analysis framework may be part of or operatively
coupled to a seismic-to-simulation framework (e.g., the PETREL.RTM.
framework, etc.).
[0087] As an example, a workflow may be a process implementable at
least in part in the OCEAN.RTM. framework. As an example, a
workflow may include one or more worksteps that access a module
such as a plug-in (e.g., external executable code, etc.).
[0088] As an example, a framework may provide for modeling
petroleum systems. For example, the commercially available modeling
framework marketed as the PETROMOD.RTM. framework (Schlumberger
Limited, Houston, Tex.) includes features for input of various
types of information (e.g., seismic, well, geological, etc.) to
model evolution of a sedimentary basin. The PETROMOD.RTM. framework
provides for petroleum systems modeling via input of various data
such as seismic data, well data and other geological data, for
example, to model evolution of a sedimentary basin. The
PETROMOD.RTM. framework may predict if, and how, a reservoir has
been charged with hydrocarbons, including, for example, the source
and timing of hydrocarbon generation, migration routes, quantities,
pore pressure and hydrocarbon type in the subsurface or at surface
conditions. In combination with a framework such as the PETREL.RTM.
framework, workflows may be constructed to provide
basin-to-prospect scale exploration solutions. Data exchange
between frameworks can facilitate construction of models, analysis
of data (e.g., PETROMOD.RTM. framework data analyzed using
PETREL.RTM. framework capabilities), and coupling of workflows.
[0089] As mentioned, a drillstring can include various tools that
may make measurements. As an example, a wireline tool or another
type of tool may be utilized to make measurements. As an example, a
tool may be configured to acquire electrical borehole images. As an
example, the fullbore Formation MicroImager (FMI) tool
(Schlumberger Limited, Houston, Tex.) can acquire borehole image
data. A data acquisition sequence for such a tool can include
running the tool into a borehole with acquisition pads closed,
opening and pressing the pads against a wall of the borehole,
delivering electrical current into the material defining the
borehole while translating the tool in the borehole, and sensing
current remotely, which is altered by interactions with the
material.
[0090] Analysis of formation information may reveal features such
as, for example, vugs, dissolution planes (e.g., dissolution along
bedding planes), stress-related features, dip events, etc. As an
example, a tool may acquire information that may help to
characterize a reservoir, optionally a fractured reservoir where
fractures may be natural and/or artificial (e.g., hydraulic
fractures). As an example, information acquired by a tool or tools
may be analyzed using a framework such as the TECHLOG.RTM.
framework. As an example, the TECHLOG.RTM. framework can be
interoperable with one or more other frameworks such as, for
example, the PETREL.RTM. framework.
[0091] As an example, various aspects of a workflow may be
completed automatically, may be partially automated, or may be
completed manually, as by a human user interfacing with a software
application. As an example, a workflow may be cyclic, and may
include, as an example, four stages such as, for example, an
evaluation stage (see, e.g., the evaluation equipment 310), a
planning stage (see, e.g., the planning equipment 320), an
engineering stage (see, e.g., the engineering equipment 330) and an
execution stage (see, e.g., the operations equipment 340). As an
example, a workflow may commence at one or more stages, which may
progress to one or more other stages (e.g., in a serial manner, in
a parallel manner, in a cyclical manner, etc.).
[0092] As an example, a workflow can commence with an evaluation
stage, which may include a geological service provider evaluating a
formation (see, e.g., the evaluation block 314). As an example, a
geological service provider may undertake the formation evaluation
using a computing system executing a software package tailored to
such activity; or, for example, one or more other suitable geology
platforms may be employed (e.g., alternatively or additionally). As
an example, the geological service provider may evaluate the
formation, for example, using earth models, geophysical models,
basin models, petrotechnical models, combinations thereof, and/or
the like. Such models may take into consideration a variety of
different inputs, including offset well data, seismic data, pilot
well data, other geologic data, etc. The models and/or the input
may be stored in the database maintained by the server and accessed
by the geological service provider.
[0093] As an example, a workflow may progress to a geology and
geophysics ("G&G") service provider, which may generate a well
trajectory (see, e.g., the generation block 324), which may involve
execution of one or more G&G software packages. Examples of
such software packages include the PETREL.RTM. framework. As an
example, a G&G service provider may determine a well trajectory
or a section thereof, based on, for example, one or more model(s)
provided by a formation evaluation (e.g., per the evaluation block
314), and/or other data, e.g., as accessed from one or more
databases (e.g., maintained by one or more servers, etc.). As an
example, a well trajectory may take into consideration various
"basis of design" (BOD) constraints, such as general surface
location, target (e.g., reservoir) location, and the like. As an
example, a trajectory may incorporate information about tools,
bottom-hole assemblies, casing sizes, etc., that may be used in
drilling the well. A well trajectory determination may take into
consideration a variety of other parameters, including risk
tolerances, fluid weights and/or plans, bottom-hole pressures,
drilling time, etc.
[0094] As an example, a workflow may progress to a first
engineering service provider (e.g., one or more processing machines
associated therewith), which may validate a well trajectory and,
for example, relief well design (see, e.g., the validation block
328). Such a validation process may include evaluating physical
properties, calculations, risk tolerances, integration with other
aspects of a workflow, etc. As an example, one or more parameters
for such determinations may be maintained by a server and/or by the
first engineering service provider; noting that one or more
model(s), well trajectory(ies), etc. may be maintained by a server
and accessed by the first engineering service provider. For
example, the first engineering service provider may include one or
more computing systems executing one or more software packages. As
an example, where the first engineering service provider rejects or
otherwise suggests an adjustment to a well trajectory, the well
trajectory may be adjusted or a message or other notification sent
to the G&G service provider requesting such modification.
[0095] As an example, one or more engineering service providers
(e.g., first, second, etc.) may provide a casing design,
bottom-hole assembly (BHA) design, fluid design, and/or the like,
to implement a well trajectory (see, e.g., the design block 338).
In some embodiments, a second engineering service provider may
perform such design using one of more software applications. Such
designs may be stored in one or more databases maintained by one or
more servers, which may, for example, employ STUDIO.RTM. framework
tools, and may be accessed by one or more of the other service
providers in a workflow.
[0096] As an example, a second engineering service provider may
seek approval from a third engineering service provider for one or
more designs established along with a well trajectory. In such an
example, the third engineering service provider may consider
various factors as to whether the well engineering plan is
acceptable, such as economic variables (e.g., oil production
forecasts, costs per barrel, risk, drill time, etc.), and may
request authorization for expenditure, such as from the operating
company's representative, well-owner's representative, or the like
(see, e.g., the formulation block 334). As an example, at least
some of the data upon which such determinations are based may be
stored in one or more database maintained by one or more servers.
As an example, a first, a second, and/or a third engineering
service provider may be provided by a single team of engineers or
even a single engineer, and thus may or may not be separate
entities.
[0097] As an example, where economics may be unacceptable or
subject to authorization being withheld, an engineering service
provider may suggest changes to casing, a bottom-hole assembly,
and/or fluid design, or otherwise notify and/or return control to a
different engineering service provider, so that adjustments may be
made to casing, a bottom-hole assembly, and/or fluid design. Where
modifying one or more of such designs is impracticable within well
constraints, trajectory, etc., the engineering service provider may
suggest an adjustment to the well trajectory and/or a workflow may
return to or otherwise notify an initial engineering service
provider and/or a G&G service provider such that either or both
may modify the well trajectory.
[0098] As an example, a workflow can include considering a well
trajectory, including an accepted well engineering plan, and a
formation evaluation. Such a workflow may then pass control to a
drilling service provider, which may implement the well engineering
plan, establishing safe and efficient drilling, maintaining well
integrity, and reporting progress as well as operating parameters
(see, e.g., the blocks 344 and 348). As an example, operating
parameters, formation encountered, data collected while drilling
(e.g., using logging-while-drilling or measuring-while-drilling
technology), may be returned to a geological service provider for
evaluation. As an example, the geological service provider may then
re-evaluate the well trajectory, or one or more other aspects of
the well engineering plan, and may, in some cases, and potentially
within predetermined constraints, adjust the well engineering plan
according to the real-life drilling parameters (e.g., based on
acquired data in the field, etc.).
[0099] Whether the well is entirely drilled, or a section thereof
is completed, depending on the specific embodiment, a workflow may
proceed to a post review (see, e.g., the evaluation block 318). As
an example, a post review may include reviewing drilling
performance. As an example, a post review may further include
reporting the drilling performance (e.g., to one or more relevant
engineering, geological, or G&G service providers).
[0100] Various activities of a workflow may be performed
consecutively and/or may be performed out of order (e.g., based
partially on information from templates, nearby wells, etc. to fill
in any gaps in information that is to be provided by another
service provider). As an example, undertaking one activity may
affect the results or basis for another activity, and thus may,
either manually or automatically, call for a variation in one or
more workflow activities, work products, etc. As an example, a
server may allow for storing information on a central database
accessible to various service providers where variations may be
sought by communication with an appropriate service provider, may
be made automatically, or may otherwise appear as suggestions to
the relevant service provider. Such an approach may be considered
to be a holistic approach to a well workflow, in comparison to a
sequential, piecemeal approach.
[0101] As an example, various actions of a workflow may be repeated
multiple times during drilling of a wellbore. For example, in one
or more automated systems, feedback from a drilling service
provider may be provided at or near real-time, and the data
acquired during drilling may be fed to one or more other service
providers, which may adjust its piece of the workflow accordingly.
As there may be dependencies in other areas of the workflow, such
adjustments may permeate through the workflow, e.g., in an
automated fashion. In some embodiments, a cyclic process may
additionally or instead proceed after a certain drilling goal is
reached, such as the completion of a section of the wellbore,
and/or after the drilling of the entire wellbore, or on a per-day,
week, month, etc. basis.
[0102] Well planning can include determining a path of a well that
can extend to a reservoir, for example, to economically produce
fluids such as hydrocarbons therefrom. Well planning can include
selecting a drilling and/or completion assembly which may be used
to implement a well plan. As an example, various constraints can be
imposed as part of well planning that can impact design of a well.
As an example, such constraints may be imposed based at least in
part on information as to known geology of a subterranean domain,
presence of one or more other wells (e.g., actual and/or planned,
etc.) in an area (e.g., consider collision avoidance), etc. As an
example, one or more constraints may be imposed based at least in
part on characteristics of one or more tools, components, etc. As
an example, one or more constraints may be based at least in part
on factors associated with drilling time and/or risk tolerance.
[0103] As an example, a system can allow for a reduction in waste,
for example, as may be defined according to LEAN. In the context of
LEAN, consider one or more of the following types of waste:
transport (e.g., moving items unnecessarily, whether physical or
data); inventory (e.g., components, whether physical or
informational, as work in process, and finished product not being
processed); motion (e.g., people or equipment moving or walking
unnecessarily to perform desired processing); waiting (e.g.,
waiting for information, interruptions of production during shift
change, etc.); overproduction (e.g., production of material,
information, equipment, etc. ahead of demand); over Processing
(e.g., resulting from poor tool or product design creating
activity); and defects (e.g., effort involved in inspecting for and
fixing defects whether in a plan, data, equipment, etc.). As an
example, a system that allows for actions (e.g., methods,
workflows, etc.) to be performed in a collaborative manner can help
to reduce one or more types of waste.
[0104] As an example, a system can be utilized to implement a
method for facilitating distributed well engineering, planning,
and/or drilling system design across multiple computation devices
where collaboration can occur among various different users (e.g.,
some being local, some being remote, some being mobile, etc.). In
such a system, the various users via appropriate devices may be
operatively coupled via one or more networks (e.g., local and/or
wide area networks, public and/or private networks, land-based,
marine-based and/or areal networks, etc.).
[0105] As an example, a system may allow well engineering,
planning, and/or drilling system design to take place via a
subsystems approach where a wellsite system is composed of various
subsystem, which can include equipment subsystems and/or
operational subsystems (e.g., control subsystems, etc.). As an
example, computations may be performed using various computational
platforms/devices that are operatively coupled via communication
links (e.g., network links, etc.). As an example, one or more links
may be operatively coupled to a common database (e.g., a server
site, etc.). As an example, a particular server or servers may
manage receipt of notifications from one or more devices and/or
issuance of notifications to one or more devices. As an example, a
system may be implemented for a project where the system can output
a well plan, for example, as a digital well plan, a paper well
plan, a digital and paper well plan, etc. Such a well plan can be a
complete well engineering plan or design for the particular
project.
[0106] FIG. 4 shows an example of a wellsite system 400,
specifically, FIG. 4 shows the wellsite system 400 in an
approximate side view and an approximate plan view along with a
block diagram of a system 470.
[0107] In the example of FIG. 4, the wellsite system 400 can
include a cabin 410, a rotary table 422, drawworks 424, a mast 426
(e.g., optionally carrying a top drive, etc.), mud tanks 430 (e.g.,
with one or more pumps, one or more shakers, etc.), one or more
pump buildings 440, a boiler building 442, an HPU building 444
(e.g., with a rig fuel tank, etc.), a combination building 448
(e.g., with one or more generators, etc.), pipe tubs 462, a catwalk
464, a flare 468, etc. Such equipment can include one or more
associated functions and/or one or more associated operational
risks, which may be risks as to time, resources, and/or humans.
[0108] As shown in the example of FIG. 4, the wellsite system 400
can include a system 470 that includes one or more processors 472,
memory 474 operatively coupled to at least one of the one or more
processors 472, instructions 476 that can be, for example, stored
in the memory 474, and one or more interfaces 478. As an example,
the system 470 can include one or more processor-readable media
that include processor-executable instructions executable by at
least one of the one or more processors 472 to cause the system 470
to control one or more aspects of the wellsite system 400. In such
an example, the memory 474 can be or include the one or more
processor-readable media where the processor-executable
instructions can be or include instructions. As an example, a
processor-readable medium can be a computer-readable storage medium
that is not a signal and that is not a carrier wave.
[0109] FIG. 4 also shows a battery 480 that may be operatively
coupled to the system 470, for example, to power the system 470. As
an example, the battery 480 may be a back-up battery that operates
when another power supply is unavailable for powering the system
470. As an example, the battery 480 may be operatively coupled to a
network, which may be a cloud network. As an example, the battery
480 can include smart battery circuitry and may be operatively
coupled to one or more pieces of equipment via a SMBus or other
type of bus.
[0110] In the example of FIG. 4, services 490 are shown as being
available, for example, via a cloud platform. Such services can
include data services 492, query services 494 and drilling services
496. As an example, the services 490 may be part of a system such
as the system 300 of FIG. 3.
[0111] FIG. 5 shows an example of a graphical user interface (GUI)
500 that includes information associated with a well plan.
Specifically, the GUI 500 includes a panel 510 where surfaces
representations 512 and 514 are rendered along with well
trajectories where a location 516 can represent a position of a
drillstring 517 along a well trajectory. The GUI 500 may include
one or more editing features such as an edit well plan set of
features 530. The GUI 500 may include information as to individuals
of a team 540 that are involved, have been involved and/or are to
be involved with one or more operations. The GUI 500 may include
information as to one or more activities 550. As shown in the
example of FIG. 5, the GUI 500 can include a graphical control of a
drillstring 560 where, for example, various portions of the
drillstring 560 may be selected to expose one or more associated
parameters (e.g., type of equipment, equipment specifications,
operational history, etc.). FIG. 5 also shows a table 570 as a
point spreadsheet that specifies information for a plurality of
wells.
[0112] In the example of FIG. 5, the drillstring graphical control
560 includes components such as drill pipe, heavy weight drill pipe
(HWDP), subs, collars, jars, stabilizers, motor(s) and a bit. A
drillstring can be a combination of drill pipe, a bottom hole
assembly (BHA) and one or more other tools, which can include one
or more tools that can help a drill bit turn and drill into
material (e.g., a formation).
[0113] As an example, the term stuck can refer to a condition, a
state, a varying degree of inability to move or remove a
drillstring from a bore. Stuck can be a condition where it is
possible to rotate a pipe or lower it back into a bore, or it might
be a condition as to an inability to move a drillstring vertically
in a vertical portion of a bore or horizontally in a horizontal
portion of a bore or otherwise in a direction along a longitudinal
axis of a bore; in one or more of such instances, rotation may or
may not be possible. Stuck may refer to a condition where an
inability to move a drillstring exists (e.g., inability to rotate
and inability to translate). A stuck condition can change with
respect to time depending on circumstances, forces, material,
fluids, pressures, etc.
[0114] As an example, a method can include detecting onset of stuck
pipe during well construction. For example, during a well
construction process, there are various mechanisms that can cause
the drill pipe, BHA, casing, wireline tool or whatever tool is in
the hole to become stuck. A method can include detecting one or
more pre-cursors to one or more sticking events and, for example,
computing the risk that a pipe and/or tool is about to become
stuck. Various different indictors may be analyzed in a
probabilistic manner, for example, using a probabilistic
framework.
[0115] U.S. Pat. No. 7,003,439 B2, to Aldred et al., issued 21 Feb.
2006, assigned to Schlumberger Technology Corporation, is
incorporated by reference herein. U.S. Pat. No. 6,549,854 B1, to
Malinverno et al., issued 15 Apr. 2002, assigned to Schlumberger
Technology Corporation, is incorporated by reference herein. An
article by Borjas et al., entitled "Real-Time Drilling Engineering:
Hydraulics and T&D Modeling for Predictive Interpretation While
Drilling", Society of Petroleum Engineers (SPE) 150069, March 2012,
is incorporated by reference herein.
[0116] As an example, a method can include propagating uncertainty
of one or more observations and risks (e.g., using a Bayesian
methodology, etc.) to compute a probability of a risk of getting
stuck (e.g., optionally in conjunction with a discrete on/off
alarm). As an example, a method can include utilizing various types
of relevant data (e.g., real-time and static measurements,
historical data, modelled predictions, etc.). As an example, a
method can include utilizing data in a dynamic manner where, for
example, data inputs are changed to assess conditions, etc. For
example, a method can operate using one type of data from one type
of equipment and then call for input of another type of data, which
may be from another type of equipment. As an example, a method can
determine on-the-fly the appropriate type of data to assess
probability of a risk of getting stuck, which may be based on one
or more types of physical phenomena as to one or more different
types of sticking mechanisms. As an example, a method can utilize a
single indictor, a sub-set of available data or a full set of
available data.
[0117] As an example, a method can be robust to different rig
configurations and available input. As an example, a method may
allow for handling of sparse data or otherwise incomplete data,
whether incomplete with respect to time, with respect to depth,
with respect to a type of mechanical operation, with respect to a
type of fluidic operation, etc. As an example, a method can include
handle different types of measurements and/or missing measurements
from one or more types of measurements.
[0118] As an example, a method can include generating information
(e.g., output or result(s)) about a possible or probable cause of a
risk (e.g., where a risk may be coming from). Such an approach can
be alternative to or additional to generating information that
indicates that there might be a problem. For example, a method can
include outputting information that indicates that a problem has a
high probability of occurrence and information that explains why
that problem is deemed to have a high probability of occurrence. As
an example, a method can include generating information and
outputting such information in one or more manners to mitigate risk
and, for example, avoid occurrence of a condition, a state, etc. As
an example, information may be output for rendering to a display
and/or for input to a controller that can control one or more
pieces of equipment.
[0119] As an example, a method can operate in real-time or near
real-time as may be appropriate for a drilling operation. For
example, a method may receive data as that data becomes available
during a drilling operation and include analyzing at least a
portion of that data to generate one or more outputs (e.g., output
information) where a delay between data reception and output
information may be of the order of a few minutes, which can provide
for, in a drilling operation context, real-time interpretation. As
an example, timing of results may be provided and/or adjusted in
relationship to a rate of penetration (ROP). Rate of penetration is
the speed at which a drill bit can break rock and thus further the
distance into a bore. ROP may be reported in units of feet per hour
or meters per hour. As an example, consider a ROP in a range from
about one meter per hour to tens of meters per hour. As an example,
a method may receive data and output information based at least in
part on such data within a time period that is less than a time to
drill approximately 10 centimeters.
[0120] As an example, a method can operate based at least in part
on a model that is built from a combination of learning from past
data and a physical model of the wellbore, etc. A model can include
equations that represent physical phenomena (e.g., differential
equations as to various types of pressure, flow, etc., phenomena).
Where a model is tied to physical phenomena, a method can more
readily generate a reason or reasons why an alarm might be high and
thus what mitigation action needs to be taken. In such an example,
a backward progression may be utilized where, for example, a
Bayesian network may provide for identifying one or more inputs as
being one or more causes of an indicator or indicators being issued
(e.g., an alarm, etc.).
[0121] As an example, a framework may be implemented using
computing resources (e.g., hardware, communication equipment, etc.)
as may be available, for example, in the cloud, a server, a
workstation, etc.
[0122] As an example, a framework can include components that can
take certain inputs and generate certain outputs. The outputs of a
component may be used as inputs of another component or other
components such that a real-time workflow can be constructed.
[0123] FIG. 6 shows an example of a framework 600 (e.g., a
computational framework) that can be adapted to generate one or
more workflows, for example, as indicated by various arrows. As
shown in the example of FIG. 6, the framework 600 can receive
information as input such as, for example, one or more rigstates
(e.g., operational states, non-operational states, etc.), surface
torque, mud cake thickness and/or quality, BHA stab type and/or
slickness, wellbore inclination, information from one or more
offset bores (e.g., wells), mud logs, real-time caliper
measurements, rate of penetration, flow rate(s), etc.
[0124] As mentioned, a framework can be at least in part
model-based. In FIG. 6, models can include a torque and drag
(T&D) model comparison component 652, a hydraulics model 654, a
geomechanics model 656, and optionally one or more other types of
models, which may be selectable and utilized in a dynamic manner
during execution of a workflow (e.g., adaptive execution of a
model-based workflow).
[0125] In the example of FIG. 6, the framework 600 is shown as
including various input components such as a pickup/slack-off
(PU/SO) determination (autocall) component 612, a stationary time
determination component 614, and a sign(s) of borehole washout
component 616. The framework 600 can include, for example, a torque
spikes at rotation start component 622, a cavings
detection/observation component 642, an amount of overbalance
component 644, a stand pipe pressure (SPP) condition decision
component 662, a wellbore stability decision component 664, a
cuttings detection/observation component 666 (e.g., cutting carried
to surface, etc.), and a mud balance component 668 (e.g., a
decision or decision making component as to mud/drilling fluid,
etc.). In such a framework, the various components can be
associated with one or more sources of information, which can
include, for example, one or more sensors that are at one or more
field sites where field operations may be performed.
[0126] As an example, as to mud balance, information pertaining to
mud balance may be acquired via one or more sensors that can
measure density (weight) of mud, cement or other liquid or slurry.
As an example, a mud balance can be determined via a fixed-volume
mud cup with a lid on one end of a graduated beam and a
counterweight on the other end. In such an example, a slider-weight
can be moved along the beam where a bubble indicates when the beam
is level. In such an example, density may be read at the point
where the slider-weight sits on the beam at level. As an example,
accuracy of mud weight may be within approximately +/-0.01
g/cm.sup.3. As an example, a mud balance may be calibrated with
water or other liquid of known density by adjusting a counter
weight. As an example, one or more pressurized mud balance sensors
may be utilized. As shown in FIG. 6, the mud balance component 668
can output information to the risk of solids-induced pack-off
component 684.
[0127] As to output information, components of the framework 600
can include, for example, a potential for differential sticking
component 672, a risk of getting differentially stuck component
682, and a risk of solids-induced pack-off component 684. The
framework 600 may be extensible in that one or more components can
be added, deleted, updated, etc. In such an example, the framework
600 may be adapted to particular types of equipment, fluid, energy
sources, etc., at a site.
[0128] As mentioned, one component of the framework 600 may
generate output that can be received by one or more other
components of the framework 600. For example, the risk of
solids-induced pack-off component 684 may receive output from the
T&D model comparison component 652, from the hydraulic model
component 654, from the wellbore stability decision component 664,
the cutting detection/observation component 666, etc. In such an
approach, the framework 600 provides for forward progression from
input(s) to output(s) as well as, for example, backward progression
where an output or outputs may be traced backwards to one or more
reasons, causes, datum, data, source of data, sources of data, etc.
In such an example, an identified cause (e.g., contributing cause,
etc.) to an output (e.g., an indicator such as an alarm, etc.), may
be utilized to generate one or more signals that can be issued from
the framework 600 to one or more pieces of equipment (e.g., to
effectuate control of such one or more pieces of equipment as to
one or more field operations, etc.).
[0129] As an example, the framework 600 may be adaptable in
real-time, for example, where caliper information becomes
available, the signs of borehole washout component 616 may be
utilized to provide output to the wellbore stability decision
component 664. As another example, where mud log data indicates
that cavings are observed per the component 642, information may be
transmitted to and received by the wellbore stability component
664. In such an approach, the quality of information provided to
the risk of solids-induced pack-off component 684 may be enhanced,
which may increase accuracy of output of the risk of solids-induced
pack-off component 684.
[0130] In the example of FIG. 6, the arrows in the framework 600
pertain to an example of a workflow, as may be enabled to assess
conditions germane to stuck pipe. Inputs to the various components
are illustrated along the left side in FIG. 6 while some outputs
are illustrated along the right side in FIG. 6. As an example,
inputs can include raw measurements, for example, acquired and/or
made at a rigsite during real-time execution and/or, for example,
acquired pre-job such as from one or more offset wells and/or, for
example, from a well plan (e.g., and/or from one or more analyses
made before drilling). In the framework 600, components toward the
right edge can compute various types of risk such as, for example,
a risk that a pipe and/or tool may be about to become stuck. In
such an example, the framework 600 may issue one or more signals,
which can include one or more of an alarm signal, a control signal
or another type of signal. As an example, the framework 600 may
progress backwards from an output such as an alarm to one or more
likely contributing causes of the alarm (e.g., one or more
components of the framework 600 and associated input, etc.). In
such an example, the framework 600 may issue one or more
recommendations (e.g., one or more recommended actions) and/or one
or more control signals, which may, for example, be issued via one
or more network interfaces addressed to one or more pieces of
equipment at a rigsite for control of at least one of the one or
more pieces of equipment at the rigsite.
[0131] In the example of FIG. 6, the framework 600 can provide for
combinations of various indicators, which may include those that
have been tried in the past (e.g., with or without success).
Various indicators may be one or more of data-driven, physics-based
model-driven, etc.
[0132] As an example, the framework 600 can include estimating
uncertainty of one or more variables and optionally propagating
such uncertainty from one component to another component (e.g., in
a chain or chains). Such an approach can allow for different
measurements of similar quantities to be merged. For example, the
two components 682 and 684 that compute risk of stuck pipe can take
multiple indicators of stuck pipe as inputs and generate a single
probability of risk.
[0133] As an example, the components 682 and 684 can be part of a
Bayesian network, which can optimally consider uncertainty of each
input to compute risk. In such an example, the Bayesian network can
allow the computation of risk in a manner that differs from a
"black-box" approach. For example, when the risk of stuck pipe is
high, a user can interrogate the framework 600 to estimate which of
the inputs are generating the high risk and why. Such an
interrogation can include performing a backwards progression
through the various components to identify an input or inputs,
which, as mentioned, may be a basis for issuing one or more signals
as to control of one or more pieces of field equipment (e.g.,
rigsite equipment, whether surface, subsurface, etc.).
[0134] As an example, a Bayesian network can include weights where
the weights are associated with data acquired from equipment such
as equipment in a field that performs one or more field operations.
For example, a Bayesian network can include weights that are
applied to data-based numbers where the data are acquired from
equipment at a rigsite, which can include surface and downhole
equipment.
[0135] In terms of an arc of a graph of a network (e.g., directed
acyclic graph (DAG), etc.), an individual arc may have a weight or
value associated with it, indicating a strength of interaction
between nodes that the arc connects. The nature of such a weight
can be application dependent. For example, it may represent a cost
associated with a particular action, the strength of a connection
between two nodes or, in the case of probabilistic models, the
probability that a particular event will occur. As an example, a
Bayesian belief network (e.g., a Bayesian network) can be conducive
to understanding a scenario or scenarios as they can be constructed
such that a parent(s) of a variable can be a direct cause. Such an
approach can help to facilitate a process of determining weights
for arcs that connect nodes of a network (e.g., assessment of
conditional probabilities, etc.).
[0136] As an example, a Bayesian network can be implemented as part
of a computational framework that includes one or more interfaces
(e.g., one or more network interfaces, etc.) that can receive data
acquired at one or more sites such as one or more rigsites. As an
example, a computational framework can include one or more
processors, memory, interfaces, etc. As mentioned, a computational
framework can include receiving data, which may include sensor data
from one or more sensors. As an example, a computational framework
can provide for sensor fusion utilizing at least in part a Bayesian
network (e.g., or Bayesian networks).
[0137] Sensor fusion refers to the class of problems where data
from various sources can be integrated to arrive at an
interpretation of a situation (e.g., a scenario). For example, data
from various rigsite sensors, which may be for different sampling
rates, different data formats, different units, etc., can be
integrated to determine a status of one or more rigsite operations,
which may include one or more operations that are associated with
sticking (e.g., actual sticking, breaking free or becoming unstuck,
risk of sticking, likelihood of success of breaking free, etc.). As
an example, a sensor fusion approach may include receiving data
from a plurality of sensors where a state can be discerned for a
system by integrating at least a portion of the received data.
[0138] As an example, a computational framework that implements one
or more Bayesian networks may provide for handling of data that may
differ as to one or more of temporal resolution and spatial
resolutions. In such an example, the framework may solve a
so-called correspondence problem that can include deciding which
event(s) from one sensor correspond to the same event(s) as
reported in one or more other sensors. As an example, a Bayesian
network approach can be implemented in a manner that tends to be
robust to missing data via combining data from a plurality of
sources (e.g., a plurality of sensors, etc.). As an example, a
Bayesian network approach may address scenarios where each sensor
individually may have a limited chance of providing an acceptable
interpretation by combining information from a plurality of sensors
to increase the likelihood of providing an acceptable
interpretation (e.g., valid in terms of physics, matching
real-world conditions, etc.).
[0139] As an example, a computational framework that implements one
or more Bayesian networks can provide for diagnosing one or more
issues that a system may be experiencing, may have experienced, may
likely experience, may experience given one or more sets of
operational conditions, etc. As an example, a computational
framework may provide for deciding when to issue a signal, which
may be a control signal and alert signal, etc.
[0140] As an example, parameters of a Bayesian network may be tuned
as conditional probability tables, which may be relative weights of
the Bayesian network. In such an example, data can be used to tune
parameters where the parameters have physical meaning as they refer
to input indicators. In such an example, where output of a
computational framework that implements at least one Bayesian
network indicates that a risk for a sticking or other issue at a
rigsite is high (e.g., compared to a threshold), as various
parameters are associated with real-world data acquired at the
rigsite, a method can include working backwards through a Bayesian
network to help identify one or more causes of the indicated risk
where such a method can include issuing one or more signals that
aim to address the one or more causes. For example, where an
indicated risk is for differential sticking of a drillstring in a
borehole that is being drilled, working backwards may identify that
a hole cleaning index (HCI) may be a cause where the hole cleaning
index is based at least in part on a flow rate for drilling fluid
(e.g., mud) such that a signal may be issued to control a pump or
pumps that pump drilling fluid to, for example, increase the flow
rate for the drilling fluid. In such an example, where the increase
in the flow rate for the drilling fluid does not adequately address
the indicated risk (e.g., sticking occurs), one or more weights may
be adjusted as to flow rate for the drilling fluid on the hole
cleaning index or, for example, a weight for the hole cleaning
index may be adjusted where another cause may be contributing, at
least in part, to the indicated risk.
[0141] As an example, a computational framework that includes a
Bayesian network or networks can provide for backward progressions
from an outcome (e.g., an indicator) to data and/or one or more
sources of data. Such an approach can help to associate the outcome
with particular, specific data and/or one or more specific sources
of data (e.g., equipment, acquisition equipment, etc.). As to
specific data, as mentioned, data may be temporal and/or spatial
with respect to resolution such that a backward progression may
provide for identifying where temporal and/or spatial aspects of
data can be improved. In such an example, a signal may be issued by
a computational framework that controls one or more temporal and/or
spatial aspects of a sensor or sensors that acquire particular
data. Such an approach can help the computational framework to
operate with an enhanced ability to provide more beneficial outputs
(e.g., operate with greater certainty, both in a forward mode and
in a backward mode, etc.).
[0142] As an example, a computational framework can be extensible
such that one or more components can be added, removed, updated,
etc. For example, where the computational framework includes a
Bayesian network as to risk of differential sticking and where a
new piece of equipment is implemented at a rigsite that outputs
data, the Bayesian network may be augmented via instantiation of a
component that adds the data as an input, which may be
appropriately weighted, etc.
[0143] As an example, a Bayesian network can provide for
computation of "potential risk" (e.g., for the case of differential
sticking, see the component 672). Such an approach can consider the
environment or set-up to determine prior risk. For example, a
workflow may consider that certain BHAs are more susceptible to
differential sticking, or that the chance of becoming
differentially stuck increases with overbalance pressure (see,
e.g., the component 644). Potential risk can be computed for a
given environment and well plan, for example, before drilling
commences and may be used to help design the well plan. As an
example, the framework may provide output that can assist in
choosing a BHA and/or mud weight that minimizes potential for
differential sticking. Such an approach may utilize a component or
components that may be part of a Bayesian network and/or a
physics-based model (e.g., of a process of differential
sticking).
[0144] As an example, one or more outputs from the framework 600
may be received as one or more inputs to a system that can generate
information suitable for rendering to a display (e.g., instructions
to control a graphics processing unit, etc.). For example, where a
BHA is being designed, information may be output to a well plan GUI
such as the GUI 510. In such an example, the drillstring GUI 560
may highlight a portion of the drillstring graphic that can be
selected based at least in part on output from the framework 600.
For example, where the framework 600 indicates a risk of getting
stuck at a certain depth during drilling operation, a tool may be
highlighted and information rendered to the display as to the type
of sticking event, the risk of the sticking event occurring, the
conditions and/or causes of the sticking event (e.g., and
associated risk and/or uncertainty and/or contribution to the cause
or causes) and, for example, a recommended change or adaption to
the drillstring, whether physically or operationally that may be
implemented in an effort to reduce the risk of getting stuck.
[0145] As an example, an output from a framework such as the
framework 600 may include a suggested alteration to a well
trajectory and/or to a completion of a well. As an example, an
output from the framework 600 may provide for highlighting one or
more points in the point spreadsheet 570 as to one or more
parameters of a well or wells. As an example, a user may accept a
change and/or otherwise adapt a well plan based at least in part on
output from the framework 600. For example, a user may utilize the
GUI 500 to edit one or more portions of a well plan, optionally
based at least in part on information communicated with one or more
members of the team 540.
[0146] As illustrated in the example framework 600 of FIG. 6, which
includes arrows as to an example of a workflow, risk of getting
stuck can be based on two mechanisms: risk of differential sticking
per the component 682 and risk of solids-induced pack-off per the
component 684. As mentioned, these components can be part of a
Bayesian network. Such a network may be extended to compute the
risk of getting stuck based on one or more other mechanisms (e.g.,
key seating, shale swelling, etc.), alternatively or
additionally.
[0147] As to various operations, conditions, etc., associated with
sticking, consider hole cleaning, hydraulics, and torque and drag
as factors that can be considered. As shown in FIG. 6, the
framework 600 can include components associated with such factors
(see, e.g., the component 654 as to hydraulics and hole cleaning
index (HCI), the components 662 and 652 as to torque and/or drag,
etc.).
[0148] As an example, a method can include one or more of assessing
stand pipe pressure (SPP) actual versus model (e.g., monitor
separation of curves and determine whether a result is to be part
of a workflow, etc.; see also the component 662 of the framework
600), assessing equivalent circulating density (ECD) actual versus
model (e.g., monitor separation of values and determine whether a
result is to be part of a workflow, etc.), assessing torque and
drag (T&D) actual versus model (e.g., monitor for deviation of
trend and model and determine whether a result is to be part of a
workflow, etc.; see also the component 652 of the framework
600).
[0149] As an example, a framework may be operatively coupled to one
or more services such as, for example, one or more of the THEMA.TM.
services (Schlumberger Limited, Houston, Tex.).
[0150] A service may provide for analysis of real-time data streams
from a number of sensors to provide real-time status of a well, for
example, by combining depth- and time-based data related to
wellbore pressure balance, drilling mechanics, and/or hole
condition.
[0151] A service may provide wellbore pressure balance for
detection of fluid influx or loss in the wellbore, even at very low
volumes. Early kick detection may be supported by pump-off gas
analysis to identify potential underbalance situations. An
automatic flowback fingerprint may be captured.
[0152] A service may provide for drilling mechanics analysis such
as wear and behavior of a drill bit, which may be assessed by
monitoring one or more drilling parameters (e.g., axial (bit
bouncing) and torsional (stick/slip) vibration frequencies and
energy) through measurements made by one or more sensors. Potential
issues may include bit balling, drillstring vibration, and bit
wear, which may be predicted as to risk and risk potential where
one or more drilling parameters may be optimized to improve ROP and
increase equipment life.
[0153] A service may provide for assessing hole condition such as
wellbore stability and hole-cleaning efficiency, which may be
analyzed in real-time by comparing measurements of relevant
parameters (e.g., pickup, slack-off, and free-rotating weights;
torque; and equivalent circulating density (ECD)), with theoretical
values calculated using one or more models. As an example,
hole-condition monitoring can be linked with data from one or more
cuttings flowmeters.
[0154] As an example, one or more of a hydraulics physics-based
model, a geomechanics physics-based model, or other type of
physics-based model may be implemented as part of a framework, for
example, to perform one or more workflows. As an example, the
framework 600 of FIG. 6 may be operatively coupled to a framework
such as the PETREL.RTM. framework (e.g., optionally via the
OCEAN.RTM. framework, etc.), which may include and/or be
operatively coupled to one or more physics-based models.
[0155] As an example, a computational framework may output
information with respect to one or more operational parameters as
to one or more operators, one or more service providers, one or
more suppliers, etc. As an example, a computational framework may
output information that associates decision making and one or more
operators (e.g., individual, team, etc.). Such an approach may help
to identify how decisions are made during operations in the field.
Such an approach may help to assess decision making by an
individual, a team, etc., which may provide for tuning of one or
more parameters of a computational framework (e.g., one or more
Bayesian network parameters, etc.).
[0156] As an example, a computational framework can respond to
real-time decision making in the field during operations. As a
Bayesian network can include inputs as to acquired, real-time data,
outputs of the Bayesian network can depend on the acquired,
real-time data. As an example, a computational framework may
associate changes in real-time data with real-time decision making
by one or more operators, controllers, etc. As an example, an
output of the computational framework can include issuing a query
to one or more onsite devices such as, for example, an operations
computer, etc. As an example, consider issuing a query to a device
that asks "was the mud flow rate increased?" In such an example, a
response can be received by the computational framework, which may
be utilized to adjust one or more parameters (e.g., one or more
weights, etc.).
[0157] FIG. 7 shows an example of a method 700 that can include a
reception block 710 for receiving information during a drilling
operation for a drillstring disposed in a bore in a formation; an
estimation block 720 for estimating uncertainty associated with the
information; an analysis block 730 for analyzing at least a portion
of the information using a physics-based model to generate a
result; a computation block 740 for computing, via a Bayesian
network, a risk probability of the drilling string sticking in the
bore in the formation based at least in part on the result and the
estimated uncertainty; and an issuance block 750 for, based at
least in part on the risk probability, issuing a signal. In such an
example, the signal may be one or more types of signals. For
example, a signal may be an alert, a control signal (e.g., a
command, etc.), or another type of signal. As an example, a signal
can be received by one or more pieces of equipment to control one
or more pieces of equipment. As an example, a signal may be an
actuation signal that upon receipt by a piece of equipment actuates
the piece of equipment such that the piece of equipment performs an
action, changes state, etc. As to a controller, a sensor, etc., a
signal may instruct such equipment to transmit information upstream
and/or downstream, change an acquisition parameter, change a
control parameter, etc.
[0158] In the example of FIG. 7, a system 790 includes one or more
information storage devices 791, one or more computers 792, one or
more networks 795 and instructions 796. As to the one or more
computers 792, each computer may include one or more processors
(e.g., or processing cores) 793 and memory 794 for storing the
instructions 796, for example, executable by at least one of the
one or more processors. As an example, a computer may include one
or more network interfaces (e.g., wired or wireless), one or more
graphics cards, a display interface (e.g., wired or wireless),
etc.
[0159] The method 700 is shown in FIG. 7 in association with
various computer-readable media (CRM) blocks 711, 721, 731, 741,
and 751. Such blocks generally include instructions suitable for
execution by one or more processors (or cores) to instruct a
computing device or system to perform one or more actions. While
various blocks are shown, a single medium may be configured with
instructions to allow for, at least in part, performance of various
actions of the method 700. As an example, a CRM block can be a
computer-readable storage medium that is non-transitory, not a
carrier wave and not a signal. As an example, such blocks can
include instructions that can be stored in memory such as the
memory 794 of the system 790 and can be executable by one or more
of the processors 793 of the system 790.
[0160] As an example, a method can include receiving information
during a drilling operation for a drillstring disposed in a bore in
a formation; estimating uncertainty associated with the
information; analyzing at least a portion of the information using
a physics-based model to generate a result; and computing, via a
Bayesian network, a risk probability of the drilling string
sticking in the bore in the formation based at least in part on the
result and the estimated uncertainty. As mentioned, a method can
include issuing a signal based at least in part on a risk
probability (see, e.g., FIG. 13 as to a graphic 1350 of probability
information, etc.). In such an example, the Bayesian network can
include a risk of differential sticking component and a risk of
pack-off sticking component and, for example, a potential for
differential sticking component. In such an example, a method can
include determining potential for differential sticking via the
differential sticking component prior to performing the drilling
operation.
[0161] As an example, a method can include computing a risk
probability during a drilling operation. As an example, a
physics-based model can be one or more of a torque and drag model,
a hydraulics model, and a geomechanics model. As an example, a
method can include receiving information that is drilling fluid
information, which can be mud information where mud is a drilling
fluid.
[0162] As an example, a method can include determining at least one
cause of a risk probability. As an example, a method can include
determining at least one action to mitigate a risk probability
(e.g., reduce the probability of the risk). In such an example, at
least one action can include an action that aims to prevent
cuttings from packing off around the drillstring. In such an
example, the action can be an action that is at least one of
increasing rotation rate and increasing circulation rate to clean
the bore. In such an example, an action can include associated
parameters that decrease likelihood of creating a hole or
washout.
[0163] As an example, a system can include a processor; memory
accessible by the processor; processor-executable instructions
stored in the memory and executable to instruct the system to:
receive information during a drilling operation for a drillstring
disposed in a bore in a formation; estimate uncertainty associated
with the information; analyze at least a portion of the information
using a physics-based model to generate a result; and compute, via
a Bayesian network, a risk probability of the drilling string
sticking in the bore in the formation based at least in part on the
result and the estimated uncertainty. Such a system may include
instructions to issue one or more signals based at least in part on
one or more risk probabilities.
[0164] As an example, one or more computer-readable storage media
can include processor-executable instructions to instruct a
computing system to: receive information during a drilling
operation for a drillstring disposed in a bore in a formation;
estimate uncertainty associated with the information; analyze at
least a portion of the information using a physics-based model to
generate a result; and compute, via a Bayesian network, a risk
probability of the drilling string sticking in the bore in the
formation based at least in part on the result and the estimated
uncertainty. In such an example, instructions can be included to
issue a signal or signals based at least in part on one or more
risk probabilities.
[0165] FIG. 8 shows some example events 800 that include
differential sticking, geopressure, unconsolidated zone, fracture
or faulted zone, undergauge hole, and seating. As shown in FIG. 8,
the example events 800 can be associated with interactions between
a drillstring and a formation, drillstring related operations and a
drillstring, drillstring related operations and a formation,
etc.
[0166] FIG. 9 shows some example events 900 that include reactive
formation, mobile formation, collapsed casing, junk in hole,
cement-related and drillstring vibration. As shown in FIG. 9, the
example events 900 can be associated with interactions between a
drillstring and a formation, drillstring related operations and a
drillstring, drillstring related operations and a formation,
etc.
[0167] FIG. 10 shows some example events 1000 that include a
wellbore geometry event and a poor hole cleaning event. As shown in
FIG. 10, the example events 1000 can be associated with
interactions between a drillstring and a formation, drillstring
related operations and a drillstring, drillstring related
operations and a formation, etc.
[0168] As an example, loose or unconsolidated formation sands or
gravels can collapse into a borehole and pack-off a drillstring as
supporting rock is removed by a bit. Schists, laminated shales,
fractures and faults can create loose rock that caves into the hole
and jam a drillstring. As an example, one or more of such factors
may be taken into consideration by a framework such as the
framework 600 (e.g., as inputs, models, etc.).
[0169] In regions where tectonic stresses are high, rock is being
deformed by movement of the Earth's crust. In such areas, the rock
around a bore may collapse into the bore. In some cases,
hydrostatic pressure to stabilize a hole may be much higher than
the fracture initiation pressure of exposed formations. As an
example, one or more of such factors may be taken into
consideration by a framework such as the framework 600 (e.g., as
inputs, models, etc.).
[0170] As an example, a mobile formation (e.g., salt or shale) can
behave in a plastic manner. When compressed by overburden, material
may flow and squeeze into a bore, thereby constricting or deforming
the hole and trapping the tubulars. As an example, one or more of
such factors may be taken into consideration by a framework such as
the framework 600 (e.g., as inputs, models, etc.).
[0171] As an example, overpressured shales can be characterized by
formation pore pressures that exceed normal hydrostatic pressure.
Insufficient mud weight in these formations may permit a hole to
become unstable and collapse around pipe. As an example, one or
more of such factors may be taken into consideration by a framework
such as the framework 600 (e.g., as inputs, models, etc.).
[0172] Reactive shales and clays tend to absorb water from drilling
fluid. Over time--ranging from hours to days--they can swell into a
bore. As an example, one or more of such factors may be taken into
consideration by a framework such as the framework 600 (e.g., as
inputs, models, etc.).
[0173] As an example, drillstring vibration may cause caving of a
bore. Such cavings can pack around a pipe, causing it to stick.
Downhole vibration may be controlled by monitoring parameters such
as weight on bit, rate of penetration and rotary speed, which can
be adjusted from a driller's console or, for example, via issuance
of one or more signals from a framework. As an example, one or more
of such factors may be taken into consideration by a framework such
as the framework 600 (e.g., as inputs, models, etc.).
[0174] As an example, differential sticking may happen when a
drillstring is held against a bore by hydrostatic overbalance
between bore pressure and pore pressure of a permeable formation.
Such an issue may occur when a stationary or slow-moving
drillstring contacts a permeable formation, and where a thick
filtercake is present. As an example, a depleted reservoir may be a
cause of differential sticking. As an example, one or more of such
factors may be taken into consideration by a framework such as the
framework 600 (e.g., as inputs, models, etc.).
[0175] As an example, keyseating can take place when rotation of a
drillpipe wears a groove into the borehole wall. When the
drillstring is tripped, the bottomhole assembly (BHA) or
larger-diameter tool joints can be pulled into the keyseat and
become jammed. A keyseat may also form at the casing shoe if a
groove is worn in the casing or the casing shoe splits. Such an
issue can occur at abrupt changes in inclination or azimuth, for
example, while pulling out of the hole and after sustained periods
of drilling between wiper trips. Wireline logging tools and cables
may be susceptible to keyseating. As an example, one or more of
such factors may be taken into consideration by a framework such as
the framework 600 (e.g., as inputs, models, etc.).
[0176] As an example, an undergauge hole may develop while drilling
hard, abrasive rock. As the rock wears away the bit and stabilizer,
the bit drills an undergauge, or smaller than specified, hole. When
a subsequent in-gauge bit is run, it can encounter resistance in
the undergauge section of hole. If a string is run into a hole too
quickly or without reaming, a bit can jam in the undergauge
section. Such an issue may occur when running a new bit, after
coring, while drilling abrasive formations, when a PDC bit is run
after a roller cone bit, etc. As an example, one or more of such
factors may be taken into consideration by a framework such as the
framework 600 (e.g., as inputs, models, etc.).
[0177] As an example, cement blocks may pack-off a drillstring, for
example, when hard cement around a casing shoe breaks off and falls
into a new openhole interval drilled out from under casing.
Uncured, or green, cement may trap a drillstring after a casing
job. For example, when the top of cement is encountered while
tripping in the hole, a higher than expected pressure surge may be
generated by a BHA, causing the cement to set instantaneously
around the BHA. As an example, one or more of such factors may be
taken into consideration by a framework such as the framework 600
(e.g., as inputs, models, etc.).
[0178] As an example, a collapsed casing can occur when pressures
exceed a casing collapse pressure rating or when casing wear or
corrosion weakens the casing. The casing may also buckle as a
result of aggressive running practices. Such conditions may be
discovered when a BHA is run in the hole and hangs up inside the
casing. As an example, one or more of such factors may be taken
into consideration by a framework such as the framework 600 (e.g.,
as inputs, models, etc.).
[0179] As an example, one or more hole cleaning problems may
prevent solids from being transported out of a bore. When cuttings
settle at the low side of deviated wellbores, they may form layered
beds that may pack around a BHA. Cuttings and cavings may also
slide down an annulus when pumps are turned off, thus packing
around a drillstring. Such issues may occur due to one or more of
low annular flow rates, inadequate mud properties, insufficient
mechanical agitation and short circulation time. As an example, one
or more of such factors may be taken into consideration by a
framework such as the framework 600 (e.g., as inputs, models,
etc.).
[0180] As mentioned, a framework, which can be a computational
framework, can include one or more Bayesian networks. A Bayesian
network, Bayes network, belief network, Bayes(ian) model or
probabilistic directed acyclic graphical (DAG) model is a
probabilistic graphical model (a type of statistical model) that
represents a set of random variables and their conditional
dependencies via a directed acyclic graph (DAG). For example, a
Bayesian network may represent the probabilistic relationships
between sticking as a result and causes of sticking. Given causes
of sticking as related to conditions, a network can be used to
compute the probabilities of the presence of various results (e.g.,
sticking).
[0181] As mentioned, a network can include nodes and arcs where
arcs may be assigned weights. As mentioned, such weights may
determine, at least in part, how acquired data impacts an outcome.
As mentioned one or more weights may be adjustable, optionally
based on data (e.g., one or more real-world outcomes, etc.).
[0182] Bayesian networks can be DAGs whose nodes represent random
variables in the Bayesian sense: they may be observable quantities,
latent variables, unknown parameters or hypotheses. Edges can
represent conditional dependencies; nodes that are not connected
(there is no path from one of the variables to the other in the
Bayesian network) represent variables that are conditionally
independent of each other. Each node can be associated with a
probability function that takes, as input, a particular set of
values for the node's parent variables, and gives (as output) the
probability (or probability distribution, if applicable) of the
variable represented by the node. For example, if m {\displaystyle
m} m parent nodes represent m {\displaystyle m} m Boolean variables
then the probability function could be represented by a table of 2
m {\displaystyle 2 {m}} 2 {m} entries, one entry for each of the 2
m {\displaystyle 2 {m}} 2 {m} possible combinations of its parents
being true or false.
[0183] Various algorithms may perform inference and learning in
Bayesian networks. Bayesian networks that model sequences of
variables may be referred to as dynamic Bayesian networks.
Generalizations of Bayesian networks that can represent and solve
decision problems under uncertainty may be characterized as
influence diagrams.
[0184] An influence diagram (ID) can be a relatively compact
graphical and mathematical representation of a decision situation
that is a generalization of a Bayesian network, in which
probabilistic inference problems and decision making problems
(e.g., following the maximum expected utility criterion) can be
modeled and solved.
[0185] An ID can be a directed acyclic graph (DAG) with three types
(plus one subtype) of node and three types of arc (or arrow)
between nodes. Nodes can include decision nodes, corresponding to
each decision to be made; uncertainty nodes, corresponding to each
uncertainty to be modeled; deterministic nodes, corresponding to a
special kind of uncertainty that its outcome is deterministically
known whenever the outcome of some other uncertainties are also
known; and value nodes, corresponding to each component of
additively separable Von Neumann-Morgenstern utility function.
[0186] An influence diagram can include arcs such as functional
arcs (ending in value node) that indicate that one of the
components of additively separable utility function is a function
of all the nodes at their tails; conditional arcs (ending in
uncertainty node) that indicate that the uncertainty at their heads
is probabilistically conditioned on all the nodes at their tails;
conditional arcs (ending in deterministic node) that indicate that
the uncertainty at their heads is deterministically conditioned on
all the nodes at their tails; and informational arcs (ending in
decision node) that indicate that the decision at their heads is
made with the outcome of all the nodes at their tails known
beforehand.
[0187] As an example, decision nodes and incoming information arcs
can collectively state alternatives (e.g., what can be done when
the outcome of certain decisions and/or uncertainties are known
beforehand). As an example, uncertainty/deterministic nodes and
incoming conditional arcs can collectively model information (e.g.,
what are known and their probabilistic/deterministic
relationships). As an example, value nodes and incoming functional
arcs can collectively quantify a preference (e.g., how things are
preferred over one another).
[0188] In an ID, alternative, information, and preference are
termed decision basis in decision analysis, as they represent three
components of a valid decision situation.
[0189] An influence diagram representing a situation where a
decision-maker is planning their vacation can include a decision
node (Vacation Activity), uncertainty nodes (Weather Condition,
Weather Forecast), and a value node (Satisfaction). In such an
example, functional arcs (ending in Satisfaction), a conditional
arc (ending in Weather Forecast), and an informational arc (ending
in Vacation Activity) can be included. Functional arcs ending in
Satisfaction indicate that Satisfaction is a utility function of
Weather Condition and Vacation Activity. In other words, their
satisfaction can be quantified if they know what the weather is
like and what their choice of activity is (note that they do not
value Weather Forecast directly). A conditional arc ending in
Weather Forecast indicates their belief that Weather Forecast and
Weather Condition can be dependent. An informational arc ending in
Vacation Activity indicates that they will know Weather Forecast,
not Weather Condition, when making their choice. In other words,
actual weather will be known after they make their choice, and
forecast is what they can count on at this stage. In the foregoing
example, it follows semantically, for example, that Vacation
Activity is independent on (irrelevant to) Weather Condition given
Weather Forecast is known.
[0190] As an example, a Bayesian network can include accounting for
uncertainty in probabilistic inference, node absorption,
sensitivity analysis, etc. As an example, a value may be classified
as an uncertain value or a type of uncertain value. For example,
different types of uncertain values can be accounted for and
propagated accordingly in a Bayesian network. As an example, a type
of uncertainty can be specified via a Gaussian approach such as,
for example, via a mean and a standard deviation. As an example,
uncertainties in measurements from one or more sensors may be
expressed with a .+-. notation and indicate a Gaussian
distribution. As another example, an uncertain value may be
expressed as an interval with one or more of a lower endpoint and
an upper endpoint (e.g., 0.ltoreq.X<10) where a likelihood
within the interval is one and a likelihood outside the interval is
zero. As yet another example, consider an unbounded Interval, which
may, for example, extend to a very large number or to a known
maximum value or minimum value for the variable. In such an
example, a likelihood within the interval can be one and a
likelihood outside the interval can be zero. Another example of
uncertainty is a set of possibilities where, for example, {s1, s2,
. . . sn} can be a set where each si is a state name. Such a set
may be, for example, unbounded interval or Gaussian as well. As an
example, consider {lo, med} and {red, blue, green}. As an example,
a type of uncertainty can be a set of impossibilities. Various
other types of uncertainties can include likelihood (e.g., s1 p1,
s2 p2, . . . sn pn) (e.g., {flow above X 0.8, flow below X 0.3}).
As an example, arbitrary likelihood distributions for continuous
variables can be formed by a series of adjacent intervals, each
with its own probability. Or, for example, elements may overlap
where their likelihoods may be combined. For example, {[0,10] 0.1,
[2,4] 0.2} can be the combination of a rect function extending from
0 to 10 with height 0.1, and another rect from 2 to 4 with a height
of 0.2. Another type of distribution can be a weighted combination
of Gaussians. For example, {3+-1 0.2, 7+-2 0.4} can be a bi-modal
distribution with peaks at 3 and 7. As an example, a method can
include mixing weighted Gaussians, intervals, and discrete states
within a single { . . . } likelihood vector. As an example, yet
another type of uncertainty can involve relative likelihood. As an
example, a type of uncertainty may be complete uncertainty, which
may be utilized for missing information, faulty equipment (e.g., a
faulty sensor, etc.).
[0191] As an example, a system can be a drilling event analysis
system, which can include an analysis engine, which may include a
Bayesian network. As an example, consider the APACHE STORM engine
(Apache Software Foundation, Forest Hill, Md.). As another example,
consider the NETICA framework (Norsys Software Corp., Vancouver,
Canada).
[0192] As an example, a method can include identifying one or more
types of events by implementing a topology that includes a directed
acyclic graph. For example, the APACHE STORM application can
include utilization of a topology that includes a directed acyclic
graph (DAG). A DAG can be a finite directed graph with no directed
cycles that includes many vertices and edges, with each edge
directed from one vertex to another, such that there is no way to
start at any vertex v and follow a consistently-directed sequence
of edges that eventually loops back to v again. As an example, a
DAG can be a directed graph that includes a topological ordering, a
sequence of vertices such that individual edges are directed from
earlier to later in the sequence. As an example, a DAG may be used
to model different kinds of information.
[0193] Risk probability may be described as a measure of the
likelihood that the consequences described in a risk statement will
occur and may be expressed, for example, as a numerical value. For
example, risk probability can be greater than zero where a risk
poses a threat and, for example, risk probability can be less than
100 percent where it is other than a certain problem (e.g., a known
problem).
[0194] Probabilistic risk assessment or PRA involves evaluation of
risks associated with the use of various types of technology, which
can include associated implementation of various techniques. As an
example, risk may be characterized by two quantities: Magnitude of
Severity (e.g., intensity or seriousness of the situation) and
Probability of Occurrence (e.g., chance a high risk event could
occur, which may be in part based on historical occurrences of
similar events). Whether or not it is feasible to invest in the
risk of concern may be determined based on the probability of the
event and its severity. For example, risk being equal to a product
of frequency and consequence.
[0195] As an example, a physics-based model approach can enhance
PRA through use of information such as real-time data that can be
input to one or more physics-based models. Such an approach can
help to address low frequency and high consequence events. As an
example, a framework may assess information and/or results with
respect to underlying uncertainty, which may be characterized as to
quantifiability, and linked with estimation of probability.
[0196] As an example, a framework can provide for generation of one
or more probability and impact matrixes that can uses a combination
of probability and impact scores of individual risks and
rank/prioritize them to help to determine which risks need detailed
risk response plans (e.g., actions, etc.).
[0197] Likelihood can refer to the possibility of a risk potential
occurring measured in qualitative values such as low, medium, or
high. Likelihood can be a qualitative assessment that is subjective
(e.g., possibly with little objective measurement).
[0198] Probability can refer to the percentage of possibilities
that one or more foreseen outcomes will occur based on parameters
of values. Probability can be a quantitative measurement of outcome
(e.g., there is a 70% chance of rain tomorrow).
[0199] Probabilities may be utilized where information may be
limited. A probability assessment can provide a level of
uncertainty about a risk event occurring. Scoring on a scale of
low, medium, high may provide little valuable information about the
occurrence uncertainties. A probability can provide information
that can be actionable by a machine and/or a person for making one
or more decisions. Perfect information about risk removes
uncertainty.
[0200] As an example, a computational framework such as the
framework 600 of FIG. 6 may be implemented to perform one or more
methods. As an example, a method can include receiving data from
one or more field operations and transmitting output based at least
in part on the received data. As an example, a framework may be
interactive such that a method can be interactive. For example, a
method can include receiving information that may be generated by a
computing device operatively coupled to a display that can render
one or more graphical user interfaces (GUIs) that include one or
more graphical controls (e.g., actuatable features that, when
actuated, cause a command, a signal, etc. to be generated, which
may be transmitted).
[0201] FIG. 11 shows an example of a method 1100 that includes a
login block 1110, a render block 1120 for rendering a multi-well
dashboard (see, e.g., a graphical user interface 1200 of FIG. 12,
etc.), a selection block 1130 for selecting an alerted well (e.g.,
as may be indicated via the GUI 1200, etc.), a render block 1140
for rendering a stuck pipe quick view graphical user interface
(see, e.g., a graphical user interface 1300 of FIG. 13, etc.), and
a render block 1150 for rendering a stuck pipe drilldown analysis
graphical user interface (see, e.g., a graphical user interface
1400 of FIG. 14, etc.). The method 1100 may be implemented at least
in part via a computational framework such as, for example, the
framework 600 of FIG. 6.
[0202] As shown in FIG. 11, the method 1100 (e.g., or workflow) may
include interactions with various blocks. For example, the block
1150 may continue at the block 1120 or the block 1140. As an
example, a system can include a plurality of GUIs that can be
rendered to one or more displays, simultaneous, sequentially, etc.
A user may interact with one or more GUIs as part of a monitoring
process for a plurality of wells (e.g., rigsites). In such an
example, the user may address one or more alerts, statuses, etc.,
that are based on data received from equipment at one or more wells
(e.g., rigsites). As to GUIs, as mentioned, FIGS. 12, 13 and 14
show example GUIs 1200, 1300 and 1400, respectively.
[0203] As to the render block 1140, a GUI may render contextual
information, historical trends, observables in a multi-pass manner,
etc. As an example, a rendered GUI may include information that
helps to address a cause of stuck pipe or a risk of a pipe
sticking. For example, a GUI may render information that allows a
user to answer a question "where is it coming from" as to sticking
or risk of sticking. In response, a system can include rendering
information as to one or more actions that can be taken to solve a
sticking problem and/or to reduce risk of sticking. As an example,
a system may issue one or more signals to one or more pieces of
equipment (e.g., rigsite equipment, field equipment, etc.) to
address sticking or a risk of sticking. In such an example, the one
or more signals may be issued via one or more network interfaces of
the system. As an example, a signal may be issued automatically. As
an example, a signal may be issued responsive to receipt of input
via a GUI such as, for example, via receipt of an actuation signal
as to one or more graphical controls that cause the system to issue
a corresponding signal. In such an example, input may be via a
pointing device (e.g., mouse, trackball, etc.), via a voice command
(e.g., via a microphone of the system), via a touch (e.g., a
trackpad, a keystroke, a touchscreen display, etc.), etc.
[0204] As to the render block 1150, a GUI may include an analysis
tab and a "what if?" tab for various functionalities, features,
etc., that can be performed at least in part by a system. For
example, a GUI may include analysis information such as a time
plot, a depth plot, a synchronization log with a bottom hole
assembly (BHA), a broomstick plot, a scatter plot, etc. Such
information may allow a user and/or a system to drilldown as to why
sticking has occurred, is occurring and/or might occur (e.g., where
conditions may exist that give rise to an elevated risk of
sticking, etc.). As an example, a GUI may provide for rendering of
evidence gathered by a system, where such evidence may be data,
modeled data, simulation data, historical data, etc., as associated
with sticking. As to a "what if?" analysis, a system may allow for
interactions via one or more GUIs as to a variety of scenarios. In
such an example, a user may assess results for one or more
scenarios to understand better one or more actions to take with
respect to field operations at one or more wells (e.g., one or more
rigsites). For example, consider a scenario that utilizes a
particular rate of penetration (ROP) or scenarios that utilize a
range of rates of penetrations (ROPs) for one or more drilling
operations. In such an example, the system may calculate
information such as hole cleaning information (e.g., a hole
cleaning index (HCI), etc.) where hole cleaning is a physical
factor related to sticking.
[0205] As an example, a system (e.g., a computational framework,
etc.) may generate one or more graphics, optionally in the form of
videos (e.g., animations) that can show an approximate graphical
view of a drillstring (e.g., BHA, etc.) in a subterranean
environment that is being drilled. For example, consider FIG. 10
and the poor hole cleaning graphic, which illustrates cutting being
accumulated in a lower portion of an annular region between a
drillstring (e.g., BHA) and a borehole. In such an example, where
cuttings are not "cleaned" away upwardly, risk of sticking can
increase and/or sticking may occur. FIG. 10 also shows curvature of
the borehole along with some amount of curvature of the
drillstring. In such an example, factors such as BHA equipment,
trajectory from a well plan, etc., can be taken into account in an
analysis, which, as mentioned, may be part of a "what if?" analysis
that aims to arrive at a course of action to take in the field to
reduce risk of getting stuck and/or unstick a drillstring in a
borehole. As an example, a system may include storing information
as to successful scenarios for particular conditions where, for
example, given such conditions in the future (e.g., according to a
well plan, real-time data, etc.), the system may provide one or
more recommendations as to one or more particular actions that can
be taken to address one or more sticking related problems.
[0206] As shown in FIG. 11, the render block 1150 for stuck pipe
drilldown analysis can include outputting one or more actionable
recommendations. As mentioned, a system may issue one or more
signals via one or more interfaces (e.g., network interfaces, etc.)
where such one or more signals are commands, provide information,
etc. that is received by one or more pieces of equipment that are
thereby controlled at least in part thereby. For example, consider
a ROP recommendation where a top drive may be instructed to operate
in a particular manner to control ROP. As another example, consider
a mud flow recommendation where a mud pump may be instructed to
operation in a particular manner to control flow of mud (e.g.,
drilling fluid). As mentioned, hole cleaning can be a factor as to
sticking and, for example, increasing flow of drilling fluid by
increasing a pump rate of a mud pump may help to clean at least a
portion of a borehole of cuttings. As shown in FIG. 10, such a
portion may be at or near a BHA where a BHA may be in a curved
portion of a borehole where an annular region about the BHA may
tend to establish one or more fluid flow paths (e.g., with respect
to gravity, etc.) that may give rise to one or more types of
cleaning issues (e.g., flow being more prominent in one portion of
an annular region as cutting settle under the influence of gravity
in another portion of the annular region, etc.).
[0207] FIG. 12 shows an example of the GUI 1200 as mentioned with
respect to FIG. 11. As shown, the GUI 1200 includes various
features including an alert field that includes one or more
graphical controls (e.g., dismiss, acknowledge, view, acknowledge
and view, etc.). The GUI 1200 includes information such as well
name, measured depth (MD), bit depth (BD), rig state (e.g., Pull
Up), next survey (e.g., date, time, etc.), last survey (e.g., MD,
INCL, AZI, etc.), rig connection, action, alert, type of alert,
etc. As shown, the GUI 1200 can be an operations dashboard and may
include a query field such that a query may be entered to search
for information. For example, a user may enter a query as to a well
name, as to depth, as to state, as to survey, etc. where the GUI
1200 then receives search results from a system that performs the
query, noting that the GUI 1200 may be part of that system, whether
locally or remotely (e.g., via a web browser interface, etc.). In
the example of FIG. 12, the GUI 1200 can be a multi-well dashboard.
As shown, two well names are present while one of the wells has
associated information illustrated in the GUI 1200.
[0208] FIG. 13 shows an example of the GUI 1300 as mentioned with
respect to FIG. 11. The GUI 1300 includes a panel 1310 where
surfaces representations 1312 and 1314 are rendered along with well
trajectories where a location 1316 can be selected, for example,
via a touch to a touchscreen, a pointing device that controls a
cursor, etc. In the example of FIG. 13, the panel 1310 may be akin
to the panel 510 of the GUI 500 of FIG. 5 (e.g., as to well
planning, etc.). The GUI 1300 of FIG. 13 also shows a graphical
representation of a drillstring (e.g., a BHA, etc.) 1330 where a
portion 1336 may be selected. FIG. 13 also shows a graphic 1350 of
probabilities versus level of risk of sticking. As an example, the
graphic 1350 can correspond to a selected location such as the
selected location 1316 or, for example, to a portion of a
drillstring such as the selected portion 1336. In such an example,
an operator may interrogate a scenario as to risk of sticking for a
location along a trajectory and/or with respect to a piece of
equipment of a drillstring with respect to one or more locations
along a trajectory. As an example, the graphical representation of
the drillstring 1330 may include coding such that levels of risk
are highlighted to indicate what portion or portions of the
drillstring may be a cause of a particular level of risk. For
example, a high level of risk may be associated with one or more
pieces of equipment that make up a portion or portions of the
drillstring. Such levels of risk may be associated with one or more
of physical dimensions of a piece of equipment and/or one or more
of operational parameters of a piece of equipment. Where a level of
risk is associated with an operational parameter or operational
parameters, an operator may interrogate further to understand what
parameter value or values may be a cause or a possible solution to
diminish risk of sticking where a control signal may be issued to
equipment that controls one or more portions of the drillstring to
diminish the risk of sticking during one or more field
operations.
[0209] As shown in FIG. 13, the panel 1310 may render multiple
trajectories where, for example, one of the trajectories may be
active or highlighted as corresponding to a selected well, which
may be selected, for example, based at least in part on an
indicator being generated by a computational framework (see, e.g.,
FIG. 6, FIG. 15, FIG. 21, etc.). As mentioned, a computational
framework can include one or more Bayesian networks, which may, for
example, be implemented via processor-executable instructions that
are executable by one or more processors to generate one or more
Bayesian networks and related calculations for determining
information such as information associated with sticking of
equipment in a borehole in a geologic environment, etc. As an
example, output of such a computational framework may be in the
form of probability versus level of risk of sticking (e.g., for a
plurality of predefined risk levels, etc.). As an example, such
information may be output with respect to one or more of depth
(e.g., bit depth, measured depth, total vertical depth, etc.),
equipment, etc.
[0210] As mentioned, regarding uncertainty, a computational
framework can estimate values of indicators and of risk as well as
uncertainty. As an example, uncertainty may be represented in one
or more different manners. For example, consider a mean and
standard deviation or a portion of a probability distribution or a
full probability distribution (e.g., consider a vector of numbers
representing the probability that the risk is a certain amount). As
an example, a computational framework can include storing an
uncertainty metric for one or more indicators as well as computing
a full probability distribution of risk. In such an example,
consider an indicator such as amount of break-over torque, which
may have an associated standard deviation as an uncertainty metric.
As shown in the example of FIG. 13, the graphic 1350 includes a
distribution, which may be a full distribution with respect to risk
as a dimension (e.g., levels of risk). Thus, as an example, a
computational framework may output a single number for risk (e.g.,
risk of getting stuck is 95%) and/or may output a distribution
(e.g., at least a portion of a distribution). As an example, a
Bayesian network of a computational framework may output a
distribution such as the distribution in the graphic 1350 of the
GUI 1300 of FIG. 13. As mentioned, such a graphic may be
interactive in that input can be received to facilitate
identification of one or more causes of a probability of the
distribution (e.g., as an equipment characteristic, as an
operational condition, as a trajectory characteristic, as a
formation characteristic, etc.).
[0211] As to the framework 600 of FIG. 6 and/or the system 1500 of
FIG. 15 and/or the system 2100 of FIG. 21, uncertainty can be
computed and passed from one component to another. For example, a
mean and a standard deviation may be computed for sensor data as
input where the mean and standard deviation are passed along and
into one or more Bayesian networks. In such an example, the
standard deviation can be an uncertainty metric and output of a
Bayesian network can be a probability distribution that is based at
least in part on such an uncertainty metric. In such a manner,
uncertainties can be estimated and taken into account to provide
output as to one or more indicators associated with drilling
operations (e.g., indicators relevant to sticking, unsticking,
etc.).
[0212] As an example, the GUI 1300 may render historical, future
and/or present information. As an example, a time line may be
presented as to data acquired and analysis results thereof.
[0213] As mentioned, the GUI 1300 may aim to help answer a question
"where is sticking or risk of sticking coming from?" or, "what is
the cause of sticking or risk of sticking?". The GUI 1300 can
present contextual information, one or more historical trends of
potential and observable multi-pass types of information, etc. As
an example, the GUI 1300 may present information based on a
plurality of wells, which may be wells in a common field (e.g., a
common type of formation, etc.). Such information may be multi-pass
in that each of the plurality of wells represents a pass through
particular rock(s) in a subterranean environment (e.g., a type or
types of formations). In such an example, information for one or
more wells may be for a common reservoir that is to be produced by
a plurality of wells. As an example, a user may transition from the
GUI 1300 to the GUI 1400 of FIG. 14 where, for example, one or more
features of the GUI 1400 may be utilized for purposes of analysis
(e.g., further analysis, etc.).
[0214] FIG. 14 shows an example of the GUI 1400 as mentioned with
respect to FIG. 11. In the example GUI 1400, various fields are
shown for various types of performance information as to drilling
of a well or wells. In the example of FIG. 14, the GUI 1400 shows
information for a particular well having a well name, which may be
a selected well from a plurality of wells (e.g., selected via
interactions with a GUI, selected by a system as having an issue,
etc.). As shown, the GUI 1400 can include log information which may
include operational log information (e.g., ROP, HKLD, etc.) and/or
one or more other types of log information. As an example, log
information can include sensor information as to measurement while
drilling (MWD) and/or logging while drilling (LWD) operations. As
an example, log information can include sensor information as to
one or more operations of a BHA in a downhole environment. For
example, consider vibration information as to vibrations
experienced by a BHA during one or more drilling operations.
[0215] The GUI 1400 may be rendered along with various types of
gathered information (e.g., gathered evidence from one or more
sources). As an example, the GUI 1400 can include a "what if?"
graphical control that can be actuated to allow a user to
understand better one or more scenarios as to the well where a
system may output one or more actionable recommendations (e.g., to
follow one or more of the scenarios, etc.).
[0216] FIG. 15 shows an example of a system 1500 that is
illustrated with various example workflows. As an example, the
system 1500 can be or include a computational framework. As an
example, the system 1500 can include one or more features of the
framework 600 of FIG. 6 and, for example, the framework 600 of FIG.
6 can include one or more features of the system 1500 of FIG.
15.
[0217] As shown, the system 1500 can include various operational
blocks including, for example, a contextual information block 1514,
a channel data block 1514, a hydraulics engine block 1522 (e.g.,
for modeling hydraulic phenomena based at least in part on
contextual information and/or channel data, etc.), a stationary
time computation block 1524 (e.g., in a BOT detection function,
etc.), a results block 1532, an output block 1534 (e.g.,
displayable output results, etc.), a channel block 1540 (e.g., as
to information acquired at a data rate for one or more conditions,
etc.), a contextual information block 1552, a channel block 1554, a
contextual information block 1562 (e.g., for information that can
be utilized in one or more potential of differential sticking
(PoDS) computations, which may include static and/or changed
conditions types of contextual information, etc.), and a
differential sticking potential block 1570 that can compute one or
more potentials for differential sticking (PoDSs) given various
inputs, which include various types of outputs from one or more
features of the system 1500.
[0218] As shown in the example of FIG. 15, the differential
sticking potential block 1570 can include an overbalance pressure
computation block 1582 that can output data as to overbalance per a
data overbalance block 1584, which can provide such data to a PoDS
computation block 1592. As shown in FIG. 15, the data overbalance
block 1584 can include information that is based at least in part
on output of the hydraulics engine block 1522 along with, for
example, information from one or more channels (see, e.g., the
channel blocks 1514, 1540, 1554, etc.).
[0219] As shown in FIG. 15, the system 1500 can include one or more
blocks associated with hole conditions, which may be part of a hole
conditions manager (HCM) functionality of the system 1500 (e.g., as
an integral part of the system 1500, as a plug-in, as an API
accessible function, etc.). As to hole conditions, these can
include various types of conditions that may be factors as to
sticking or increased risk of a drillstring becoming stuck in a
borehole. HCM functionality may provide for dynamically updating
one or more wellbore condition models to provide substantially
instantaneous display of borehole condition information. A US
Patent Application Publication having a Publication No.
US20150134257 A1, entitled Automatic Wellbore Condition Indicator
and Manager, to Schlumberger Technology Corporation, is
incorporated by reference herein (257 publication). The '257
publication provides information as to calculation of hole
conditions factors (HCFs), hole conditions indexes (HCIs), etc. HCF
is a parameter that may be derived from other parameters related to
various hole conditions such as, for example, drag (axial friction
between a bore wall and a drillstring), torque applied to a
drillstring to effect rotation thereof, equivalent circulating
density (ECD, e.g., equivalent density of drilling fluid when
moving through a drillstring and wellbore) and equivalent static
density (ESD) of the drilling fluid, drilling fluid standpipe
pressure (SPP, e.g., pressure at a discharge side of a pump), a
hole cleaning index (HCI), Filter Cake Quality, and a Vibration
Parameter.
[0220] As shown in FIG. 15, the differential sticking potential
block 1570 can include computing various types of HCM
interpretations as outputs per the output blocks 1594 and 1596. As
shown, one or more outputs may be outputs for a Bayesian Network
(BN) and one or more outputs may be outputs for rendering to a
display or displays.
[0221] As an example, the system 1500 may be utilized to implement
at least a portion of the method 700 of FIG. 7. As shown in FIG. 7,
the method 700 includes an analysis block 730 that utilizes a
physics-based model and includes a compute block 740 that utilizes
a Bayesian network (BN). As an example, the system 1500 may include
one or more features of the system 790 of FIG. 7.
[0222] As an example, the system 1500 may provide for one or more
functionalities of the framework 600 of FIG. 6, which includes, for
example, the hydraulics model 654 (see, e.g., the hydraulics engine
block 1522), which may output information as to a hole cleaning
index (HCI), which may be utilized for one or more purposes.
[0223] The framework 600 of FIG. 6, as mentioned, includes a
stationary time determination component 614, which may take as
input one or more rigstates and that may output information to the
potential for differential sticking component 672 (e.g., a PoDS
component).
[0224] As shown in the example of FIG. 6, the framework 600 can
include the amount of overbalance component 644 that receives input
from the hydraulics model 654 (e.g., a hydraulics engine) where the
amount of overbalance component 644 can output information to the
potential for differential sticking component 672. As shown in the
example of FIG. 6, the framework 600 can include a risk of getting
differentially stuck component 682 that receives input from the
potential for differential sticking component 672 and, for example,
information as to torque (e.g., torque and drag). As mentioned, the
system 1500 of FIG. 15 and the framework 600 of FIG. 6 may include
one or more common features and may be considered examples of
various types of systems, frameworks, etc. that can provide for
drillstring sticking management (e.g., and optionally control,
etc.).
[0225] FIG. 16 shows an example of a method 1600 and examples of
graphical user interfaces 1612, 1622 and 1632. In the example of
FIG. 16, the method 1600 includes a measurement block 1610 for
acquiring measurements (e.g., measurement information, data, etc.);
an indicators block 1620 for generating indicators (see, e.g., a
stuck pipe indicator as a solid black line that crosses the GUIs
1612, 1622 and 1632); and a Bayesian network block 1630 for
outputting information such as, for example, risk of sticking
information, which may be classified into different categories
(e.g., high, medium, low, etc.). As mentioned, where a risk of
sticking is high, a method can include identifying one or more
causes (e.g., likely causes) of the risk. In such an example, the
method may include issuing one or more signals (e.g., an alert as
to a cause, a control signal, etc.).
[0226] As shown in FIG. 16, the GUI 1632 is coded to indicate high,
medium and low levels of probabilities at various times that
correspond to times of the plots in the GUIs 1612 and 1622. As
mentioned with respect to FIG. 13, output of a computational
framework can include probabilities, which may be rendered as in
the graphic 1350 of FIG. 13 or, for example, as in the GUI 1632 of
FIG. 16. In particular, the GUI 1632 of FIG. 16 shows, for each
time, a coded portion of low level, medium level and/or high level.
As can be seen in the GUI 1632, for early times, the probability
distribution is largely low level (coded white) and then
transitions to a region where it is largely a combination of medium
level (coded hatched) and high level (coded checkered). The rig
then transitions to a region (e.g., a period of time) where there
is a relative balance between low level and medium level and then
to another region where high level risk increases and,
correspondingly, low level risk decreases (e.g., as to probability
distribution). As mentioned, a solid black line indicates "stuck
pipe". The time for the stuck pipe can be visually inspected across
the GUIs 1612, 1622 and 1632 where output of a computational
framework, inputs to the computational framework and/or
intermediate results of the computational framework can be examined
(e.g., interrogated) as to one or more causes of the stuck pipe.
Per the GUI 1612, factors such as hook load (HKLD), flow rate,
downhole equivalent circulating density (ECD), surface torque may
be considered as one or more contributing factors to an elevated
level of risk as illustrated in the GUI 1632; noting that one or
more other factors may be considered, alternatively or
additionally.
[0227] As to hook load, it can be a sensor value or sensor values
acquired by a sensor or sensors at a rigsite that measure (e.g.,
sense) force pulling down on a hook (see, e.g., the traveling block
211, the crown block 173, 213, the top drive 240, etc.). The force
may be a total force that includes the weight of the drillstring in
air, the drill collars and any ancillary equipment, reduced by
force(s) that tend to reduce that weight. For example, some
examples of forces that might reduce the weight include friction
along a borehole wall (e.g., especially in deviated wells) and
buoyant forces on the drillstring caused by its immersion in
drilling fluid. As hook load can be associated with friction, hook
load can be a factor that can be taken into account in one or more
calculations as to sticking, unsticking, etc. As mentioned, hook
load can depend on buoyant forces, which can be associated with
drilling fluid (e.g., mud). As such, one or more characteristics of
drilling fluid (e.g., density, circulating density, etc.) may be
related factors that can be taken into account in one or more
calculations as to sticking, unsticking, etc.
[0228] As mentioned, uncertainty information such as in the form of
one or more uncertainty metrics may accompany a value (e.g., a
measured or sensed value). For example, a standard deviation may
accompany a hook load value, which may be estimated (e.g.,
calculated) via a framework, a system, etc. and/or via equipment
that may be rigsite equipment. As an example, a load sensor or load
sensors can acquire load data (e.g., hook load data) from which one
or more uncertainty metrics can be estimated (e.g., standard
deviation, etc.) such that a framework or a system may account for
such uncertainty. As mentioned, such uncertainty can be taken into
account via one or more Bayesian networks that can output
probability information (see, e.g., the GUI 1632 of FIG. 16).
[0229] As mentioned, the graphical information of FIG. 16 may
correspond to information of the framework 600 of FIG. 6 (e.g., or
the system 1500 of FIG. 15, etc.) such that an operator may
determine a primary contributing factor (e.g., or other factors)
that may provide information as to one or more actions that can be
taken in the field, optionally automatically or semi-automatically
via issuance of a signal or signals from the framework 600, the
system 1500 of FIG. 15, the system 2100 of FIG. 21, etc. As an
example, an operator may interact with a framework, a system, etc.,
to render various GUIs such as a GUI of the framework components of
the framework 600 and GUIs as in FIG. 16 where relationships can be
examined, for example, to determine one or more causes and/or one
or more actions that can mitigate a situation (e.g., stuck pipe,
risk of pipe sticking, etc.). As an example, an interrogation as to
a cause may progress from risk information to indicator information
to measurement information, for example, working backwards in the
method 1600 of FIG. 16 (e.g., or in the GUIs 1612, 1622, 1632
and/or the framework components of the framework 600, the system
1500, etc.).
[0230] FIG. 17 shows examples of graphical user interfaces 1710 and
1720, which may be part of a common graphical user interface
associated with a single well or drilling operations at a single
rigsite. As shown, the GUI 1710 includes a plot of depth versus
time as to drilling activity where the plot can include data as to
bit depth and data as to hole depth. As shown, the GUI 1720 can
include information versus depth where the information may be risk
information of one or more physical conditions occurring that can
cause sticking of at least a portion of a drillstring. As shown in
the example of FIG. 17, the GUI 1720 can include highlighting of
risk information according to one or more criteria (e.g., levels,
etc.). In the example of FIG. 17, an operator at a rigsite or
remote from a rigsite may readily assess progress of drilling with
respect to time and associate depths, times and risks. For example,
an operator may discern that progress has diminished over a range
of depths for a certain period of time where risk of sticking was
elevated at those depths (see, e.g., depths greater than
approximately 3400 and days 11 September and 12 September. During
such periods of time, a framework may be outputting the risk
information for rendering to a GUI while also outputting one or
more signals as to actions, whether recommended and/or control
actions. In such an approach, a framework may be dynamically
available for one or more operators and/or controllers.
[0231] FIG. 18 illustrates examples of graphical user interfaces
1801, 1803, 1805 and 1900 where the GUI 1805 may include a filter
control 1807 for filtering information associated with a plurality
of sites. As shown in FIG. 18, the GUIs 1801, 1803 and 1805 can be
part of a drilldown workflow for selecting one or more sites. As
shown, the GUI 1805 can include indicators that show the status of
one or more sites. In the example of FIG. 18, a plurality of sites
may be selected and provided (e.g., via identifiers, coordinates,
etc.) to a framework for rendering associated information to the
GUI 1900.
[0232] FIG. 19 shows an example of the graphical user interface
1900 as mentioned in FIG. 18. In the example of FIG. 19, the GUI
1900 includes various types of information for a plurality of sites
along with various graphical controls that an operator may interact
with to cause one or more actions to occur. For example, an
operator may review various sites as to indicators such that an
indicator may be addressed or dismissed. As shown, a "Go To Well"
graphical control may cause a framework to transition from the GUI
1900 of FIG. 19 to, for example, one or more of the GUIs 1710 and
1720 of FIG. 17, which can be individual site GUIs. As mentioned,
the GUI 1805 of FIG. 18 can include the graphical control 1807 for
purposes of filtering (e.g., selecting one or more sites based on
one or more criteria). In the example of FIG. 19, the GUI 1900
includes a series of graphical controls 1905 for "All", "Group" and
"Filter" such that an operator may selectively control information
that is rendered via the GUI 1900.
[0233] FIG. 20 shows an example of a system 2000 associated with an
example of a wellsite system 2001 and also shows an example
scenario 2002. As shown in FIG. 20, the system 2000 can include a
front-end 2003 and a back-end 2005 from an outside or external
perspective (e.g., external to the wellsite system 2001, etc.). In
the example of FIG. 20, the system 2000 includes a drilling
framework 2020, a stream processing and/or management block 2040,
storage 2060 and optionally one or more other features that can be
defined as being back-end features. In the example of FIG. 20, the
system 2000 includes a drilling workflow framework 2010, a stream
processing and/or management block 2030, applications 2050 and
optionally one or more other features that can be defined as being
front-end features.
[0234] As an example, the stream processing and/or management block
2030 of the system 2000 may include one or more features of the
framework 600 of FIG. 6 and/or the system 1500 of FIG. 15. As shown
in FIG. 20, the scenario 2002 can include receiving various types
of information that can include drilling framework information
where such information may be processed and output to one or more
applications 2052, which can include local and/or remote
applications. As an example, output of the stream processing and/or
management block 2030 may include one or more signals as to one or
more alerts, control signals, etc.
[0235] As an example, a user operating a user device can interact
with the front-end 2003 where the front-end 2003 can interact with
one or more features of the back-end 2005. As an example, such
interactions may be implemented via one or more networks, which may
be associated with a cloud platform (e.g., cloud resources,
etc.).
[0236] As to the example scenario 2002, the drilling framework 2020
can provide information associated with, for example, the wellsite
system 2001. As shown, the stream related blocks 2030 and 2040, a
query service 2085 and the drilling workflow framework 2010 may
receive information and direct such information to storage, which
may include a time series database 2062, a blob storage database
2064, a document database 2066, a well information database 2068, a
project(s) database 2069, etc. As an example, the well information
database 2068 may receive and store information such as, for
example, customer information (e.g., from entities that may be
owners of rights at a wellsite, service providers at a wellsite,
etc.). As an example, the project database 2069 can include
information from a plurality of projects where a project may be,
for example, a wellsite project.
[0237] FIG. 21 shows an example of a system 2100 that includes a
publishing engine 2111, an interpretation engine 2112, an equipment
registry 2113, a data engine 2114 and a communication engine 2115
as well as application programming interfaces (APIs) 2121 and 2131
and operatively coupled databases 2141, 2142, 2143 and 2144.
[0238] As an example, the system 2100 of FIG. 21 may include one or
more features of the framework 600 of FIG. 6 and/or the system 1500
of FIG. 15. For example, the interpretation engine 2112 may include
one or more Bayesian networks that can receiving information and
generate output as, for example, one or more signals. As shown in
FIG. 21, the system 2100 can include receiving various types of
information that can include drilling related information (e.g.,
via one or more of the APIs 2121 and/or one or more of the
databases 2141, 2142, 2143 and 2144, which can include one or more
channels of data, etc.) where such information may be processed and
output, optionally via one or more the APIs 2131. As an example,
output of the system 2100 may include one or more signals as to one
or more alerts, control signals, etc.
[0239] In the example of FIG. 21, the components 2111, 2112, 2113,
2114 and 2115 can be hosted by a cloud computing platform. As an
example, the equipment registry 2113 can be a registry associated
with an equipment provisioning framework that may operate, for
example, via resources provided in a cloud computing platform. As
an example, the equipment registry 2113 can be rig site specific
where each rig site includes a dedicated equipment registry. As an
example, the system 2100 may include a plurality of equipment
registries for a plurality of rig sites.
[0240] In the example of FIG. 21, the data engine 2114 may
correspond to and/or be operatively coupled to one or more features
of the wellsite system 400 of FIG. 4. As shown in the example of
FIG. 21, the data engine 2114 can be operatively coupled to one or
more of the APIs 2121 and to the equipment registry 2113 (e.g., or
registries). As shown, the data engine 2114 can be operatively
coupled to the databases 2141 to 2144. Further, the data engine
2114 can be operatively coupled to the publishing engine 2111 and
the interpretation engine 2112 as well as, for example, one or more
of the APIs 2131.
[0241] In the example of FIG. 21, various components may be in a
trusted or secure zone where, for example, the APIs 2121 and/or the
APIs 2131 provide predefined calls and responses for components in
the trusted or secure zone. As an example, the APIs 2131 can expose
functionality of one or more components in the trusted or secure
zone. For example, a computing device with a browser application
may issue an API call to the system 2100 where the system 2100
responds to the API call with information transmitted to the
computing device that can be rendered to a display via the browser
application. In such an example, the computing device may be
prohibited from accessing functionality of components in a trusted
or secure zone where such functionality is not exposed via an API
defined call.
[0242] As an example, the APIs 2121 can be utilized by rig site
equipment, for example, for purposes of provisioning, data
transmission, control commands, etc. As an example, the APIs 2121
can provide for handshakes between rig site equipment and one or
more components of the system 2100 where information may be
transmitted before, during or after a handshake.
[0243] As an example, the system 2100 can receive drilling
framework information from one or more rig sites (e.g., via a
system as in FIG. 4) and/or other information from one or more rig
sites.
[0244] As an example, the interpretation engine 2112 can issue one
or more notifications, which may be based on one or more events.
For example, the interpretation engine 2112 can receive
information, determine an event and issue a notification for that
event. As an example, a notification of the interpretation engine
2112 can be issued to one or more destination addresses, for
example, according to the communication engine 2115, which may
operating according to information in a communication matrix.
[0245] As shown in the example of FIG. 21, the interpretation
engine 2112 can be implemented for a single site 2116 and/or for
multiple sites 2118. For example, the interpretation engine 2112
can include algorithms for handling single site information and
algorithms for handling multiple site information. As an example,
where the system 2100 receives information for a plurality of well
plans the plurality of well plans may be analyzed individually
and/or collectively. As an example, a well plan can be a digital
well plan for an individual well to be drilled and/or completed
and/or a well plan can be a digital master well plan for a
plurality of wells to be drilled and/or completed. As an example, a
digital master well plan can include information as to equipment
and/or other resources (e.g., human resources, power, water, mud,
etc.) that may be utilized at a plurality of sites. In such an
example, an event that occurs at one site may possibly impact one
or more other sites.
[0246] As an example, a method can include generating reports using
a system such as, for example, the system 2100. As shown in the
example of FIG. 21, the publishing engine 2111 may respond to
requests received as API calls by generating and issuing one or
more reports.
[0247] As shown in FIG. 21, the system 2100 can include features
for acquiring information about a rig, which can be state
information. As an example, a system may operate automatically to
determine a state or states based at least in part on information
received by the system, which can include information acquired via
one or more sensors, one or more devices with input mechanisms for
user input, etc. As an example, a report may be generated based at
least in part on a state or states (e.g., based at least in part on
state information). As an example, a report may be triggered based
on a push system and/or a pull system. For example, an oilfield
operator may query a system to determine one or more states of the
system (e.g., where a state can be a system state, a subsystem
state, etc.). As an example, a report may be triggered based on
state information, time, or another type of trigger.
[0248] As an example, the system 2100 can include receiving data
associated with one or more drilling operations, analyzing at least
a portion of the data and identifying one or more events and
classifying the events. For example, the interpretation engine 2112
can include interpreting information to identify one or more events
and to classify the one or more events. In such an example, an
event may be classified as being associated with a particular type
of performance (e.g., drilling, formation, equipment, etc.) and,
for example, may be classified as being a "good" event or a "bad"
event, optionally along one or more axes. For example, an axis from
bad to good may be utilized and, for example, an associated cost,
which may range from negative to positive. Thus, an event may be
classified as being good with a positive financial or other type of
cost return on achievement of that event. Such an event may be
desirable to achieve while drilling. As an example, another type of
event may be classified as being bad with a low financial or other
type of cost impact. In such an example, avoidance of such an event
may be considered to be optional due to low impact on cost;
whereas, for example, a bad event with a high financial or other
type of cost impact may be assessed for avoidance. Such an
assessment may impact a drilling plan, etc., for one or more wells,
which are being drilled, being planned to be drilled, etc.
[0249] As an example, the interpretation engine 2112 can be or
include an inference engine. As an example, an inference engine can
use logic represented as IF-THEN rules. For example, consider a
format of such rules as IF <logical expression> THEN
<logical expression>. IF-THEN statements (e.g., Modus Ponens)
can represent various types of logic including, for example, human
psychology as humans can utilize IF-THEN types of representations
of knowledge. As an example, an inference engine may implement
inductive algorithms that can predict a next state (e.g., next
event, worsening of an event, improvement of an event, etc.) based
upon a given series of information. As an example, an inductive
framework can combine algorithmic information theory with a
Bayesian framework (e.g., a computational framework that includes
at least one Bayesian network).
[0250] As an example, the interpretation engine 2112 can be part of
the knowledge base, learning and evaluation blocks or operatively
coupled thereto. In such an example, the interpretation engine 2112
may receive information from a knowledge base (e.g., one or more
sources of information), may learn by training one or more
algorithms (e.g., including retraining one or more algorithms), and
may evaluate information based at least in part on one or more
trained algorithms. As an example, an "expert" may review
information output by an interpretation engine where the expert may
approve, disapprove, modify, comment, etc. as to such output. In
such an example, a mechanism may capture the expert feedback and
utilize at least a portion of the feedback for purposes of training
the interpretation engine.
[0251] As an example, an expert station may be a computing system
that is operatively coupled to an interpretation engine that can
intervene in operation of the interpretation engine. For example,
where output is deemed lacking, input may be received via an expert
station to comment on such output, to halt transmission of such
output, to cause reinterpretation of information to generate new
output, etc.
[0252] As an example, a system can be a drilling event analysis
system, which can include an analysis engine, which may be a
machine learning engine (e.g., Bayesian, etc.). As an example,
consider the APACHE STORM engine (Apache Software Foundation,
Forest Hill, Md.).
[0253] The APACHE STORM engine can be implemented as a distributed
real-time computation system. Such a system can receive and process
unbounded streams of data. Such a system may provide for real-time
analytics, online machine learning, continuous computation,
distributed RPC, ETL, etc. Such a system may integrate with one or
more queueing and database technologies. As an example, a topology
may be constructed that consumes streams of data and processes
those streams in one or more manners, optionally repartitioning
streams between each stage of computation. As an example, a
topology can be constructed as a graph of computation where, for
example, a node in a topology includes processing logic, and links
between nodes indicate how data may be passed around between
nodes.
[0254] As an example, data may be available in the WITSML.TM.
standard (Wellsite Information Transfer Standard Markup Language,
Energistics, Sugar Land, Tex.) developed as part of an industry
initiative to interfaces for technology and applications (e.g., to
monitor wells, manage wells, drilling, fracturing, completions,
workovers, etc.). For example, a machine learning system can
receive data organized in the WITSML.TM. standard where such data
may pertain to one or more of drilling, completions, interventions
data exchange, etc. As an example, a system may be operatively
coupled to resources associated with one or more entities (e.g.,
energy companies, service companies, drilling contractors,
application vendors, regulatory agencies, etc.). While WITSML.TM.
is mentioned, one or more other types of data schemes may be
utilized.
[0255] As an example, a method includes receiving information
during a drilling operation for a drillstring disposed in a bore in
a formation; estimating uncertainty associated with the
information; analyzing at least a portion of the information using
a physics-based model to generate a result; computing, via a
Bayesian network, a risk probability of the drilling string
sticking in the bore in the formation based at least in part on the
result and the estimated uncertainty; and, based at least in part
on the risk probability, issuing a signal. In such an example, the
signal can be or include a control signal that controls drilling
equipment of the drilling operation or, for example, the signal can
be or include an alert (e.g., that causes a computing device,
system, equipment, etc. to render information visually, audibly,
etc.).
[0256] As an example, a method can include, responsive to comparing
a risk probability to a threshold, identifying a primary
contributing factor to a risk probability. In such an example, a
primary contributing factor may be a factor that gives rise to a
risk probability being at an elevated level such that if the
contributing factor were altered, the risk probability may be below
a particular threshold. As an example, a risk probability can
include a plurality of contributing factors where such factors may
optionally be ranked as to their effect on the risk probability. In
such an example, a top ranked factor may be a primary contributing
factor and others may be considered secondary (e.g., or tertiary,
or related, etc.). As an example, depending on physical phenomena
associated with sticking (e.g., or unsticking), one or more factors
can be related (see, e.g., FIG. 6, FIG. 15, etc.). As an example, a
method can include identifying one or more contributing factors
(e.g., and/or ranking, etc.) by progressing backwards through a
Bayesian network to identify an input to the Bayesian network. In
such an example, the identified input may be further analyzed, for
example, by progressing backwards (see, e.g., FIG. 6, FIG. 15,
etc.), which may identify one or more associated inputs.
[0257] As an example, a method can include implementing a Bayesian
network that includes a risk of differential sticking component and
a risk of pack-off sticking component (see, e.g., FIG. 6, etc.). In
such an example, the Bayesian network can include a potential for
differential sticking component. In such an example, a method can
include determining potential for differential sticking via the
differential sticking component prior to performing a drilling
operation.
[0258] As an example, a method can include computing (see, e.g.,
the computation block 740 of the method 700 of FIG. 7) during a
drilling operation (see, e.g., the reception block 710 of the
method 700 of FIG. 7). In such an example, the method can include
issuing one or more signals (see, e.g., the issuance block 750 of
the method 700 of FIG. 7) where at least one of the one or more
signals actuates at least one piece of equipment associated with
the drilling operation, for example, to control an aspect of the
drilling operation (e.g., mud flow, rpm of a bit, sensing,
etc.).
[0259] As an example, a method can include implementing a
physics-based model such as a torque and drag model. As an example,
a method can include implementing a physics-based model such as a
hydraulics model. As an example, a method can include implementing
a physics-based model such as a geomechanics model. As an example,
a method can include implementing a plurality of physics-based
models, which can include one or more of a torque and drag model, a
hydraulics model and a geomechanics model.
[0260] As an example, a method can include receiving information
such as drilling fluid information (see, e.g., the reception block
710 of the method 700 of FIG. 7). In such an example, the drilling
fluid information (e.g., mud information, etc.) may be associated
with an ability to clean cuttings from a borehole that is being
drilled (see, e.g., the example events 1000 of FIG. 10). Such
information may pertain to characteristics of the drilling fluid,
flow of the drilling fluid, etc.
[0261] As an example, a method can include computing that includes
determining at least one cause of a risk probability. As mentioned,
such a method may include progressing backwards through a network,
computational components, etc. to one or more inputs, which may be
one or more factors that cause a risk probability to be at a
particular level (e.g., at an elevated level, etc.).
[0262] As an example, a method can include computing that includes
determining at least one action to mitigate a risk probability. In
such an example, the action may be associated with one or more
identified causes (e.g., contributing factors, etc.) as to a risk
probability being at a particular level (e.g., at an elevated
level, etc.). In such an example, consider at least one action that
includes an action that aims to prevent cuttings from packing off
around a drillstring. In such an example, consider an action that
is or includes at least one of increasing rotation rate and
increasing circulation rate to clean a bore (e.g., of a borehole
being drilled, etc.). In such an example, the action can include,
for example, associated parameters that decrease likelihood of
creating a hole or washout.
[0263] As an example, a system can include a processor; memory
accessible by the processor; processor-executable instructions
stored in the memory and executable to instruct the system to:
receive information during a drilling operation for a drillstring
disposed in a bore in a formation; estimate uncertainty associated
with the information; analyze at least a portion of the information
using a physics-based model to generate a result; compute, via a
Bayesian network, a risk probability of the drilling string
sticking in the bore in the formation based at least in part on the
result and the estimated uncertainty; and, based at least in part
on the risk probability, issue a signal. In such an example, the
system may include instructions suitable for instructing the system
to perform one or more actions of one or more methods.
[0264] As an example, one or more computer-readable storage media
can include processor-executable instructions to instruct a
computing system to: receive information during a drilling
operation for a drillstring disposed in a bore in a formation;
estimate uncertainty associated with the information; analyze at
least a portion of the information using a physics-based model to
generate a result; compute, via a Bayesian network, a risk
probability of the drilling string sticking in the bore in the
formation based at least in part on the result and the estimated
uncertainty; and, based at least in part on the risk probability,
issue a signal. In such an example, the one or more
computer-readable storage media may include instructions suitable
for instructing the computing system to perform one or more actions
of one or more methods.
[0265] As an example, a method may be implemented in part using
computer-readable media (CRM), for example, as a module, a block,
etc. that include information such as instructions suitable for
execution by one or more processors (or processor cores) to
instruct a computing device or system to perform one or more
actions. As an example, a single medium may be configured with
instructions to allow for, at least in part, performance of various
actions of a method. As an example, a computer-readable medium
(CRM) may be a computer-readable storage medium (e.g., a
non-transitory medium) that is not a carrier wave.
[0266] According to an embodiment, one or more computer-readable
media may include computer-executable instructions to instruct a
computing system to output information for controlling a process.
For example, such instructions may provide for output to sensing
process, an injection process, drilling process, an extraction
process, an extrusion process, a pumping process, a heating
process, etc.
[0267] In some embodiments, a method or methods may be executed by
a computing system. FIG. 22 shows an example of a system 2200 that
can include one or more computing systems 2201-1, 2201-2, 2201-3
and 2201-4, which may be operatively coupled via one or more
networks 2209, which may include wired and/or wireless
networks.
[0268] As an example, a system can include an individual computer
system or an arrangement of distributed computer systems. In the
example of FIG. 22, the computer system 2201-1 can include one or
more modules 2202, which may be or include processor-executable
instructions, for example, executable to perform various tasks
(e.g., receiving information, requesting information, processing
information, simulation, outputting information, etc.).
[0269] As an example, a module may be executed independently, or in
coordination with, one or more processors 2204, which is (or are)
operatively coupled to one or more storage media 2206 (e.g., via
wire, wirelessly, etc.). As an example, one or more of the one or
more processors 2204 can be operatively coupled to at least one of
one or more network interface 2207. In such an example, the
computer system 2201-1 can transmit and/or receive information, for
example, via the one or more networks 2209 (e.g., consider one or
more of the Internet, a private network, a cellular network, a
satellite network, etc.).
[0270] As an example, the computer system 2201-1 may receive from
and/or transmit information to one or more other devices, which may
be or include, for example, one or more of the computer systems
2201-2, etc. A device may be located in a physical location that
differs from that of the computer system 2201-1. As an example, a
location may be, for example, a processing facility location, a
data center location (e.g., server farm, etc.), a rig location, a
wellsite location, a downhole location, etc.
[0271] As an example, a processor may be or include a
microprocessor, microcontroller, processor module or subsystem,
programmable integrated circuit, programmable gate array, or
another control or computing device.
[0272] As an example, the storage media 2206 may be implemented as
one or more computer-readable or machine-readable storage media. As
an example, storage may be distributed within and/or across
multiple internal and/or external enclosures of a computing system
and/or additional computing systems.
[0273] As an example, a storage medium or storage media may include
one or more different forms of memory including semiconductor
memory devices such as dynamic or static random access memories
(DRAMs or SRAMs), erasable and programmable read-only memories
(EPROMs), electrically erasable and programmable read-only memories
(EEPROMs) and flash memories, magnetic disks such as fixed, floppy
and removable disks, other magnetic media including tape, optical
media such as compact disks (CDs) or digital video disks (DVDs),
BLUERAY.RTM. disks, or other types of optical storage, or other
types of storage devices.
[0274] As an example, a storage medium or media may be located in a
machine running machine-readable instructions, or located at a
remote site from which machine-readable instructions may be
downloaded over a network for execution.
[0275] As an example, various components of a system such as, for
example, a computer system, may be implemented in hardware,
software, or a combination of both hardware and software (e.g.,
including firmware), including one or more signal processing and/or
application specific integrated circuits.
[0276] As an example, a system may include a processing apparatus
that may be or include a general purpose processors or application
specific chips (e.g., or chipsets), such as ASICs, FPGAs, PLDs, or
other appropriate devices.
[0277] FIG. 23 shows components of a computing system 2300 and a
networked system 2310. The system 2300 includes one or more
processors 2302, memory and/or storage components 2304, one or more
input and/or output devices 2306 and a bus 2308. According to an
embodiment, instructions may be stored in one or more
computer-readable media (e.g., memory/storage components 2304).
Such instructions may be read by one or more processors (e.g., the
processor(s) 2302) via a communication bus (e.g., the bus 2308),
which may be wired or wireless. The one or more processors may
execute such instructions to implement (wholly or in part) one or
more attributes (e.g., as part of a method). A user may view output
from and interact with a process via an I/O device (e.g., the
device 2306). According to an embodiment, a computer-readable
medium may be a storage component such as a physical memory storage
device, for example, a chip, a chip on a package, a memory card,
etc.
[0278] According to an embodiment, components may be distributed,
such as in the network system 2310. The network system 2310
includes components 2322-1, 2322-2, 2322-3, . . . 2322-N. For
example, the components 2322-1 may include the processor(s) 2302
while the component(s) 2322-3 may include memory accessible by the
processor(s) 2302. Further, the component(s) 2322-2 may include an
I/O device for display and optionally interaction with a method.
The network may be or include the Internet, an intranet, a cellular
network, a satellite network, etc.
[0279] As an example, a device may be a mobile device that includes
one or more network interfaces for communication of information.
For example, a mobile device may include a wireless network
interface (e.g., operable via IEEE 802.11, ETSI GSM,
BLUETOOTH.RTM., satellite, etc.). As an example, a mobile device
may include components such as a main processor, memory, a display,
display graphics circuitry (e.g., optionally including touch and
gesture circuitry), a SIM slot, audio/video circuitry, motion
processing circuitry (e.g., accelerometer, gyroscope), wireless LAN
circuitry, smart card circuitry, transmitter circuitry, GPS
circuitry, and a battery. As an example, a mobile device may be
configured as a cell phone, a tablet, etc. As an example, a method
may be implemented (e.g., wholly or in part) using a mobile device.
As an example, a system may include one or more mobile devices.
[0280] As an example, a system may be a distributed environment,
for example, a so-called "cloud" environment where various devices,
components, etc. interact for purposes of data storage,
communications, computing, etc. As an example, a device or a system
may include one or more components for communication of information
via one or more of the Internet (e.g., where communication occurs
via one or more Internet protocols), a cellular network, a
satellite network, etc. As an example, a method may be implemented
in a distributed environment (e.g., wholly or in part as a
cloud-based service).
[0281] As an example, information may be input from a display
(e.g., consider a touchscreen), output to a display or both. As an
example, information may be output to a projector, a laser device,
a printer, etc. such that the information may be viewed. As an
example, information may be output stereographically or
holographically. As to a printer, consider a 2D or a 3D printer. As
an example, a 3D printer may include one or more substances that
can be output to construct a 3D object. For example, data may be
provided to a 3D printer to construct a 3D representation of a
subterranean formation. As an example, layers may be constructed in
3D (e.g., horizons, etc.), geobodies constructed in 3D, etc. As an
example, holes, fractures, etc., may be constructed in 3D (e.g., as
positive structures, as negative structures, etc.).
[0282] Although only a few examples have been described in detail
above, those skilled in the art will readily appreciate that many
modifications are possible in the examples. Accordingly, all such
modifications are intended to be included within the scope of this
disclosure as defined in the following claims. In the claims,
means-plus-function clauses are intended to cover the structures
described herein as performing the recited function and not only
structural equivalents, but also equivalent structures. Thus,
although a nail and a screw may not be structural equivalents in
that a nail employs a cylindrical surface to secure wooden parts
together, whereas a screw employs a helical surface, in the
environment of fastening wooden parts, a nail and a screw may be
equivalent structures. It is the express intention of the applicant
not to invoke 35 U.S.C. .sctn. 112, paragraph 6 for any limitations
of any of the claims herein, except for those in which the claim
expressly uses the words "means for" together with an associated
function.
* * * * *