U.S. patent application number 13/752505 was filed with the patent office on 2013-06-06 for system and method for performing optimized downhole stimulation operations.
This patent application is currently assigned to SCHLUMBERGER TECHNOLOGY CORPORATION. The applicant listed for this patent is Schlumberger Technology Corporation. Invention is credited to Carlos Abad, Charles-Edouard Cohen, Kevin W. England, Utpal Ganguly, Xiaowei Weng, Wenyue Xu.
Application Number | 20130140031 13/752505 |
Document ID | / |
Family ID | 48523180 |
Filed Date | 2013-06-06 |
United States Patent
Application |
20130140031 |
Kind Code |
A1 |
Cohen; Charles-Edouard ; et
al. |
June 6, 2013 |
SYSTEM AND METHOD FOR PERFORMING OPTIMIZED DOWNHOLE STIMULATION
OPERATIONS
Abstract
A method of performing a stimulation operation for an
unconventional wellsite having natural fractures and hydraulic
fractures. The method involves providing at least one treatment
parameter with a corresponding objective function value and
performing a fracture operation based on the treatment parameter.
The fracture operation involves defining a treatment schedule,
conducting a hydraulic fracture operation, and estimating
production. The objective function value is based on an objective
function. The method also involves modifying the treatment
parameter and performing the fracture operation based on the
modified treatment parameter. The modified treatment parameter has
a corresponding modified objective function value based on the
objective function. The method continues with optimizing the
treatment operation by comparing the objective function value with
the modified objective function value, and repeating the modifying
and optimizing for new modified treatment parameters until
convergence about a desired outcome whereby an optimized parameter
is defined at convergence.
Inventors: |
Cohen; Charles-Edouard;
(Houston, TX) ; Weng; Xiaowei; (Katy, TX) ;
Abad; Carlos; (Richmond, TX) ; England; Kevin W.;
(Houston, TX) ; Ganguly; Utpal; (Sugar Land,
TX) ; Xu; Wenyue; (Sugar Land, TX) |
|
Applicant: |
Name |
City |
State |
Country |
Type |
Schlumberger Technology Corporation; |
Sugar Land |
TX |
US |
|
|
Assignee: |
SCHLUMBERGER TECHNOLOGY
CORPORATION
Sugar Land
TX
|
Family ID: |
48523180 |
Appl. No.: |
13/752505 |
Filed: |
January 29, 2013 |
Related U.S. Patent Documents
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Application
Number |
Filing Date |
Patent Number |
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13338784 |
Dec 28, 2011 |
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13752505 |
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61592371 |
Jan 30, 2012 |
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61460372 |
Dec 30, 2010 |
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61464134 |
Feb 28, 2011 |
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Current U.S.
Class: |
166/308.1 |
Current CPC
Class: |
E21B 43/26 20130101 |
Class at
Publication: |
166/308.1 |
International
Class: |
E21B 43/26 20060101
E21B043/26 |
Claims
1. A method of performing an optimized stimulation operation for a
wellsite having a wellbore extending into a subterranean formation,
the wellsite being an unconventional wellsite having complex
fractures extending through the subterranean formation, the method
comprising: providing at least one treatment parameter with a
corresponding at least one objective function value, the at least
one objective function value based on an objective function;
performing a fracture operation based on the at least one treatment
parameter, the fracture operation comprising defining a treatment
schedule, conducting a hydraulic fracture operation, and estimating
production; modifying the at least one treatment parameter and
performing the fracture operation based on the modified at least
one treatment parameter, the modified at least one treatment
parameter having a corresponding modified at least one objective
function value based on the objective function; optimizing the
treatment operation by comparing the at least one objective
function value with the modified at least one objective function
value; and repeating the modifying and optimizing for at least one
new modified at least one treatment parameter until convergence
about a desired outcome whereby an optimized parameter is defined
at convergence.
2. The method of claim 1, further comprising collecting data about
the wellsite.
3. The method of claim 1, further comprising treating the wellsite
according to the treatment schedule and the fracture operation
corresponding to the optimized parameter.
4. The method of claim 1, further comprising adjusting the
treatment schedule and the fracture operation based on one of new
data, a new desired outcome, at least one new treatment parameter,
at least one new objective function, and combinations thereof.
5. The method of claim 1, wherein the fracture operation comprises
one of hydraulically fracturing the subterranean formation about
the wellbore and simulating hydraulic fracturing.
6. The method of claim 1, wherein the objective function is a
scalar variable.
7. The method of claim 6, wherein the objective function comprises:
f ( x ( t ) , y ) = a . y b + c . x ( t ) . ##EQU00002##
8. The method of claim 1, wherein the convergence occurs where the
desired outcome is one of minimized or maximized.
9. The method of claim 1, wherein the at least one treatment
parameter and the modified at least one treatment parameter
comprises a range of values and wherein the performing, modifying
and optimizing are repeated for the range of values.
10. The method of claim 9, further comprising selecting at least
one value from the range of values based on a relation between the
at least one objective function value and the estimated
production.
11. The method of claim 1, wherein the at least one treatment
parameter and the at least one modified treatment parameter
comprises at least one of proppant parameters, fluid parameters,
financial parameters, treatment parameters, wellsite parameters and
combinations thereof.
12. The method of claim 1, further comprising holding at least one
objective function value constant during the performing, optimizing
and repeating.
13. A method of performing an optimized stimulation operation for a
wellsite having a wellbore extending into a subterranean formation,
the wellsite being an unconventional wellsite having complex
fractures extending through the subterranean formation, the method
comprising: collecting data about the wellsite; providing at least
one treatment parameter with a corresponding at least one objective
function value, the at least one objective function value based on
an objective function; performing a fracture operation based on the
at least one treatment parameter, the fracture operation comprising
defining a treatment schedule, conducting a hydraulic fracture
operation, and estimating production; modifying the at least one
treatment parameter and performing the fracture operation based on
the modified at least one treatment parameter, the modified at
least one treatment parameter having a corresponding modified at
least one objective function value based on the objective function;
optimizing the treatment operation by comparing the at least one
objective function value with the modified at least one objective
function value; and repeating the modifying and optimizing for at
least one new modified at least one treatment parameter until
convergence about a desired outcome whereby an optimized parameter
is defined at convergence. treating the wellsite according to the
treatment schedule and hydraulic fracture operation corresponding
to the optimized parameter.
14. The method of claim 13, wherein the collecting comprises one of
measuring wellsite data, receiving historical data, collecting data
from other wellsites, and combinations thereof.
15. The method of claim 13, further comprising adjusting the
treating based on one of new data, a new desired outcome, at least
one new treatment parameter, at least one new objective function,
and combinations thereof.
16. A method of performing an optimized stimulation operation for a
wellsite having a wellbore extending into a subterranean formation,
the wellsite being an unconventional wellsite having complex
fractures extending through the subterranean formation, the method
comprising: performing reservoir characterization using a reservoir
characterization model to generate a mechanical earth model based
on integrated wellsite data; generating a stimulation plan by
performing well planning, staging design, stimulation design and
production prediction based on the mechanical earth model, the
stimulation design and production prediction, comprising: providing
at least one treatment parameter with a corresponding at least one
objective function value, the at least one objective function value
based on an objective function; performing a fracture operation
based on the at least one treatment parameter, the fracture
operation comprising defining a treatment schedule, conducting a
hydraulic fracture operation, and estimating production; modifying
the at least one treatment parameter and performing the fracture
operation based on the modified at least one treatment parameter,
the modified at least one treatment parameter having a
corresponding modified at least one objective function value based
on the objective function; optimizing the treatment operation by
comparing the at least one objective function value with the
modified at least one objective function value; and repeating the
modifying and optimizing for at least one new modified at least one
treatment parameter until convergence about a desired outcome
whereby an optimized parameter is defined at convergence.
17. The method of claim 16, further comprising measuring at least a
portion of the combination of petrophysical, geomechanical,
geological and geophysical data at the wellsite.
18. The method of claim 16, further comprising optimizing the
stimulation plan based on the optimized parameter.
19. The method of claim 18, further comprising executing the
optimized stimulation plan at the wellsite.
20. The method of claim 19, further comprising measuring real time
data from the wellsite during the executing the optimized
stimulation plan.
21. The method of claim 20, further comprising performing real time
interpretation based on the measured real time data.
22. The method of claim 21, further comprising performing real time
stimulation design and production prediction based on the real time
interpretation.
Description
BACKGROUND
[0001] The present disclosure relates to techniques for performing
oilfield operations. More particularly, the present disclosure
relates to techniques for performing stimulation operations, such
as perforating, injecting, and/or fracturing, a subterranean
formation having at least one reservoir therein. The statements in
this section merely provide background information related to the
present disclosure and may not constitute prior art.
[0002] Oilfield operations may be performed to locate and gather
valuable downhole fluids, such as hydrocarbons. Oilfield operations
may include, for example, surveying, drilling, downhole evaluation,
completion, production, stimulation, and oilfield analysis.
Surveying may involve seismic surveying using, for example, a
seismic truck to send and receive downhole signals. Drilling may
involve advancing a downhole tool into the earth to form a
wellbore. Downhole evaluation may involve deploying a downhole tool
into the wellbore to take downhole measurements and/or to retrieve
downhole samples. Completion may involve cementing and casing a
wellbore in preparation for production. Production may involve
deploying production tubing into the wellbore for transporting
fluids from a reservoir to the surface. Stimulation may involve,
for example, perforating, fracturing, injecting, and/or other
stimulation operations, to facilitate production of fluids from the
reservoir.
[0003] Oilfield analysis may involve, for example, evaluating
information about the wellsite and the various operations, and/or
performing well planning operations. Such information may be, for
example, petrophysical information gathered and/or analyzed by a
petrophysicist; geological information gathered and/or analyzed by
a geologist; or geophysical information gathered and/or analyzed by
a geophysicist. The petrophysical, geological and geophysical
information may be analyzed separately with dataflow therebetween
being disconnected. A human operator may manually move and analyze
the data using multiple software and tools. Well planning may be
used to design oilfield operations based on information gathered
about the wellsite.
SUMMARY
[0004] 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.
[0005] The techniques disclosed herein relate to stimulation
operations involving reservoir characterization using a mechanical
earth model and integrated wellsite data (e.g., petrophysical,
geological, geomechanical, and geophysical data). The stimulation
operations may also involve well planning staging design,
stimulation design and production prediction optimized in a
feedback loop. The stimulation plan may be optimized by performing
the stimulation design and production prediction in a feedback
loop. The optimization may also be performed using the staging and
well planning in the feedback loop. The stimulation plan may be
executed and the stimulation plan optimized in real time. The
stimulation design may be based on staging for unconventional
reservoirs, such as tight gas sand and shale reservoirs.
[0006] In another aspect, one or more embodiments of the present
disclosure relates to optimizing the design of the treatment in
order to maximize the production. Optimizing may include a
simulation workflow going from the simulation of hydraulic
fracturing to production forecast for evaluating the cumulated
production obtained from a particular treatment design. The method
may further comprise modifying the parameters of the treatment to
try to increase the production. Embodiments of the present
disclosure may also relate to proppant selection and fluid
selection in a treatment design and optimization of completion
parameters (such as number of perforation clusters and perforation
cluster spacing and location of the perforation clusters based on
the petrophysical and geomechanical evaluation of the reservoir)
for hydraulic fracturing in shale.
[0007] In yet another aspect, the disclosure relates to a method of
performing a stimulation operation for a wellsite having a wellbore
extending into a subterranean formation. The wellsite is an
unconventional wellsite having natural fractures and hydraulic
fractures extending through the subterranean formation. The method
involves providing at least one treatment parameter with a
corresponding objective function value and performing a fracture
operation based on the treatment parameter. The fracture operation
involves defining a treatment schedule, conducting a hydraulic
fracture operation, and estimating production. The objective
function value is based on an objective function. The method also
involves modifying the treatment parameter and performing the
fracture operation based on the modified treatment parameter. The
modified treatment parameter has a corresponding modified objective
function value based on the objective function. The method
continues with optimizing the treatment operation by comparing the
objective function value with the modified objective function
value, and repeating the modifying and optimizing for new modified
treatment parameters until convergence about a desired outcome
whereby an optimized parameter is defined at convergence. The
method may also involve collecting data about the wellsite,
treating the wellsite and adjusting the treating.
BRIEF DESCRIPTION OF THE DRAWINGS
[0008] Embodiments of the method and system for performing a
downhole stimulation operation are described with reference to the
following figures. Like reference numerals are intended to refer to
similar elements for consistency. For purposes of clarity, not
every component may be labeled in every drawing.
[0009] FIGS. 1.1-1.4 are schematic views illustrating various
oilfield operations at a wellsite.
[0010] FIGS. 2.1-2.4 are schematic views of data collected by the
operations of FIGS. 1.1-1.4.
[0011] FIG. 3.1 is a schematic view of a wellsite illustrating
various downhole stimulation operations.
[0012] FIGS. 3.2-3.4 are schematic views of various fractures of
the wellsite of FIG. 3.1.
[0013] FIG. 4.1 is a schematic flow diagram depicting a downhole
stimulation operation.
[0014] FIGS. 4.2 and 4.3 are schematic diagrams depicting portions
of the downhole stimulation operation.
[0015] FIG. 5.1 is a schematic diagram and FIG. 5.2 is a flow chart
illustrating a method of staging a stimulation operation in a tight
gas sandstone formation.
[0016] FIG. 6 is a schematic diagram depicting a set of logs
combined to form a weighted composite log.
[0017] FIG. 7 is a schematic diagram depicting a reservoir quality
indicator formed from a first and a second log.
[0018] FIG. 8 is a schematic diagram depicting a composite quality
indicator formed from a completion and a reservoir quality
indicator.
[0019] FIG. 9 is a schematic diagram depicting a stage design based
on a stress profile and a composite quality indicator.
[0020] FIG. 10 is a schematic diagram depicting stage boundary
adjustment to enhance the homogeneity of composite quality
indicators.
[0021] FIG. 11 is a schematic diagram depicting stage splitting
based on a composite quality indicator.
[0022] FIG. 12 is a diagram depicting perforation placement based
on a quality indicator.
[0023] FIG. 13 is a flow diagram illustrating a method of staging a
stimulation operation for a shale reservoir.
[0024] FIG. 14 is a flow diagram illustrating a method of
performing a downhole stimulation operation.
[0025] FIG. 15 is a schematic diagram depicting a stimulation
operation at a wellsite.
[0026] FIG. 16 is a flow chart depicting a method of performing an
optimized stimulation operation.
[0027] FIG. 17 is a flow chart depicting iteration of a portion of
the method of FIG. 16.
[0028] FIG. 18 is a schematic diagram depicting an example
parametric study.
[0029] FIG. 19 is a schematic diagram depicting optimization of
proppant mesh.
[0030] FIG. 20 is a schematic diagram depicting optimization of
viscosity.
[0031] FIG. 21 is a schematic diagram depicting optimization of
proppant type.
[0032] FIG. 22 is a schematic diagram depicting optimization of
fluid viscosity and proppant type.
DETAILED DESCRIPTION
[0033] The description that follows includes exemplary systems,
apparatuses, methods, and instruction sequences that embody
techniques of the subject matter herein. However, it is understood
that the described embodiments may be practiced without these
specific details.
[0034] The present disclosure relates to design, implementation and
feedback of stimulation operations performed at a wellsite. The
stimulation operations may be performed using a reservoir centric,
integrated approach. These stimulation operations may involve
integrated stimulation design based on multi-disciplinary
information (e.g., used by a petrophysicist, geologist,
geomechanicist, geophysicist and reservoir engineer), multi-well
applications, and/or multi-stage oilfield operations (e.g.,
completion, stimulation, and production). Some applications may be
tailored to unconventional wellsite applications (e.g., tight gas,
shale, carbonate, coal, etc.), complex wellsite applications (e.g.,
multi-well), and various fracture models (e.g., conventional planar
bi-wing fracture models for sandstone reservoirs or complex network
fracture models for naturally fractured low permeability
reservoirs), and the like. As used herein unconventional reservoirs
relate to reservoirs, such as tight gas, sand, shale, carbonate,
coal, and the like, where the formation is not uniform or is
intersected by natural fractures (all other reservoirs are
considered conventional).
[0035] The stimulation operations may also be performed using
optimization, tailoring for specific types of reservoirs (e.g.,
tight gas, shale, carbonate, coal, etc.), integrating evaluations
criteria (e.g., reservoir and completion criteria), and integrating
data from multiple sources. The stimulation operations may be
performed manually using conventional techniques to separately
analyze dataflow, with separate analysis being disconnected and/or
involving a human operator to manually move data and integrate data
using multiple software and tools. These stimulation operations may
also be integrated, for example, streamlined by maximizing
multi-disciplinary data in an automated or semi-automated
manner.
Oilfield Operations
[0036] FIGS. 1.1-1.4 depict various oilfield operations that may be
performed at a wellsite, and FIGS. 2.1-2.4 depict various
information that may be collected at the wellsite. FIGS. 1.1-1.4
depict simplified, schematic views of a representative oilfield or
wellsite 100 having subsurface formation 102 containing, for
example, reservoir 104 therein and depicting various oilfield
operations being performed on the wellsite 100. FIG. 1.1 depicts a
survey operation being performed by a survey tool, such as seismic
truck 106.1, to measure properties of the subsurface formation. The
survey operation may be a seismic survey operation for producing
sound vibrations. In FIG. 1.1, one such sound vibration 112
generated by a source 110 reflects off a plurality of horizons 114
in an earth formation 116. The sound vibration(s) 112 may be
received in by sensors, such as geophone-receivers 118, situated on
the earth's surface, and the geophones 118 produce electrical
output signals, referred to as data received 120 in FIG. 1.1.
[0037] In response to the received sound vibration(s) 112
representative of different parameters (such as amplitude and/or
frequency) of the sound vibration(s) 112, the geophones 118 may
produce electrical output signals containing data concerning the
subsurface formation. The data received 120 may be provided as
input data to a computer 122.1 of the seismic truck 106.1, and
responsive to the input data, the computer 122.1 may generate a
seismic and microseismic data output 124. The seismic data output
124 may be stored, transmitted or further processed as desired, for
example by data reduction.
[0038] FIG. 1.2 depicts a drilling operation being performed by a
drilling tool 106.2 suspended by a rig 128 and advanced into the
subsurface formations 102 to form a wellbore 136 or other channel.
A mud pit 130 may be used to draw drilling mud into the drilling
tools via flow line 132 for circulating drilling mud through the
drilling tools, up the wellbore 136 and back to the surface. The
drilling mud may be filtered and returned to the mud pit. A
circulating system may be used for storing, controlling or
filtering the flowing drilling muds. In this illustration, the
drilling tools are advanced into the subsurface formations to reach
reservoir 104. Each well may target one or more reservoirs. The
drilling tools may be adapted for measuring downhole properties
using logging while drilling tools. The logging while drilling tool
may also be adapted for taking a core sample 133 as shown, or
removed so that a core sample may be taken using another tool.
[0039] A surface unit 134 may be used to communicate with the
drilling tools and/or offsite operations. The surface unit may
communicate with the drilling tools to send commands to the
drilling tools, and to receive data therefrom. The surface unit may
be provided with computer facilities for receiving, storing,
processing, and/or analyzing data from the operation. The surface
unit may collect data generated during the drilling operation and
produce data output 135 which may be stored or transmitted.
Computer facilities, such as those of the surface unit, may be
positioned at various locations about the wellsite and/or at remote
locations.
[0040] Sensors (S), such as gauges, may be positioned about the
oilfield to collect data relating to various operations as
described previously. As shown, the sensor (S) may be positioned in
one or more locations in the drilling tools and/or at the rig to
measure drilling parameters, such as weight on bit, torque on bit,
pressures, temperatures, flow rates, compositions, rotary speed
and/or other parameters of the operation. Sensors (S) may also be
positioned in one or more locations in the circulating system.
[0041] The data gathered by the sensors may be collected by the
surface unit and/or other data collection sources for analysis or
other processing. The data collected by the sensors may be used
alone or in combination with other data. The data may be collected
in one or more databases and/or transmitted on or offsite. All or
select portions of the data may be selectively used for analyzing
and/or predicting operations of the current and/or other wellbores.
The data may be historical data, real time data or combinations
thereof. The real time data may be used in real time, or stored for
later use. The data may also be combined with historical data or
other inputs for further analysis. The data may be stored in
separate databases, or combined into a single database.
[0042] The collected data may be used to perform analysis, such as
modeling operations. For example, the seismic data output may be
used to perform geological, geophysical, and/or reservoir
engineering analysis. The reservoir, wellbore, surface and/or
processed data may be used to perform reservoir, wellbore,
geological, and geophysical or other simulations. The data outputs
from the operation may be generated directly from the sensors, or
after some preprocessing or modeling. These data outputs may act as
inputs for further analysis.
[0043] The data may be collected and stored at the surface unit
134. One or more surface units may be located at the wellsite, or
connected remotely thereto. The surface unit may be a single unit,
or a complex network of units used to perform the necessary data
management functions throughout the oilfield. The surface unit may
be a manual or automatic system. The surface unit 134 may be
operated and/or adjusted by a user.
[0044] The surface unit may be provided with a transceiver 137 to
allow communications between the surface unit and various portions
of the current oilfield or other locations. The surface unit 134
may also be provided with or functionally connected to one or more
controllers for actuating mechanisms at the wellsite 100. The
surface unit 134 may then send command signals to the oilfield in
response to data received. The surface unit 134 may receive
commands via the transceiver or may itself execute commands to the
controller. A processor may be provided to analyze the data
(locally or remotely), make the decisions and/or actuate the
controller. In this manner, operations may be selectively adjusted
based on the data collected. Portions of the operation, such as
controlling drilling, weight on bit, pump rates or other
parameters, may be optimized based on the information. These
adjustments may be made automatically based on computer protocol,
and/or manually by an operator. In some cases, well plans may be
adjusted to select optimum operating conditions, or to avoid
problems.
[0045] FIG. 1.3 depicts a wireline operation being performed by a
wireline tool 106.3 suspended by the rig 128 and into the wellbore
136 of FIG. 1.2. The wireline tool 106.3 may be adapted for
deployment into a wellbore 136 for generating well logs, performing
downhole tests and/or collecting samples. The wireline tool 106.3
may be used to provide another method and apparatus for performing
a seismic survey operation. The wireline tool 106.3 of FIG. 1.3
may, for example, have an explosive, radioactive, electrical, or
acoustic energy source 144 that sends and/or receives electrical
signals to the surrounding subsurface formations 102 and fluids
therein.
[0046] The wireline tool 106.3 may be operatively connected to, for
example, the geophones 118 and the computer 122.1 of the seismic
truck 106.1 of FIG. 1.1. The wireline tool 106.3 may also provide
data to the surface unit 134. The surface unit 134 may collect data
generated during the wireline operation and produce data output 135
which may be stored or transmitted. The wireline tool 106.3 may be
positioned at various depths in the wellbore to provide a survey or
other information relating to the subsurface formation.
[0047] Sensors (S), such as gauges, may be positioned about the
wellsite 100 to collect data relating to various operations as
described previously. As shown, the sensor (S) is positioned in the
wireline tool 106.3 to measure downhole parameters which relate to,
for example porosity, permeability, fluid composition and/or other
parameters of the operation.
[0048] FIG. 1.4 depicts a production operation being performed by a
production tool 106.4 deployed from a production unit or Christmas
tree 129 and into the completed wellbore 136 of FIG. 1.3 for
drawing fluid from the downhole reservoirs into surface facilities
142. Fluid flows from reservoir 104 through perforations in the
casing (not shown) and into the production tool 106.4 in the
wellbore 136 and to the surface facilities 142 via a gathering
network 146.
[0049] Sensors (S), such as gauges, may be positioned about the
oilfield to collect data relating to various operations as
described previously. As shown, the sensor (S) may be positioned in
the production tool 106.4 or associated equipment, such as the
Christmas tree 129, gathering network, surface facilities and/or
the production facility, to measure fluid parameters, such as fluid
composition, flow rates, pressures, temperatures, and/or other
parameters of the production operation.
[0050] While only simplified wellsite configurations are shown, it
will be appreciated that the oilfield or wellsite 100 may cover a
portion of land, sea and/or water locations that hosts one or more
wellsites. Production may also include injection wells (not shown)
for added recovery or for storage of hydrocarbons, carbon dioxide,
or water, for example. One or more gathering facilities may be
operatively connected to one or more of the wellsites for
selectively collecting downhole fluids from the wellsite(s).
[0051] It should be appreciated that FIGS. 1.2-1.4 depict tools
that can be used to measure not only properties of an oilfield, but
also properties of non-oilfield operations, such as mines,
aquifers, storage, and other subsurface facilities. Also, while
certain data acquisition tools are depicted, it will be appreciated
that various measurement tools (e.g., wireline, measurement while
drilling (MWD), logging while drilling (LWD), core sample, etc.)
capable of sensing parameters, such as seismic two-way travel time,
density, resistivity, production rate, etc., of the subsurface
formation and/or its geological formations may be used. Various
sensors (S) may be located at various positions along the wellbore
and/or the monitoring tools to collect and/or monitor the desired
data. Other sources of data may also be provided from offsite
locations.
[0052] The oilfield configurations of FIGS. 1.1-1.4 depict examples
of a wellsite 100 and various operations usable with the techniques
provided herein. Part, or all, of the oilfield may be on land,
water and/or sea. Also, while a single oilfield measured at a
single location is depicted, reservoir engineering may be utilized
with any combination of one or more oilfields, one or more
processing facilities, and one or more wellsites.
[0053] FIGS. 2.1-2.4 are graphical depictions of examples of data
collected by the tools of FIGS. 1.1-1.4, respectively. FIG. 2.1
depicts a seismic trace 202 of the subsurface formation of FIG. 1.1
taken by seismic truck 106.1. The seismic trace may be used to
provide data, such as a two-way response over a period of time.
FIG. 2.2 depicts a core sample 133 taken by the drilling tools
106.2. The core sample may be used to provide data, such as a graph
of the density, porosity, permeability or other physical property
of the core sample over the length of the core. Tests for density
and viscosity may be performed on the fluids in the core at varying
pressures and temperatures. FIG. 2.3 depicts a well log 204 of the
subsurface formation of FIG. 1.3 taken by the wireline tool 106.3.
The wireline log may provide a resistivity or other measurement of
the formation at various depts. FIG. 2.4 depicts a production
decline curve or graph 206 of fluid flowing through the subsurface
formation of FIG. 1.4 measured at the surface facilities 142. The
production decline curve may provide the production rate Q as a
function of time t.
[0054] The respective graphs of FIGS. 2.1, 2.3, and 2.4 depict
examples of static measurements that may describe or provide
information about the physical characteristics of the formation and
reservoirs contained therein. These measurements may be analyzed to
define properties of the formation(s), to determine the accuracy of
the measurements and/or to check for errors. The plots of each of
the respective measurements may be aligned and scaled for
comparison and verification of the properties.
[0055] FIG. 2.4 depicts an example of a dynamic measurement of the
fluid properties through the wellbore. As the fluid flows through
the wellbore, measurements are taken of fluid properties, such as
flow rates, pressures, composition, etc. As described below, the
static and dynamic measurements may be analyzed and used to
generate models of the subsurface formation to determine
characteristics thereof. Similar measurements may also be used to
measure changes in formation aspects over time.
Stimulation Operations
[0056] FIG. 3.1 depicts stimulation operations performed at
wellsites 300.1 and 300.2. The wellsite 300.1 includes a rig 308.1
having a vertical wellbore 336.1 extending into a formation 302.1.
Wellsite 300.2 includes rig 308.2 having wellbore 336.2 and rig
308.3 having wellbore 336.3 extending therebelow into a
subterranean formation 302.2. While the wellsites 300.1 and 300.2
are shown having specific configurations of rigs with wellbores, it
will be appreciated that one or more rigs with one or more
wellbores may be positioned at one or more wellsites.
[0057] Wellbore 336.1 extends from rig 308.1, through
unconventional reservoirs 304.1-304.3. Wellbores 336.2 and 336.3
extend from rigs 308.2 and 308.3, respectfully to unconventional
reservoir 304.4. As shown, unconventional reservoirs 304.1-304.3
are tight gas sand reservoirs and unconventional reservoir 304.4 is
a shale reservoir. One or more unconventional reservoirs (e.g.,
such as tight gas, shale, carbonate, coal, heavy oil, etc.) and/or
conventional reservoirs may be present in a given formation.
[0058] The stimulation operations of FIG. 3.1 may be performed
alone or in conjunction with other oilfield operations, such as the
oilfield operations of FIGS. 1.1 and 1.4. For example, wellbores
336.1-336.3 may be measured, drilled, tested and produced as shown
in FIGS. 1.1-1.4. Stimulation operations performed at the wellsites
300.1 and 300.2 may involve, for example, perforation, fracturing,
injection, and the like. The stimulation operations may be
performed in conjunction with other oilfield operations, such as
completions and production operations (see, e.g., FIG. 1.4). As
shown in FIG. 3.1, the wellbores 336.1 and 336.2 have been
completed and provided with perforations 338.1-338.5 to facilitate
production.
[0059] Downhole tool 306.1 is positioned in vertical wellbore 336.1
adjacent tight gas sand reservoirs 304.1 for taking downhole
measurements. Packers 307 are positioned in the wellbore 336.1 for
isolating a portion thereof adjacent perforations 338.2. Once the
perforations are formed about the wellbore fluid may be injected
through the perforations and into the formation to create and/or
expand fractures therein to stimulate production from the
reservoirs.
[0060] Reservoir 304.4 of formation 302.2 has been perforated and
packers 307 have been positioned to isolate the wellbore 336.2
about the perforations 338.3-338.5. As shown in the horizontal
wellbore 336.2, packers 307 have been positioned at stages S.sub.t2
and S.sub.t2 of the wellbore. As also depicted, wellbore 304.3 may
be an offset (or pilot) well extended through the formation 302.2
to reach reservoir 304.4. One or more wellbores may be placed at
one or more wellsites. Multiple wellbores may be placed as
desired.
[0061] Fractures may be extended into the various reservoirs
304.1-304.4 for facilitating production of fluids therefrom.
Examples of fractures that may be formed are schematically shown in
FIGS. 3.2 and 3.4 about a wellbore 304. As shown in FIG. 3.2,
natural fractures 340 extend in layers about the wellbore 304.
Perforations (or perforation clusters) 342 may be formed about the
wellbore 304, and fluids 344 and/or fluids mixed with proppant 346
may be injected through the perforations 342. As shown in FIG. 3.3,
hydraulic fracturing may be performed by injecting through the
perforations 342, creating fractures along a maximum stress plane
.sigma..sub.hmax and opening and extending the natural
fractures.
[0062] FIG. 3.4 shows another view of the fracturing operation
about the wellbore 304. In this view, the injected fractures 348
extend radially about the wellbore 304. The injected fractures may
be used to reach the pockets of microseismic events 351 (shown
schematically as dots) about the wellbore 304. The fracture
operation may be used as part of the stimulation operation to
provide pathways for facilitating movement of hydrocarbons to the
wellbore 304 for production.
[0063] Referring back to FIG. 3.1, sensors (S), such as gauges, may
be positioned about the oilfield to collect data relating to
various operations as described previously. Some sensors, such as
geophones, may be positioned about the formations during fracturing
for measuring microseismic waves and performing microseismic
mapping. The data gathered by the sensors may be collected by the
surface unit 334 and/or other data collection sources for analysis
or other processing as previously described (see, e.g., surface
unit 134). As shown, surface unit 334 is linked to a network 352
and other computers 354.
[0064] A stimulation tool 350 may be provided as part of the
surface unit 334 or other portions of the wellsite for performing
stimulation operations. For example, information generated during
one or more of the stimulation operations may be used in well
planning for one or more wells, one or more wellsites and/or one or
more reservoirs. The stimulation tool 350 may be operatively linked
to one or more rigs and/or wellsites, and used to receive data,
process data, send control signals, etc., as will be described
further herein. The stimulation tool 350 may include a reservoir
characterization unit 363 for generating a mechanical earth model
(MEM), a stimulation planning unit 365 for generating stimulation
plans, an optimizer 367 for optimizing the stimulation plans, a
real time unit 369 for optimizing in real time the optimized
stimulation plan, a control unit 368 for selectively adjusting the
stimulation operation based on the real time optimized stimulation
plan, an updater 370 for updating the reservoir characterization
model based on the real time optimized stimulation plan and post
evaluation data, and a calibrator 372 for calibrating the optimized
stimulation plan as will be described further herein. The
stimulation planning unit 365 may include a staging design tool 381
for performing staging design, a stimulation design tool 383 for
performing stimulation design, a production prediction tool 385 for
prediction production and a well planning tool 387 for generating
well plans.
[0065] Wellsite data used in the stimulation operation may range
from, for example, core samples to petrophysical interpretation
based on well logs to three dimensional seismic data (see, e.g.,
FIGS. 2.1-2.4). Stimulation design may employ, for example,
oilfield petrotechnical experts to conduct manual processes to
collate different pieces of information. Integration of the
information may involve manual manipulation of disconnected
workflows and outputs, such as delineation of a reservoir zones,
identification of desired completion zones, estimation of
anticipated hydraulic fracture growth for a given completion
equipment configurations, decision on whether and where to place
another well or a plurality of wells for better stimulation of the
formation, and the like. This stimulation design may also involve
semi-automatic or automatic integration, feedback and control to
facilitate the stimulation operation.
[0066] Stimulation operations for conventional and unconventional
reservoirs may be performed based on knowledge of the reservoir.
Reservoir characterization may be used, for example, in well
planning, identifying optimal target zones for perforation and
staging, design of multiple wells (e.g., spacing and orientation),
and geomechanical models. Stimulation designs may be optimized
based on a resulting production prediction. These stimulation
designs may involve an integrated reservoir centric workflow which
include design, real time (RT), and post treatment evaluation
components. Well completion and stimulation design may be performed
while making use of multi-disciplinary wellbore and reservoir
data.
[0067] FIG. 4.1 is a schematic flow diagram 400 depicting a
stimulation operation, such as those shown in FIG. 3.1. The flow
diagram 400 is an iterative process that uses integrated
information and analysis to design, implement and update a
stimulation operation. The method involves pre-treatment evaluation
445, a stimulation planning 447, real time treatment optimization
451, and design/model update 453. Part or all of the flow diagram
400 may be iterated to adjust stimulation operations and/or design
additional stimulation operations in existing or additional
wells.
[0068] The pre-stimulation evaluation 445 involves reservoir
characterization 460 and generating a three-dimensional mechanical
earth model (MEM) 462. The reservoir characterization 460 may be
generated by integrating information, such as the information
gathered in FIGS. 1.1-1.4, to perform modeling using united
combinations of information from historically independent technical
regimes or disciplines (e.g., petrophysicist, geologist,
geomechanic and geophysicist, and previous fracture treatment
results). Such reservoir characterization 460 may be generated
using integrated static modeling techniques to generate the MEM 462
as described, for example, in US Patent Application Nos.
2009/0187391 and 2011/0660572. By way of example, software, such as
PETREL.TM., VISAGE.TM., TECHLOG.TM., and GEOFRAME.TM. commercially
available from SCHLUMBERGER.TM., may be used to perform the
pre-treatment evaluation 445.
[0069] Reservoir characterization 460 may involve capturing a
variety of information, such as data associated with the
underground formation and developing one or more models of the
reservoir. The information captured may include, for example,
stimulation information, such as reservoir (pay) zone,
geomechanical (stress) zone, and natural fracture distribution. The
reservoir characterization 460 may be performed such that
information concerning the stimulation operation is included in
pre-stimulation evaluations. Generating the MEM 462 may simulate
the subterranean formation under development (e.g., generating a
numerical representation of a state of stress and rock mechanical
properties for a given stratigraphic section in an oilfield or
basin).
[0070] Conventional geomechanical modeling may be used to generate
the MEM 462. Examples of MEM techniques are provided in US Patent
Application No. 2009/0187391. The MEM 462 may be generated by
information gathered using, for example, the oilfield operations of
FIGS. 1.1-1.4, 2.1-2.4 and 3. For example, the 3D MEM may take into
account various reservoir data collected beforehand, including the
seismic data collected during early exploration of the formation
and logging data collected from the drilling of one or more
exploration wells before production (see, e.g., FIGS. 1.1-1.4). The
MEM 462 may be used to provide, for example, geomechanical
information for various oilfield operations, such as casing point
selection, optimizing the number of casing strings, drilling stable
wellbores, designing completions, performing fracture stimulation,
etc.
[0071] The generated MEM 462 may be used as an input in performing
stimulation planning 447. The 3D MEM may be constructed to identify
potential drilling wellsites. In one embodiment, when the formation
is substantially uniform and is substantially free of major natural
fractures and/or high-stress barriers, it can be assumed that a
given volume of fracturing fluid pumped at a given rate over a
given period of time will generate a substantially identical
fracture network in the formation. Core samples, such as those
shown in FIGS. 1.2 and 2.2 may provide information useful in
analyzing fracture properties of the formation. For regions of the
reservoir manifesting similar properties, multiple wells (or
branches) can be placed at a substantially equal distance from one
another and the entire formation may be sufficiently
stimulated.
[0072] The stimulation planning 447 may involve well planning 465,
staging design 466, stimulation design, 468 and production
prediction 470. In particular, the MEM 462 may be an input to the
well planning 465 and/or the staging design 466 and stimulation
design 468. Some embodiments may include semi-automated methods to
identify, for example, well spacing and orientation, multistage
perforation design and hydraulic fracture design. To address a wide
variation of characteristics in hydrocarbon reservoirs, some
embodiments may involve dedicated methods per target reservoir
environments, such as, but not limited to, tight gas formations,
sandstone reservoirs, naturally fractured shale reservoirs, or
other unconventional reservoirs.
[0073] The stimulation planning 447 may involve a semi-automated
method used to identify potential drilling wellsites by
partitioning underground formations into multiple set of discrete
intervals, characterizing each interval based on information such
as the formation's geophysical properties and its proximity to
natural fractures, then regrouping multiple intervals into one or
multiple drilling wellsites, with each wellsite receiving a well or
a branch of a well. The spacing and orientation of the multiple
wells may be determined and used in optimizing production of the
reservoir. Characteristics of each well may be analyzed for stage
planning and stimulation planning. In some cases, a completion
advisor may be provided, for example, for analyzing vertical or
near vertical wells in tight-gas sandstone reservoir following a
recursive refinement workflow.
[0074] Well planning 465 may be performed to design oilfield
operations in advance of performing such oilfield operations at the
wellsite. The well planning 465 may be used to define, for example,
equipment and operating parameters for performing the oilfield
operations. Some such operating parameters may include, for
example, perforating locations, operating pressures, stimulation
fluids, and other parameters used in stimulation. Information
gathered from various sources, such as historical data, known data,
and oilfield measurements (e.g., those taken in FIGS. 1.1-1.4), may
be used in designing a well plan. In some cases, modeling may be
used to analyze data used in forming a well plan. The well plan
generated in the stimulation planning may receive inputs from the
staging design 466, stimulation design 468, and production
prediction 470 so that information relating to and/or affecting
stimulation is evaluated in the well plan.
[0075] The well planning 465 and/or MEM 462 may also be used as
inputs into the staging design 466. Reservoir and other data may be
used in the staging design 466 to define certain operational
parameters for stimulation. For example, staging design 466 may
involve defining boundaries in a wellbore for performing
stimulation operations as described further herein.
[0076] Examples of staging design are described in US Patent
Application No. 2011/0247824. Staging design may be an input for
performing stimulation design 468.
[0077] Stimulation design defines various stimulation parameters
(e.g., perforation placement) for performing stimulation
operations. The stimulation design 468 may be used, for example,
for fracture modeling. Examples of fracture modeling are described
in US Patent Application No. 2008/0183451, 2006/0015310 and PCT
Publication No. WO2011/077227. Stimulation design may involve using
various models to define a stimulation plan and/or a stimulation
portion of a well plan.
[0078] Stimulation design may integrate 3D reservoir models
(formation models), which can be a result of seismic
interpretation, drilling geo-steering interpretation, geological or
geomechanical earth model, as a starting point (zone model) for
completion design. For some stimulation designs, a fracture
modeling algorithm may be used to read a 3D MEM and run forward
modeling to predict fracture growth. This process may be used so
that spatial heterogeneity of a complex reservoir may be taken into
account in stimulation operations. Additionally, some methods may
incorporate spatial X-Y-Z sets of data to derive an indicator, and
then use the indicator to place and/or perform a wellbore
operation, and in some instance, multiple stages of wellbore
operations as will be described further herein.
[0079] Stimulation design may use 3D reservoir models for providing
information about natural fractures in the model. The natural
fracture information may be used, for example, to address certain
situations, such as cases where a hydraulically induced fracture
grows and encounters a natural fracture (see, e.g., FIGS. 3.2-3.4).
In such cases, the fracture can continue growing into the same
direction and divert along the natural fracture plane or stop,
depending on the incident angle and other reservoir geomechanical
properties. This data may provide insights into, for example, the
reservoir dimensions and structures, pay zone location and
boundaries, maximum and minimum stress levels at various locations
of the formation, and the existence and distribution of natural
fractures in the formation. As a result of this simulation,
nonplanar (i.e. networked) fractures or discrete network fractures
may be formed. Some workflows may integrate these predicted
fracture models in a single 3D canvas where microseismic events are
overlaid (see, e.g., FIG. 3.4). This information may be used in
fracture design and/or calibrations.
[0080] Microseismic mapping may also be used in stimulation design
to understand complex fracture growth. The occurrence of complex
fracture growth may be present in unconventional reservoirs, such
as shale reservoirs. The nature and degree of fracture complexity
may be analyzed to select an optimal stimulation design and
completion strategy. Fracture modeling may be used to predict the
fracture geometry that can be calibrated and the design optimized
based on real time Microseismic mapping and evaluation. Fracture
growth may be interpreted based on existing hydraulic fracture
models. Some complex hydraulic fracture propagation modeling and/or
interpretation may also be performed for unconventional reservoirs
(e.g., tight gas sand and shale) as will be described further
herein. Reservoir properties, and initial modeling assumptions may
be corrected and fracture design optimized based on microseismic
evaluation.
[0081] Examples of complex fracture modeling are provided in SPE
paper 140185, the entire content of which is hereby incorporated by
reference. This complex fracture modeling illustrates the
application of two complex fracture modeling techniques in
conjunction with microseismic mapping to characterize fracture
complexity and evaluate completion performance. The first complex
fracture modeling technique is an analytical model for estimating
fracture complexity and distances between orthogonal fractures. The
second technique uses a gridded numerical model that allows complex
geologic descriptions and evaluation of complex fracture
propagation. These examples illustrate how embodiments may be
utilized to evaluate how fracture complexity is impacted by changes
in fracture treatment design in each geologic environment. To
quantify the impact of changes in fracture design using complex
fracture models despite inherent uncertainties in the MEM and
"real" fracture growth, microseismic mapping and complex fracture
modeling may be integrated for interpretation of the microseismic
measurements while also calibrating the complex stimulation model.
Such examples show that the degree of fracture complexity can vary
depending on geologic conditions.
[0082] Production prediction 470 may involve estimating production
based on the well planning 465, staging design 466 and stimulation
design 468. The result of stimulation design 468 (i.e. simulated
fracture models and input reservoir model) can be carried over to a
production prediction workflow, where a conventional analytical or
numerical reservoir simulator may operate on the models and
predicts hydrocarbon production based on dynamic data. The
preproduction prediction 470 can be useful, for example, for
quantitatively validating the stimulation planning 447 process.
[0083] Part or all of the stimulation planning 447 may be
iteratively performed as indicated by the flow arrows. As shown,
optimizations may be provided after the staging design 466,
stimulation design 468, and production prediction 470, and may be
used as a feedback to optimize 472 the well planning 465, the
staging design 466 and/or the stimulation design 468. The
optimizations may be selectively performed to feedback results from
part or all of the stimulation planning 447 and iterate as desired
into the various portions of the stimulation planning process and
achieve an optimized result. The stimulation planning 447 may be
manually carried out, or integrated using automated optimization
processing as schematically shown by the optimization 472 in
feedback loop 473.
[0084] FIG. 4.2 schematically depicts a portion of the stimulation
planning operation 447. As shown in this figure, the staging design
446, stimulation design 468 and production prediction 470 may be
iterated in the feedback loop 473 and optimized 472 to generate an
optimized result 480, such as an optimized stimulation plan. This
iterative method allows the inputs and results generated by the
staging design 466 and stimulation design 468 to `learn from each
other` and iterate with the production prediction for optimization
therebetween.
[0085] Various portions of the stimulation operation may be
designed and/or optimized. Examples of optimizing fracturing are
described, for example, in U.S. Pat. No. 6,508,307. In another
example, financial inputs, such as fracture costs which may affect
operations, may also be provided in the stimulation planning 447.
Optimization may be performed by optimizing stage design with
respect to production while taking into consideration financial
inputs. Such financial inputs may involve costs for various
stimulation operations at various stages in the wellbore as
depicted in FIG. 4.3.
[0086] FIG. 4.3 depicts a staging operation at various intervals
and related net present values associated therewith. As shown in
FIG. 4.3, various staging designs 455.1 and 455.2 may be considered
in view of a net present value plot 457. The net present value plot
457 is a graph plotting mean post-tax net present value (y-axis)
versus standard deviation of net present value (x-axis). The
various staging designs may be selected based on the financial
analysis of the net present value plot 457. Techniques for
optimizing fracture design involving financial information, such as
net present value are described, for example, in U.S. Pat. No.
7,908,230, the entire contents of which are hereby incorporated by
reference. Various techniques, such as, Monte Carlo simulations may
be performed in the analysis.
[0087] Referring back to FIG. 4.1, various optional features may be
included in the stimulation planning 447. For example, a multi-well
planning advisor may be used to determine if it is necessary to
construct multiple wells in a formation. If multiple wells are to
be formed, the multi-well planning advisor may provide the spacing
and orientation of the multiple wells, as well as the best
locations within each for perforating and treating the formation.
As used herein, the term "multiple wells" may refer to multiple
wells each being independently drilled from the surface of the
earth to the subterranean formation; the term "multiple wells" may
also refer to multiple branches kicked off from a single well that
is drilled from the surface of the earth (see, e.g., FIG. 3.1). The
orientation of the wells and branches can be vertical, horizontal,
or anywhere in between.
[0088] When multiple wells are planned or drilled, simulations can
be repeated for each well so that each well has a staging plan,
perforation plan, and/or stimulation plan. Thereafter, multi-well
planning can be adjusted if necessary. For example, if a fracture
stimulation in one well indicates that a stimulation result will
overlap a nearby well with a planned perforation zone, the nearby
well and/or the planned perforation zone in the nearby well can be
eliminated or redesigned. On the contrary, if a simulated fracture
treatment cannot penetrate a particular area of the formation,
either because the pay zone is simply too far away for a first
fracture well to effectively stimulate the pay zone or because the
existence of a natural fracture or high-stress barrier prevents the
first fracture well from effectively stimulating the pay zone, a
second well/branch or a new perforation zone may be included to
provide access to the untreated area. The 3D reservoir model may
take into account simulation models and indicate a candidate
location to drill a second well/branch or to add an additional
perforation zone. A spatial X'-Y'-Z' location may be provided for
the oilfield operator's ease of handling.
Post Planning Stimulation Operations
[0089] Embodiments may also include real time treatment
optimization (or post job workflows) 451 for analyzing the
stimulation operation and updating the stimulation plan during
actual stimulation operations. The real time treatment optimization
451 may be performed during implementation of the stimulation plan
at the wellsite (e.g., performing fracturing, injecting or
otherwise stimulating the reservoir at the wellsite). The real time
treatment optimization may involve calibration tests 449, executing
448 the stimulation plan generated in stimulation planning 447, and
real time oilfield stimulation 455.
[0090] Calibration tests 449 may optionally be performed by
comparing the result of stimulation planning 447 (i.e. simulated
fracture models) with the observed data. Some embodiments may
integrate calibration into the stimulation planning process,
perform calibrations after stimulation planning, and/or apply
calibrations in real-time execution of stimulation or any other
treatment processes. Examples of calibrations for fracture or other
stimulation operations are described in US Patent Application No.
2011/0257944, the entire contents of which are hereby incorporated
by reference.
[0091] Based on the stimulation plan generated in the stimulation
planning 447 (and calibration 449 if performed), the oilfield
stimulation 445 may be executed 448. Oilfield stimulation 455 may
involve real time measurement 461, real time interpretation 463,
real time stimulation design 465, real time production 467 and real
time control 469. Real time measurement 461 may be performed at the
wellsite using, for example, the sensors S as shown in FIG. 3.1.
Observed data may be generated using real time measurements 461.
Observation from a stimulation treatment well, such as bottomhole
and surface pressures, may be used for calibrating models
(traditional pressure match workflow). In addition, microseismic
monitoring technology may be included as well. Such spatial/time
observation data may be compared with the predicted fracture
model.
[0092] Real time interpretation 463 may be performed on or off site
based on the data collected. Real time stimulation design 465 and
production prediction 467 may be performed similar to the
stimulation design 468 and production prediction 470, but based on
additional information generated during the actual oilfield
stimulation 455 performed at the wellsite. Optimization 471 may be
provided to iterate over the real time stimulation design 465 and
production prediction 467 as the oilfield stimulation progresses.
Real time stimulation 455 may involve, for example, real time
fracturing. Examples of real time fracturing are described in US
Patent Application No. 2010/0307755, the entire contents of which
are hereby incorporated by reference.
[0093] Real time control 469 may be provided to adjust the
stimulation operation at the wellsite as information is gathered
and an understanding of the operating conditions is gained. The
real time control 469 provides a feedback loop for executing 448
the oilfield stimulation 455. Real time control 469 may be
executed, for example, using the surface unit 334 and/or downhole
tools 306.1-306.4 to alter operating conditions, such as
perforation locations, injection pressures, etc. While the features
of the oilfield stimulation 455 are described as operating in real
time, one or more of the features of the real time treatment
optimization 451 may be performed in real time or as desired.
[0094] The information generated during the real time treatment
optimization 451 may be used to update the process and feedback to
the reservoir characterization 445. The design/model update 453
includes post treatment evaluation 475 and update model 477. The
post treatment evaluation involves analyzing the results of the
real time treatment optimization 451 and adjusting, as necessary,
inputs and plans for use in other wellsites or wellbore
applications.
[0095] The post treatment evaluation 475 may be used as an input to
update the model 477. Optionally, data collected from subsequent
drilling and/or production can be fed back to the reservoir
characterization 445 (e.g., the 3D earth model) and/or stimulation
planning 447 (e.g., well planning module 465). Information may be
updated to remove errors in the initial modeling and simulation, to
correct deficiencies in the initial modeling, and/or to
substantiate the simulation. For example, spacing or orientation of
the wells may be adjusted to account the newly developed data. Once
the model is updated 477, the process may be repeated as desired.
One or more wellsites, wellbores, stimulation operations or
variations may be performed using the method 400.
[0096] In a given example, a stimulation operation may be performed
by constructing a 3D model of a subterranean formation and
performing a semi-automated method involving dividing the
subterranean formation into a plurality of discrete intervals,
characterizing each interval based on the subterranean formation's
properties at the interval, grouping the intervals into one or more
drilling sites, and drilling a well in each drilling site.
Tight Gas Sand Applications
[0097] An example stimulation design and downstream workflow useful
for unconventional reservoirs involving tight gas sandstone (see,
e.g., reservoirs 304.1-304.3 of FIG. 3.1) are provided. For tight
gas sandstone reservoir workflow, a conventional stimulation (i.e.
hydraulic fracturing) design method may be used, such as a single
or multi-layer planar fracture model.
[0098] FIGS. 5A and 5B depict examples of staging involving a tight
gas sand reservoir. A multi-stage completion advisor may be
provided for reservoir planning for tight gas sandstone reservoirs
where a plurality of thin layers of hydrocarbon rich zones (e.g.,
reservoirs 304.1-304.3 of FIG. 3.1) may be scattered or dispersed
over a large portion of the formation adjacent the wellbore (e.g.,
336.1). A model may be used to develop a near wellbore zone model,
where key characteristics, such as reservoir (pay) zone and
geomechanical (stress) zone, may be captured.
[0099] FIG. 5A shows a log 500 of a portion of a wellbore (e.g.,
the wellbore 336.1 of FIG. 3.1). The log may be a graph of
measurements, such as resistivity, permeability, porosity, or other
reservoir parameters logged along the wellbore. In some cases, as
shown in FIG. 6, multiple logs 600.1, 600.2 and 600.3 may be
combined into a combined log 601 for use in the method 501. The
combined log 601 may be based on a weighted linear combination of
multiple logs, and corresponding input cutoffs may be weighted
accordingly.
[0100] The log 500 (or 601) may correlate to a method 501 involving
analyzing the log 500 to define (569) boundaries 568 at intervals
along the log 500 based on the data provided. The boundaries 568
may be used to identify (571) pay zones 570 along the wellbore. A
fracture unit 572 may be specified (573) along the wellbore.
Staging design may be performed (575) to define stages 574 along
the wellbore. Finally, perforations 576 may be designed (577) along
locations in the stages 574.
[0101] A semi-automated method may be used to identify partitioning
of a treatment interval into multiple sets of discrete intervals
(multi-stages) and to compute a configuration of perforation
placements, based on these inputs. Reservoir (petrophysical)
information and completion (geomechanical) information may be
factored into the model, simultaneously. Zone boundaries may be
determined based on input logs. Stress logs may be used to define
the zones. One can choose any other input log or a combination of
logs which represents the reservoir formation.
[0102] Reservoir pay zones can be imported from an external (e.g.,
petrophysical interpretation) workflow. The workflow may provide a
pay zone identification method based on multiple log cutoffs. In
the latter case, each input log value (i.e. default logs) may
include water saturation (Sw), porosity (Phi), intrinsic
permeability (Kint) and volume of clay (Vcl), but other suitable
logs can be used. Log values may be discriminated by their cutoff
values. If all cutoff conditions are met, corresponding depth may
be marked as a pay zone. Minimum thickness of a pay zone, KH
(permeability multiplied by zone height) and PPGR (pore pressure
gradient) cutoff conditions may be applied to eliminate poor pay
zones at the end. These pay zones may be inserted into the stress
based zone model. The minimum thickness condition may be examined
to avoid creation of tiny zones. The pay zones may also be selected
and the stress based boundary merged therein. In another
embodiment, 3D zone models provided by the reservoir modeling
process may be used as the base boundaries and the output zones,
finer zones, may be inserted.
[0103] For each identified pay zones, a simple fracture height
growth estimation computation based on a net pressure or a bottom
hole treating pressure may be performed, and the overlapping pays
combined to form a fracture unit (FracUnit). Stimulation stages may
be defined based on one or more of the following conditions:
minimum net height, maximum gross height and minimum distance
between stages.
[0104] The set of FracUnits may be scanned, and possible
combinations of consecutive FracUnits examined. Certain
combinations that violate certain conditions may be selectively
excluded. Valid combinations identified may act as staging
scenarios. A maximum gross height (=stage length) may be varied,
and combinatory checks run repeatedly for each of the variations.
Frequently occurring staging scenarios may be counted from a
collection of all outputs to determine final answers. In some
cases, no `output` may be found because no single staging design
may be ascertained that meets all conditions. In such case, the
user can specify the priorities among input conditions. For
example, maximum gross height may be met, and minimum distance
between stages may be ignored to find the optimum solution.
[0105] Perforation locations, shot density and number of shots, may
be defined based on a quality of pay zone if the stress variations
within a stage are insignificant. If the stress variations are
high, a limited entry method may be conducted to determine
distribution of shots among fracture units. A user can optionally
choose to use a limited entry method (e.g., stage by stage) if
desired. Within each FracUnit, a location of perforation may be
determined by a selected KH (permeability multiplied by perforation
length).
[0106] A multi-stage completion advisor may be used for reservoir
planning for a gas shale reservoir. Where a majority of producing
wells are essentially horizontally drilled (or drilled deviated
from a vertical borehole) an entire lateral section of a borehole
may reside within a target reservoir formation (see, e.g.,
reservoir 304.4 of FIG. 1). In such cases, variability of reservoir
properties and completion properties may be evaluated separately.
The treatment interval may be partitioned into a set of contiguous
intervals (multi-stages). The partitioning may be done such that
both reservoir and completion properties are similar within each
stage to ensure the result (completion design) offers maximum
coverage of reservoir contacts.
[0107] In a given example, stimulation operations may be performed
utilizing a partially automated method to identify best multistage
perforation design in a wellbore. A near wellbore zone model may be
developed based upon key characteristics, such as reservoir pay
zone and geomechanical stress zone. A treatment interval may be
partitioned into multiple set of discrete intervals, and a
configuration of perforation placement in the wellbore may be
computed. A stimulation design workflow including single or
multi-layer planar fracture models may be utilized.
Shale Applications
[0108] FIGS. 7-12 depict staging for an unconventional application
involving a gas shale reservoir (e.g., reservoir 304.4 in FIG.
3.1). FIG. 13 depicts a corresponding method 1300 for staging
stimulation of a shale reservoir. For gas shale reservoirs, a
description of naturally fractured reservoirs may be utilized.
Natural fractures may be modeled as a set of planar geometric
objects, known as discrete fracture networks (see, e.g., FIGS.
3.2-3.4). Input natural fracture data may be combined with the 3D
reservoir model to account for heterogeneity of shale reservoirs
and network fracture models (as opposed to planar fracture model).
This information may be applied to predict hydraulic fracture
progressions.
[0109] A completion advisor for a horizontal well penetrating
formations of shale reservoirs is illustrated in FIGS. 7 through
12. The completions advisor may generate a multi-stage stimulation
design, comprising a contiguous set of staging intervals and a
consecutive set of stages. Additional inputs, such as fault zones
or any other interval information may also be included in the
stimulation design to avoid placing stages.
[0110] FIGS. 7-9 depict the creation of a composite quality
indicator for a shale reservoir. The reservoir quality and
completion quality along the lateral segment of borehole may be
evaluated. A reservoir quality indicator may include, for example,
various requirements or specifications, such as total organic
carbon (TOC) greater than or equal to about 3%, gas in place (GIP)
greater than about 100 scf/ft.sup.3, Kerogen greater than high,
shale porosity greater than about 4%, and relative permeability to
gas (Kgas) greater than about 100 nD. A completions quality
indicator may include, for example, various requirements or
specifications, such as stress that is `-low`, resistivity that is
greater than about 15 Ohm-m, clay that is less than 40%, Young's
modulus (YM) is greater than about 2.times.10.sup.6 psi, Poisson's
ratio (PR) is less than about 0.2, neutron porosity is less than
about 35% and density porosity is greater than about 8%.
[0111] FIG. 7 schematically depicts a combination of logs 700.1 and
700.2. The logs 700.1 and 700.2 may be combined to generate a
reservoir quality indicator 701. The logs may be reservoir logs,
such as permeability, resistivity, and porosity logs from the
wellbore. The logs have been adjusted to a square format for
evaluation. The quality indicator may be separated (1344) into
regions based on a comparison of logs 700.1 and 700.2, and
classified under a binary log as Good (G) and Bad (B) intervals.
For a borehole in consideration, any interval where all reservoir
quality conditions are met may be marked as Good, and everywhere
else set as Bad.
[0112] Other quality indicators, such as a completions quality
indicator, may be formed in a similar manner using applicable logs
(e.g., Young's modulus, Poisson's ration, etc. for a completions
log). Quality indicators, such as reservoir quality 802 and
completion quality 801 may be combined (1346) to form a composite
quality indicator 803 as shown in FIG. 8.
[0113] FIGS. 9-11 depict stage definition for the shale reservoir.
A composite quality indicator 901 (which may be the composite
quality indicator 803 of FIG. 8) is combined (1348) with a stress
log 903 segmented into stress blocks by a stress gradient
differences. The result is a combined stress & composite
quality indicator 904 separated into GB, GG, BB and BG
classifications at intervals. Stages may be defined along the
quality indicator 904 by using the stress gradient log 903 to
determine boundaries. A preliminary set of stage boundaries 907 are
determined at the locations where the stress gradient difference is
greater than a certain value (e.g., a default may be 0.15 psi/ft).
This process may generate a set of homogeneous stress blocks along
the combined stress and quality indicator.
[0114] Stress blocks may be adjusted to a desired size of blocks.
For example, small stress blocks may be eliminated where an
interval is less than a minimum stage length by merging it with an
adjacent block to form a refined composite quality indicator 902.
One of two neighboring blocks which has a smaller stress gradient
difference may be used as a merging target. In another example,
large stress blocks may be split where an interval is more than a
maximum stage length to form another refined composite quality
indicator 905.
[0115] As shown in FIG. 10, a large block 1010 may be split (1354)
into multiple blocks 1012 to form stages A and B where an interval
is greater than a maximum stage length. After the split, a refined
composite quality indicator 1017 may be formed, and then split into
a non-BB composite quality indicator 1019 with stages A and B. In
some cases as shown in FIG. 10, grouping large `BB` blocks with
non-`BB` blocks, such as `GG` blocks, within a same stage, may be
avoided.
[0116] If a `BB` block is large enough as in the quality indicator
1021, then the quality indicator may be shifted (1356) into its own
stage as shown in the shifted quality indicator 1023. Additional
constraints, such as hole deviation, natural and/or induced
fracture presence, may be checked to make stage characteristics
homogeneous.
[0117] As shown in FIG. 11, the process in FIG. 10 may be applied
for generating a quality indicator 1017 and splitting into blocks
1012 shown as stages A and B. BB blocks may be identified in a
quality indicator 1117, and split into a shifted quality indicator
1119 having three stages A, B and C. As shown by FIGS. 10 and 11,
various numbers of stages may be generated as desired.
[0118] As shown in FIG. 12, perforation clusters (or perforations)
1231 may be positioned (1358) based on stage classification results
and the composite quality indicator 1233. In shale completion
design, the perforations may be placed evenly (in equal distance,
e.g., every 75 ft (22.86 m)). Perforations close to the stage
boundary (for example 50 ft (15.24 m)) may be avoided. The
composite quality indicator may be examined at each perforation
location. Perforation in `BB` blocks may be moved adjacent to the
closest `GG`, `GB` or `BG` block as indicated by a horizontal
arrow. If a perforation falls in a `BG` block, further fine grain
GG, GB, BG, BB reclassification may be done and the perforation
placed in an interval that does not contain a BB.
[0119] Stress balancing may be performed to locate where the stress
gradient values are similar (e.g. within 0.05 psi/ft) within a
stage. For example, if the user input is 3 perforations per stage,
a best (i.e. lowest stress gradient) location which meets
conditions (e.g., where spacing between perforations and are within
the range of stress gradient) may be searched. If not located, the
search may continue for the next best location and repeated until
it finds, for example, three locations to put three
perforations.
[0120] If a formation is not uniform or is intersected by major
natural fractures and/or high-stress barriers, additional well
planning may be needed. In one embodiment, the underground
formation may be divided into multiple sets of discrete volumes and
each volume may be characterized based on information such as the
formation's geophysical properties and its proximity to natural
fractures. For each factor, an indicator such as "G" (Good), "B"
(Bad), or "N" (Neutral) can be assigned to the volume. Multiple
factors can then be synthesized together to form a composite
indicator, such as "GG", "GB", "GN", and so on. A volume with
multiple "B"s indicates a location may be less likely to be
penetrated by fracture stimulations. A volume with one or more "G"s
may indicate a location that is more likely to be treatable by
fracture stimulations. Multiple volumes can be grouped into one or
more drilling wellsites, with each wellsite representing a
potential location for receiving a well or a branch. The spacing
and orientation of multiple wells can be optimized to provide an
entire formation with sufficient stimulation. The process may be
repeated as desired.
[0121] While FIGS. 5A-6 and FIGS. 7-12 each depict specific
techniques for staging, various portions of the staging may
optionally be combined. Depending on the wellsite, variations in
staging design may be applied.
[0122] FIG. 14 is a flow diagram illustrating a method (1400) of
performing a stimulation operation. The method involves obtaining
(1460) petrophysical, geological and geophysical data about the
wellsite, performing (1462) reservoir characterization using a
reservoir characterization model to generate a mechanical earth
model based on integrated petrophysical, geological and geophysical
data (see, e.g., pre-stimulation planning 445). The method further
involves generating (1466) a stimulation plan based on the
generated mechanical earth model. The generating (1466) may
involve, for example, well planning, 465, staging design, 466,
stimulation design 468, production prediction 470 and optimization
472 in the stimulation planning 447 of FIG. 4. The stimulation plan
is then optimized (1464) by repeating (1462) in a continuous
feedback loop until an optimized stimulation plan is generated.
[0123] The method may also involve performing (1468) a calibration
of the optimized stimulation plan (e.g., 449 of FIG. 4). The method
may also involve executing (1470) the stimulation plan, measuring
(1472) real time data during execution of the stimulation plan,
performing real time stimulation design and production prediction
(1474) based on the real time data, optimizing in real time (1475)
the optimized stimulation plan by repeating the real time
stimulation design and production prediction until a real time
optimized stimulation plan is generated, and controlling (1476) the
stimulation operation based on the real time optimized stimulation
plan. The method may also involve evaluating (1478) the stimulation
plan after completing the stimulation plan and updating (1480) the
reservoir characterization model (see, e.g., design/model updating
453 of FIG. 4). The steps may be performed in various orders and
repeated as desired.
Optimizing Production Using Parametric Analysis
[0124] The present disclosure also relates to a method of
performing an optimized stimulation operation. The method may be
used, for example, as part or all (or in combination with) the
stimulation design 468, production prediction 470 and optimizing
472 of the stimulation operation 400 of FIG. 4.1. The method
involves optimizing stimulation by iteratively defining a treatment
schedule and a hydraulic fracture operation, and estimating
production by varying a select treatment parameter over time.
Results are compared between objective function values
corresponding to the treatment parameters for multiple treatment
parameters and repeated until convergence. At least one optimized
parameter is defined at convergence. A well plan may be generated
and the wellsite may be treated according to the treatment schedule
and the hydraulic fracture operation corresponding to the converged
treatment parameter.
[0125] A `conventional` wellsite as used herein refers to a
wellsite having a reservoir with natural fractures that do not
interact with man-made (or hydraulic) fractures. As used herein, an
`unconventional` wellsite is a wellsite having a reservoir with
interacting fractures natural and/or man-made fractures. An
unconventional reservoir may be, for example, tight gas, shale,
carbonate, coal, heavy oil, etc. (see, e.g., FIGS. 304.1-304.4).
One or more unconventional reservoirs and/or conventional
reservoirs may be present in a given formation. Complex fractures
refer to fractures that are man-made or a combination of natural
and man-made. The complex fractures may include, for example,
bi-wing, separate, interlocking, propagated and/or other fracture
configurations. The man-made fractures may be created, for example,
by hydraulic fracturing.
[0126] FIG. 15 depicts an example environment in which embodiments
of the present disclosure may be performed. The environment
includes wellsite 1500 having a wellbore 1504 extending from a
wellhead 1508 at a surface location and through a subterranean
formation 1502 therebelow. A fracture network 1506 extends about
the wellbore 1504. A pump system 1529 is positioned about the
wellhead 1508 for passing fluid through tubing 1542.
[0127] The pump system 1529 is depicted as being operated by a
controller, such as a field operator 1527 or a surface system, for
recording maintenance and operational data and/or performing
maintenance in accordance with a prescribed maintenance plan. The
controller may provide manual and/or automatic control of part or
all of the wellsite operations. The controller may also collect
data from the wellsite from various sensors located thereabout for
measuring wellsite and/or treatment parameters. The pumping system
1529 pumps fluid from the surface to the wellbore 1504.
[0128] The pump system 1529 includes a plurality of water tanks
1531, which feed water to a gel hydration unit 1533. The gel
hydration unit 1533 combines water from the tanks 1531 with a
gelling agent to form a gel. The gel is then sent to a blender 1535
where it is mixed with a proppant from a proppant transport 1537 to
form a fracturing fluid. The gelling agent may be used to increase
the viscosity of the fracturing fluid, and to allow the proppant to
be suspended in the fracturing fluid. It may also act as a friction
reducing agent to allow higher pump rates with less frictional
pressure.
[0129] The fracturing fluid is then pumped from the blender 1535 to
the treatment trucks 1520 with plunger pumps as shown by solid
lines 1543. Each treatment truck 1520 receives the fracturing fluid
at a low pressure and discharges it to a common manifold 1539
(sometimes called a missile trailer or missile) at a high pressure
as shown by dashed lines 1541. The missile 1539 then directs the
fracturing fluid from the treatment trucks 1520 to the wellbore
1504 as shown by solid line 1515. One or more treatment trucks 1520
may be used to supply fracturing fluid at a desired rate.
[0130] Each treatment truck 1520 may be normally operated at any
rate, such as well under its maximum operating capacity. Operating
the treatment trucks 1520 under their operating capacity may allow
for one to fail and the remaining to be run at a higher speed in
order to make up for the absence of the failed pump. A computerized
control system may be employed to direct the entire pump system
1529 during the fracturing operation.
[0131] Various fluids, such as conventional stimulation fluids with
proppants, may be used to create fractures. Other fluids, such as
viscous gels, "slick water" (which may have a friction reducer
(polymer) and water) may also be used to hydraulically fracture
various conventional or unconventional wells. Such "slick water"
may be in the form of a thin fluid (e.g., nearly the same viscosity
as water) and may be used to create more complex fractures, such as
multiple micro-seismic fractures detectable by monitoring.
[0132] As also shown in FIG. 15, the fracture network includes
fractures located at various positions around the wellbore 1504.
The various fractures may be natural fractures 1544 present before
injection of the fluids, or hydraulic fractures 1546 generated
about the formation 1502 during injection.
[0133] Optimization of stimulation operations may involve an
understanding of parameters relating to the stimulation operations
in view of desired outcomes, such as increased production.
Production from unconventional wellsites, such as those with shale
gas reservoirs, may depend on the efficiency of treatments, such as
hydraulic fracturing treatments. The economics of shale gas
reservoirs can be challenging, for example, in cases of high
completion cost and an uncertainty on a production rate that tends
to decline rapidly. Nevertheless, cumulated experience in the
industry has in recent years constantly improved the production
from shale gas reservoirs, and practices in treatment design have
been identified for multistage fracturing of horizontal wells.
[0134] Further developments in treatment design may require a
deeper understanding of the complex physics involved in both
hydraulic fracturing and production, such as stress shadow,
proppant transport and interaction with pre-existing natural
fractures. Currently, dedicated new simulation workflows, from
fracturing to production, may provide an understanding of the
complex relation between parameters of a treatment design and their
impact on production.
[0135] A sensitivity analysis on several parameters of a treatment
design may be used to determine the properties of the reservoir and
their impact on the final cumulated production. Treatment
parameters that may affect production may include, for example,
proppant parameters, fluid parameters, financial parameters,
fracture parameters, and wellsite parameters. Proppant parameters
may include, for example, proppant type, volume of proppant, mix of
proppant type (e.g., injection of several proppants at the same
time, Injection of several proppants separately at different
times), volume of fluid system (e.g., predefined association of
fluid type, proppant type, additives, and specific pumping
technique (i.e., cyclical variations in pressure pumping at
surface)), proppant concentration, variation of proppant
concentration, etc. Fluid parameters may include, for example,
fluid type (e.g., fluid composition, fluid concentration, types of
additives), mix of fluid type, volume of fluid, pumping rate, flow
back strategy, etc.
[0136] Financial parameters may include, for example, material
costs, budget limitations, net present value, revenue, etc. See,
for example, the financial parameters discussed with respect to
FIG. 4.3. Fracture parameters may include, for example, number and
location of fracturing treatment stages in a wellbore, number and
location of perforation clusters in each stage, number of
perforations in each cluster, perforation parameters such as
phasing, perforation diameter, Simultaneous or alternate fracturing
operations in neighboring wellbores, etc. Wellsite parameters may
include, for example, conventional vs. unconventional formations,
etc. Other parameters as discussed herein may also be used.
[0137] The relations between various treatment parameters and
production may be shown with a simulation workflow that combines a
hydraulic fracturing simulator for shale reservoir with a
semi-analytical production model dedicated to fractured gas
reservoir. The relation between the production and various
treatment parameters, such as proppant selection, proppant volume,
proppant concentration, fluid selection, and pumping schedule, may
be determined.
[0138] Simulations may be used to confirm some of the best
practices, but also explain the underlying reasons. The detailed
analysis of the simulation results may be used to reveal the
relation between parameters, such as proppant transport in a
complex fracture network, and other parameters, such as propped
length, propped conductivity and cumulated production. Simulations
may also be used to highlight the influence of the reservoir
parameters, such as permeability, stress anisotropy and density of
natural fractures. The conclusions from the sensitivity analysis
may be used to provide guidelines for optimized fracturing
treatment.
[0139] Parameters may be selected based on, for example, a relation
between the treatment design and production. The cumulated
production may be given by a simulation workflow from fracturing to
production. Embodiments may also include selection of certain
treatment parameters. For example, proppant selection and fluid
selection may be used in a treatment design and optimization of
completion parameters for hydraulic fracturing operations. In an
example involving a simulation workflow, an optimized set of
treatment parameters may be used to define a treatment design for
hydraulic fracturing in fractured reservoir, such as shale, so that
production is maximized and costs are minimized.
[0140] In some cases, financial parameters, such as the economic
return on investment (NPV), may dictate the optimum treatment
design. For example, at some point in the iteration of treatment
design (COST) and production results (REVENUE) there will be point
where the increase in COST is not offset by the increase in
production (REVENUE) and therefore would be the best (or optimized)
economic result for the given well under the given circumstances.
Methods to achieve this goal may involve, for example, a parametric
(or sensitivity analysis) study of these parameters, or an
iterative inversion method that searches for the best parameter
that minimizes an objective function considering cost and cumulated
production. Ad-hoc, means of effectively performing the parametric
analysis can be used, such as orthogonal and non-orthogonal
experimental design methods. Proposed treatment designs may involve
time dependent changes to the parameters of choice resulting in
optimized treatment pumping schedules.
[0141] FIG. 16 depicts an example method 1600 for performing an
optimized stimulation operation 1600. In the example provided, the
method is performed on an unconventional wellsite with complex
fractures, such as the wellsite 1500 of FIG. 15. While the method
is described with respect to unconventional wellsites, the method
may also be used with conventional or unconventional wellsites with
natural fractures, bi-wing, separate, integral and/or other
fracture arrangements.
[0142] The method involves collecting 1602 data at the wellsite.
The wellsite data may relate to one or more of the treatment
parameters. The wellsite data may be data measured at the wellsite,
for example, as described in FIGS. 1.1-2.4, or collected from
databases, other wellsites, historical data, client data, etc. In
another example, data may be collected from sensors disposed about
the simulation environment of FIG. 15. Data may be collected for
real time and/or subsequent use.
[0143] The method also involves providing 1604 at least one
treatment parameter with a corresponding objective function value.
One or more of the treatment parameters may be used separately or
in combination. Initially, one or more of the treatment parameters
may be selected. The treatment parameters may be selected for a
range of values. Each treatment parameter has a corresponding
objective function value.
[0144] The objective function value may be a value generated for
each parameter based on an objective function. The objective
function value is based on an objective function. The objective
function may define a relationship between the parameters and a
desired outcome. The objective function may be a scalar variable
based on a selected outcome. The definition of the objective
function can vary and be more complex. For example, where the
objective function represents market price, such price may include
the impact of inflation, or prices of hydrocarbons (oil and gas)
varying in time. An algorithm may be defined for the objective
function as will be described further herein.
[0145] The method 1600 continues by performing 1606 a fracture
operation based on the treatment parameter(s). The fracture
operation involves defining a treatment schedule, conducting a
hydraulic fracture operation, and estimating production as will be
described further herein.
[0146] The method 1600 continues by modifying 1608 the treatment
parameter and the corresponding objective function value and
performing a fracture operation based on the modified at least one
treatment parameter. The modifying may use the same process as the
performing 1606 and provide a modified treatment parameter having a
modified objective function value based on the objective function.
Optimizing 1610 may then be performed for the treatment operation
by comparing the objective function value with the modified
objective function value.
[0147] The modifying and optimizing may be iteratively repeated
1612 for new modified treatment parameter until convergence about a
desired outcome. The optimizing may be performed for a range of
values, parameters and/or objective functions to generate multiple
outcomes. An optimized parameter may be selected at convergence.
Thus, the optimized parameter may be defined based on a
consideration of a variety of parameters over a number of ranges
based on given desired outcomes. The optimized parameter may yield
the optimized overall result, such as optimized production at a
minimum cost given a variety of parameters, such as proppant type,
fluid viscosities, etc.
[0148] The method 1600 may also involve treating 1614 the wellsite
according to the treatment schedule and hydraulic fracture
operation corresponding to the converged objective function. A well
plan may be defined based on the optimized parameter(s).
Optionally, new data, a new desired outcome, at least one new
treatment parameter, at least one new objective function and other
new information may be considered and the treating adjusted
accordingly. Adjustments may be made in real time, or as
appropriate. The method may be repeated using part or all of the
new information. Additional action may be taken, such as part or
all of the methods herein. For example, post planning as described
herein may also be performed.
[0149] The method 1600 may be performed partially or wholly
automatically or manually. The method 1600 may be implemented at
the wellsite, for example, at the controller (e.g., operator 1527),
or from an offsite location. The method 1600 may also be used in
combination with conventional methods, such as those involving
fracture length.
[0150] FIG. 17 depicts a portion of the method 1600 depicting the
performing 1606 and optimizing 1608 of FIG. 16 in greater detail.
As shown in this figure, the method 1600 employs an iterative
inversion method. At least a portion of the performing 1606 may be
performed using a simulator, such as FRACADE.TM., UFM.TM. and
UPM.TM. commercially available from SCHLUMBERGER TECHNOLOGY
CORPORATION.TM. (see: www.slb.com).
[0151] The performing 1606 involves defining 1716 a treatment
schedule, conducting 1718 a hydraulic fracture operation at the
wellsite, and estimating 1720 production based on the treatment
schedule and the hydraulic fracture operation. The treatment
schedule defines a portion of the well plan relating to the
stimulation. Treatment schedules define the equipment, materials
and overall plan for performing a treatment operation, such as the
operation of FIG. 15. FIG. 15 is an example of a hydraulic fracture
operation that may be used to form the man-made and/or complex
fractures as depicted. The hydraulic operation may also involve
simulating the hydraulic fracturing using a simulator, such as
UFM.TM.. The production may be estimated based on the hydraulic
fracturing using, for example, a production simulator, such as
UPM.TM..
[0152] The hydraulic operation may involve, for example,
hydraulically fracturing the wellsite as shown in FIG. 15 and/or
simulating hydraulic fracturing.
[0153] The performing 1606 may be conducted iteratively using
multiple parameters as shown by loop 1721. The performing 1606 is
done in combination with the optimizing 1608 as depicted. The
optimizing involves collecting 1722 the objective function values
for each of the parameters based on an objective function. The
objective functions for each of the parameters are tested 1724 for
convergence. The testing 1724 for convergence may involve comparing
the objective functions to determine if the objective function
achieves the desired outcome (e.g., minimum cost, maximum
production, etc.)
[0154] If convergence is achieved, then an optimized parameter is
generated 1726 at convergence. If not, then a new parameter is
selected and the process repeated for the new parameter and its
corresponding new objective function value. The repeating may be
performed by iteratively selecting a new parameter for comparison
with one or more of the previous parameters and/or one or more new
objective function values for comparison with one or more of the
previous objective function values.
[0155] The methods of FIGS. 16 and 17 may involve iteratively
modifying the initial treatment parameter (or input parameter) in
order to optimize (e.g., minimize) the objective function. The
optimization can be done by using different optimization
algorithms, such as gradient based methods. Example methods may
include simplex, complex, Gauss-Newton, Levenberg-Marquardt, and
the like.
[0156] The objective function may be used to account for selected
conditions, such as the production and the economics of the
completion and treatment. For example, the objective function may
consider the conditions in such a way that the smaller the value of
the objective function, the better the design of the treatment. For
example, if the objective functions may be minimized when the
cumulated production at a given time increases and the cost of the
fracturing decreases, the objective function (f) could have the
following form:
f ( x ( t ) , y ) = a . y b + c . x ( t ) ( Equation 1 )
##EQU00001##
with x being the cumulated production after a given time t; y
representing the cost of the operation (completion and
stimulation); a, b and c being positive constants to be defined by
the user; and c being defined based on the market price of the
hydrocarbon produced. Equation provides an example objective
function that may be used. A corresponding objective function value
may be generated for each parameter using the objective function.
These objective function values may be compared in the optimizing
(see, e.g., 1612 of FIGS. 16 and 1724 of FIG. 17).
[0157] The loop 1721 may begin with an initial guess P.sub.i of the
parameter to be considered. As the method iterates 1721, a modified
parameter P.sub.i+1 is generated. The objective function value
V.sub.i of the initial guess P, is compared with the objective
function value V.sub.i+1 of the modified parameter P.sub.i+1 and
tested for convergence 1724. If the comparison determines that the
objective function meets certain criteria (e.g., minimized,
maximized, or near within a desired threshold), then an optimized
parameter P.sub.opt is generated 1726 from the converged objective
function. If not, a new parameter P.sub.i+n of the objective
function may be selected 1728, and the method repeated for the new
value. The method may continue until convergence is achieved.
[0158] In a given example, if the parameter P is the concentration
of proppant to be injected during the treatment, the initial guess
P.sub.i may be a value that is considered as a standard practice,
like 1 ppa. The optimization algorithm then modifies this parameter
P.sub.i+i, and performs the operation 1606. The optimization 1608
is performed using the objective function to analyze if the
modification of the parameter P.sub.i+1 minimizes or maximizes the
objective function, when compared to the value obtained with the
initial guess P.sub.i. Depending on this analysis, the algorithm
considers a new guess P.sub.i+n for the concentration before
re-starting the loop. By successive iterations the algorithm will
converge toward a value that produces the lowest (or optimized)
value from the objective function. This example considers a single
parameter, but it can be extended to more parameters.
[0159] FIG. 18 shows an example of a parametric study 1800 of
iterating the method 1600 for multiple parameters. The idea of a
parametric study is to first run a certain number of simulations
with a specific parameter varying in a way that describes the
relation between the production and the parameter investigated.
Then an optimum choice or value for this parameter may be chosen
based on this analysis. For example, the optimum for a particular
parameter at this stage may be a method to address more of the
"weighting factor" for such a parameter when holding the other
input data parameters constant.
[0160] In some cases, the impact of a plurality of parameters may
be evaluated simultaneously. For example, where a multi-parameter
simulation is performed in order to determine the most optimum
overall result. Such multi-parameter simulation will likely involve
simultaneously solving multiple parameters with multiple sets of
equations. A technique, such as optimized design of experiments,
can be used to evaluate the parameter space most effectively.
[0161] As shown in FIG. 18, the parametric study 1800 involves
defining 1 through n number of treatment designs 1850. The
different simulation cases are defined in such a way that the
parametric space of the investigated parameter is properly covered.
For example, if the parameter to be investigated is a proppant
concentration that can vary between 0 and 3 ppa, at least seven
simulation cases with different proppant concentration being equal
to, for example, 0, 0.5, 1, 1.5, 2, 2.5 and 3 ppa, or the like, may
be considered.
[0162] The parametric study 1800 also involves performing 1 through
n number of production simulations 1852. Production simulations
1852 are performed for each treatment design. The simulation
workflow to these simulation cases are applied, and the resulting
production output collected.
[0163] An analysis 1854 of the results of the treatment design 1850
and production simulations 1852 may be performed. The analysis 1854
of the results may be used to provide the operator with the
relation between the parameter and the production, and based on
this analysis the operator can select the simulation case that
provides the best (e.g., most profitable) production. As for the
automated algorithm, the choice of the optimum cases can be made
not only on the production data, but some other parameters, such as
cost can also be included.
[0164] For example, a parametric study may be used to identify a
proppant type ratio in a particular treatment. In the following
example, the simulation is used to find the best volume ratio in a
pumping schedule between two pre-selected proppant type (80/100
mesh and 40/70 mesh). The invention may not be limited to the
particular ratio described in this example, and that a number of
ratios may be available. In this example, this parametric study is
made of a series of nine simulations, in which the treatments and
the reservoirs are identical except for the mix of proppant type.
These simulations vary in their volume ratio of proppant type
between 80/100 mesh sand and 40/70 mesh. Therefore, nine
simulations were run in this case, starting with a case with only
900 Mgal of slurry made of 80/100 mesh sand at 1 ppa. The second
case adds 100 Mgal of slurry made of 40/70 mesh sand at 1 ppa at
the end of the treatment, and decreases the previous stage of
80/100 mesh sand from 900 to 800 Mgal. Therefore the total volume
of proppant is similar in the two cases.
[0165] The other cases are defined similarly, for example, by
increasing the volume of 40/70 mesh sand and decreasing the volume
of 80/100 mesh sand. The final stage is made of only 900 Mgal of
40/70 mesh sand. The method to define the simulation cases is aimed
at covering evenly the parametric space of the ratio between the
two types of proppants. Once all the simulation cases are defined,
the simulation workflow is applied to each of them. The analysis of
the simulations indicates the proppant ratio that gives the best
cumulated production.
[0166] FIGS. 19-22 depict various examples implementing the
optimization methods. These figures illustrate examples of various
parameters that are considered in order to optimize production. As
illustrated in these figures one or more parameters over a range of
values may be considered during optimization. The methods may also
be used to consider one or more desired outcomes, such as
production as depicted and/or other outcomes (e.g., NPV).
[0167] FIG. 19 shows an example parametric study 1900. This figure
illustrates a parametric study on the impact on production of
proppant ratio between three meshes 1955.1-1955.3 (between 80/100
mesh and 40/70 mesh). The three meshes 1955.1-1955.3 generate
production curves 1957.1-1957.3, respectively. The production
curves 1957.1-1957.3 are used to generate a plot 1959 of production
(y-axis) (MMcsf) versus days (x-axis). In this figure, normalized
cumulated production curves 1956.1-1956.3 are generated for three
different times (1 year, 3 years and 10 years) and compared for all
nine simulations. Maximum production for each curve 1956.1-1956.3
is generated in the region 1957.
[0168] In another example shown in FIG. 20, fluid viscosity may be
optimized in accordance with the present disclosure. FIG. 20
provides a production plot 2000 depicting production (y-axis)
versus time (x-axis) for various viscosities. Curves 2062.1-2062.4
are depicted for viscosities 1 cp, 5 cp, 10 cp and 50 cp,
respectively. This example compares similar treatment designs
except for the viscosity of the fluid.
[0169] The viscosity (defined by the Newtonian rheological model)
from 1 cp, 5 cp, 10 cp and 50 cp is modified. After applying the
simulation workflow with these treatments, the different cumulated
production after 1000 days of production is compared. The results
shown in the plot 2000 illustrate that the best production is
obtained with the fluid of 1 cp.
[0170] In yet another example shown in FIG. 21, proppant type may
be optimized in accordance with the present disclosure hereof. FIG.
21 provides a production plot 2100 depicting production (y-axis)
versus time (x-axis) for various types of proppants. Curves
2162.1-2162.4 are depicted for four different proppants,
respectively.
[0171] This example is a comparison of several treatments mixing
600,000 gallons of slurry made of 40/70 mesh sand followed by
300,000 gallons of slurry made of another type of proppant (235/270
mesh sand, 80/100 mesh sand, 30/50 mesh sand and 20/40 mesh sand).
After applying the simulation workflow on the different treatments,
this parametric study indicates that the best production is
obtained with the mix of 80/100 mesh sand and 40/70 mesh sand.
[0172] FIG. 22 provides another example of optimization involving a
combination of parameters. This figure depicts a plot 2200 of
viscosity (left y-axis) and cumulative production verses (right
y-axis) and cumulative production (x-axis) and proppant diameter
(x-axis). The darkened region 2268 indicates a region of maximum
production.
[0173] In this example, the optimization approach is similar to the
previous examples. In this version, multiple simulation cases may
be used to visualize multiple parameters. FIG. 22 shows proppant
size used in a pumping schedule containing only one proppant type
and one fluid type. The fluid is assumed to be a Newtonian fluid
and the viscosity may be replaced by the concentration of a gelling
agent and a crosslinker (e.g., slickwater) added to the base
fluid.
[0174] In this case, 28 simulation cases were run to correspond to
a matrix made of four different proppant sizes (e.g., 890/100,
40/70, 30/50 and 20/40 mesh sand) and seven different fracturing
fluid viscosities (e.g., 1, 2, 5, 10, 20, 50, 100 cp). The color
scale depicts cumulated production after three years as may be
predicted, for example, by the simulation 1606 of FIG. 16. Optimum
production is achieved with a combination of proppant size 30/50
mesh sand with 5 cp viscosity fracturing fluid. Thus, both fluid
and proppant type may be optimized in combination. The combination
may be used to account for interdependence between parameters and
their impact on production.
[0175] The methods as performed herein may be performed in any
order and repeated as desired to achieve any desired output. In
some cases certain portions of the method may be performed
simultaneously, sequentially, repeatedly and/or in combination.
Various of the methods may use or be used in combination with other
methods, such as methods described in US Patent Publication No.
2012/0185225, US Patent Publication No. 2012/0179444, or US Patent
Publication No. 2008/0183451, the entire contents of which are
hereby incorporated by reference.
[0176] Although only a few example embodiments have been described
in detail above, those skilled in the art will readily appreciate
that many modifications are possible in the example embodiments
without materially departing from this invention. 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.
[0177] In a given example, a stimulation operation may be performed
involving evaluating variability of reservoir properties and
completion properties separately for a treatment interval in a
wellbore penetrating a subterranean formation, partitioning the
treatment interval into a set of contiguous intervals (both
reservoir and completion properties may be similar within each
partitioned treatment interval, designing a stimulation treatment
scenario by using a set of planar geometric objects (discrete
fracture network) to develop a 3D reservoir model, and combining
natural fracture data with the 3D reservoir model to account
heterogeneity of formation and predict hydraulic fracture
progressions.
* * * * *
References