Note: Descriptions are shown in the official language in which they were submitted.
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SHALE GAS PRODUCTION FORECASTING
BACKGROUND
[0001] Exploration and development of formations such as shale gas
formations continue to gain interest. Various examples of technologies,
techniques,
etc. described herein pertain to, for example, for exploration, development,
production, etc. of formations.
SUMMARY
[0002] A method can include providing data for at least one shale gas
formation; performing a statistical analysis on the data for each of the at
least one
shale gas formation; providing a simulation model; history matching the
simulation
model for each of the at least one shale gas formation based at least in part
on the
performed statistical analysis to generate a history matched model for each of
the at
least one shale gas formation; and forecasting production for another shale
gas
formation by plugging in data for the other shale gas formation into each
generated
history matched model. One or more computer-readable storage media can include
computer-executable instructions to instruct a computing system to: access
data for
at least one shale gas formation; perform a statistical analysis on the data
for each of
the at least one shale gas formation; provide a simulation model; history
match the
simulation model for each of the at least one shale gas formations based at
least in
part on the performed statistical analysis to generate a history matched model
for
each of the at least one shale gas formations; and forecast production for
another
shale gas formation by plugging in data for the other shale gas formation into
each
generated history matched model. A system can include one or more processors;
memory; and instructions stored in the memory and executable by at least one
of the
one or more processors to instruct the system to access data for at least one
formation that has produced hydrocarbons; perform a statistical analysis on
the data
for each of the at least one formation; provide a model; history match the
model for
each of the at least one formation based at least in part on the performed
statistical
analysis to generate a history matched model for each of the at least one
formation;
and forecast production of hydrocarbons for another formation by plugging in
data for
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the other formation into each generated history matched model. Various other
apparatuses, systems, methods, etc., are also disclosed.
[0002a] According to one aspect of the present invention, there is
provided a
method comprising: identifying shale gas formations having production data;
for each
shale gas formation of at least some of the shale gas formations: using
equipment to
acquire measurements from the shale gas formation, the measurements
representing
characteristics of the shale gas formation, wherein the characteristics
include at least
one of: reservoir pressure, net thickness, horizontal section of a well,
number of
fracturing stages per well; performing a statistical analysis on data for each
of the
shale gas formations, wherein at least some of the data is based on the
measurements; providing a simulation model; history matching the simulation
model
for each of the shale gas formations based at least in part on the performed
statistical
analysis to generate a history matched model for each of the shale gas
formations,
wherein the history matching comprises performing a sensitivity analysis to
assist
with selection of parameters for history matching, wherein the selection of
parameters
comprises selecting parameters to which production of a corresponding shale
gas
formation is sensitive based at least in part on a parameter sensitivity
ranking;
forecasting production for another shale gas formation by plugging in data for
the
other shale gas formation into each generated history matched model to
generate
results for the other shale gas formation from each of the generated history
matched
models; and controlling at least one piece of equipment based at least in part
on the
results, the controlling also including incorporating feedback about a
geological
environment.
[0002b] According to another aspect of the present invention, there is
provided
one or more non-transitory computer-readable storage media comprising computer-
executable instructions that, when executed, cause a computing system to:
identify
shale gas formations having production data; for each shale gas formation of
at least
some of the shale gas formations: receive measurements that were acquired from
the
shale gas formation using equipment, the measurements representing
characteristics
of the shale gas formation, wherein the characteristics include at least one
of:
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reservoir pressure, net thickness, horizontal section of a well, number of
fracturing
stages per well; perform a statistical analysis on data for each of the shale
gas
formations, wherein at least some of the data is based on the measurements;
provide
a simulation model; history match the simulation model for each of the shale
gas
formations based at least in part on the performed statistical analysis to
generate a
history matched model for each of the shale gas formations, wherein the
history
match performs a sensitivity analysis to assist with selection of parameters
for history
matching, wherein the selection of parameters comprises selecting parameters
to
which production of a corresponding shale gas formation is sensitive based at
least in
part on a parameter sensitivity ranking; forecast production for another shale
gas
formation by plugging in data for the other shale gas formation into each
generated
history matched model to generate results for the other shale gas formation
from
each of the generated history matched models; and control at least one piece
of
equipment based at least in part on the results and based on feedback about a
geological environment.
[0002c] According to still another aspect of the present invention, there
is
provided a system comprising: one or more processors; memory; and instructions
stored in the memory and executable by at least one of the one or more
processors
to instruct the system to: identify formations that have produced
hydrocarbons; for
each formation of at least some of the formations: receive measurements that
were
acquired from the formation using equipment, the measurements representing
characteristics of the formation, wherein the characteristics include at least
one of:
reservoir pressure, net thickness, horizontal section of a well, number of
fracturing
stages per well; perform a statistical analysis on data for each of the
formations,
wherein at least some of the data is based on the measurements; provide a
model;
history match the model for each of the formations based at least in part on
the
performed statistical analysis to generate a history matched model for each of
the
formations, wherein the history match performs a sensitivity analysis to
assist with
selection of parameters for history matching, wherein the selection of
parameters
comprises selecting parameters to which production of a corresponding shale
gas
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formation is sensitive based at least in part on a parameter sensitivity
ranking;
forecast production of hydrocarbons for another formation by plugging in data
for the
other formation into each generated history matched model to generate results
for the
other shale gas formation from each of the generated history matched models;
and
control at least one piece of equipment based at least in part on the results
and
based on feedback about a geological environment.
[0003] This summary is provided to introduce a selection of concepts that
are
further described below in the detailed description. This summary is not
intended to
identify key or essential features of the claimed subject matter, nor is it
intended to be
used as an aid in limiting the scope of the claimed subject matter.
BRIEF DESCRIPTION OF THE DRAWINGS
[0004] Features and advantages of the described implementations can be
more readily understood by reference to the following description taken in
conjunction
with the accompanying drawings.
[0005] Fig. 1 illustrates an example system that includes various
components
for modeling a geologic environment;
[0006] Fig. 2 illustrates an example of a method and an example of a
system;
[0007] Fig. 3 illustrates an example of a method;
[0008] Fig. 4 illustrates an example of a model;
[0009] Fig. 5 illustrates an example of a method;
[0010] Figs. 6, 7 and 8 illustrates an example of a method;
[0011] Fig. 9 illustrates examples of probabilities;
[0012] Fig. 10 illustrates examples of Estimated Ultimate Recoveries
(EURs);
[0013] Fig. 11 illustrates examples of parameters and examples of
sensitivities
for examples of parameters;
[0014] Fig. 12 illustrates examples simulation results; and
[0015] Fig. 13 illustrates example components of a system and a networked
system.
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DETAILED DESCRIPTION
[0016] The following description includes the best mode presently
contemplated for practicing the described implementations. This description is
not to
be taken in a limiting sense, but rather is made merely for the purpose of
describing
the general principles of the implementations. The scope of the described
implementations should be ascertained with reference to the issued claims.
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[0017] Geologic formations include rock, which may be characterized by, for
example, porosity values and by permeability values. Porosity may be defined
as a
percentage of volume occupied by pores, void space, volume within rock that
can
include fluid, etc. Permeability may be defined as an ability to transmit
fluid,
measurement of an ability to transmit fluid, etc.
[0018] The term "effective porosity" may refer to interconnected pore
volume
in rock, for example, that may contribute to fluid flow in a formation. As
effective
porosity aims to exclude isolated pores, effective porosity may be less than
total
porosity. As an example, a shale formation may have relatively high total
porosity
yet relatively low permeability due to how shale is structured within the
formation.
[0019] As an example, shale may be formed by consolidation of clay- and
silt-
sized particles into thin, relatively impermeable layers. In such an example,
the
layers may be laterally extensive and form caprock. Caprock may be defined as
relatively impermeable rock that forms a barrier or seal with respect to
reservoir rock
such that fluid does not readily migrate beyond the reservoir rock. As an
example,
the permeability of caprock capable of retaining fluids through geologic time
may be
of the order of about 10-6 to about 10-8 D (darcies).
[0020] The term "shale" may refer to one or more types of shales that may
be
characterized, for example, based on lithology, etc. In shale gas formations,
gas
storage and flow may be related to combinations of different geophysical
processes.
For example, regarding storage, natural gas may be stored as compressed gas in
pores and fractures, as adsorbed gas (e.g., adsorbed onto organic matter), and
as
soluble gas in solid organic materials.
[0021] Gas migration and production processes in gas shale sediments can
occur, for example, at different physical scales. As an example, production in
a
newly drilled wellbore may be via large pores through a fracture network and
then
later in time via smaller pores. As an example, during reservoir depletion,
thermodynamic equilibrium among kerogen, clay and the gas phase in pores can
change, for example, where gas begins to desorb from kerogen exposed to a pore
network.
[0022] Sedimentary organic matter tends to have a high sorption capacity
for
hydrocarbons (e.g., adsorption and absorption processes). Such capacity may
depend on factors such as, for example, organic matter type, thermal maturity
(e.g.,
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high maturity may improve retention) and organic matter chemical composition.
As
an example, a model may characterize a formation such that a higher total
organic
content corresponds to a higher sorption capacity.
[0023] With respect to a shale formation that includes hydrocarbons (e.g.,
a
hydrocarbon reservoir), its hydrocarbon producing potential may depend on
various
factors such as, for example, thickness and extent, organic content, thermal
maturity,
depth and pressure, fluid saturations, permeability, etc. As an example, a
shale
formation that includes gas (e.g., a gas reservoir) may include nanodarcy
matrix
permeability (e.g., of the order of le D) and narrow, calcite-sealed natural
fractures.
In such an example, technologies such as stimulation treatment may be applied
in
an effort to produce gas from the shale formation, for example, to create new,
artificial fractures, to stimulate existing natural fractures (e.g.,
reactivate calcite-
sealed natural fractures), etc.
[0024] Shale may vary by, for example, one or more of mineralogical
characteristics, formation grain sizes, organic contents, rock fissility, etc.
Attention to
such factors may aid in designing an appropriate stimulation treatment. For
example, an evaluation process may include well construction (e.g., drilling
one or
more vertical, horizontal or deviated wells), sample analysis (e.g., for
geomechanical
and geochemical properties), open-hole logs (e.g., petrophysical log models)
and
post-fracture evaluation (e.g., production logs). Effectiveness of a
stimulation
treatment (e.g., treatments, stages of treatments, etc., may determine flow
mechanism(s), well performance results, etc.
[0025] As an example, a stimulation treatment may include pumping fluid
into
a formation via a wellbore at pressure and rate sufficient to cause a fracture
to open.
Such a fracture may be vertical and include wings that extend away from the
wellbore, for example, in opposing directions according to natural stresses
within the
formation. As an example, proppant (e.g., sand, etc.) may be mixed with
treatment
fluid to deposit the proppant in the generated fractures in an effort to
maintain
fracture width over at least a portion of a generated fracture. For example, a
generated fracture may have a length of about 500 ft extending from a wellbore
where proppant maintains a desirable fracture width over about the first 250
ft of the
generated fracture.
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[0026] In a stimulated shale gas formation, fracturing may be applied over
a
region deemed a "drainage area" (e.g., consider at least one well with at
least one
artificial fracture), for example, according to a development plan. In such a
formation, gas pressure (e.g., within the formation's "matrix") may be higher
than in
generated fractures of the drainage area such that gas flows from the matrix
to the
generated fractures and onto a wellbore. During production of the gas, gas
pressure
in a drainage area tends to decrease (e.g., decreasing the driving force for
fluid flow,
for example, per Darcy's law, Navier-Stokes equations, etc.). As an example,
gas
production from a drainage area may continue for decades; however, the
predictability of decades long production (e.g., a production forecast) can
depend on
many factors, some of which may be uncertain (e.g., unknown, unknowable,
estimated with probability bounds, etc.).
[0027] Various shale gas formations have and are producing gas
economically, which has widened interest gas production in other areas. For
example, several shale gas exploration projects are under-way in diverse
regions of
the world, including Europe and Africa. However, a lack of understanding of
various
elements controlling well productivity, and limitations of available tools to
adequately
characterize a shale gas formation and forecast production from wells drilled
therein,
make it difficult to predict likely commercial value of a project. Factors
that may
impact a value assessment may include, for example, drilling costs, associated
number of wells to develop a shale gas region, production return that each
well can
deliver, etc.
[0028] As an example, a method can generate, based at least in part on a
statistical analysis of data from a selected shale gas formation (e.g., a
play), a
history matched reservoir simulation model that can represent shale gas
behavior
during production (e.g., a production phase) and that can be used for
forecasting
purposes in a new exploration area, for which data may be of limited
availability. For
example, a history matched reservoir simulation model (e.g., for a selected
different
existing, well-characterized play) may be recast using a limited amount of
available
data to generate a production outcome for a new exploration area (new play).
In
such an example, the statistical analysis may provide a case or scenario for
production with respect to time, such as one of a P10 production curve, a P50
production curve, a Pave production curve or a P90 production curve. Such an
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example may be referred to as a single formation and single production curve
method. As an example, such a method may include fitting the single production
curve to provide a fit curve and then extrapolating the fit curve in time. A
simulation
model may then be history matched to the extrapolated production curve (e.g.,
a fit
decline curve that models decline of production, for example, via exponential
decay,
harmonic decay, hyperbolic decay, etc.). The history matched model may then be
used for forecasting purposes in a new exploration area, for which data may be
of
limited availability. As to decay or decline curves, each may include one or
more
parameters that may be fit (e.g., via error minimization, plotting, etc.). As
an
example, an exponential decline curve may include a fit parameter "a" (e.g.,
q(t) =
qi*exp(-a*t)), a hyperbolic decline curve may include fit parameters "a" and
"b" (e.g.,
q(t) = qi/((1+a*b1)^(1/b))) and a harmonic decline curve may be a form of a
hyperbolic decline curve where the parameter "b" is unity (e.g., (e.g., q(t) =
qi/(1+a*
t)).
[0029] As an example, a method can generate, based at least in part on
statistical analyses of data from a selected shale gas formation (e.g., a
play), a
series of history matched reservoir simulation model that can represent shale
gas
behavior during production (e.g., a production phase) and that can be used for
forecasting purposes in a new exploration area, for which data may be of
limited
availability. For example, a series of history matched reservoir simulation
model
(e.g., for a selected different existing, well-characterized play) may be
recast using a
limited amount of available data to generate a production outcome for a new
exploration area (new play). In such an example, the statistical analyses may
provide various cases or scenarios for production with respect to time, such
as, for
example, two or more of a P10 production curve, a P50 production curve, a Pave
production curve or a P90 production curve. Such an example may be referred to
as
a single formation and multiple production curve method. As an example, such a
method may include fitting each of the production curves to provide fit curves
and
then extrapolating each fit curve in time (e.g., a decade or more). A
simulation
model may then be history matched to each of the extrapolated production
curves
(e.g., a fit decline curve that models decline of production, for example, via
exponential decay, harmonic decay, hyperbolic decay, etc.). Such an approach
may
provide a series of history matched models, for example, one for each fit
curve. As
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an example, multiple fit curves may be used for history matching to generate a
single
history matched model, for example, one that may generate simulation results
that
match a P10 case, a P50 case, a Pave case, a P90 case responsive to input
parameters that may have associated uncertainties. The history matched model
or
models may then be used for forecasting purposes in a new exploration area,
for
which data may be of limited availability.
[0030] As an example, a method can generate, based at least in part on
statistical analyses of data from selected shale gas formations (plays), a
series of
history matched reservoir simulation models that can represent shale gas
behavior
during production (e.g., a production phase) and that can be used for
forecasting
purposes in a new exploration area, for which data may be of limited
availability. For
example, history matched reservoir simulation models (e.g., for a selected
number of
different existing, well-characterized plays) may be recast using a limited
amount of
available data to generate a series of production outcomes for a new
exploration
area (new play). In such an example, the statistical analyses may provide
various
cases or scenarios for production with respect to time. As an example, for
each of
the selected gas formations, the statistical analyses may provide one or more
production curves, such as, for example, one or more of a P10 production
curve, a
P50 production curve, a Pave production curve and a P90 production curve. Such
an example may be referred to as a multiple formation and multiple production
curve
method (e.g., where each formation has at least one associated production
curve).
As an example, such a method may include fitting each of the production curves
to
provide fit curves and then extrapolating each fit curve in time (e.g., a
decade or
more). A simulation model may then be history matched to each of the
extrapolated
production curves (e.g., a fit decline curve that models decline of
production, for
example, via exponential decay, harmonic decay, hyperbolic decay, etc.). Such
an
approach may provide a series of history matched models, for example, one for
each
fit curve. As an example, multiple fit curves may be used for history matching
to
generate a single history matched model for each formation, for example, one
that
may generate simulation results that match a P10 case, a P50 case, a Pave
case, a
P90 case responsive to input parameters that may have associated
uncertainties.
As an example, a Pave case (e.g., a fit curve for Pave data) for a formation
may be
provided for purposes of history matching a simulation model to provide a
history
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matched model for that formation. The history matched models may then be used
for forecasting purposes in a new exploration area, for which data may be of
limited
availability.
[0031] As an example, history matching may be focused through sensitivity
analysis to identify parameters that have the greatest impact on production,
which
may be, for example, reservoir parameters and/or operational/controlled
parameters. When considering a new exploration area (new play), one or more
history matched simulation models (e.g., for existing plays), as loaded with
the
limited data for the new exploration area (new play), can output a predicted
production profile or profiles, the latter of which may, for example, range
from
optimistic to pessimist cases. As an example, parameters for development of
that
area may be optimized while accounting for possible behaviors described by the
one
or more models. In such an example, one or more history matched models for one
or more corresponding existing shale gas formations may be considered proxy or
surrogate models for another shale gas formation. A model may deemed a
surrogate model, for example, where it has been history matched using data
from a
formation other than a formation of interest. Such a model may then "carry"
data for
a formation of interest to provide estimates, approximations, etc. of how that
formation of interest may behave (e.g., responsive to an existing plan,
existing
development efforts, a prospective plan, prospective development efforts,
etc.).
Through plugging in at least a portion of available data for a "foreign
formation", one
or more surrogate models may help estimate ultimate recovery (EUR) from that
foreign formation, for example, via one or more simulations with respect to
future
time using the one or more surrogate models.
[0032] As an example, a method may create a tool, for example, that may be
applied to one or more areas of interest. For example, such a tool may be
applied to
an area of interest to produce forecasts within uncertainty bounds. As an
example,
such forecasts may be for a new exploration area where the forecasts are
supported
by the latest understood gas flow behavior in nanodarcy permeability rock
(e.g.,
shale) and incorporate gas desorption physics.
[0033] As an example, a tool may be a numerical model or a set of numerical
models, for example, that may be provided in the form of instructions
executable by
a processor of a computer, a computing device, a computing system, etc. For
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example, such instructions may be stored in memory accessible by a processor.
As
an example, a tool may be part of a modeling framework, part of a simulation
framework, part of a modeling and simulation framework, a framework plug-in, a
framework add-on, etc. As an example, output from a tool may be directed to
one or
more pieces of equipment, for example, to at least in part control a process,
to plan a
process, etc. For example, output from a tool may be input to a pad
construction
process, a drilling process, a stimulation process, a production process, etc.
[0034] As an example, a method may include providing a generalized model
for well in shale formations (e.g., with constructs for modeling
characteristics such as
a matrix, a well, natural fractures, hydraulic fractures and stimulated
fractures);
providing production data for at least one developed shale gas formation
(e.g., a field
or a play); normalizing at least a portion of the data in time and providing,
for each of
the at least one formation, a corresponding set of probabilities (e.g., P10,
P50/median, Pave (P average) and P90); determining production curves for the
at
least one formation based on a respective set of the probabilities; matching
each
production curve for the at least one formation using different types of
decline
curves; extrapolating production to future times (e.g., about a decade or
more) based
at least in part on one or more best fit parameters (e.g., to optionally
estimate
"ultimate" productions for the at least one formation); optionally verifying
extrapolated
curves for the at least one formation; performing sensitivity analysis aided
history
match of the generalized model to provide a specific, history matched model
for each
of the at least one formation (e.g., where sensitivity analysis identifies
parameters
with the biggest impact on production); plugging in data from a newly selected
formation into each of the at least one history matched model; and simulating
production for the newly selected formation using each of the at least one
history
matched model. As an example, results from a simulation may be used to assess
the newly selected formation (e.g., as to production potential, etc.). As an
example,
one or more models for modeling the newly selected formation may be used to
simulate one or more development scenarios (e.g., hydraulic fractures, number
of
wells, etc.) for the newly selected formation.
[0035] In oil and gas formations, a so-called "chance of success" may be
determined, for example, as an estimate of the chance of geophysical,
geochemical,
etc. elements within a prospect working. A chance of success may be described
as
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a probability, as being optimistic, as being pessimistic, as being high risk,
as being
low risk, etc. For example, a high risk prospect may have a less than about 10
percent chance of working, while a medium risk prospect may have about a 10
percent to about a 20 percent chance of working. As an example, a low risk
prospect may have a chance of working of about over 20 percent.
[0036] As an example, probabilities may be selected from those that find
use
in oil and gas exploration and development. For example, a "proven reserve"
may
be defined as "reasonably certain" to be producible using current technology
at
current prices, with current commercial terms and government consent may be
known in the industry as 1P; while some may refer to it as P90 (e.g., having
about a
90 percent certainty of being produced). A so-called "probable reserve" may be
defined as "reasonably probable" of being produced using current or likely
technology at current prices, with current commercial terms and government
consent
may be deemed 2P (e.g., proven plus probable) or P50 (e.g., having about a 50
percent certainty of being produced). A so-called "possible reserve" may be
defined
as having a chance of being developed under favorable circumstances and may be
deemed 3P (e.g., proven plus probable plus possible) or P10 (e.g., having
about a
percent certainty of being produced).
[0037] Below, an example of a system is described followed by various
technologies, including examples of techniques, which may, for example,
include
modeling one or more formations and, for example, using modeling results to
take
steps toward development, production, etc. As an example, modeling result
information (e.g., values, states, etc.) may be transmitted to one or more
pieces of
equipment, which may include controllers, actuators, etc. that can act at
least in part
on such information (e.g., to start a process, stop a process, alter a
process, etc.).
[0038] Fig. 1 shows an example of a system 100 that includes various
management components 110 to manage various aspects of a geologic environment
150 (e.g., an environment that includes a sedimentary basin, a reservoir 151,
one or
more fractures 153, etc.). For example, the management components 110 may
allow for direct or indirect management of sensing, drilling, injecting,
extracting, etc.,
with respect to the geologic environment 150. In turn, further information
about the
geologic environment 150 may become available as feedback 160 (e.g.,
optionally
as input to one or more of the management components 110).
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[0039] In the example of Fig. 1, the management components 110 include a
seismic data component 112, an additional information component 114 (e.g.,
well/logging data), a processing component 116, a simulation component 120, an
attribute component 130, an analysis/visualization component 142 and a
workflow
component 144. In operation, seismic data and other information provided per
the
components 112 and 114 may be input to the simulation component 120.
[0040] In an example embodiment, the simulation component 120 may rely on
entities 122. Entities 122 may include earth entities or geological objects
such as
wells, surfaces, reservoirs, etc. In the system 100, the entities 122 can
include
virtual representations of actual physical entities that are reconstructed for
purposes
of simulation. The entities 122 may include entities based on data acquired
via
sensing, observation, etc. (e.g., the seismic data 112 and other information
114). An
entity may be characterized by one or more properties (e.g., a geometrical
pillar grid
entity of an earth model may be characterized by a porosity property). Such
properties may represent one or more measurements (e.g., acquired data),
calculations, etc.
[0041] In an example embodiment, the simulation component 120 may rely on
a software framework such as an object-based framework. In such a framework,
entities may include entities based on pre-defined classes to facilitate
modeling and
simulation. A commercially available example of an object-based framework is
the
MICROSOFT .NETTm framework (Redmond, Washington), which provides a set of
extensible object classes. In the .NETTm framework, an object class
encapsulates a
module of reusable code and associated data structures, Object classes can be
used to instantiate object instances for use in by a program, script, etc. For
example, borehole classes may define objects for representing boreholes based
on
well data.
[0042] In the example of Fig. 1, the simulation component 120 may process
information to conform to one or more attributes specified by the attribute
component
130, which may include a library of attributes. Such processing may occur
prior to
input to the simulation component 120 (e.g., consider the processing component
116). As an example, the simulation component 120 may perform operations on
input information based on one or more attributes specified by the attribute
component 130. In an example embodiment, the simulation component 120 may
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construct one or more models of the geologic environment 150, which may be
relied
on to simulate behavior of the geologic environment 150 (e.g., responsive to
one or
more acts, whether natural or artificial). In the example of Fig. 1, the
analysis/visualization component 142 may allow for interaction with a model or
model-based results. As an example, output from the simulation component 120
may be input to one or more other workflows, as indicated by a workflow
component
144.
[0043] As an example, the simulation component 120 may include one or
more features of a simulator such as the ECLIPSETM reservoir simulator
(Schlumberger Limited, Houston Texas), the I NTERSECTIm reservoir simulator
(Schlumberger Limited, Houston Texas), etc. As an example, a reservoir or
reservoirs may be simulated with respect to one or more enhanced recovery
techniques (e.g., consider a thermal process such as SAGD, etc.).
[0044] In an example embodiment, the management components 110 may
include features of a commercially available simulation framework such as the
PETREL seismic to simulation software framework (Schlumberger Limited,
Houston, Texas). The PETREL framework provides components that allow for
optimization of exploration and development operations. The PETREL framework
includes seismic to simulation software components that can output information
for
use in increasing reservoir performance, for example, by improving asset team
productivity. Through use of such a framework, various professionals (e.g.,
geophysicists, geologists, and reservoir engineers) can develop collaborative
workflows and integrate operations to streamline processes. Such a framework
may
be considered an application and may be considered a data-driven application
(e.g.,
where data is input for purposes of simulating a geologic environment).
[0045] In an example embodiment, various aspects of the management
components 110 may include add-ons or plug-ins that operate according to
specifications of a framework environment. For example, a commercially
available
framework environment marketed as the OCEAN framework environment
(Schlumberger Limited, Houston, Texas) allows for integration of add-ons (or
plug-
ins) into a PETREL framework workflow. The OCEAN framework environment
leverages .NET tools (Microsoft Corporation, Redmond, Washington) and offers
stable, user-friendly interfaces for efficient development. In an example
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embodiment, various components may be implemented as add-ons (or plug-ins)
that
conform to and operate according to specifications of a framework environment
(e.g.,
according to application programming interface (API) specifications, etc.).
[0046] Fig. 1 also shows an example of a framework 170 that includes a
model simulation layer 180 along with a framework services layer 190, a
framework
core layer 195 and a modules layer 175. The framework 170 may include the
commercially available OCEAN framework where the model simulation layer 180
is
the commercially available PETREL model-centric software package that hosts
OCEAN framework applications. In an example embodiment, the PETREL
software may be considered a data-driven application. The PETREL software can
include a framework for model building and visualization. Such a model may
include
one or more grids.
[0047] The model simulation layer 180 may provide domain objects 182, act
as a data source 184, provide for rendering 186 and provide for various user
interfaces 188. Rendering 186 may provide a graphical environment in which
applications can display their data while the user interfaces 188 may provide
a
common look and feel for application user interface components.
[0048] In the example of Fig. 1, the domain objects 182 can include entity
objects, property objects and optionally other objects. Entity objects may be
used to
geometrically represent wells, surfaces, reservoirs, etc., while property
objects may
be used to provide property values as well as data versions and display
parameters.
For example, an entity object may represent a well where a property object
provides
log information as well as version information and display information (e.g.,
to display
the well as part of a model).
[0049] In the example of Fig. 1, data may be stored in one or more data
sources (or data stores, generally physical data storage devices), which may
be at
the same or different physical sites and accessible via one or more networks.
The
model simulation layer 180 may be configured to model projects. As such, a
particular project may be stored where stored project information may include
inputs,
models, results and cases. Thus, upon completion of a modeling session, a user
may store a project. At a later time, the project can be accessed and restored
using
the model simulation layer 180, which can recreate instances of the relevant
domain
objects.
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[0050] In the example of Fig. 1, the geologic environment 150 may include
layers (e.g., stratification) that include a reservoir 151 and that may be
intersected by
a fault 153. As an example, the geologic environment 150 may be outfitted with
any
of a variety of sensors, detectors, actuators, etc. For example, equipment 152
may
include communication circuitry to receive and to transmit information with
respect to
one or more networks 155. Such information may include information associated
with downhole equipment 154, which may be equipment to acquire information, to
assist with resource recovery, etc. Other equipment 156 may be located remote
from a well site and include sensing, detecting, emitting or other circuitry.
Such
equipment may include storage and communication circuitry to store and to
communicate data, instructions, etc. As an example, one or more satellites may
be
provided for purposes of communications, data acquisition, etc. For example,
Fig. 1
shows a satellite in communication with the network 155 that may be configured
for
communications, noting that the satellite may additionally or alternatively
include
circuitry for imagery (e.g., spatial, spectral, temporal, radiometric, etc.).
[0051] Fig. 1 also shows the geologic environment 150 as optionally
including
equipment 157 and 158 associated with a well that includes a substantially
horizontal
portion that may intersect with one or more fractures 159. For example,
consider a
well in a shale formation that may include natural fractures, artificial
fractures (e.g.,
hydraulic fractures) or a combination of natural and artificial fractures. As
an
example, a well may be drilled for a reservoir that is laterally extensive. In
such an
example, lateral variations in properties, stresses, etc. may exist where an
assessment of such variations may assist with planning, operations, etc. to
develop
the reservoir (e.g., via fracturing, injecting, extracting, etc.). As an
example, the
equipment 157 and/or 158 may include components, a system, systems, etc. for
fracturing, seismic sensing, analysis of seismic data, assessment of one or
more
fractures, etc.
[0052] As mentioned, the system 100 may be used to perform one or more
workflows. A workflow may be a process that includes a number of worksteps. A
workstep may operate on data, for example, to create new data, to update
existing
data, etc. As an example, a may operate on one or more inputs and create one
or
more results, for example, based on one or more algorithms. As an example, a
system may include a workflow editor for creation, editing, executing, etc. of
a
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workflow. In such an example, the workflow editor may provide for selection of
one
or more pre-defined worksteps, one or more customized worksteps, etc. As an
example, a workflow may be a workflow implementable in the PETREL software,
for example, that operates on seismic data, seismic attribute(s), etc. As an
example,
a workflow may be a process implementable in the OCEAN framework. As an
example, a workflow may include one or more worksteps that access a module
such
as a plug-in (e.g., external executable code, etc.).
[0053] Fig. 2 shows an example of a method 200 and an example of a system
260. The method 200 includes a provision block 210 for providing data for at
least
one formation (e.g., formations 1 to N, where N is greater than or equal to
0), a
performance block 220 for performing statistical analysis(es) (e.g., using at
least a
portion of the provided data), a provision block 230 for providing a
simulation model
(e.g., a formation model), a match block 240 for matching simulation results
and
results from the statistical analysis(es) (e.g., to generate at least one
history matched
formation model) and a forecast block 250 for forecasting, for example,
production
for a formation X (e.g., which is a formation other than one of the formations
1 to N).
[0054] In the example of Fig. 2, the system 260 includes one or more
information storage devices 262, one or more computers 264, one or more
networks
270 and one or more modules 280. As to the one or more computers 264, each
computer may include one or more processors (e.g., or processing cores) 266
and
memory 268 for storing instructions (e.g., modules), for example, executable
by at
least one of the one or more processors. As an example, a computer may include
one or more network interfaces (e.g., wired or wireless), one or more graphics
cards,
a display interface (e.g., wired or wireless), etc. As an example, a system
may
include one or more modules, which may be provided to analyze data, control a
process, perform a task, perform a workstep, perform a workflow, etc.
[0055] The method 200 is shown in Fig. 2 in association with various
computer-readable media (CRM) blocks 211, 221, 231, 241 and 251. Such blocks
generally include instructions suitable for execution by one or more
processors (or
processor cores) to instruct a computing device or system to perform one or
more
actions. While various blocks are shown, a single medium may be configured
with
instructions to allow for, at least in part, performance of various actions of
the
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method 200. As an example, a computer-readable medium (CRM) may be a
computer-readable storage medium.
[0056] Fig. 3 shows
an example of a method 300. As shown the method 300
can include a provision block 310 for providing a generalized model for well
in shale
formations (e.g., with constructs for modeling characteristics such as a
matrix 311, a
well 312, natural fractures 313, hydraulic fractures 314, stimulated fractures
315 and
optionally other characteristics 316); a provision block 320 for providing
production
data for at least one developed shale gas formation (e.g., consider selecting
from
formations 321, 322, 323, 324, 325 or other geologic environment 326); a
normalization block 330 for normalizing at least a portion of the data in time
and
providing, for at least one formation, a corresponding set of probabilities
(see, e.g.,
P10 331, P50/median 332, Pave (P average) 333, P90 334 and optionally one or
more other probabilities 335); a determination block 340 for determining
production
curves for the at least one formations based on a respective set of the
probabilities(see, e.g., PC 341, PC 342, PC 343, PC 344, PC 345, and
optionally
other curve 346); a fit block 350 for fitting each production curve for the at
least one
formation using one or more type of decline curve (e.g., exponential,
hyperbolic,
harmonic, etc.) to provide fit decline curves (e.g., FDC 351, FDC 352, FDC
353, FDC
354, FDC 355 or other fit curve 356); an extrapolate block 360 for
extrapolating
production to future times (e.g., using fit decline curves to provide
extrapolated
curves per EC 361, EC 362, EC 363, EC 364, EC 365 and optionally other
extrapolated curve 366), for example, based at least in part on best fit
parameters
(e.g., to optionally estimate "ultimate" productions for the at least one
formation); an
optional verification block for verifying extrapolated curves for the at least
one
formation; a performance block 370 for performing sensitivity analysis aided
history
match of the generalized model to provide a specific, history matched model
for each
of the at least one formation, for example, where sensitivity analysis
identifies
parameters with the biggest impact on production (see, e.g., history matched
models
371, 372, 373, 374, 375 and optionally other matched model 376); and a
simulation
block 380 for plugging in data from a newly selected formation into each of
the at
least one history matched model and simulating production for the newly
selected
formation using each of the at least one history matched models (see, e.g.,
simulation results 381, 382, 383, 384, 385, and optionally other results 386).
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[0057] As shown in the example of Fig. 3, per a plan and/or development
block 390, as an example, results from the simulations may be used to assess
the
newly selected formation (e.g., as to production potential, etc.), for
example, one or
more models for modeling the newly selected formation may be used to simulate
one
or more planning and/or development scenarios for the newly selected formation
(see, e.g., well 392, hydraulic fractures 394, stimulated fracture 395 and
optionally
other features for planning, development and/or production 396).
[0058] Fig. 4 shows an example of a model 401 that includes constructs to
model (e.g., equations), for example, a matrix 411, a well 412, natural
fractures 413,
hydraulic fractures 414, stimulated fractures 415 and stimulated inter-
hydraulic
fracture region 416. In the example of Fig. 4, the model 401 may encompass a
drainage area, for example, defined as covering a surface area and as having a
depth or depths. Given parameter values for the various constructs (e.g.,
locations,
characteristics, etc.), the model 401 may be formulated with respect to a grid
405 to
form a numerical model suitable for providing solutions via a numerical
solver.
[0059] In the example of Fig. 4, the grid 405 is shown as a three-
dimensional
grid with a well head 409 for a well that extends along an x-axis where
hydraulic
fractures and other constructs may be modeled within the grid 405. As an
example,
by inputting the model and parameters into a numerical solver, results may be
generated. For example, results may include pressure values. In the example of
Fig. 4, contours are shown with respect to the grid 405 that may represent
pressure
isobars where outer isobars are at higher pressures than an inner isobar,
which may
correspond to pressure in a horizontal wellbore. As mentioned, where pressure
is
higher in a matrix and fractures that intersect a wellbore than in the
wellbore, fluid
may flow from the matrix and fractures to the wellbore. As fluid is depleted
from the
matrix, pressure may drop and hence production may drop. The model 401, as
gridded per the grid 405, may be used to simulate production with respect to
time, for
example, for future times to estimate how depletion occurs and to estimate an
ultimate recovery (e.g., EUR).
[0060] As an example, the model 401 may be a model suitable for use in a
framework such as the ECLIPSE framework. As an example, the model may
implement a dual porosity approach (e.g., a continuum approach) for at least a
portion of a formation (e.g., a drainage area). As an example, such a model
may
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include one or more constructs analogous to a coal bed/methane gas model, for
example, where such one or more constructs are adapted to a shale gas
formation.
[0061] As an example, a model may include equations for dual porosity and
equations for sorption (e.g., desorption). With respect to a grid, grid cells
may be
considered to be "coal"; noting that the model is applied to shale.
[0062] Due to the very low stress anisotropy in shale gas formations,
hydraulic
fractures may be non-planar fractures that may develop a complex fracture
network.
Expansion of these non-planar hydraulic fractures may be represented in a
model as
a wide simulation cell that includes a relatively high permeability.
[0063] For a fracture system, natural fractures within a shale gas
formation
may also be considered. Such natural fractures may be found to be mineralized
(e.g., calcite, etc.) or inactive. A model may include equations that provide
for
reactivation of such fractures, for example, responsive to hydraulic
fracturing where
microfractures are reopened to provide for fluid flow. Such fractures may be
considered as being stimulated fractures. As an example, a model may include
four
types of permeable media: matrix, natural fractures, stimulated fractures, and
hydraulic fractures. As to a desorption process, a model may include equations
that
account for a Langmuir pressure and a Langmuir volume.
[0064] Fig. 5 shows an example of a method 500 that includes a production
curve block 510 for generating production curves 532 for regions 512 and
associated
production data 514 (e.g., production data with respect to time for each of
the
regions 512); a matching block 520 for matching a model to each of the
production
curves to generate individual matched production curves 534 and multiple
matched
models; and a forecast or extrapolation block 540 for forecasting or
extrapolating
production curves for multiple formations (see, e.g., a plot 542). As an
example,
data for a region other than one of the regions 512 may be provided and input
to one
or more of the matched models to, for example, generate a forecast for that
region.
As an example, data for a region other than one of the regions 512 may be
provided
and input to more than one of the matched models to, for example, generate
forecasts for that region. For example, Fig. 5 shows the plot 542 as including
four
sets of curves where each set includes a production decline curve and a
cumulative
production curve. In the example of Fig. 5, each set of curves corresponds to
a
particular formation (e.g., Formation 1, Formation 2, Formation 3 and
Formation 4).
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[0065] As an example, the sets of curves in the plot 542 may correspond to
four production forecast cases run by introducing Silurian shale field data
into
already calibrated US shale play analogues (e.g., surrogate models). Such
curves
may be analyzed as to chance of success, etc. For example, in trials for the
Silurian
shale field based on data for the Barnett, Woodford, Haynesville and
Fayetteville
formations, the most optimistic scenario was given by the Haynesville case,
followed
by the ones for Barnett, Woodford and Fayetteville, respectively (e.g., where
cumulative production was taken to a present production value).
[0066] Figs. 6, 7 and 8 show an example of a method 600. As shown in Fig.
6, the method 600 includes an identification or selection block 610 for
identifying or
selecting gas shale basins (e.g., formations) with enough available production
data
per well for running a statistical analysis (e.g., >100 data points at a given
time). For
example, a map is shown with formations Fl, F2, F3 and F4, which may
correspond
to, for example, Haynesville (F1), Barnett (F2), Fayetteville (F3) and
Woodford (F4)
formations. As shown in Fig. 6, the method 600 includes a retrieval block 620
for
retrieving core characteristics of the shale formations, for example,
including
compiling a range of values for one or more of the following: (a)
GIIP/section, (b)
reservoir pressure, (c) net thickness, (d) TOC, (e) Ro, (f) horizontal section
of the
well, (g) number of fracturing stages per well, and (h) number of clusters per
stage.
As shown in Fig. 6, the method 600 can include a plot block 630, for example,
for
plotting information retrieved through spider graph plots 632 to select an
appropriate
analogue per basin (e.g., per formation). In such an example, differences may
be
ascertained, for example, via calculations and/or visual inspection of area
encompassed by values for different regions of a formation, different
formations, etc.,
plotted as a spider graph plot (see, e.g., different lines in Fig. 6). A
method may
optionally include one or more spider graphs (e.g., or radar charts) for
displaying
multivariate data (e.g., as a two-dimensional chart of three or more
quantitative
variables represented on axes starting from the same point).
[0067] As an example, an equation 634 may be implemented for purposes of
assessing data, information, etc. In the equation 634, q is a gas flow (e.g.,
at
standard conditions), k is a permeability, h is a length dimension (e.g., a
thickness
vertically for a horizontal well), Pe and p,,,f are pressures (e.g., an
effective pressure
and a "bottom hole" pressure), T is a temperature (e.g., formation temperature
about
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a wellbore, for example, in a drainage region), i.tg is a gas viscosity, z is
a
compressibility factor, re and ryõ are radii (e.g., an effective drainage
radius and a
wellbore radius), s is a skin effect coefficient, D may be a non-Darcy
coefficient and
the term DQg may, for example, provide for a rate dependent skin factor. As an
example, the equation 632 may include a constant, for example, as to units
(e.g.,
1422, etc.). As an example, an equation may account for radial flow of gas
(e.g.,
from a formation to a wellbore). As an example, an equation may account for
non-
Darcy flow (e.g., an inertial or turbulent flow factor). As an example, an
equation
may be in a pressure-squared approximation form. As an example, flow may be
modeled in various regimes, which may include Forchheimer, beyond Forchheimer,
etc. As an example, a model may account for presence of one or more proppant
materials in a fracture (e.g., flow with respect to proppant structure,
packing, etc.).
As an example, a model may include one or more Langmuir equations, for
example,
to model sorption and/or desorption (e.g., for adsorption and/or desorption of
molecules on a material or materials, for example, including one or more
Langmuir
adsorption constants).
[0068] Fig. 7 shows examples of some additional blocks of the method 600.
As shown, the method 600 can include a filter block 640 for filtering (e.g.,
for a
selected analogue) non-representative well production data (e.g., old wells,
horizontal lengths < about 500m, etc.) and for normalizing filtered data. For
example, consider the data 642, which may be filtered and normalized to
product the
filtered and normalized data 644. The method 600 may include a calculation
block
650 for calculating a set of low, mid and high production trend cases using
the well
production data from a selected analogue (e.g., consider P10, P50, P90, Pave,
etc.).
For example, a plot 652 shows Pave and a plot 654 shows P90 according to such
calculations. The method 600 may include a matching block 660 for matching
calculated production trend cases using different decline curves. For example,
consider the plots 662 and 664, which show Pave and P90 fit to various types
of
decline curves, for example, with corresponding fit parameter values. As
mentioned,
such fitting may include fitting to an exponential curve, a harmonic curve, a
hyperbolic curve, etc. In the example of Fig. 7, the hyperbolic curve fits
Pave with a
fit parameter value of about 1.4 while the hyperbolic curve fits P90 with a
fit
parameter value of about 1.5.
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[0069] Fig. 8 shows examples of some additional blocks of the method 600.
For example, the method 600 can include an extrapolation block 670 for
extrapolating each matched curve for various production trend cases (e.g.,
P10, P50
and P90) up to approximately 30 years for FUR and forecasts (see, e.g., a plot
672
for P90). As shown, the method 600 can include an estimation block 680 for
estimate a number of development wells based on analogue well spacing, which
can
take into account the prospective area of the studied basin (e.g., formation
of
interest). For example, consider a model 682 for a drainage area X that
includes a
well. As an example, a well may serve a drainage area of approximately 80 to
approximately 100 acres. As shown in Fig. 8, the method 600 may include a
forecast block 690 for forecasting field production for a particular
development plan.
For example, using time to drill and complete one well in a studied basin and
drilling
rig availability (see, e.g., a table 692), the forecast block 690 may output a
number of
field production forecasts, for example, one per production trend case. An
example
plot 694 is shown in Fig. 8 with daily production and cumulative production
over time
(e.g., up to about 250 months into the future).
[0070] Figs. 9, 10 and 11 show various examples of results from an example
of a method. As an example, based on statistical analysis of various shale gas
basins, a synthetic reservoir simulation model can represent shale gas wells
and be
used for forecasting purposes in exploration areas where information may be
limited
in its availability. As an example, such a model can include accurate
identification of
parameters that impact on production (e.g., reservoir parameters, operational
/controlled parameters, etc.) to allow for prediction of production profiles
and, for
example, optimization of controlled parameters.
[0071] As to an example involving statistical analysis, a method can
include
collecting historical production information for thousands of shale gas wells
from
various basins, categorizing the information categorized, for example, to
exclude
information from certain types of wells (e.g., vertical and short lateral
wells), and
screening the information in time (e.g., to retain information from more
recent wells)
to help assess aspects of technology with respect to time, to isolate one or
more
techniques (e.g., resulting from improved understanding of rock mechanical
behavior
and fracturing process with microseismic, 3D seismic for sweet-spot hunting,
etc.),
etc.
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[0072] After collecting, categorizing and screening, such a method may
include normalizing production data in time to derive a set of, for example,
P10, P50,
Pave, and P90 values from a reversed cumulative distribution curve (e.g., for
determination of production curves for each basin).
[0073] Fig. 9 shows examples of production trend cases 900 for a shale gas
formation, including a plot 910 for P10, a plot 920 for P50/Median, a plot 930
for
Pave and a plot 940 for P90. Each of the trend cases extends to about 70
months
(e.g., about 5.8 years), for example, where maximum daily production increases
from
P10 to P50 to Pave to P90.
[0074] As an example, a method may include, for a set of curves for each
formation, fitting or matching using different decline curve types. In turn,
the best
fitting parameters may be used to extrapolate historical data per formation to
get an
estimated ultimate recovery, for example, up to about 30 years. As an example,
extrapolation of the fitted or matched decline curves may optionally be
compared to
information from one or more additional sources, for example, to verify
consistency
of the extrapolated results.
[0075] Fig. 10 shows examples of estimated ultimate recoveries (EURs) 1000
for various trend cases such as the trend cases 900 of Fig. 9. For example,
Fig. 10
shows a plot 1010 for P90 where a fit parameter for a hyperbolic curve has a
value
of about 1.7 and where daily production falls from about 900 to less than
about 100
over about 400 months; a plot 1020 for P50 where a fit parameter for a
hyperbolic
curve has a value of about 1.6 and where daily production falls from about
2000 to
less than about 100 over about 400 months; a plot 1030 for Pave where a fit
parameter for a hyperbolic curve has a value of about 1.7 and where daily
production
falls from about 2500 to less than about 100 over about 400 months, and a plot
1040
for P10 where a fit parameter for a hyperbolic curve has a value of about 1.6
and
where daily production falls from about 4000 to less than about 100 over about
400
months. Also shown in each of the plots 1010, 1020, 1030 and 1040 is EUR where
P90 has an EUR of about 1.06 BCF, P50 has an EUR of about 2.56 BCF, Pave has
an EUR of about 3.14 BCF and P10 has a EUR of about 5.64 BCF. Such values
may be compared to "break-even" prices (e.g., based at least in part on gas
prices)
to determine which scenario may be economically viable, if any.
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[0076] As an example, a method may include performing a sensitivity
analysis. Such an analysis may be performed using a model. As an example, a
model may be a dual-porosity model together with an instant sorption model
within a
simulation grid with about 106 cells in the x direction, about 107 cells in
the y
direction and about two cells in the z direction. Such a dual-porosity
compositional
model may model a fractured matrix, for example, with dimensions of about
5,280
feet in the x direction, about 5,285 feet in the y direction, and about 261
feet in the z
direction. As an example, initial porosity and permeability values in the
matrix may
be set at about 0.06 and about 0.00017 mD, respectively. In fractured medium,
the
initial porosity value may be set to about 0.0004 and the initial permeability
value
may be set to about 0.00017 mD. As an example, a reservoir datum depth may be
set at about 11,231 feet, and reservoir pressure may be defined as about 7,000
psi,
where gas/water contact is located at the bottom of the 100 percent gas-
saturated
formation.
[0077] As to a well, as an example, a model may include a horizontal well
of
about 3,250 ft, which may be placed in approximately the middle of the model
and,
for example, divided into about seven hydraulic fracturing stages with two
clusters in
each, for a total of 14 hydraulic fractures, contained within the first layer.
As an
example, a hydraulic fracture length may be set to about 305 feet. As an
example, a
non-Darcy skin of 0.05 day/Mcf (e.g., caused by a gas turbulent flow regime)
may be
taken into account.
[0078] When in low-stress anisotropy gas shale formations, hydraulic
fractures
tend to be nonplanar and a complex fracture network may develop. The expansion
of these nonplanar hydraulic fractures may be represented in a model as being
highly permeable using a simulation cell about 50 ft wide. Natural fractures
may also
be considered where upon stimulation to form stimulated fractures, the initial
permeability value may be set to about 0.1 mD and about 20 mD in the hydraulic
fractures. As an example, desorption may include setting a Langmuir pressure
to
about 1,125 psi and a Langmuir volume to about 0.065 Mcf/ton.
[0079] As an example, provided with a model and various values for
parameters of the model a sensitivity analysis may be performed, for example,
to
identify those elements with the greatest impact on the reservoir simulation
results.
As an example, a sensitivity analysis may consider a decade or more of
"simulation"
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time. To start, parameters thought to have a high impact on production may be
selected and their values were varied. Table 1 below shows variation of
uncertain
parameters in particular gas shale formations.
[0080] Table 1
Variable Low Base High
Matrix porosity (p.u) 0.02 0.06 0.1
Matrix permeability (mD) 1.00E-05 0.00017 0.001
Natural fracture porosity (p.u) 5.00E-05 0.0004 0.02
Natural fracture permeability (mD) 1.00E-05 0.00017 0.001
Shape factor (ft2) 0.08 1.2 8
, Langmuir volume (Mcf/ton) 0 0.065 0.11
Langmuir pressure (psi) 500 1,125.00 3,000.00
Well length (ft) 2,000.00 3,250.00 4,650.00
Hydraulic fracture length (ft) 105 305 1,005.00
Hydraulic fracture permeability (mD) 10 20 200
Stimulated fracture permeability (mD) 0.05 0.1 1
Non-Darcy skin (day/Mcf) 0.08 0.05 0.01
Layer thickness (ft) 190.5 261 402
Bottom-hole pressure (psi) 5,000.00 3,000.00 1,000.00
Reservoir pressure (psi) 5,000.00 7,000.00 9,000.00
[0081] Fig. 11 shows examples of parameters 1130 as being classified as
being certain (underlined) and uncertain (italicized) as well as a tornado
plot 1150
from a cumulative production sensitivity analysis. As indicated in the plot
1150,
hydraulic fracture length and well section length may be positioned as the
elements
having the greatest impact on simulation results; noting that simulation
results
demonstrated that hydraulic fracture permeability had an impact in earlier
time, but
limited impact in later time. On the other hand, the permeability of natural
fractures
showed limited impact in earlier time, but greater impact in later time. This
sensitivity
analysis provided valuable information to focus history matching efforts
(e.g.,
production matching).
[0082] As to history matching or production matching, as an example, a
method can include averaging matched curves for each shale gas formation from
a
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type curve analysis and then matching using a current simulation model. As
parameters affecting production may have already been identified by performing
a
sensitivity analysis, the parameters may be varied within a range to adjust
for the
cumulative production and production rate. As an example, Table 2 below shows
final tuned parameters for the aforementioned model (see also plots of Fig. 9
and
10).
[0083] Table 2
Variable Haynesville Barnett Fayetteville Woodford
Hydraulic fracture perm. (mD) 15 5 4.5 4.5
Hydraulic fracture length (ft) 605 505 505 305
Non-Darcy skin (day/Mcf) 0.5 0.7 0.7 0.6
Natural fracture permeability (mD) 0.0000083 0.00037 0.0000076
0.000043
Stimulated fracture perm. (mD) 0.00005 0.00004 0.00012
0.000045
Matrix porosity 0.02 0.07 0.07 0.06
Natural fracture porosity 0.0014 0.04 0.025 0.0004
Layer thickness (ft) 270 116 190 200
Formation pressure (psi) 9,883 4,472 1,919 4,281
Formation depth (ft) 11,231 7,710 4,265.10 9,514.40
Well average length (ft) 3,250 2,950 3,950 2,950
[0084] As shown in Table 2, the average values appear to be low for
hydraulic
fracture permeability and high for non-Darcy skin; however, one may consider
that
this results from an assumption that hydraulic fractures are opened and
producing in
the synthetic model; thus, such low values may suggest flow instabilities
through the
hydraulic fractures. Also, the length of the hydraulic fractures was
considered equal
in the model; whereas, this may differ from the field. Thus, an assumption
that they
are of equal length may affect their estimated values or contribute to
uncommon
values for hydraulic fracture permeability and non-Darcy skin.
[0085] Formation characteristics may differ from one shale formation to
another. As an example, matrix porosity may tend to have a similar value
across
selected shale formations; however, porosity and permeability of natural
fractures
may vary considerably, which may be attributed to specific and distinct
mineralogy
found in each shale play and, for example, mechanical behavior of the rock
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impacting the fracture treatment result. As an example, natural fractures may
be
open, or partially or totally filled, enabling or hindering flow.
[0086] Per trial results, permeability of stimulated fractures tended to be
higher than in natural fractures found in a formation; however, one exception
was
found for the Barnett formation. A model suggests that, for the Barnett
formation, in
the vicinity of the hydraulic fractures there is damaged rather than
stimulation. Such
information, uncovered by such a model, may be valuable as it may help to
detect
possible problems related to hydraulic fracturing treatment design.
[0087] Fig. 12 shows examples of simulation results 1200 for a model that
was adapted to Haynesville formation data, to Barnett formation data, to
Fayetteville
formation data, and to Woodford formation data. Specifically, a plot 1210
shows
simulation results for a Haynesville adapted model, a plot 1220 shows
simulation
results for a Barnett adapted model, a plot 1230 shows simulation results for
a
Fayetteville adapted model, and a plot 1240 shows simulation results for a
Woodford
adapted model. Contours in the plots 1210, 1220, 1230 and 1240 indicate
pressure
depletion for the drainage areas, each with a respective well. Various
hydraulic
fractures are also indicated as being modeled, for example, as filled circles
along
each wellbore. In the example of Fig. 12, the models included grid cells, for
example, as shown with respect to the model of Fig. 4. The plots 1210, 1220,
1230
and 1240 may be considered cut-away views, for example, to illustrate
pressures
with respect to respective wellbores (e.g., to show pressure depletion in a
vicinity of
a wellbore). Such results (e.g., model output) may optionally be used in an
algorithm, workflow, etc., for example, for planning stimulation treatment,
setting one
or more stimulation treatment control parameters, etc.
[0088] As an example, a method can include generating one or more synthetic
models for application to an exploration project, for example, to predict
future
production. For example, consider a Silurian shale formation where, as input
for
application of one or more synthetic models, well landing depth, formation
pressure,
formation depth, top and layer thickness are provided. In such an example,
operational parameters may be set according to values divined from a number of
formations such as the Barnett, Fayetteville, Haynesville and Woodford
formations.
For example, consider a well length of about 3,600 ft, a well bore diameter of
about
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0.5833 ft, and a perforation interval spacing of about 150 feet (e.g., two
shots per
cluster).
[0089] As an example, four production forecast cases may be run by
introducing the Silurian shale field data into calibrated shale formation
model
analogues (e.g., synthetic surrogate models). Referring again to the plot 542
of Fig.
5, it shows gas production analysis in each of these cases, where the most
optimistic
scenario is given by the Haynesville case, followed by the ones for Barnett,
Woodford and Fayetteville, respectively. The results of this analysis are
shown in
Table 3, below. These results confirm the gas production scenarios.
[0090] Table 3
Formation Cumulative Gas Vol.
Haynesville 65,522.55373 Mcf
Barnett 37,236.83619 Mcf
Fayettevile 35,260.06362 Mcf
Woodford 37,172.69063 Mcf
[0091] As an example, even with limited information, simulation models may
be developed for that make it possible to have at least one production
forecast per
well in new shale gas exploration basins. Such an approach to forecasting
production in shale gas exploration may be relatively robust from an
engineering
point of view, for example, represent a suitable way to address the
uncertainty of
shale gas project forecasting from the exploration stage.
[0092] As an example, a method can include providing data for at least one
shale gas formation; performing a statistical analysis on the data for each
shale gas
formation; providing a simulation model; history matching the simulation model
for
each of the at least one different shale gas formations based at least in part
on the
performed statistical analyses to generate a history matched model for each of
the at
least one shale gas formations; and forecasting production for another shale
gas
formation by plugging in data for the other shale gas formation into each
generated
history matched model. In such an example, where data are provided for two or
more shale gas formations, those shale gas formations may be different
formations.
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[0093] As an example, a statistical analysis may generate a set of
production
curves for a shale gas formation. In such an example, a method may include
fitting
curves to each of the production curves in a set of the set of production
curves to
generate sets of fit curves, for example, for each of at least one shale gas
formations. As an example, one or more types of fit declines curve may be used
that
model decline of production, for example, via exponential decay, harmonic
decay,
hyperbolic decay, etc. As an example, such a method may include extrapolating
each of the production curves in time using each of the fit curves (e.g.,
optionally by
a year or more). As an example, a fitting process may fit more than one curve,
type
of curve, etc. to a production curve, for example, a method may use different
curves
at different times to represent diverse flowing periods (e.g., exhibited by a
production
curve).
[0094] As an example, a method may include performing history matching that
adjusts parameter values of a simulation model for each set of fit curves
(e.g., fit
decline curves) to generate a history matched model for each of at least one
shale
gas formation. As an example, history matching may adjust parameter values of
a
simulation model for one fit curve from each set of fit curves to generate a
history
matched model for each of at least one shale gas formation. As an example, the
one fit curve may be a fit curve for a respective Pave production curve. As an
example, adjusting may be performed by an algorithm, for example, that acts to
minimize error between target values (e.g., or a target curve) and model
simulation
values.
[0095] As an example, history matching may include adjusting parameter
values for a model of formation that has produced hydrocarbons until output
from the
model approximates historic hydrocarbon production (e.g., and/or optionally
other
criteria) of the formation. As an example, historical production and pressures
may
be matched to within some tolerance. Accuracy of history matching may depend
on,
for example, quality of a model and quality and quantity of pressure and
production
data. As an example, once a model has been history matched, it may be used to
simulate future behavior of the formation. As described with respect to
various
examples herein, one or more history matched models may be loaded with data
for
another formation, for example, to simulate behavior of that other formation,
In such
an example, data for the other formation may be limited, for example, due to
one or
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more of various factors (e.g., exploration phase has not progressed to
production,
production is uncertain, etc.). As an example, one or more history matched
models,
as associated with one or more formations, may be used as one or more proxy or
surrogate models for another, different formation.
[0096] As an example, a method may include performing a sensitivity
analysis
to assist with selection of parameters for history matching, for example,
where the
selection of parameters includes parameters to which production is sensitive.
As an
example, such a method may include ranking parameters and, for example,
selecting at least one parameter based on the ranking (e.g., a parameter
sensitivity
ranking).
[0097] As an example, a model may model a matrix, natural fractures,
hydraulic fractures and stimulated fractures. For example, a model may include
parameters that may be set for a matrix portion of the model, a natural
fracture
portion of the model, a hydraulic fracture portion of the model and a
stimulated
fracture portion of the model. As an example, a model may model desorption of
a
hydrocarbon from organic matter in shale (e.g., include one or more desorption
equations). As an example, a model may include at least one Langmuir
parameter,
for example, associated with a Langmuir isotherm for adsorbed gas on kerogen.
[0098] As an example, a method may include generating simulation results
for
a shale gas formation and controlling at least one piece of equipment based at
least
in part on the simulation results.
[0099] As an example, one or more computer-readable storage media can
include computer-executable instructions to instruct a computing system to:
access
data for at least one shale gas formation; perform a statistical analysis on
the data
for each of the at least one shale gas formation; provide a simulation model;
history
match the simulation model for each of the at least one shale gas formation
based at
least in part on the performed statistical analysis for each of the at least
one shale
gas formation to generate a history matched model for each of the at least one
shale
gas formation; and forecast production for another shale gas formation by
plugging
in data for the other shale gas formation into each generated history matched
model.
As an example, instructions may be included to generate simulation results for
the
other shale gas formation and to control at least one piece of equipment based
at
least in part on the simulation results.
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[00100] As an example, a statistical analysis may generate a set of
production
curves for each of at least one shale gas formation and instructions may be
provided
to fit curves to each of the production curves in each set of production
curves to
generate sets of fit curves for each of the at least one shale gas formation.
As an
example, fit curves may include at least one of an exponential curve, a
hyperbolic
curve and a harmonic curve. For example, given a set of production curves such
as
P10, a Pave and a P90, a fitting process may fit each with an exponential
decline
curve, a hyperbolic decline curve and a harmonic decline curve to generate a
total of
nine fit decline curves (e.g., three for each production curve). In such an
example,
where data are provided for two shale gas formations, as an example, a total
of
eighteen fit decline curves may be generated. From such fit decline curves,
one or
more may be selected for further analysis, processing, etc. As an example, one
or
more decline curves may be used to fit a production curve with respect to
time. For
example, a mixed approach may include a hyperbolic decline curve followed in
time
by an exponential decline curve. The resulting fit decline curve from a mixed
approach may be, for example, extrapolated in time (e.g., using an exponential
tail
portion that extends from a hyperbolic head portion).
[00101] As an example, a system can include one or more processors;
memory; and instructions stored in the memory and executable by at least one
of the
one or more processors to instruct the system to access data for at least one
formation that has produced hydrocarbons; perform a statistical analysis on
the data;
provide a model; history match the model for each of the at least one
formation
based at least in part on the performed statistical analysis to generate a
history
matched model for each of the at least one formation; and forecast production
of
hydrocarbons for another formation by plugging in data for the other formation
into
each generated history matched model. In such an example, a formation may be
or
include a shale gas formation. As an example, instructions may be provided to
instruct a system to provide a model that can model a matrix, natural
fractures,
hydraulic fractures and stimulated fractures. As an example, a model may model
a
drainage area as including a matrix, one or more natural fractures, one or
more
hydraulic fractures and optionally one or more stimulated fractures.
[00102] Fig. 13 shows components of an example of a computing system 1300
and an example of a networked system 1310. The system 1300 includes one or
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more processors 1302, memory and/or storage components 1304, one or more input
and/or output devices 1306 and a bus 1308. In an example embodiment,
instructions may be stored in one or more computer-readable media (e.g.,
memory/storage components 1304). Such instructions may be read by one or more
processors (e.g., the processor(s) 1302) via a communication bus (e.g., the
bus
1308), which may be wired or wireless. The one or more processors may execute
such instructions to implement (wholly or in part) one or more attributes
(e.g., as part
of a method). A user may view output from and interact with a process via an
I/O
device (e.g., the device 1306). In an example embodiment, a computer-readable
medium may be a storage component such as a physical memory storage device,
for example, a chip, a chip on a package, a memory card, etc. (e.g., a
computer-
readable storage medium).
[00103] In an example embodiment, components may be distributed, such as in
the network system 1310. The network system 1310 includes components 1322-1,
1322-2, 1322-3,. . . 1322-N. For example, the components 1322-1 may include
the
processor(s) 1302 while the component(s) 1322-3 may include memory accessible
by the processor(s) 1302. Further, the component(s) 1302-2 may include an I/O
device for display and optionally interaction with a method. The network may
be or
include the Internet, an Intranet, a cellular network, a satellite network,
etc.
[00104] As an example, a device may be a mobile device that includes one or
more network interfaces for communication of information. For example, a
mobile
device may include a wireless network interface (e.g., operable via IEEE
802.11,
ETSI GSM, BLUETOOTH , satellite, etc.). As an example, a mobile device may
include components such as a main processor, memory, a display, display
graphics
circuitry (e.g., optionally including touch and gesture circuitry), a SIM
slot,
audio/video circuitry, motion processing circuitry (e.g., accelerometer,
gyroscope),
wireless LAN circuitry, smart card circuitry, transmitter circuitry, GPS
circuitry, and a
battery. As an example, a mobile device may be configured as a cell phone, a
tablet, etc. As an example, a method may be implemented (e.g., wholly or in
part)
using a mobile device. As an example, a system may include one or more mobile
devices.
[00105] As an example, a system may be a distributed environment, for
example, a so-called "cloud" environment where various devices, components,
etc.
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interact for purposes of data storage, communications, computing, etc. As an
example, a device or a system may include one or more components for
communication of information via one or more of the Internet (e.g., where
communication occurs via one or more Internet protocols), a cellular network,
a
satellite network, etc. As an example, a method may be implemented in a
distributed
environment (e.g., wholly or in part as a cloud-based service).
[00106] As an example, information may be input from a display (e.g.,
consider
a touchscreen), output to a display or both. As an example, information may be
output to a projector, a laser device, a printer, etc. such that the
information may be
viewed. As an example, information may be output stereographically or
holographically. As to a printer, consider a 2D or a 3D printer. As an
example, a 3D
printer may include one or more substances that can be output to construct a
3D
object. For example, data may be provided to a 3D printer to construct a 3D
representation of a subterranean formation. As an example, layers may be
constructed in 3D (e.g., horizons, etc.), geobodies constructed in 3D, etc. As
an
example, holes, fractures, etc., may be constructed in 3D (e.g., as positive
structures,
as negative structures, etc.).
[00107] 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. 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.
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