U.S. patent application number 16/088949 was filed with the patent office on 2019-04-25 for methods, systems and devices for modelling reservoir properties.
The applicant listed for this patent is NEXEN ENERGY ULC. Invention is credited to David CAMPAGNA, Graham Andrew Robert NOBLE, Stefan ZANON.
Application Number | 20190120022 16/088949 |
Document ID | / |
Family ID | 59962305 |
Filed Date | 2019-04-25 |
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United States Patent
Application |
20190120022 |
Kind Code |
A1 |
ZANON; Stefan ; et
al. |
April 25, 2019 |
METHODS, SYSTEMS AND DEVICES FOR MODELLING RESERVOIR PROPERTIES
Abstract
Aspects of the present disclosure may provide devices, systems
and methods for modelling resource production for which there may
be incomplete information and/or unknown parameters. In some
embodiments, the method includes applying an analytical fracture
model and reducing a the number of models to be matched in a set of
potential subterranean formation models.
Inventors: |
ZANON; Stefan; (Calgary,
CA) ; CAMPAGNA; David; (Calgary, CA) ; NOBLE;
Graham Andrew Robert; (Calgary, CA) |
|
Applicant: |
Name |
City |
State |
Country |
Type |
NEXEN ENERGY ULC |
Calgary |
|
CA |
|
|
Family ID: |
59962305 |
Appl. No.: |
16/088949 |
Filed: |
March 30, 2016 |
PCT Filed: |
March 30, 2016 |
PCT NO: |
PCT/CA2016/050369 |
371 Date: |
September 27, 2018 |
Current U.S.
Class: |
1/1 |
Current CPC
Class: |
E21B 43/00 20130101;
E21B 41/0092 20130101; E21B 49/00 20130101; E21B 43/26
20130101 |
International
Class: |
E21B 41/00 20060101
E21B041/00 |
Claims
1. A method of modelling hydrocarbon production rates for a
subterranean formation, the method comprising: obtaining, by at
least one processor, production data for at least one well in the
subterranean formation; based at least in part on geological data
for the subterranean formation, identify, at the at least one
processor, a range of potential values for each of a plurality of
parameters, the plurality of parameters including at least one
parameter representative of geological characteristics of the
subterranean formation, and fracture parameters; where each set of
values including a selection from each of the ranges for the
plurality of parameters defining a potential subterranean formation
model, and where sets of values including different combinations of
values for the plurality of parameters define a set of potential
subterranean formation models; matching at least a portion of the
set of potential subterranean formation models to the production
data for the at least one well by iteratively: inputting, by the at
least one processor, a set of parameter values selected from the
ranges of potential values to an analytical fracture model to
generate a production model for the subterranean formation for the
particular subterranean formation model defined by the inputted set
of values, the production model a function of a stimulated area
value; determining at least one stimulated area value for the
production model, and comparing production values for the
production model with the production data for the at least one well
to generate an error value; and selecting parameter values for
inputting in a subsequent iteration based on a machine learning
algorithm and past error values to reduce a number of analyzed
subterranean formation models that do not fit the production
profile; identifying production models which fit the production
profile from the production data within a defined error threshold;
with the identified production models which fit the production
profile from the production data for the at least one well in the
subterranean formation, selecting a range of stimulated area values
from a subset of the identified models having the lowest generated
error values; and based on a frequency distribution of the
stimulated area values from the subset of the identified models
having the lowest productivity value error scores, creating a
forecast production model for at least a portion of the
subterranean resource, the forecast production model having input
parameters representative of geological characteristics of at least
the portion of the subterranean formation, and an input parameter
associated with the stimulated area value and limited to the
selected range.
2. The method of claim 1, wherein the stimulated area value is a
function of permeability and fracture area.
3. The method of claim 1, wherein the analytical fracture model is
a function of the stimulated area value.
4. The method of claim 1, wherein the analytical fracture model
includes dimensionless time and dimensionless pressure logs to
generate the production model for a series of time steps.
5. The method of claim 1 wherein the production data is collected
over a period of time.
6. The method of claim 1 wherein the geological data for the
subterranean formation is collected from at least one sensing
device or a petrophysical analysis of well logs.
7. The method of claim 1 wherein identifying the range of potential
values for each of a plurality of parameters comprises identifying
a granularity at which values can be selected within the range of
potential values.
8. The method of claim 1 wherein the fracture parameters include
parameters associated with a number of fractures and at least one
fracture area dimension.
9. The method of claim 1 comprising: creating forecast models of
different portions of the subterranean resource, each forecast
production model having input parameters representative of
geological characteristics of the respective portion of the
subterranean formation.
10. The method of claim 9 comprising: using the forecast models,
generating a visual map illustrating different production forecasts
for the different portions of the subterranean formation.
11. A system for modelling hydrocarbon production rates for a
subterranean formation, the system comprising at least one
processor configured for: obtaining production data for at least
one well in the subterranean formation; based at least in part on
geological data for the subterranean formation, identify a range of
potential values for each of a plurality of parameters, the
plurality of parameters including at least one parameter
representative of geological characteristics of the subterranean
formation, and fracture parameters; where each set of values
including a selection from each of the ranges for the plurality of
parameters defining a potential subterranean formation model, and
where sets of values including different combinations of values for
the plurality of parameters define a set of potential subterranean
formation models; matching at least a portion of the set of
potential subterranean formation models to the production data for
the at least one well by iteratively: inputting a set of parameter
values selected from the ranges of potential values to an
analytical fracture model to generate a production model for the
subterranean formation for the particular subterranean formation
model defined by the inputted set of values, the production model a
function of a stimulated area value; determining at least one
stimulated area value for the production model, and comparing
production values for the production model with the production data
for the at least one well to generate an error value; and selecting
parameter values for inputting in a subsequent iteration based on a
machine learning algorithm and past error values to reduce a number
of analyzed subterranean formation models that do not fit the
production profile; identifying production models which fit the
production profile from the production data within a defined error
threshold; with the identified production models which fit the
production profile from the production data for the at least one
well in the subterranean formation, selecting a range of stimulated
area values from a subset of the identified models having the
lowest generated error values; and based on a frequency
distribution of the stimulated area values from the subset of the
identified models having the lowest productivity value error
scores, creating a forecast production model for at least a portion
of the subterranean resource, the forecast production model having
input parameters representative of geological characteristics of at
least the portion of the subterranean formation, and an input
parameter associated with the stimulated area value and limited to
the selected range.
12. The system of claim 11, wherein the stimulated area value is a
function of permeability and fracture area.
13. The system of claim 11, wherein the analytical fracture model
is a function of the stimulated area value.
14. The system of claim 11, wherein the analytical fracture model
includes dimensionless time and dimensionless pressure logs to
generate the production model for a series of time steps.
15. The system of claim 11 wherein the production data is collected
over a period of time.
16. The system of claim 11 wherein the geological data for the
subterranean formation is collected from at least one sensing
device or a petrophysical analysis of well logs.
17. The system of claim 11 wherein identifying the range of
potential values for each of a plurality of parameters comprises
identifying a granularity at which values can be selected within
the range of potential values.
18. The system of claim 11 wherein the fracture parameters include
parameters associated with a number of fractures and at least one
fracture area dimension.
19. The system of claim 11 wherein the at least one processor is
configured for: creating forecast models of different portions of
the subterranean resource, each forecast production model having
input parameters representative of geological characteristics of
the respective portion of the subterranean formation.
20. The system of claim 19 wherein the at least one processor is
configured for: using the forecast models, generating a visual map
illustrating different production forecasts for the different
portions of the subterranean formation.
21. A computer-readable medium or media having stored thereon
computer-readable instructions which when executed by at least one
processor configured the at least one processor for: obtaining, by
the at least one processor, production data for at least one well
in the subterranean formation; based at least in part on geological
data for the subterranean formation, identify, at the at least one
processor, a range of potential values for each of a plurality of
parameters, the plurality of parameters including at least one
parameter representative of geological characteristics of the
subterranean formation, and fracture parameters; where each set of
values including a selection from each of the ranges for the
plurality of parameters defining a potential subterranean formation
model, and where sets of values including different combinations of
values for the plurality of parameters define a set of potential
subterranean formation models; matching at least a portion of the
set of potential subterranean formation models to the production
data for the at least one well by iteratively: inputting, by the at
least one processor, a set of parameter values selected from the
ranges of potential values to an analytical fracture model to
generate a production model for the subterranean formation for the
particular subterranean formation model defined by the inputted set
of values, the production model a function of a stimulated area
value; determining at least one stimulated area value for the
production model, and comparing production values for the
production model with the production data for the at least one well
to generate an error value; and selecting parameter values for
inputting in a subsequent iteration based on a machine learning
algorithm and past error values to reduce a number of analyzed
subterranean formation models that do not fit the production
profile; identifying production models which fit the production
profile from the production data within a defined error threshold;
with the identified production models which fit the production
profile from the production data for the at least one well in the
subterranean formation, selecting a range of stimulated area values
from a subset of the identified models having the lowest generated
error values; and based on a frequency distribution of the
stimulated area values from the subset of the identified models
having the lowest productivity value error scores, creating a
forecast production model for at least a portion of the
subterranean resource, the forecast production model having input
parameters representative of geological characteristics of at least
the portion of the subterranean formation, and an input parameter
associated with the stimulated area value and limited to the
selected range.
Description
FIELD
[0001] The present application relates to the field of reservoir
modelling, and particularly to methods, systems and devices for
modelling unconventional oil reservoir production based on
collected physical data.
BACKGROUND
[0002] Hydrocarbon exploration involves trade-offs between the
number and spacing of wells (and associated costs) and the
geological and commercial risk based on available data which may
impact production forecasting and resource development
planning.
[0003] Type curves can be used to estimate reservoir production for
new wells by averaging existing wells. However, in some instances,
type curves may be difficult to adjust for differences, and may not
account for subtle changes across an area or over time.
[0004] Simulations can also be used to model reservoir properties;
however, simulations may require large amounts of input data which
may be expensive to obtain, and are expensive from both a time and
computational resource perspective.
[0005] Methods, systems and devices which can reduce computational
requirements and/or input data requirements are desirable.
SUMMARY
[0006] In accordance with one aspect, there is provided a method of
modelling hydrocarbon production rates for a subterranean
formation. The method includes: obtaining, by at least one
processor, production data for at least one well in the
subterranean formation; based at least in part on geological data
for the subterranean formation, identify, at the at least one
processor, a range of potential values for each of a plurality of
parameters, the plurality of parameters including at least one
parameter representative of geological characteristics of the
subterranean formation, and fracture parameters; where each set of
values including a selection from each of the ranges for the
plurality of parameters defining a potential subterranean formation
model, and where sets of values including different combinations of
values for the plurality of parameters define a set of potential
subterranean formation models; matching at least a portion of the
set of potential subterranean formation models to the production
data for the at least one well by iteratively: inputting, by the at
least one processor, a set of parameter values selected from the
ranges of potential values to an analytical fracture model to
generate a production model for the subterranean formation for the
particular subterranean formation model defined by the inputted set
of values, the production model a function of a stimulated area
value; determine at least one stimulated area value for the
production model, and comparing production values for the
production model with the production data for the at least one well
to generate an error value; and selecting parameter values for
inputting in a subsequent iteration based on a machine learning
algorithm and past error values to reduce a number of analyzed
subterranean formation models that do not fit the production
profile; identifying production models which fit the production
profile from the production data within a defined error threshold;
with the identified production models which fit the production
profile from the production data for the at least one well in the
subterranean formation, selecting a range of stimulated area values
from a subset of the identified models having the lowest generated
error values; and based on a frequency distribution of the
stimulated area values from the subset of the identified models
having the lowest productivity value error scores, creating a
forecast production model for at least a portion of the
subterranean resource, the forecast production model having input
parameters representative of geological characteristics of at least
the portion of the subterranean formation, and an input parameter
associated with the stimulated area value and limited to the
selected range.
[0007] In accordance with another aspect, there is provided a
system for modelling hydrocarbon production rates for a
subterranean formation. The system includes at least one processor
configured for: obtaining production data for at least one well in
the subterranean formation; based at least in part on geological
data for the subterranean formation, identify a range of potential
values for each of a plurality of parameters, the plurality of
parameters including at least one parameter representative of
geological characteristics of the subterranean formation, and
fracture parameters; where each set of values including a selection
from each of the ranges for the plurality of parameters defining a
potential subterranean formation model, and where sets of values
including different combinations of values for the plurality of
parameters define a set of potential subterranean formation models;
matching at least a portion of the set of potential subterranean
formation models to the production data for the at least one well
by iteratively: inputting a set of parameter values selected from
the ranges of potential values to an analytical fracture model to
generate a production model for the subterranean formation for the
particular subterranean formation model defined by the inputted set
of values, the production model a function of a stimulated area
value; determine at least one stimulated area value for the
production model, and comparing production values for the
production model with the production data for the at least one well
to generate an error value; and selecting parameter values for
inputting in a subsequent iteration based on a machine learning
algorithm and past error values to reduce a number of analyzed
subterranean formation models that do not fit the production
profile; identifying production models which fit the production
profile from the production data within a defined error threshold;
with the identified production models which fit the production
profile from the production data for the at least one well in the
subterranean formation, selecting a range of stimulated area values
from a subset of the identified models having the lowest generated
error values; and based on a frequency distribution of the
stimulated area values from the subset of the identified models
having the lowest productivity value error scores, creating a
forecast production model for at least a portion of the
subterranean resource, the forecast production model having input
parameters representative of geological characteristics of at least
the portion of the subterranean formation, and an input parameter
associated with the stimulated area value and limited to the
selected range.
[0008] In accordance with another aspect, there is provided a
computer-readable medium or media having stored thereon
computer-readable instructions which when executed by at least one
processor configured the at least one processor for: obtaining, by
the at least one processor, production data for at least one well
in the subterranean formation; based at least in part on geological
data for the subterranean formation, identify, at the at least one
processor, a range of potential values for each of a plurality of
parameters, the plurality of parameters including at least one
parameter representative of geological characteristics of the
subterranean formation, and fracture parameters; where each set of
values including a selection from each of the ranges for the
plurality of parameters defining a potential subterranean formation
model, and where sets of values including different combinations of
values for the plurality of parameters define a set of potential
subterranean formation models; matching at least a portion of the
set of potential subterranean formation models to the production
data for the at least one well by iteratively: inputting, by the at
least one processor, a set of parameter values selected from the
ranges of potential values to an analytical fracture model to
generate a production model for the subterranean formation for the
particular subterranean formation model defined by the inputted set
of values, the production model a function of a stimulated area
value; determine at least one stimulated area value for the
production model, and comparing production values for the
production model with the production data for the at least one well
to generate an error value; and selecting parameter values for
inputting in a subsequent iteration based on a machine learning
algorithm and past error values to reduce a number of analyzed
subterranean formation models that do not fit the production
profile; identifying production models which fit the production
profile from the production data within a defined error threshold;
with the identified production models which fit the production
profile from the production data for the at least one well in the
subterranean formation, selecting a range of stimulated area values
from a subset of the identified models having the lowest generated
error values; and based on a frequency distribution of the
stimulated area values from the subset of the identified models
having the lowest productivity value error scores, creating a
forecast production model for at least a portion of the
subterranean resource, the forecast production model having input
parameters representative of geological characteristics of at least
the portion of the subterranean formation, and an input parameter
associated with the stimulated area value and limited to the
selected range.
[0009] Many further features and combinations thereof concerning
embodiments described herein will appear to those skilled in the
art following a reading of the present disclosure.
DESCRIPTION OF THE FIGURES
[0010] In the figures,
[0011] FIG. 1 is a cross sectional view of an example geological
formation and well;
[0012] FIG. 2 is an example system to which aspects of the present
disclosure may be applied;
[0013] FIG. 3 is a perspective sectional view of an example
horizontal well portion with fractures;
[0014] FIG. 4 is a top view of an example horizontal well portion
with fractures;
[0015] FIGS. 5, 6 and 7 are flowcharts illustrating aspects of
example methods for modelling reservoir properties;
[0016] FIGS. 8 and 9 is are stimulated area vs. error value
plots;
[0017] FIG. 10 shows an example geographic map showing different
production forecasts for different portions of a resource; and
[0018] FIG. 11 shows a graph showing examples of different
probabilistic forecasts of production rates.
DETAILED DESCRIPTION
[0019] In hydrocarbon development, accurate estimations of rates of
production can help provide information regarding the value and/or
viability of a project/resource. These estimations may also guide
the number, location and/or orientation of wells. Due to the high
cost of development, there can be significant financial incentives
to properly describe uncertainty in outcomes so informed decisions
can be made as much as possible. In some examples, it may be
important to keep the cost and amount of time spent acquiring the
information low.
[0020] In some embodiments, aspects of the present disclosure may
provide analytic devices, systems and methods for modelling
resource production which are computationally less intensive or
less time consuming than a complex simulation. In some embodiments,
aspects of the present disclosure may have a higher degree of
confidence in their models than a type curve or other similar
model.
[0021] In some embodiments, aspects of the present disclosure may
provide devices, systems and methods for modelling resource
production based on incomplete or less information than would be
necessary for other processes.
[0022] In broad embodiments, aspects of the disclosure may, in some
instances, provide a practical method for spatially modelling value
to target areas of highest potential investment return.
[0023] FIG. 1 illustrates a cross-sectional view of a subterranean
resource or geological formation 110 which may include a number of
different layers of materials having different physical
characteristics as illustrated in FIG. 1 by the lines in the
formation. It should be understood that these lines are
illustrative only and that geological formations may have any
number of layers or types of material which may not have distinct
delineations but may be gradual or may contain mixtures or
combinations of different material. There may also be lateral
and/or vertical variations in the types of material contained
within any of the geological formations.
[0024] In evaluating the subsurface or subterranean formations, in
some examples, data is collected from one or more wells 100 drilled
into or around the formations. In some examples, the wells are
exploratory wells, production wells or wells for any other purpose.
The wells may include vertical wells 100, horizontal wells 105, or
any wells of any direction or structure, and/or any combination
thereof.
[0025] In some examples, data collected from the well(s) 100 can
include or can be used to create logs of the geologic formations
penetrated by the well(s). The data can be collected from core
samples or by measurements taken by devices in the borehole.
[0026] In some examples, the well data collected or generated from
well measurements can include, but are not limited to, gamma ray
logs, bulk density logs, neutron density logs, induction
resistivity logs, and/or well core or image data.
[0027] In some examples, the geological formation may include
hydrocarbon-bearing layers having low permeability such as shale or
tight sandstone. Such formations may be suitable for hydrocarbon
extraction using hydraulic fracturing technologies. In some
instances, such unconventional plays may have large spatial
variability, may involve multiple hydrocarbon fluids phases, and/or
may have uncertainties in fracturing areas and permeability.
[0028] As such, in some instances, traditional techniques for
conventional oil well drilling may be unsuitable for these
unconventional resources.
[0029] The embodiments of the devices, systems and methods
described herein may be implemented in a combination of both
hardware and software. These embodiments may be implemented on
programmable computers, each computer including at least one
processor, a data storage system (including volatile memory or
non-volatile memory or other data storage elements or a combination
thereof), and at least one communication interface.
[0030] Program code may be applied to input data to perform the
functions described herein and to generate output information. The
output information may be applied to one or more output devices. In
some embodiments, the communication interface may be a network
communication interface. In embodiments in which elements may be
combined, the communication interface may be a software
communication interface, such as those for inter-process
communication. In still other embodiments, there may be a
combination of communication interfaces implemented as hardware,
software, and combination thereof. In some examples, devices having
at least one processor may be configured to execute software
instructions stored on a computer readable tangible, non-transitory
medium.
[0031] The following discussion provides many example embodiments.
Although each embodiment represents a single combination of
inventive elements, other examples may include all possible
combinations of the disclosed elements. Thus if one embodiment
comprises elements A, B, and C, and a second embodiment comprises
elements B and D, other remaining combinations of A, B, C, or D,
may also be used.
[0032] The technical solution of embodiments may be in the form of
a software product. The software product may be stored in a
non-volatile or non-transitory storage medium, which can be a
compact disk read-only memory (CD-ROM), a USB flash disk, or a
removable hard disk. The software product includes a number of
instructions that enable a computer device (personal computer,
server, or network device) to execute the methods provided by the
embodiments.
[0033] The embodiments described herein are implemented by physical
computer hardware, including computing devices, servers, receivers,
transmitters, processors, memory, displays, and networks. The
embodiments described herein provide useful physical machines and
particularly configured computer hardware arrangements. The
embodiments described herein are directed to electronic machines
and methods implemented by electronic machines adapted for
processing and transforming electromagnetic signals which represent
various types of information. The embodiments described herein
pervasively and integrally relate to machines, and their uses; and
the embodiments described herein have no meaning or practical
applicability outside their use with computer hardware, machines,
and various hardware components. Substituting the physical hardware
particularly configured to implement various acts for non-physical
hardware, using mental steps for example, may substantially affect
the way the embodiments work. Such computer hardware limitations
are clearly essential elements of the embodiments described herein,
and they cannot be omitted or substituted for mental means without
having a material effect on the operation and structure of the
embodiments described herein. The computer hardware is essential to
implement the various embodiments described herein and is not
merely used to perform steps expeditiously and in an efficient
manner.
[0034] FIG. 2 shows an example system 200 include one or more
devices 205 which may be used to model or predict hydrocarbon
production rates. In some examples, a device 205 may be a
computational device such as a computer, server, tablet or mobile
device, or other system, device or any combination thereof suitable
for accomplishing the purposes described herein. In some examples,
the device 205 can include one or more processor(s) 210, memories
215, and/or one or more devices/interfaces 220 necessary or
desirable for input/output, communications, control and the like.
The processor(s) 210 and/or other components of the device(s) 205
or system 250 may be configured to perform one or more aspects of
the processes described herein.
[0035] In some examples, the device(s) 205 may be configured to
receive or access data from one or more volatile or non-volatile
memories 215, or external storage devices 225 directly coupled to a
device 205 or accessible via one or more wired and/or wireless
network(s) 260. In external storage device(s) 225 can be a network
storage device or may be part of or connected to a server or other
device.
[0036] In some examples, the data may be accessed from one or more
public databases. Such data can, in some examples, include less
well characteristic and/or production data than may be available
for well data from an internal source. For example, well production
data may be provided as an aggregate of multiple wells or for an
area without any information as to the number or size of fractures.
In another example, well production data may be provided over time
in less granular time periods. In some embodiments, the methods,
devices, and systems described herein may generate models which
account for such unknown(s).
[0037] In some examples, the device(s) 205 may be configured to
receive or access data from sensors or devices 230 in the field.
These sensors or devices 230 may be configured for collecting or
measuring well, seismic or other geological and/or physical data.
In some examples, the sensor(s)/device(s) 230 can be configured to
communicate the collected data to the device(s) 205 and/or storage
device(s) 225 via one or more networks 260 or otherwise. In some
examples, the sensors or devices 230 may be connected to a local
computing device 240 which may be configured to receive the data
from the sensors/devices 230 for local storage and/or communication
to the device(s) 205 and/or storage device(s) 225.
[0038] In some examples, a client device 250 may connect to or
otherwise communicate with the device(s) 205 to gain access to the
data and/or to instruct or request that the device(s) 205 perform
some or all of the aspects described herein.
[0039] With reference to FIG. 3, in some embodiments, a well may
have multiple hydraulic fractures 310, which may be transverse or
in any other orientation with respect to the well. The wellbore 120
in FIG. 3 is horizontal; however, in other examples, fractures can
be made relative to a wellbore of any orientation.
[0040] FIG. 4 shows a top-down view of the horizontal well 120
including four fractures 310. In some embodiments, a well with
multiple fractures can be modelled as equally-spaced bi-wing
transverse fractures. In some embodiments, the model can assume
that there is a no flow boundary 315 at a midpoint between each
fracture.
[0041] On the right, FIG. 4 shows an example drainage area for a
single fracture 310. In some embodiments, fracture parameters can
include a number of fractures, a fracture drainage area half-length
Y.sub.e, a fracture drainage area half width X.sub.e, a fracture
half-length X.sub.f, and the like.
[0042] In some examples, the system can be configured to treat each
fracture of a multi-fracture well as multiple wells with a single
fracture each. In some instances, this may simplify the models and
may reduce the computational load on the system.
[0043] The model can account for spatial differences in geology
through geostatistical realization. Multiple models are run on a
finite and discrete number of areas with a larger defined region.
Distributions can then be calculated for each area. The model can
represent one region over a play or can be run on several.
[0044] FIG. 5 shows a flowchart illustrating aspects of an example
method 500 for modelling hydrocarbon production rates for a
subterranean formation. At 510, one or more processor(s) 210 and/or
other aspects of device(s) 205 may be configured to receive,
access, compile or otherwise obtain production data for at least
one well in the subterranean formation 110.
[0045] In some examples, the production data can be obtained from
one or more memories 215, storage devices 215, 225, and/or sensors
or field devices 230, 240. In some examples, the production data
can include periodic (e.g. daily, weekly, monthly, etc.) production
values, cumulative production values or any other data values from
which suitable production data can be calculated.
[0046] In some embodiments, the production data for the well(s)
should span at least 6 months of normalized production in order to
produce meaningful results.
[0047] The processor(s) can also obtain drilling data, completion
data, and/or data associated with geological properties.
[0048] In some examples, data associated with geological properties
can include well logs such as gamma ray well log(s), bulk density
well log(s), neutron density well log(s), resistivity well log(s),
core and/or well image data, nuclear magnetic resonance log(s),
and/or any other well log that can be measured in the well.
[0049] In some examples, the processor(s) drilling and/or
completion data can include fracture heights, fracture half
lengths, a number of fractures, a fracture drainage area
half-lengths, a fracture drainage area half widths, a fracture
half-lengths, well lengths, and the like.
[0050] In some embodiments, data may be obtained from internal data
sources, or data collected from drilled wells and fracturing
processes.
[0051] In some embodiments, data may be obtained from public
sources such as a data retrieved from a government or otherwise
public fracturing database. This public data may include production
data for which production values for multiple fractures and/or
wells may be combined into a single value. In some examples, the
public data may not include all fracture characteristics or
geological data.
[0052] In accordance with some embodiments, the methods, systems
and devices herein may accommodate for incomplete data sources such
as public data or limited confidential data from a third party
while still providing a production model with a reasonable degree
of confidence.
[0053] In some embodiments, the processor(s) may combine internal
and external data sources.
[0054] At 520, the processor(s) identify a range of values for each
parameter in a set of parameters for the subterranean formation. In
some examples, the range of values for a parameter can be
identified by determining a possible range of values based on
geological data. In some embodiments, the processors can receive or
otherwise obtain ranges of values from one or more input sources
which may be based at least in part on the collected geological
data for the subterranean formation.
[0055] The set of parameters can include one or more geological
parameters representative of geological characteristics of the
subterranean formation. For example, geological parameters can
include one or more of formation height, reservoir depth, porosity,
permeability, water saturation, and the like. In some examples, the
geological parameters can include pressure properties, and/or rock
and fluid properties including, for example, pressure gradient,
initial pressure, operating conditions such as early or late well
flowing pressure, months to well flowing pressure change,
temperature gradient, reservoir temperature, rock compressibility,
water compressibility, API (American Petroleum Institute) gravity,
gas-to-oil ratio, combined gas gravity, relative gas permeability,
residual gas saturation, critical gas saturation, gas viscosity,
condensate gas ratio and the like.
[0056] In some examples, the set of parameters can include well
parameters such as completion parameters and fracture parameters.
These parameters can include fracture heights, fracture half
lengths, a number of fractures, fracture drainage area
half-lengths, fracture drainage area half widths, fracture
half-lengths, well lengths, and the like.
[0057] In some instances, one or more parameters may be fixed or
constant based on the parameter itself or the obtained data. These
parameter(s) will have a single value. However, other parameters
may be unknown or may not be pinpointed exactly, so the
processor(s) generate or receive a range of potential values for
the parameter(s). These ranges can be based on the obtained
geological and/or other obtained data.
[0058] For example, the processor(s) can generate or receive
parameter ranges which would be reasonable or possible based on log
data, geostatistical models and/or extrapolated data between wells.
In some examples, the processor(s) can generate or receive
parameter ranges based on correlations with other parameters. For
example, pressure(s) may be correlation with reservoir depth. In
some examples, the range of values may be based on correlations
with other geological formations having similar geological
data.
[0059] In some embodiments, the processor(s) can generate or
receive a granularity or increment by which the ranges of parameter
values can be varied in the system.
[0060] Each combination of values including a selection from each
range for the set of parameters corresponds to, defines or
otherwise represents a potential subterranean formation model, and
the total set of different combinations of values for the set of
parameters defines a set of potential subterranean formation
models.
[0061] At 530, the processor(s) match the set of potential
subterranean formation models to the production data for the
well(s). The matching is based on an analytical fracture model. In
some examples, the analytical fracture model can generate a
production model based on the geological parameters. However, as
described herein, the base analytical model can include a number of
unknowns which cannot be solved directly. In some embodiments, a
number of these unknowns can be combined into a single stimulated
area value. In some examples, the stimulated area value can be
based on a number of parameters in the set of parameters defining
or otherwise associated with properties of a subterranean formation
model.
[0062] In some examples, using a combination of values for the set
of parameters as inputs to the analytical fracture model, the
processor(s) can generate a production model for the potential
subterranean formation model corresponding to the set of input
values. In some examples, the processor(s) can determine a
stimulated area value for the generated production model.
[0063] The processor(s) compare the generated production model with
the production data from the well(s) to generate an error value
between the production of the generated model and the physical well
production data. In some examples, the error value may be a total
error over the production period, an average error, a maximum
error, or any other error metric. In some embodiments, the error
value may be based on individual production values for each time
period.
[0064] In some embodiments, the error value may be based on one or
more of cumulative gas production, gas production rate, cumulative
oil production, oil production rate, and the like.
[0065] In some embodiments, the processor(s) are configured to
select parameter values for inputting in a subsequent iteration
based on a machine learning algorithm and previous error values. In
some examples, the processor(s) can be configured to use a genetic
algorithm to select subsequent parameter values for generating
subsequent models.
[0066] In some embodiments, the genetic algorithm can optimize or
improve the searching of a large solution space. It can include
selecting several sets of random values from all parameters and
solving for the objective matching function. The results which
provide the best matching function result can be merged together to
find new sets of values for testing. This process is iterated with
the best matching function results from each iteration used in
subsequent sets of values for testing. In addition to or
alternatively to previous inputs sets being combined, in some
embodiments, random variations can be introduced to input sets. In
some instances, this can reduce the chance of solutions converging
on a local optimal rather than a global optimal.
[0067] In some instances, this may reduce the number of models to
be analyzed, and may reduce the computation time and resources
required to complete the model matching process.
[0068] At 540, the processor(s) identify the models from the set of
potential subterranean formation models which fit the production
profile from the production data within a defined error threshold.
In some examples, this includes identifying the models having an
error value less than the defined error threshold. In some
examples, the error threshold may be a statically defined
threshold. In other examples, the error threshold may be based on a
defined percentile of the models.
[0069] In some embodiments, the error at different stages of
production can be weighted differently. For example, if early
production results are more important, the error for earlier stages
of production can be weighted more than error for later stages of
production; and vice versa. These weightings can be applied to
objective matching functions.
[0070] In some embodiments, the determination of the stimulated
area values includes performing two runs to deliver a stable
region. On the first run, a consistent number (e.g. 5000) of trials
are performed on each run. On the second run, the model can be
rerun with the last good solution as a starting point to ensure
tight clustering of values. Again a consistent number of runs
should be used. In some embodiments, three criteria are then used
to define an acceptable solution space. First, a minimum absolute
error of at least 20% should be achieved for a run to be considered
valid. All runs should be completed with similar weighting to
ensure the results are comparable. Second, the mean error can be
examined as it progresses through the solution space. Cases that
are within a best fit mean error will be retained. Only trials with
a deviation of less than 1% will be used. Third, the maximum error
of 2.times. the minimum error should be used to bound the cases.
The corresponding total error for this stable region of running
average stimulated area value can be used to pick a minimum and
maximum stimulated area value. In other embodiments, different
mechanisms for determining the stimulated area values may be
used.
[0071] At 550, the processor(s) select a range of stimulated area
values from the identified models which fit the production profile
from the production data within the defined error threshold. In
some examples, the selected range is based on the models having the
lowest error values. In some examples, the selected range is based
on a concentration of stimulated area values which correspond to
models having a low error value.
[0072] At 560, the processor(s) create a forecast production model
for the subterranean resource having inputs based on the geological
data, and stimulated value input(s) based on the selected range.
The process for forecasting production model is described in the
following sections.
Analytical Model
[0073] As described above, in some embodiments, the matching
process at 530 is based on an analytical fracture model. In some
examples, the analytical fracture model can involve a gas model, an
oil model or both.
[0074] FIG. 6 shows an example flowchart 600 outlining aspects of
an example gas model. As described herein or otherwise, the
processor(s) generate or receive physical input values or ranges of
physical input values based on obtained geological data. In some
examples, such geological data can be derived from petrophysical
interpretation of well logs from adjacent or nearby wells. In some
embodiments, the geological data can include time steps, pressure
differences (initial, early, late), net pay, gas permeability,
porosity, drainage area half width, fracture spacing half distance,
condensate gas ratio, and the like.
TABLE-US-00001 PARAMETER Units Min Max Increments Skin Shape 0.5 3
0.1 Function Krg 0.1 0.5 0.02 Formation Net m 53.24 70.69 0.25
Thickness Frac Height % 60% 100% 2% Frac half length m 20 85 2 Well
Length m 1200 1391 5 Number of Fracs Count 9 12 1 Reservoir Depth m
3236 3328 0 Pressure Gradient psi/ft 0.759927 0.834638 0.01 Early
Pwf psig 2000 7500.0 100 Late Pwf psig 200 2000.0 20 Pwf Change
Time Months 0 6.0 1 Temperature F/ft 0.02 0.03 0.001 Gradient Rock
Compres- 1/psi 1.0E-07 6.0E-06 2.0E-07 sibility Porosity % 5% 6%
0.1% Permeability mD -4 -2.52 0.01 (log scale) Water Saturation %
10% 18% 0.25% CGR Bbl/Mmcf 50.00 89.00 5.00
[0075] From the physical inputs, the processor(s) may calculate
other inputs which may be dependent or otherwise correlated with
the physical inputs or other calculated inputs. These calculated
inputs or correlations may include gas z factor calculations,
critical temperatures and pressures for miscellaneous gasses, gas
viscosity correlations, gas compressibility correlations, gas
pseudopressure miscellaneous gasses, temperature, skin, k slippage
and the like. These calculations may be performed with any
currently known or future techniques.
[0076] The table above illustrates example physical and calculated
inputs which may be used to create models for the subterranean
formation.
[0077] Based on Darcy's Law, in its simplest form, radial flow q is
based on both an effective permeability, and a fracture area:
q = kA * .DELTA. p .mu. * L ##EQU00001##
[0078] Assuming all other factors are constant, if permeability
increases, the fracture area must be lower to achieve the same flow
rate. Since these parameters are both unknown, the relationship
between these parameters can be determined to select appropriate
forecast models which will produce accurate results. These unknowns
can be combined into a stimulated area value which may be solved
based on the known production data.
[0079] In a simplified linear flow equation:
1 q = m t + b ' ##EQU00002##
[0080] Oil and gas flow equations are different:
m = 31.3 B hx f k .mu. .0. c t * 1 p i - p wf ##EQU00003##
[0081] Oil:
m = 315.4 T h .0. .mu. g c t * 1 p i - p wf * 1 x f k
##EQU00004##
[0082] Gas:
[0083] As highlighted these linear flow equations share the
unknowns of pay thickness h, the fracture half-width x.sub.f, and
the square root of the permeability k. In some embodiments,
processor(s) can be configured to combine these into a single
stimulated area value. This stimulated area value can, in some
embodiments, be the stimulated area A (h*x.sub.f) times the root of
the effective permeability k.
[0084] In some embodiments, the analytical model for gas may be
based on an equivalent gas flow rate equation:
q g = ( skin ) ( # of fracs ) ( .DELTA. p ) ( h ) ( K gas ) ( Gas
Constant ) ( 460 + T ) ( P wd Gas Frac ) Where skin = permeability
adjustment factor due to liquid drop out .DELTA. p = difference in
pressure across wellface h = pay thickness K gas = gas permeability
T = reservoir temperature in degrees Farenheit P wd Gas Frac =
dimensionless pressure ( 1 ) ##EQU00005##
[0085] The dimensionless pressure can be based on a Gringarten
(Gringarten, A. C., Ramey, H. J., "Unsteady-State Pressure
Distributions created by a well with a single Infinite-Conductivity
Vertical Fracture", Stanford University, August 1974) approach
using dimensionless time:
Tda Gas = 0.00633 * K gas * Green Function Time .mu. g * .0. * C t
Gas * 4 * X e * Y e ( 2 ) ##EQU00006##
[0086] Where [0087] .mu..sub.g=gas viscosity [0088] O=porosity
[0089] C.sub.t.sub.Gas=gas compressability [0090] Xe=drainage area
half width, and [0091] Ye=drainage area half-length or half
distance between fracs
[0092] With equation (2), the processor(s) can generate a log of
dimensionless time values. In some examples, this model may assume
and treat each fracture as a single vertical well with a single
horizontal fracture. In some embodiments, the processor(s) handle
each fracture in a well with multiple fractures as individual
identical single fracture wells with a well length based on the
original well length divided by the number of fractures.
[0093] Based on the above and using Green Equations, the
processor(s) can generate an analytical model including a log of
dimensionless time and pressure estimates. From these logs and
equation (1), a single phase gas forecast estimate can be provided
for each time step. These cumulative gas volumes are at surface
conditions prior to liquids being removed.
[0094] The relationship of pressure drop over time p/z can be used
to calculate pressure steps as the gas is produced. In some
embodiments, the processor(s) generate the p/z relationship based
on initial pressure, and final pressure from the measured or
calculated data. For example, an initial pressure can be a known
measured data point, or it can be calculated based on depth and
gradient data. In some examples, the gradient may be a varied
parameter during the matching process.
[0095] Based on iterations of:
P / Z slope = Pwf - Pi Qg ##EQU00007##
the pressures at each interval can be generated.
[0096] In some embodiments, each time interval can be divided into
two or more subintervals. The processor(s) can be configured to
determine the pressure, P/Z slope and Q for each subinterval. The
average pressure across these subintervals is then used as the
pressure for the whole interval. In some examples, this can
potentially provide a more accurate estimation of the changing
pressure as the well ages.
[0097] In some embodiments, the processor(s) can be configured to
account for changes to the gas/liquid composition changes with
pressure. Based on the condensate gas ratio parameter, the
processor(s) can generate or access a database of PVT
(pressure-volume-temperature) tables at different yield bands.
[0098] In addition to the analytical model for gas production, in
some embodiments, the processor(s) can generate an analytical model
which alternatively or additional accounts for oil production. FIG.
7 shows an example flowchart 700 outlining aspects of an example
oil model. As described herein or otherwise, the processor(s)
generate or receive physical input values or ranges of physical
input values based on obtained geological data. In some examples,
such geological data can be derived from petrophysical
interpretation of well logs from adjacent or nearby wells. In some
embodiments, the physical inputs can include time steps, pressure
differences, net pay, effective permeability, porosity, drainage
area half width, fracture spacing half distance, condensate gas
ratio or gas oil ratio, initial water saturation and the like.
[0099] From the physical inputs, the processor(s) may calculate
other inputs which may be dependent or otherwise correlated with
the physical inputs or other calculated inputs. These calculated
inputs or correlations may include oil viscosity, gas viscosity,
oil expansion factor, gas expansion factor, bubble point pressure,
oil compressibility, formation volume factors, solution gas-oil
ratio, formation volume factor, oil expansion factor, and the like.
In some examples, these calculated inputs may be calculated
relative to a bubble point pressure. These calculations may be
performed with any currently known or future techniques.
[0100] As described above, the processor(s) can generate the oil
analytical model by determining a stimulated area value (e.g. A
root K) similar to the gas model. However, the dimensionless time
value function for oil can be based on:
Tda = 0.00633 * K oil * Green Function Time .mu. o * .0. * C t * 4
* ( ( X e * Y e ) / ( 0.3048 ) 2 ) , and ##EQU00008## q o = ( # of
fracs ) ( .DELTA. p ) ( h ) ( k eff ) ( k ro ) ( 141.2 ) ( .mu. o )
( P wd ) ( B ti ) ##EQU00008.2##
[0101] Where [0102] Tda=dimensionless time [0103]
.DELTA.p=difference in pressure across wellface [0104] h=pay
thickness or layer thickness*frac height [0105] K.sub.eff=absolute
effective permeability [0106] K.sub.oil=relative permeability to
oil [0107] P.sub.wd=dimensionless pressure [0108] .mu..sub.o=gas
viscosity [0109] C.sub.t=gas compressability [0110] Xe=drainage
area half width [0111] Ye=drainage area half-length or half
distance between fracs [0112] # of fracs=number of fractures in the
horizontal well [0113] Bti=Total expansion factor
[0114] Based on the above and using Green Equations, the
processor(s) can generate an analytical oil model including a log
of dimensionless time and pressure estimates. From these logs and
equations, an oil forecast estimate can be provided for each time
step. These cumulative gas volumes are at surface conditions prior
to liquids being removed.
[0115] In some embodiments, the processor(s) can apply material
balance equations to adjust the oil production model to account for
different gas-oil ratios at different pressures.
[0116] Absolute permeability is the measure of the ability of a
single phase fluid to move through a porous medium. When multiple
phases (ie. Gas and oil, or oil and water) are present at the same
time inefficiencies are created resulting in a permeability that is
a fraction of the absolute permeability. This is referred to as
relative permeability. Relative permeability changes as a function
of the saturation of one phase as a percentage of pore volume.
Relative permeability curves describe this relationship. The curves
can be determined through special core analysis or assumed based on
an analog. In the oil production model, relative perm can be used
to account for changes in productivity as the reservoir is depleted
and gas is introduced out of solution. The relative permeability
adjustment causes an appropriate reduction in productivity.
Well Matching
[0117] As discussed above, the processor(s) match the set of
potential subterranean formation models to the production data for
the well(s) based on an analytical fracture model. In some
embodiments, the processor(s) can match the potential subterranean
formation models based on both an oil analytical fracture model and
a gas analytical fracture model. In some embodiments, each model
can produce its own production models and associated stimulated
area values. In some examples, the processor(s) can use the results
of the two different models (gas or oil) to determine a probability
that a portion or all of a subterranean formation will use one
model over the other.
[0118] After matching the models, the processor(s) can store and/or
compile all the production models and their corresponding error
values and stimulated area values. FIG. 8 shows a plot 800 of
stimulated area vs. error with each data point representing a
matched production model. In some examples, the processor(s) can be
configured to generate such a plot and output it on an output
device such as a display, printer, or communication device.
[0119] The processor(s) can be configured to identify a subset 810
of the production models (see FIGS. 8 and 9) which have an error
value below a defined threshold or which otherwise fit the
production profile from the production data within a defined error
threshold.
Forecasting Model
[0120] At 560, the processors generate a forecast production model.
In some embodiments, the forecast production model is defined by a
number of input parameters representative of geological
characteristics of the subterranean formation. The forecast
production model is based on the stimulated area values identified
by the analytical model and well matching.
[0121] By applying distributions of reservoir input parameters for
the area of interest, the forecast production model can generate
production forecasts over time for the area of interest. In some
embodiments, ranges and/or distributions of input parameters for
the area of interest can be used as inputs to generate an average
production forecast for the area.
[0122] In some embodiments, the forecasting model can be run
deterministically using single values for each parameter or Monte
Carlo or similar techniques can be used to deliver a distribution
of production forecasts. Forecasts can be created for both
hydrocarbon liquids and gas.
[0123] In some embodiments, the forecast models can provide an
indication of predicted average production rates over time. In
other embodiments, the forecast models can provide probabilistic
production rates based on the possible distributions for input
parameters including the geological characteristics and the
stimulated area value. As illustrated in FIG. 11, in some
embodiments, graphs are generated to illustrated different
probabilistic production rates over time.
[0124] In some embodiments, by applying input parameters
representative of geological characteristics of a particular area
or localized region, the system can, in some instance, model
production rates which account for physical and potentially subtle
geological variations across an area.
[0125] As illustrated in FIG. 10, in some embodiments, forecast
models can be used to generate a visual map illustrating different
production forecasts across a prospective area. In some instances,
the forecast models can be used to generate a probabilistic
financial viabilities of different portions of an area.
[0126] As described herein, in some embodiments, these forecast
models can in some instances account for various unknowns in the
available production data and/or variations in geological
characteristics across a play.
[0127] Although the embodiments have been described in detail, it
should be understood that various changes, substitutions and
alterations can be made herein without departing from the scope as
defined by the appended claims.
[0128] Moreover, the scope of the present application is not
intended to be limited to the particular embodiments of the
process, machine, manufacture, composition of matter, means,
methods and steps described in the specification. As one of
ordinary skill in the art will readily appreciate from the
disclosure of the present invention, processes, machines,
manufacture, compositions of matter, means, methods, or steps,
presently existing or later to be developed, that perform
substantially the same function or achieve substantially the same
result as the corresponding embodiments described herein may be
utilized. Accordingly, the appended claims are intended to include
within their scope such processes, machines, manufacture,
compositions of matter, means, methods, or steps
[0129] As can be understood, the examples described above and
illustrated are intended to be exemplary only. The scope is
indicated by the appended claims.
* * * * *