Note: Descriptions are shown in the official language in which they were submitted.
CA 02913827 2015-12-03
METHODS, SYSTEMS AND DEVICES FOR PREDICTING
RESERVOIR PROPERTIES
FIELD
[0001] The present application relates to the field of reservoir
modelling, and particularly
to methods, systems and devices for modelling subterranean dynamic reservoir
properties
based on collected physical data.
BACKGROUND
[0002] Hydrocarbon exploration involves trade-offs between the number and
spacing of
wells (and associated costs) and the accuracy of data which may impact
production
forecasting and resource development planning. Often geological properties
obtained from
various wells use average properties over a given area to provide an
assessment of a
reservoir. It some instances, estimating with averages may provide a limited
picture of
geological variations in a subterranean formation.
SUMMARY
[0003] In accordance with one aspect, there is provided a method of
predicting
hydrocarbon production rates for a subterranean formation. The method
includes: receiving
or generating, by at least one processor, well logs from data collected from
at least one well
in the subterranean formation; generating from the well logs a predicted
production rate log
for the at least one well; receiving, by the at least one processor, a field
dataset for the
subterranean formation, the field dataset including field data at locations in
3-dimensions of
a volume of the subterranean formation; identifying the predicted production
rate log for the
at least one well as one or more targets, determining a transform relating the
field data and
the predicted rate log for the at least one well; and using the transform,
generating a
predicted production rate for each location of the volume of the subterranean
formation.
[0004] In accordance with another aspect, there is provided a device for
predicting
hydrocarbon production rates for a subterranean formation. The device includes
at least one
processor configured for: receiving or generating well logs from data
collected from at least
one well in the subterranean formation; generating from the well logs a
predicted production
CA 02913827 2015-12-03
rate log for the at least one well; receiving a field dataset for the
subterranean formation, the
field dataset including field data at locations in 3-dimensions of a volume of
the subterranean
formation; identifying the predicted production rate log for the at least one
well as one or
more targets, determining a transform relating the field data and the
predicted rate log for the
at least one well; and using the transform, generating a predicted production
rate for each
location of the volume of the subterranean formation.
[0005] In accordance with another aspect, there is provided a non-
transitory computer-
readable medium or media having stored thereon, computer-readable
instructions. The
computer-readable instructions, when executed by at least one processor,
configure the at
least one processor for: receiving or generating well logs from data collected
from at least
one well in the subterranean formation; generating from the well logs a
predicted production
rate log for the at least one well; receiving a field dataset for the
subterranean formation, the
field dataset including field data at locations in 3-dimensions of a volume of
the subterranean
formation; identifying the predicted production rate log for the at least one
well as one or
more targets, determining a transform relating the field data and the
predicted rate log for the
at least one well; and using the transform, generating a predicted production
rate for each
location of the volume of the subterranean formation.
[0006] 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
[0007] In the figures,
[0008] Fig. 1 is a cross sectional view of an example geological
formation and well;
[0009] Fig. 2 is an example system to which aspects of the present
disclosure may be
applied;
[0010] Figs. 3 and 4 are cross sectional view of example geological
formations including
example energy sources and sensors;
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[0011] Figs. 5, 6 and 7 are flowcharts illustrating aspects of an example
method for
predicting reservoir properties;
[0012] Fig. 8 is a chart showing example well log data;
[0013] Fig. 9 is a chart showing an example linear regression;
[0014] Fig. 10 is a visual representation of example predicted production
rates in three-
dimensions as illustrated along wells and through an example geological
formation;
[0015] Fig. 11 is a top view map of an example drainage area showing
different predicted
production rates; and
[0016] Fig. 12 is a visual representation of example predicted production
rates as they
vary along the length of the various wells.
DETAILED DESCRIPTION
[0017] 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 and location of wells. Due to the high
cost of
development, there can be significant financial incentives to have as much and
as accurate
information as possible. In some examples, it may be important to keep the
cost and amount
of time spent acquiring the information low.
[0018] In some estimation methods such as fractal contouring, average
well values are
determined and then can be geologically contoured by a geologist or computer
algorithm to
estimate information between wells.
[0019] In some embodiments, aspects of the present disclosure may provide
more
potentially accurate and/or more granular predicted rates of production based
on measured
geological properties between wells.
[0020] 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
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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.
[0021] In evaluating the subsurface or subterranean formations, in some
examples, data
can be collected from one or more wells 100 drilled into or around the
formations. In some
examples, the wells can be exploratory wells, production wells or wells for
any other
purpose. The wells can include vertical wells, horizontal wells, or any wells
of any direction
or structure, and/or any combination thereof.
[0022] 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.
[0023] 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.
[0024] 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.
[0025] 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
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some examples, devices having at least one processor may be configured to
execute
software instructions stored on a computer readable tangible, non-transitory
medium.
[0026] 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.
[0027] 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.
[0028] 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.
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[0029] Fig. 2 shows an example system 200 include one or more devices 205
which may
be used to 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.
[0030] 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.
[0031] 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.
[0032] 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.
[0033] With reference to Figs. 3 and 4, subterranean resource or
geological formation
data can be collected from the field to ascertain information between wells
and/or
information otherwise not provided by well log data.
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[0034] Similar to Fig. 1, Figs 3 and 4 illustrate a cross-sectional views
of a subterranean
resource or geological formation 110 which may include a number of different
layers of
materials having different physical characteristics as illustrated 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. In some
instances, each individual geological formation may have lateral and / or
vertical variations in
the types of materials of which they are comprised.
[0035] Field data may be used to create logs for porosity, saturation,
permeability, and/or
other similar static properties and has been used to create a sum or average
for these
attributes over a two-dimensional area. However, these averages or totals may
provide a
limited granularity as to the dynamic flow properties of a geological
formation.
[0036] In Fig. 3, a controlled energy source 310 such as a seismic
vibrator, dynamite or
other explosive, air gun, or the like can be configured to generate seismic
waves. Sensors
300 such as geophones, accelerometers, MEMS devices, seismometers, receivers
and the
like can be positioned at the surface or elsewhere to collect seismic data
associated with the
geological formation(s) 110 based on reflected or otherwise detected seismic
waves.
[0037] In some examples, the sensors 300 can be positioned to generate a
three-
dimensional (3D) log or grid of data points for locations within the
subterranean formation(s).
[0038] In some examples, the sensors 300 can be positioned such that
reflected waves
have a larger angle of incidence 0 which can in some examples provide greater
density
information for the geological formations. In some examples, the amount of
density
information acquired from a detected wave may be a function of the sine of the
angle of
incidence. By way of example, the following is an equation relating density to
the sine of the
angle of impedance:
-
Acr Ap _4,62 Ap Afi
R(0), + tan 0)( sin' 0 + 13:
sin 2 0 - tan 2 0 AP
\2 2 a p a, 2 p 2 _ P
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where 8 is the angle, R(8) is the seismic response with angle, a is the
average
compressional wave velocity, 13 is the average shear wave velocity, p is the
average density,
and A is an operator indicating the change in the above properties across a
boundary.
[0039] In some examples, one or more sensors 300 can be positioned to
collect data
from wide-angle waves such as waves having an angle of incidence of 45 degrees
or greater
to provide a desired level of density information. The sensors can also be
positioned as a
function of the depth of the target formation.
[0040] Any number or arrangement of sensors and energy source locations
can be used
to create the desired resolution of data points in the 3D volume of the
formations.
[0041] Fig. 4 shows another example whereby sensors 300 can be positioned
in a well to
collect vertical seismic profile data. In another example, energy source(s)
310 can be
positioned in a well, and sensors 300 can be positioned at the surface to
collect reverse
vertical seismic profile data. In another example, energy source(s) 310 can be
positioned in
one well, and sensors 300 can be positioned in a second well to collect cross-
well seismic
data.
[0042] In some examples, combinations of seismic or other field data
collected from any
number of source/sensor arrangements can be combined to create one or more
field data
sets.
[0043] In some examples, field data regarding the subterranean formations
can be
additionally or alternatively collected. Such data can include electromagnetic
data, gravity
data and the like.
[0044] Any subset or combination of the aforementioned data sources may
be used to
collect field data for use in the prediction of hydrocarbon production rates.
[0045] Fig. 5 shows a flowchart illustrating aspects of an example method
500 for
predicting hydrocarbon production rates for a subterranean resource. At 510,
one or more
processor(s) 210 and/or other aspects of device(s) 205 may be configured to
receive or
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generate well logs from data collected from at least one well in the
subterranean formation
110.
[0046] In some examples, the well logs can be received or accessed from
one or more
memories 215, storage devices 215, 225, and/or sensors or field devices 230,
240. In some
examples, the well logs can include 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.
[0047] As illustrated by the flow diagram in Fig. 6, in some examples,
well logs, such as
logs derived directly from measured values, can be used to generate additional
well logs. In
some examples, these generated well logs can include, but are not limited to,
logs for
fractional porosity, fractional oil saturation, permeability in any direction,
and/or height or
exploitable bitumen in place. Fig. 6 includes example equations and models for
deriving,
generating or otherwise determining logs and values which may be used to
generate a
predicted production rate log, and which, in some examples, are based on data
representative of physical measurements or attributes of a well or formation.
In some
examples, some of the petrophysical and/or other equations/models may be
industry
standards as described, for example, in "Crain's Petrophysical Handbook", Ross
Crain;
Woodhouse, R. "Athabasca Tar Sand Reservoir Properties Derived from Cores and
Logs",
Transactions SPWLA 17th Annual Logging Symposium, Paper T,1976; Deutsch
Permeability - Hosseini, A.H., Leuangthong, O., and Deutsch, C.V., "An
integrated
Approach to Permeability Modeling Using Micro Models", SPE-117517-PP, 2008;
SPE/PS/CHOA Heavy Oil Symposium, October 20-23, 2008, Calgary, AB.
[0048] In some examples, the reservoir height H can be one of or the
lesser of the height
of the formation, the height of the bitumen within the formation, or the
height of the first
barrier to fluid flow above the base of the bitumen within the formation. The
reservoir height
can be measured from the base of the formation, the base of the bitumen, or
the elevation of
a well placed in the formation.
[0049] In some examples, some or all of the generated well logs may be
generated by
the processor(s) 210 of device 205 from received or accessed well log data. In
some
examples, some or all of the generated well logs may have been previously
generated by a
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remote, field 240 or other device and may be stored at and accessed/received
from one or
more storage device(s) 225, 215 and/or memory(ies) 215.
[0050] At 520, processor(s) can be configured to generate a predicted
production rate log
for each of one or more wells based on the received or generated well logs. In
steam-
assisted gravity drainage (SAGD) hydrocarbon production techniques, the
estimated
maximum production rate can be determined by the equation:
2.436Solf jaH
Q = 2 L _______________________________________
7111)s
wherein Q is the oil rate, L is the horizontal length of the well, cl) is the
fractional porosity,
AS, is the difference between initial oil saturation and residual oil
saturation to steam, K is
permeability, g, is the acceleration due to gravity, a is the thermal
diffusivity of the reservoir,
H is the reservoir height, m is a constant between 3 and 4 depending on the
oil viscosity to
temperature relation, and vs is the kinematic viscosity of crude at steam
temperature.
[0051] In some examples, by combining the values that are generally
constant over a
large area, the equation can be expressed as:
Q = C L Vc136S0KH
wherein C is an estimated constant. In some examples, processor(s) can be
configured to
calculate C for a reservoir based on measured maximum production rate data for
one or
more wells associated with the reservoir. In some examples, processor(s) can
be configured
to calculate C using a prediction simulator such as Computer Modelling Group
Ltd.'s STARS
software. In some examples, the simulator can be configured to divide a
reservoir into cells
and to solve differential equations at the cell boundaries to determine values
for calculating
or estimating C.
[0052] In some examples, the processor(s) can be configured to determine
C based on a
measured and/or simulated production rate for one or more wells. The
processor(s) can be
configured to obtain Q by dividing by the length of the respective one or more
wells. Based
on the values for Q, L, cI, AS,, K and H for each well for which production
rates were
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measured or simulated, the processor(s) can apply the equation above to
estimate the value
of C for each well.
[0053] In some examples, the processor(s) can be configured to estimate C
for an entire
region/project/resource, etc using a measure of a central value for C (e.g.
mean, average,
median, mode values). In some examples, the overall estimate for C may be
based on the
estimated C values for each well and the well locations relative to each
other.
[0054] In some examples, with this later equation, the processor(s) at
520 can be
configured to generate a predicted production rate log for one or more wells
based on each
well's respective porosity, saturation, permeability, and height or
exploitable bitumen in place
(EBIP) logs. An illustrative example of well logs and the resulting predicted
production rate
log can been seen in Fig. 8.
[0055] At 530, one or more processor(s) 210 and/or other aspects of
device(s) 205 may
be configured to receive or generate a field dataset for the subterranean
formation, the field
dataset including field data (such as seismic, electromagnetic, electrical
resistivity and/or
gravity data) at different points or locations within the 3D volume of the
subterranean
formation. In some examples, the field dataset can be visualized as a 3D grid
or mesh of
values from the collected data from seismic sensors and the like.
[0056] In some embodiments, processor(s) 210 and/or other aspects of
device(s) 205
may be configured to the field dataset may be configured to generate the field
dataset in a
defined grid of any spacing. In some examples, field data samples can be
placed into the
defined grid based on their geometry relative to the grid.
[0057] In some examples, seismic or other field data may be acquired at a
specified
sampling rate to obtain desired grid data granularity. For example, seismic
sampling rates
may affect the collected data grid spacing in the vertical direction. In some
instances, a
sample rate of 1 ms may provide a vertical grid data granularity of 1 meters.
[0058] The spatial sampling of data in a grid may be related to one-half
the spacing
between the source(s) in one direction and the sensor(s) in the other
direction. For example,
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when source(s) and sensor(s) are spaced 20 meters apart, the horizontal grid
data
granularity may be 10 meters by 10 meters.
[0059] By combining the above, example sampling rate and source/sensor
spacing, the
3D spatial grid may be 10 m x 10 m x lm. Other spacings and sampling rates may
also be
used.
[0060] In some examples the field dataset can include a grid of data
representing density,
impedance, reflectivity, water saturations and the like.
[0061] With reference to Fig. 7, in some examples, field datasets, such
as data derived
directly from measured values, can be used to generate additional data grids.
In some
examples, these generated data grids can include, but are not limited to, 3D
logs/arrays/grids for porosity, saturation, permeability, and/or height or
exploitable bitumen in
place.
[0062] In some examples, some or all of the generated field data grids
may be generated
by the processor(s) 210 of device 205 from received or accessed field data. In
some
examples, some or all of the generated field data grids may have been
previously
generated/transformed by a remote, field 240 or other device and may be stored
at and
accessed/received from one or more storage device(s) 225, 215 and/or
memory(ies) 215.
[0063] For example, it has been observed that, in some instances, well
logs used for the
predicted production rate log (e.g. fractional porosity logs, fractional oil
saturation logs, and
permeability logs) show a strong correlation to the density logs. Therefore,
in accordance
with aspects of the present disclosure, predicted density logs from wide-angle
seismic or
other field data may be used to generate a predicted production rate log.
Similarly, in some
examples, other attributes of the seismic or other field data may be related
to the predicted
rate log. In some examples, a collection of such attributes may be determined
and used
together to predict the predicted rate log via multi-linear regression or
neural network
methods.
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[0064] At 540, identifying the predicted production rate log(s) from the
well data as
target(s), processor(s) can be configured to determine a transform relating
the field data and
the predicted production rate log(s) from the well data.
[0065] In some examples, the processor(s) can be configured to determine
a transform
by applying linear and/or multi-linear regression. In some examples, the
processor(s) can be
configured to determine a transform by generating/training a neural network,
agenetic
algorithm, or any other multi-parameter estimation method.
[0066] In some examples, determining a transform may include generating a
weighted
equation, neural network or other equation or model which defines or models a
relationship
between the desired output log(s) (e.g. predicted production rate log) from
well log data and
the seismic or other field datasets. In some examples, the field datasets can
be elastic logs
(e.g. sonic, shear-wave sonic, density) and/or properties that may be
calculated from the
elastic logs (e.g. Young's modulus, Poisson's ratio, compressibility, etc.).
In some examples,
the processor(s) can be configured to use the seismic response equation, R(0),
above or a
similar amplitude versus offset or other equation to identify the elastic
properties which are
related to the desired output log(s) (e.g. predicted production rate log). In
some examples,
the elastic properties may include P-impedance, S-impedance and density.
[0067] In some examples, the processor(s) can be configured to calculate
the identified
elastic properties using the seismic response or other equation, and the
desired output log(s)
using the elastic properties at the well locations. Based on the desired
output log(s) from the
elastic properties and the output log(s) from the well data, the processor(s)
can be
configured to generate a transform using a multi-linear regression, neural
network, genetic
algorithm or other suitable model.
[0068] Fig. 9 shows a chart illustrating a linear regression and a
resulting transform
relating the field (seismic) data and the predicted production rate log(s).
[0069] At 550, using this transform, the processor(s) can be configured
to generate a
predicted production rate for each location of the volume of the subterranean
formation for
which field data was received/generated.
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[0070] In some examples, the processor(s) can be configured to generate
the transform
and/or generate the predicted production rate for each location using any of
the equations
described herein (e.g. the equation for Q above) or otherwise. In some
embodiments, the
processor(s) can be configured to apply these equations based on a constant
factor C which
can, in some examples, be measured or simulated based on well data as
described above
or otherwise.
[0071] In this manner, the methods, systems and devices described herein
can generate
a grid of predicted production rates i.e. a value for each 3D unit in a
subterranean formation
volume. In some examples, this grid of predicted production rates may provide
a more
refined/granular characterization of the value/viability of specific portions
of a reservoir in
three dimensions between core holes. In some examples, as the grid is based on
data
associated with physical field data between the wells, the predicted
production rates may be
more accurate than rough estimates taken by averaging between wells.
[0072] In some examples, the 3D grid may provide a visual representation
for quantifying
production, resources, cost, risk and/or value for specific portions of a
resource.
[0073] At 560, the processor(s) may optionally be configured to generate
a visual 3D map
or representation of the resource's predicted production rates on a per unit
volume basis. In
some examples, the processor(s) may be configured to sum or otherwise
aggregate the
production rates over identified drainage areas of the resource/formation.
[0074] At 570, the processor(s) may receive data or inputs identifying
locations/positions
of wells and/or drainage areas. In some examples, these wells may be existing
or
prospective wells. Based on the unit volume grid of predicted production rates
and/or other
predictions and the locations/positions of the inputted well/drainage area
data, the
processor(s) may be configured to sum or aggregate the associated predicted
production
rate units to generate a predicted production rate, cost, and/or economic
viability for the
inputted wells/drainage areas.
[0075] For example, Fig. 10 illustrates a perspective view illustrating a
three-dimensional
visual representation of predicted production rates along different wells 1010
and through
different regions of a subterranean resource 1020. The different shades of
grey (or color if
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visible) represent different levels predicted production rates for each unit
volume included in
the visual representation.
[0076] For example, Fig. 11 illustrates a top view map 1110 of a drainage
area showing
the different predicted production rates for the various regions, and some
SAGD well
locations 1120. In some examples, the top view may be generated by summing
volumetric
unit predicted production rates below the surface of each unit area of the top
view visual
representation 1110. In some examples, this may include summing wedges, boxes,
columns, etc. of per unit volume predicted production rates below the surface
of each unit
area.
[0077] Fig. 12 shows a visual representation of the predicted production
rates as they
vary along the length of the identified wells. In some examples, this granular
modelling of
production rates may provide information to determine not only the potential
best location(s)
for well development, but also the potentially most valuable portions of those
wells.
[0078] In some embodiments, the methods and devices described herein may
be
computationally more efficient than previous methods wherein static formation
properties
such as porosity, saturation and permeability were individually estimated over
a 2D area
using multi-linear regression or neural networks for each property. These 2D
maps were
then combined to predict a maximum 20 rate map.
[0079] In contrast, in addition to creating 3D granular information
regarding a dynamic
property such as production rate, the methods, devices and systems described
herein may,
in some embodiments, decrease computation requirements by a factor of three by
only using
a single multi-linear regression, neural network or other model. In some
embodiments, the
reduction in computational steps by the methods, devices and systems described
herein
may reduce (compounding) error bars on the estimated output log(s).
[0080] In some example embodiments, the methods and devices described above
may
be similarly applied to generate 3D granular data sets for predicted steam
use, energy use,
water use, steam-to-oil ratios and/or water-to-steam ratios. In some examples,
this may
involve generating log(s) for these predicted attributes based on well data at
520;
determining, at 540, a transform relating seismic data to the well predicted
logs; and based
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CA 02913827 2015-12-03
on the transform, at 550, generating a predicted 3D data set for one or more
of these
predicted attributes. In some examples, since these predicted attributes may
affect the
economics of a well or a project, the methods and devices described herein may
similarly be
used to predicting the economic viability of a well/project/resource/etc.
[0081] 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.
[0082] 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
[0083] As can be understood, the examples described above and illustrated
are intended
to be exemplary only. The scope is indicated by the appended claims.
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