U.S. patent application number 16/514670 was filed with the patent office on 2020-02-06 for detecting fluid types using petrophysical inversion.
The applicant listed for this patent is Jan Schmedes, Di Yang. Invention is credited to Jan Schmedes, Di Yang.
Application Number | 20200041692 16/514670 |
Document ID | / |
Family ID | 67515139 |
Filed Date | 2020-02-06 |
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United States Patent
Application |
20200041692 |
Kind Code |
A1 |
Schmedes; Jan ; et
al. |
February 6, 2020 |
Detecting Fluid Types Using Petrophysical Inversion
Abstract
A method and apparatus for hydrocarbon management, including
generating a fluid saturation model for a subsurface region.
Generating such a model may include: performing a brine flood
petrophysical inversion to generate inversion results; iteratively
repeating: classifying rock types (including at least one
artificial rock type) based on the inversion results; generating a
trial fluid saturation model based on the classified rock types;
performing a trial petrophysical inversion with the trial fluid
saturation model to generate trial results; and updating the
inversion results with the trial results; and generating the fluid
saturation model for the subsurface region based on the inversion
results. The petrophysical inversion may include a facies-based
inversion and/or may invert for water saturation. Generating such a
model may include: performing a brine flood petrophysical
inversion, performing a hydrocarbon flood petrophysical inversion;
identifying misfits in the inversion results, and generating a
trial fluid saturation model based on the misfits.
Inventors: |
Schmedes; Jan; (Bellaire,
TX) ; Yang; Di; (Spring, TX) |
|
Applicant: |
Name |
City |
State |
Country |
Type |
Schmedes; Jan
Yang; Di |
Bellaire
Spring |
TX
TX |
US
US |
|
|
Family ID: |
67515139 |
Appl. No.: |
16/514670 |
Filed: |
July 17, 2019 |
Related U.S. Patent Documents
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Application
Number |
Filing Date |
Patent Number |
|
|
62712780 |
Jul 31, 2018 |
|
|
|
62871479 |
Jul 8, 2019 |
|
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Current U.S.
Class: |
1/1 |
Current CPC
Class: |
G01V 2210/6244 20130101;
G01V 99/005 20130101; G01V 2210/645 20130101; G01V 1/306 20130101;
G01V 2210/6163 20130101; E21B 49/00 20130101; G01V 2210/663
20130101; G06N 20/00 20190101; E21B 41/0092 20130101; G01V 2210/667
20130101 |
International
Class: |
G01V 99/00 20060101
G01V099/00 |
Claims
1. A method for generating a fluid saturation model for a
subsurface region comprising: performing a first petrophysical
inversion for the subsurface region with hydrocarbon flood
parameters to generate hydrocarbon flood results; performing a
second petrophysical inversion for the subsurface region with brine
flood parameters to generate brine flood results; identifying a
first set of misfits in the hydrocarbon flood results; identifying
a second set of misfits in the brine flood results; generating a
trial fluid saturation model based on at least one of the first set
of misfits and the second set of misfits; performing a third
petrophysical inversion for the subsurface region with the trial
fluid saturation model to generate final results; and generating
the fluid saturation model for the subsurface region based on the
final results; wherein each one of the first, second, and third
petrophysical inversion is carried out using a computer, and each
one of the trial fluid saturation model and the fluid saturation
model is generated using a computer.
2. The method of claim 1, wherein at least one of the first,
second, and third petrophysical inversions comprises a facies-based
inversion.
3. The method of claim 1, further comprising at least one of:
identifying potential hydrocarbon-bearing formations in the
subsurface region based on the fluid saturation model; generating
an image of the subsurface region based on the fluid saturation
model; and managing hydrocarbons in the subsurface region based on
the fluid saturation model.
4. The method of claim 1, wherein at least one of the first set of
misfits and the second set of misfits comprises at least one of:
porosity misfits; volume of clay misfits; seismic data misfits; and
P-wave velocity misfits.
5. A method for generating a fluid saturation model for a
subsurface region comprising: performing a first petrophysical
inversion for the subsurface region with brine flood parameters to
generate brine flood results; classifying rock types in the
subsurface region based on the brine flood results, wherein the
rock types comprise at least one artificial rock type; generating a
trial fluid saturation model based on the classified rock types;
performing a second petrophysical inversion for the subsurface
region with the trial fluid saturation model to generate final
results; and generating the fluid saturation model for the
subsurface region based on the final results; wherein each one of
the first and second petrophysical inversion is carried out using a
computer, and each one of the trial fluid saturation model and the
fluid saturation model is generated using a computer.
6. The method of claim 5, wherein at least one of the first and
second petrophysical inversions comprises a facies-based
inversion.
7. The method of claim 5, further comprising identifying potential
hydrocarbon-bearing formations in the subsurface region based on
the fluid saturation model.
8. The method of claim 5, further comprising generating an image of
the subsurface region based on the fluid saturation model.
9. The method of claim 5, further comprising managing hydrocarbons
in the subsurface region based on the fluid saturation model.
10. The method of claim 5, further comprising utilizing a machine
learning system to classify the rock types.
11. The method of claim 5, further comprising performing one or
more additional petrophysical inversions for the subsurface region
with the at least one artificial rock type prior to generating the
final results.
12. The method of claim 5, wherein classifying the rock types
comprises generating one or more cross-plots of porosity and volume
of clay.
13. A method for generating a fluid saturation model for a
subsurface region comprising: using a computer, performing a first
petrophysical inversion for the subsurface region with brine flood
parameters to generate inversion results; using the computer,
iteratively repeating until convergence: classifying rock types in
the subsurface region based on the inversion results, wherein the
rock types comprise at least one artificial rock type; generating a
trial fluid saturation model based on the classified rock types;
performing a trial petrophysical inversion for the subsurface
region with the trial fluid saturation model to generate trial
results; updating the inversion results with the trial results; and
checking for convergence; and using the computer, generating the
fluid saturation model for the subsurface region based on the
inversion results.
14. The method of claim 13, wherein at least one of the first
petrophysical inversion and the trial petrophysical inversions
comprises a facies-based inversion.
15. The method of claim 13, wherein the check for convergence
comprises: comparing the inversion results from a prior iteration
to the trial results to determine a remaining error estimate; and
determining whether the remaining error estimate is below a
selected error threshold.
16. The method of claim 13, wherein the trial petrophysical
inversion inverts for water saturation.
17. The method of claim 13, further comprising managing
hydrocarbons in the subsurface region based on the fluid saturation
model.
18. The method of claim 13, further comprising utilizing a machine
learning system to classify the rock types.
19. The method of claim 13, wherein the trial petrophysical
inversion utilizes the at least one artificial rock type.
Description
CROSS-REFERENCE TO RELATED APPLICATION
[0001] This application claims the benefit of U.S. Provisional
Patent Applications 62/712,780, filed Jul. 31, 2018 entitled "Fluid
Saturation Model for Petrophysical Inversion" and 62/871479, filed
Jul. 8, 2019 entitled "Detecting Fluid Types Using Petrophysical
Inversion", the entirety of which are incorporated by reference
herein.
FIELD
[0002] This disclosure relates generally to the field of
geophysical prospecting and, more particularly, to hydrocarbon
management (including prospecting for hydrocarbons) and related
data processing. Specifically, exemplary embodiments relate to
methods and apparatus for improving computational efficiency and
accuracy of petrophysical inversion techniques applicable to
detecting fluid types.
BACKGROUND
[0003] This section is intended to introduce various aspects of the
art, which may be associated with exemplary embodiments of the
present disclosure. This discussion is believed to assist in
providing a framework to facilitate a better understanding of
particular aspects of the present disclosure. Accordingly, it
should be understood that this section should be read in this
light, and not necessarily as admissions of prior art.
[0004] An important goal of geophysical prospecting is to
accurately image subsurface structures to assist in the
identification and/or characterization of hydrocarbon-bearing
formations. Geophysical prospecting may employ a variety of
data-acquisition techniques, including seismic prospecting,
electromagnetic prospecting, well logging, etc. Such data may be
processed, analyzed, and/or examined with a goal of identifying
geological structures that may contain hydrocarbons.
[0005] An important type of geophysical data analysis is
petrophysical inversion. Petrophysical inversion generally
transforms elastic parameters, such as seismic velocity and
density, to petrophysical properties, such as porosity and volume
of clay (Vclay). For example, petrophysical inversion can transform
compressional velocity, shear velocity, and density well logs to
porosity and Vclay logs. As another example, petrophysical
inversion can utilize elastic information from seismic data,
including traditional images of reflectivity and tomographic
velocity, to predict three-dimensional volumes of porosity and
Vclay. As used herein, Vclay refers to rock volumes including
anything that is not sand (e.g., shale). That is, we will treat
clay and shale (and associated properties such as Vclay and Vshale)
interchangeably with the recognition that they are not strictly the
same from a mineralogical standpoint. For the present application's
purposes, however, it is suitable to treat them interchangeably as
one of the volumetric mineral end-members of subsurface rocks, the
other one being sand. Furthermore, petrophysical inversion can
include additional geophysical data types, namely electromagnetic
data or resistivity, which tend to have a better sensitivity to
water saturation than elastic parameters.
[0006] Petrophysical inversion typically utilizes a model of fluid
saturation that recognizes the vertical and lateral distribution of
hydrocarbons and water in a reservoir. For example, in the case of
well logs, a one-dimensional fluid saturation model may be derived
with analysis of traditional electric well logs using the Archie
equation. Building a two-dimensional or three-dimensional fluid
saturation model for petrophysical inversion is a significant
technical challenge that involves analysis and interpretation of
seismic data to laterally-bind reservoir extent and known or
suspected fluid-contact surfaces. The depth of hydrocarbon-contact
surfaces can be detected by log analysis if penetrated by a well or
hypothesized from extrapolation of pressure trends. Remaining
challenges include: what to do when contacts are not penetrated by
a well, what to do away from the well when the time-to-depth
relationship is uncertain, and how to handle the potential for
variable hydrocarbon contacts, which potential increases with
complex geology (e.g. separated fault blocks or stratigraphic
barriers to flow).
[0007] Broadly, two categories of relationships are used to relate
petrophysical properties to seismic data during petrophysical
inversion. The first relationship type is referred to as a rock
physics model (RPM). RPMs relate petrophysical rock properties,
such as porosity and Vclay (or, equivalently as noted above,
Vshale), and fluid (hydrocarbon or water) content to geophysical
rock properties, such as compressional (P-wave) and shear (S-wave)
velocities, and density. Geophysical rock properties depend on
elastic rock properties, such as bulk and shear moduli. RPMs can be
either inductive (empirical) or deductive (theoretical). RPMs can
be mathematically linear or nonlinear. RPMs may be calibrated using
direct well-bore measurements and collocated seismic data. The
second relationship type is referred to as an angle-dependent
amplitude model (ADAM). ADAMs relate amplitudes of reflected
seismic waves that have traveled through the subsurface to changes
in the geophysical properties of the rocks between one layer and
the next, as well as the angle of incidence with which the wave
impinged on the boundary. Consequently, changes in amplitude as a
function of receiver offset ("amplitude-variation with offset," or
"AVO"), and/or changes in amplitude as a function of receiver angle
("amplitude-variation with angle," or "AVA"), can be used to infer
information about these elastic parameters. To take advantage of
AVO and/or AVA, subsets of seismic reflection data corresponding to
particular offsets (or angles) or small groups of offsets (or
angles) can be processed into what are called angle stacks. ADAMs
can be linear or nonlinear in mathematical representations.
[0008] More efficient equipment and techniques to perform
petrophysical inversion, including better fluid saturation
modeling, would be beneficial.
SUMMARY
[0009] Petrophysical inversion may be utilized for hydrocarbon
management, such as reservoir characterization, causing a well to
be drilled, and/or otherwise prospecting for hydrocarbons in a
subsurface region. For example, petrophysical inversion may be
utilized to generate a fluid saturation model (e.g., to predict if
a reservoir is wet or hydrocarbon-bearing). Under a misfit-based
approach, petrophysical inversion may be utilized both with brine
flood and hydrocarbon flood assumptions. As a result, misfits under
the two assumptions may be estimated and/or analyzed. Under a
post-inversion classification-based approach, a petrophysical
inversion may be utilized under a brine flood assumption. The
petrophysical inversion may classify petrophysical parameters under
specified rock types, which may include one or more artificial rock
types. Under a classification-during-inversion approach, the rock
types may be developed (e.g., learned) during an iterative series
of petrophysical inversions.
BRIEF DESCRIPTION OF THE DRAWINGS
[0010] So that the manner in which the recited features of the
present disclosure can be understood in detail, a more particular
description of the disclosure, briefly summarized above, may be had
by reference to embodiments, some of which are illustrated in the
appended drawings. It is to be noted, however, that the appended
drawings illustrate only exemplary embodiments and are therefore
not to be considered limiting of scope, for the disclosure may
admit to other equally effective embodiments and applications.
[0011] FIG. 1 illustrates a fluid saturation model mismatch as
evidenced by geologic prior misfits and/or data misfits.
[0012] FIGS. 2A-2C illustrate exemplary methods of utilizing fluid
saturation model mismatches in petrophysical inversion. FIG. 2A
illustrates an exemplary method utilizing the misfit-based
approach. FIG. 2B illustrates an exemplary method utilizing the
post-inversion-classification approach. FIG. 2C illustrates an
exemplary method utilizing the classification-during-inversion
approach.
[0013] FIGS. 3A-3C illustrate one way to determine parameters for a
suitable artificial rock type. FIG. 3A illustrates a cross-plot of
porosity with Vclay, showing probability distributions of expected
rock types and elastic parameters under a hydrocarbon flood
assumption. FIG. 3B illustrates a cross-plot of porosity with
Vclay, showing probability distributions of expected rock types and
elastic parameters under a brine flood assumption. FIG. 3C
illustrates a cross-plot of porosity with Vclay, showing an
artificial rock type classified under the brine flood
assumption.
[0014] FIGS. 4A-4C illustrate results for the
classification-during-inversion approach. FIG. 4A illustrates
petrophysical parameters after the first iteration. FIG. 4B
illustrates petrophysical parameters after the next iteration. FIG.
4C illustrates fluid saturation model mismatch as evidenced by
geologic prior misfits and/or data misfits.
[0015] FIG. 5 illustrates a block diagram of a seismic data
analysis system upon which the present technological advancement
may be embodied.
DETAILED DESCRIPTION
[0016] It is to be understood that the present disclosure is not
limited to particular devices or methods, which may, of course,
vary. It is also to be understood that the terminology used herein
is for the purpose of describing particular embodiments only, and
is not intended to be limiting. As used herein, the singular forms
"a," "an," and "the" include singular and plural referents unless
the content clearly dictates otherwise. Furthermore, the words
"can" and "may" are used throughout this application in a
permissive sense (i.e., having the potential to, being able to),
not in a mandatory sense (i.e., must). The term "include," and
derivations thereof, mean "including, but not limited to." The term
"coupled" means directly or indirectly connected. The word
"exemplary" is used herein to mean "serving as an example,
instance, or illustration." Any aspect described herein as
"exemplary" is not necessarily to be construed as preferred or
advantageous over other aspects. The term "uniform" means
substantially equal for each sub-element, within about .+-.10%
variation. The term "nominal" means as planned or designed in the
absence of variables such as wind, waves, currents, or other
unplanned phenomena. "Nominal" may be implied as commonly used in
the fields of seismic prospecting and/or hydrocarbon
management.
[0017] As used herein, a fluid is a substance that deforms or flows
under an applied shear stress, including phases of matter such as
liquids and gases. Specifically relevant to hydrocarbon
prospecting, the term "fluid" includes oil, water, brine, and
natural gas (or simply "gas").
[0018] As used herein, "offset" refers to a distance between a
source and a receiver in a geophysical survey.
[0019] The term "seismic data" as used herein broadly means any
data received and/or recorded as part of the seismic surveying
process, including particle displacement, velocity, and/or
acceleration, pressure, reflection, shear, and/or refraction wave
data. "Seismic data" is also intended to include any data or
properties, including geophysical properties such as one or more
of: elastic properties (e.g., P- and/or S-wave velocity,
P-Impedance, S-Impedance, density, attenuation, anisotropy, and the
like); seismic stacks (e.g., seismic angle stacks); compressional
velocity models; and porosity, permeability, or the like, that the
ordinarily skilled artisan at the time of this disclosure will
recognize may be inferred or otherwise derived from such data
received and/or recorded as part of the seismic surveying process.
Thus, the disclosure may at times refer to "seismic data and/or
data derived therefrom," or equivalently simply to "seismic data."
Both terms are intended to include both measured/recorded seismic
data and such derived data, unless the context clearly indicates
that only one or the other is intended.
[0020] As used herein, "inversion" refers to any process whereby,
for a quantity y known to depend on one or more variables x (e.g.,
collectively forming a model m(x)), inferring the specific values
of x (or the specific model m(x)) that correspond to measured
values of y. For example, a model may be derived from field data to
describe the subsurface that is consistent with acquired data. For
example, seismic inversion may refer to calculating acoustic
impedance (or velocity) from a seismic trace, taken as representing
the earth's reflectivity. Inverse problems frequently contain three
elements: data, model parameters, and model structure. In the realm
of petrophysical inversion, the data element is generally
geophysical data such as seismic angle stacks, seismic velocities,
resistivity, density, etc. In particular, such data is often
obtained from a subsurface region of interest (e.g., the subsurface
region for which a subsurface properties model is being generated
via the inversion). In the realm of petrophysical inversion, the
model parameters element generally comprises one or more
petrophysical properties such as porosity, Vclay, Vshale, water
saturation, lithology, etc. In the realm of petrophysical
inversion, the model structure element is generally forward physics
or statistical model relating data and model parameters; structure
of petrophysical constraints; a priori concepts of porosity and
Vclay distributions, etc.
[0021] As used herein, "facie" refers to rock characteristics
reflective of origin and/or differentiation from other nearby rock
units. Geological characteristics indicative of facies distinctions
include petrophysical characteristics that control the fluid
behavior (e.g., porosity and Vclay).
[0022] As would be understood by one of ordinary skill in the art
with the benefit of this disclosure, a variety of petrophysical
inversion techniques may be applicable herein. Exemplary
petrophysical inversion techniques include: i) one-stage
petrophysical inversion (Aleardi, 2018; US20180156932A1), ii)
petrophysically-constrained Full Wavefield Inversion (FWI) (Zhang,
Zhen-dong, Alkhalifah, Tariq, Naeini, Ehsan Zabihi, Sun, Bingbing,
2018, "Multiparameter elastic full waveform inversion with
facies-based constraints," Geophysical Journal International, Vol
213, Issue 3, 2112-2127), and iii) joint inversion (Gao, Guozhong,
Abubakar, Aria, Habashy, Tarek M, 2012, "Joint petrophysical
inversion of electromagnetic and full-waveform seismic data,"
Geophysics, Vol 77, Issue 3, WA3-WA18). For example, joint
inversion may include any of the other petrophysical inversion
techniques wherein seismic data is used jointly with other
geophysical data, such as gravity, magnetics, and/or
electromagnetic geophysical data. Applicable types of petrophysical
inversion utilize a spatial depiction, or model, of fluid
saturation that allow transformation from elastic to petrophysical
parameters. Typically, a one-stage petrophysical inversion utilizes
the fluid model as an integral component that allows the method to
solve directly for petrophysical parameters from seismic data.
While petrophysical inversion is historically thought of as a
post-stack reservoir characterization method, recent advances in
FWI also provide techniques applicable to the current disclosure.
For example, petrophysically-constrained FWI may be analogous to a
one-stage petrophysical inversion, but the forward modeling engine
is FWI, and application of petrophysical constraints (e.g., to a
fluid model) is performed inside the FWI iteration loop.
Petrophysical inversion (e.g., lithology inversion) may be used to
predict petrophysical properties such as sand (or shale) volume and
porosity in sub-surface rocks. Specifically, petrophysical
inversion may predict facies-based petrophysical properties from
geophysical products such as seismic data and Full Wavefield
Inversion products along with an assessment of uncertainty.
Exemplary techniques for petrophysical inversion can be found in
co-pending U.S. Patent Publication No. 2018/0156932, entitled
"Method for Estimating Petrophysical Properties for Single or
Multiple Scenarios from Several Spectrally Variable Seismic and
Full Wavefield Inversion Products," and filed Oct. 19, 2017, which
is incorporated herein by reference in all jurisdictions that allow
it. Petrophysical inversion techniques are applicable to solve a
variety of technical problems. Petrophysical inversion techniques
may utilize a broad range of computational complexity and/or a
multi-dimensional fluid saturation model.
[0023] As used herein, the term "facies-based inversion" in general
refers to petrophysical inversion techniques which match
geophysical input data, such as seismic data, and infer facies for
locations in a subsurface region. Facies-based inversion generally
utilizes additional input parameters of facies, described in a
petrophysical parameter space. A simple example of facies input
includes a rock type probability distribution characterized by a
mean value and a covariance matrix. The inclusion of facies input
may beneficially add low frequency information. Generally, the
output of a facies-based inversion includes petrophysical
parameters in an absolute frequency band (e.g., low frequencies,
near to or about 0 Hz). Exemplary techniques for facies-based
inversion can be found in aforementioned U.S. Patent Publication
No. 2018/0156932.
[0024] The terms "velocity model," "density model," "physical
property model," or other similar terms as used herein refer to a
numerical representation of parameters for subsurface regions.
Generally, the numerical representation includes an array of
numbers, typically a 2-D or 3-D array, where each number--which may
be called a "model parameter"--is a value of velocity, density, or
another physical property in a cell, where a subsurface region has
been conceptually divided into discrete cells for computational
purposes. For example, the spatial distribution of velocity may be
modeled using constant-velocity units (layers) through which ray
paths obeying Snell's law can be traced. A 3-D geologic model
(particularly a model represented in image form) may be represented
in volume elements (voxels), in a similar way that a photograph (or
2-D geologic model) is represented by picture elements (pixels).
Such numerical representations may be shape-based or functional
forms in addition to, or in lieu of, cell-based numerical
representations.
[0025] As used herein, "hydrocarbon management" or "managing
hydrocarbons" includes any one or more of the following:
hydrocarbon extraction; hydrocarbon production (e.g., drilling a
well and prospecting for, and/or producing, hydrocarbons using the
well; and/or causing a well to be drilled, e.g., to prospect for
hydrocarbons); hydrocarbon exploration; identifying potential
hydrocarbon-bearing formations; characterizing hydrocarbon-bearing
formations; identifying well locations; determining well injection
rates; determining well extraction rates; identifying reservoir
connectivity; acquiring, disposing of, and/or abandoning
hydrocarbon resources; reviewing prior hydrocarbon management
decisions; and any other hydrocarbon-related acts or activities,
such activities typically taking place with respect to a subsurface
formation. The aforementioned broadly include not only the acts
themselves (e.g., extraction, production, drilling a well, etc.),
but also or instead the direction and/or causation of such acts
(e.g., causing hydrocarbons to be extracted, causing hydrocarbons
to be produced, causing a well to be drilled, causing the
prospecting of hydrocarbons, etc.).
[0026] As used herein, "obtaining" data generally refers to any
method or combination of methods of acquiring, collecting, or
accessing data, including, for example, directly measuring or
sensing a physical property, receiving transmitted data, selecting
data from a group of physical sensors, identifying data in a data
record, and retrieving data from one or more data libraries. For
example, a seismic survey may be conducted to acquire the initial
data (noting that these and other embodiments may also or instead
include obtaining other geophysical data in addition or, or instead
of, seismic data--such as obtaining electrical resistivity
measurements). In these and other embodiments, models may be
utilized to generate synthetic initial data (e.g., computer
simulation). In some embodiments, the initial data may be obtained
from a library of data from previous seismic surveys or previous
computer simulations. In some embodiments, a combination of any two
or more of these methods may be utilized to generate the initial
data.
[0027] The term "label" generally refers to identifications and/or
assessments of correct or true outputs provided for a given set of
inputs. Labels may be of any of a variety of formats, including
text labels, data tags (e.g., binary value tags), pixel attribute
adjustments (e.g., color highlighting), n-tuple label (e.g.,
concatenation and/or array of two or more labels), etc.
[0028] If there is any conflict in the usages of a word or term in
this specification and one or more patent or other documents that
may be incorporated herein by reference, the definitions that are
consistent with this specification should be adopted for the
purposes of understanding this disclosure.
[0029] One of the many potential advantages of the embodiments of
the present disclosure is that accurate fluid saturation models may
be efficiently developed. Other potential advantages include one or
more of the following, among others that will be apparent to the
skilled artisan with the benefit of this disclosure: working in the
petrophysical parameter space, well log data, and prior fit may be
utilized as criteria to determine fluid types; some techniques
utilize only a single inversion routine to identify
hydrocarbon-bearing sands; and some techniques utilize an
artificial rock type to model implausible sets of petrophysical
parameters. Embodiments of the present disclosure can thereby be
useful in the discovery and/or extraction of hydrocarbons from
subsurface formations.
[0030] A petrophysical inversion (e.g., facies-based inversion)
will generally use a fluid saturation model of the subsurface
region. When the fluid saturation model mismatches the geophysical
data (i.e., makes incorrect fluid assumptions for at least a
portion of the subsurface region), the petrophysical inversion
pushes the inversion solution (predicted result) away from the
prior distributions. For example, if a hydrocarbon reservoir is
inverted under an assumption that it is wet (e.g., containing
brine), the inverted reservoir sand will be predicted to be too
"clean" (i.e., have too high porosity and/or too low Vclay). Hence
the prior distribution assumption is violated if the wrong fluid is
assumed in the fluid saturation model.
[0031] FIG. 1 illustrates fluid saturation model mismatch as
evidenced by geologic prior misfits and/or data misfits. Graphs
140, 150, and 160 illustrate inversion parameters under a
hydrocarbon flood fluid saturation model. Graphs 170, 180, and 190
illustrate inversion parameters under a brine flood fluid
saturation model. Along the horizontal axes, graphs 140 and 170
illustrate porosity parameters, graphs 150 and 180 illustrate
volume of clay (Vclay or Vcl) parameters, and graphs 160 and 190
illustrate P-wave velocity (Vp) parameters. The vertical axis
represents signal time, which can be simply converted to reflector
depth. Each graph illustrates stacked reservoir sands in zones 110,
120, and 130. Well log data (e.g., from a petrophysical model) is
illustrated as line WL in each of the graphs 140, 150, 170, and
180. Such well log data may be utilized to generate seismic data
and/or P-wave velocity data. In this example, for the forward
modeling of the seismic data and P-wave velocity data, the zones
110 and 120 contain hydrocarbons, and zone 130 contains brine. In
each of the graphs 140, 150, 170, and 180, the line INV is the
inverted result (predicted result), and the line PR is the final
prior distribution.
[0032] Graphs 140, 150, and 160, being inversion parameters under a
hydrocarbon flood fluid saturation model, assume the sands in all
three zones are hydrocarbon bearing. Note that, for graphs 140 and
150, the predicted result (line INV) matches reasonably well with
the prior distribution (line PR), which also matches reasonably
well with the true model (e.g., well log data represented by line
WL). However, in zone 130 of these two graphs, although the
predicted result (line INV) matches reasonably well with the prior
distribution (line PR), the forward modeling does not correctly
predict the lower reservoir (zone 130) for the true model (line WL)
due to the wrong fluid assumption (e.g., a misfit of geologic
prior). The wet sand of zone 130 under-predicted in porosity. A
data misfit in P-wave velocity can also be seen in zone 130 of
graph 160.
[0033] On the other hand, graphs 170, 180, and 190, being inversion
parameters under a brine flood fluid saturation model, assume the
sands in all three zones contain brine. Note that, for graphs 170
and 180, the predicted result (line INV) does not match well with
either the prior distribution (line PR) or the true model (line WL)
in zones 110 and 120. However, in these two graphs, for zone 130,
the predicted result (line INV) matches reasonably well with the
prior distribution (line PR) which also matches reasonably well
with the true model (line WL). The prior misfits in zones 110 and
120 indicate that the hydrocarbon sands (of zones 110 and 120) are
predicted to be too clean; specifically the porosity is
over-predicted. It should be understood that a petrophysical
inversion converging upon a correct result should not have a
predicted result (line INV) with a significantly higher porosity
than the prior distributions (line PR). Hence, zones 110 and 120
illustrate a large prior misfit in graph 170. Moreover, the prior
misfits in zones 110 and 120 in graphs 170 and 180 are larger than
those of graphs 140 and 150, respectively. Consequently, FIG. 1
illustrates a fluid saturation model mismatch for the brine flood
in zones 110 and 120, and for the hydrocarbon flood in zone
130.
[0034] Fluid saturation model mismatches in petrophysical inversion
can be exploited in various ways to better predict the potential
for locating hydrocarbons in a reservoir. Three approaches will be
discussed below. The first two approaches are generally applicable
post inversion, while the third approach may be implemented as part
of a learning step during the inversion. It should be understood
that each approach presupposes that data and/or models for a
subsurface region have been appropriately obtained to facilitate
elucidated actions. For example, each approach presupposes that an
initial subsurface region model is obtained. A subsurface region
model may be made up of cells identified at locations in the
subsurface region. Each cell in the subsurface region model may
contain a representation of pore space, for example percentage of
pore space. The pore space may determine the amount of fluid that
may occupy the pore and/or cell. Each cell in the subsurface region
model may contain a representation of volume of clay, for example
percentage of volume of clay. The volume of clay may determine the
amount of bound-water, and thus decrease the amount of additional
fluid that may occupy the pore and/or cell. Seismic data may be
utilized to identify possible reflectors, layers, and/or geology of
the subsurface formation of interest.
[0035] Misfit-Based Approach
[0036] The misfit-based approach utilizing fluid saturation model
mismatches in petrophysical inversion utilizes a variant of
"velocity flooding" to solve problems associated with accurate and
efficient modeling of a subterranean region remote from a wellbore
(or other location with obtainable samples). Typically, velocity
flooding includes generating a velocity model where all of a
subterranean reservoir, or large portions thereof, has a constant,
or linearly-increasing gradient, velocity. The first iteration
would fill the model with water velocity, the second would have a
simple velocity gradient profile for sediments below the
interpreted water bottom, the third would have a constant velocity
of salt below the interpreted top salt, etc. The complexity of the
model generally grows with each subsequent iteration. In some
embodiments, instead of starting with "floods" of simple velocity
profiles, the initial velocity model may simulate the filling of
all (substantially all, or the vast majority thereof) available
pore space of a fluid saturation model of a subterranean region
with a given fluid type--even recognizing that this is not a
realistic representation of the subterranean region. This
"flooding" process is repeated iteratively with one or more
different fluid types. Suitable techniques for velocity flooding
can be found in co-pending U.S. Patent Application Ser. No.
67/712,780, entitled "Fluid Saturation Model for Petrophysical
Inversion," and filed Jul. 31, 2018, which is incorporated herein
by reference in all jurisdictions that allow it.
[0037] The misfit-based approach makes use of the fact that a
mismatched fluid saturation model drives the inversion to produce
larger misfits, both for the prior distribution and for the data
term of the objective function. FIG. 2A illustrates an exemplary
method 201 utilizing the misfit-based approach. The method 201
begins by performing a first petrophysical inversion under a
hydrocarbon flood assumption at block 215, and by performing a
second petrophysical inversion under a brine flood assumption at
block 210. The actions of blocks 210 and 215 may occur in parallel,
sequentially, and/or in any order. Each of the inversions generates
results (e.g., resulting predicted properties in a subsurface
property model), such as one or more of porosity, Vclay, seismic
data, and Vp results. In certain of these embodiments, an inversion
generates a model of one of the aforementioned properties (noting
that multiple inversions may be carried out to generate multiple
subsurface property models, resulting in, for example, a model for
each property). As illustrated in FIG. 1, a larger prior misfit
will result if a hydrocarbon-bearing sand is inverted under a brine
flood fluid saturation model. (That is, using data from a
subsurface region of interest, and inverting for a given subsurface
property(ies) (e.g., Por, Vclay, and/or Vp, as shown in FIG. 1)
using a brine flood fluid saturation model, as opposed to a
hydrocarbon flood fluid saturation model, may result in larger
misfit of predicted property(ies) as compared to measured
property(ies) of the subsurface region of interest.) Furthermore,
because the prior will restrain the solution from predicting a
more-correct result, the data (under the fluid saturation model
mismatch) cannot be fit as well as for an inversion with a better
fluid match (e.g., using a hydrocarbon flood fluid saturation
model, for hydrocarbon-bearing sand). Misfits in the results of the
two inversions (from blocks 210 and 215) may be identified at block
220. By comparing misfits for a hydrocarbon flood and a brine
flood, zones of increased relative misfit may be identified. Such
identified zones may correspond to zones where the fluid assumption
was violated. Method 201 continues at block 250 where a fluid
saturation model is generated based on the misfits from block 220.
For example, zones may then be labeled as "wet sand" or
"hydrocarbon sand" based on relative performance under the two
inversions (from blocks 210 and 215). A final petrophysical
inversion may be performed at block 270 starting with the fluid
saturation model from block 250. The output of the method 201 may
be the output of the inversion at block 270, including a final
fluid saturation model at block 290. It will be appreciated that,
although the above example is discussed in terms of inverting a
hydrocarbon-bearing sand under a brine flood fluid saturation
model, the same concepts could apply vice versa with respect to
brine-saturated sand inverted under a hydrocarbon flood fluid
saturation model.
[0038] Post-Inversion-Classification Approach
[0039] The post-inversion classification approach utilizing fluid
saturation model mismatches in petrophysical inversion analyzes
results of using a brine flood during the inversion. Any identified
prior misfits and/or data misfits may lead to rock type
classification with the use of an artificial (i.e., not
petrophysically plausible) rock type. In other words, a rock type
model may include the artificial rock type in exactly those
locations that prior misfits and/or data misfits arise during the
inversion iterations. The fluid saturation model may then be
generated based on the locations of the predicted artificial rock
type. Suitable techniques for inversion analysis with
artificial/synthetic rock types can be found in co-pending U.S.
Patent Application Ser. No. 62/731,182, entitled "Reservoir
Characterization Utilizing Resampled Seismic Data," and filed Sep.
14, 2018, which is incorporated herein by reference in all
jurisdictions that allow it.
[0040] FIG. 2B illustrates an exemplary method 202 utilizing the
post-inversion-classification approach. The method 202 begins by
performing a petrophysical inversion under a brine flood assumption
at block 210. The petrophysical inversion takes as input several
expected rock type classes, each specified by its respective
petrophysical parameters. Output of the petrophysical inversion
includes a model of the subsurface region as specified by
petrophysical parameters.
[0041] Method 202 continues at block 230 where rock types are
classified based on the petrophysical parameters from the
petrophysical inversion of block 210. For example, rock types may
be classified in petrophysical space (e.g., based on porosity and
Vclay parameters). In some embodiments, classifying rock types at
block 230 may include utilizing one or more artificial rock types.
FIGS. 3A-C illustrate one way to determine parameters for a
suitable artificial rock type. FIG. 3A illustrates a cross-plot of
porosity (along the horizontal axis) with Vclay (along the vertical
axis), showing probability distributions of expected rock types
301-305 (e.g., rock types used during the classification step in a
conventional petrophysical inversion). For example, rock type 302
represents a clean reservoir sand, having high porosity and low
Vclay. In FIG. 3A, iso-contours of elastic parameters under a
hydrocarbon flood assumption for rock type 302 (more specifically,
for the mean value of the probability distribution of rock type
302) are also plotted: S-wave velocity (Vs) at line 310, P-wave
velocity (Vp) at line 320, and density at line 330. FIG. 3B
illustrates a cross-plot of porosity with Vclay for the same
reservoir with a brine flood assumption. Note in FIG. 3B that line
320 (Vp) and line 330 (density) are moved away from the clean sand
rock type 302, indicating a geologic prior misfit. In FIG. 3C, an
additional probability distribution is constructed for artificial
rock type 306. The mean value of rock type 306 is at the
intersection of line 320 (Vp) and line 330 (density) under the
brine flood assumption. It is expected that performing a
petrophysical inversion with a brine flood assumption with rock
types 301-306 will correctly classify wet sands (e.g., zone 130 of
FIG. 1) as rock type 302, while hydrocarbon sands (e.g., zones 110
and 120 of FIG. 1) will be classified as artificial rock type 306.
Thus, the inverted porosity and Vclay from block 210 may be
utilized to classify rock types at block 230, including potentially
one or more artificial rock types, which may be indicative of a
prior misfit and/or data misfit.
[0042] In some embodiments (not illustrated in FIG. 2B), further
iterations of blocks 210 and 230 perform one or more petrophysical
inversions under a brine flood assumption with successive rock type
models updated with one or more artificial rock types. By
introducing these additional artificial rock types during the
inversions, the results are allowed to diverge further in parameter
space. This may produce somewhat better overall results.
[0043] In some embodiments, a trained machine learning system may
be utilized to classify rock types (e.g., based on cross-plots of
porosity and Vclay) at block 230. In some embodiments, expert
interpretation of inverted porosity and Vclay in the parameter
space can be used to carve out potential hydrocarbon sands, by e.g.
drawing arbitrary polygons to select all points not consistent with
the rock types shown in FIG. 3A. In some embodiments multiple
artificial rock types are selected for multiple types of reservoirs
sands. For example, an additional rock type may be created based on
the iso-contours of line 310 under a brine assumption.
[0044] Method 202 continues at block 250 where a fluid saturation
model is generated based on the rock types from block 230. For
example, expert interpretation may be utilized to build a fluid
saturation model, e.g., by removing small scale artifacts that
could result from mis-classification. A final petrophysical
inversion may be performed at block 270 starting with the fluid
saturation model from block 250. The output of the method 202 may
be the output of the inversion at block 270, including a final
fluid saturation model at block 290.
[0045] In some embodiments, the misfit-based approach and the
post-inversion classification approach may be utilized in
conjunction to lessen or remove ambiguity between an actual fluid
response and an unexpected lithology variation. For example, if the
prior misfit actually identifies an area of higher porosity sand
that is not fully captured in the modeled rock types, the
post-inversion classification approach may identify the area as
hydrocarbon-bearing sand in a brine flood inversion, while the data
misfit-based approach would suggest that a wet, but more porous,
sand is more probable.
[0046] Classification-During-Inversion Approach
[0047] The classification-during-inversion approach utilizing fluid
saturation model mismatches in petrophysical inversion allows the
fluid type to be changed during the inversion. For example, the
fluid model may be constructed during the inversion. This approach
follows a similar strategy as the post-inversion classification
approach, in that both approaches utilize one or more artificial
rock types. In the classification-during-inversion approach, the
artificial rock type is added as a prior facie at the beginning of
the inversion.
[0048] FIG. 2C illustrates an exemplary method 203 utilizing the
classification-during-inversion approach. The method 203 begins by
performing a petrophysical inversion under a brine flood assumption
at block 210. The iterative methodology of the inversion generates
porosity and Vclay results for each iteration. FIGS. 4A-4C
illustrate results for the classification-during-inversion
approach. Each graph of FIGS. 4A-4C illustrates stacked reservoir
sands in zones 410, 420, and 430. Graphs 445 in each FIG. 4A-4B
illustrate porosity parameters (along horizontal axis) with well
depth as a function of time (along vertical axis). Similarly in
each FIG. 4A-4B, graphs 455 illustrate Vclay parameters; graphs 465
illustrate the water saturation models (Sw) assumed in the
inversion; graphs 475 illustrate rock type classifications; and
graphs 485 illustrate a masking parameter that informs the
inversion of the fluid type (i.e. 1 is brine and -1 is
hydrocarbon). In FIG. 4C, graph 445 illustrates porosity parameters
(along horizontal axis) with well depth as a function of time
(along vertical axis). Similarly in FIG. 4C, graph 455 illustrates
Vclay parameters; graphs 490-a-490-d illustrate four different
angle stacks; and graph 495 illustrates a P-wave velocity parameter
(e.g., frequency range 0-12 Hz). In the true model (e.g., well log
data shown in the line WL of graphs 445 and 455 in FIG. 4C), on
which seismic synthetic data are computed, zones 410 and 420
contain hydrocarbons, while zone 430 contains brine. In graphs 445
and 455 of FIG. 4C, the line INV is the inverted result (predicted
result), and the line PR is the final prior distribution. For
example, FIG. 4A illustrates porosity (in graph 445) and Vclay (in
graph 455) after the first iteration. The two hydrocarbon
reservoirs in zones 410 and 420 show large porosity values because
the fluid assumption is violated. Graph 465 illustrates the water
saturation model used during the inversion, indicative of starting
with a brine flood assumption.
[0049] Method 203 continues at block 230 where rock types are
classified based on the inverted porosity and Vclay from block 210.
In FIG. 4A, note the classification of artificial rock type 6 in
zones 410 and 420 of graph 475 based on the porosity and Vclay in
graphs 445 and 455. In some embodiments, a machine learning system
may be utilized to classify rock types based on iterative results,
such as location of porosity and/or Vclay parameters in cross-plot
space, data misfits, etc. Additionally, 3-D information could be
included to further constrain the placement of sands.
[0050] Method 203 continues at block 250 where a fluid saturation
model is generated based on the rock types from block 230. For
example, based on the artificial rock type classification of graph
475, the two upper sands are switched from wet sands to
hydrocarbon-bearing, as illustrated in the masking parameter of
graph 485.
[0051] Method 203 continues at block 270 where an iteration of
petrophysical inversion is performed based on the fluid saturation
model of block 250. FIG. 4B illustrates results after the next
iteration, utilizing masking parameters from graph 485 of FIG. 4A
and water saturation model 465 of FIG. 4B. The water saturation in
this example is computed as a function of Vclay. Note that this
iteration employs a more complex fluid saturation model than the
simple, brine flood assumption of the first iteration.
[0052] Method 203 continues at block 280 where an iteration of
checking for convergence is performed. Note that no artificial rock
type 6 is identified in graph 475 of FIG. 4B. Consequently, it can
be assumed that zones 410 and 420 have been predicted correctly
after two iterations. In the illustration of FIGS. 4A-4C,
therefore, method 203 concludes at block 290, outputting a final
fluid saturation model. FIG. 4C illustrates the final results. As
can be seen in graphs 445 and 455 of FIG. 4C, the fluid types in
each zone 410, 420, and 430 are predicted correctly during the
inversion.
[0053] In some embodiments, block 580 identifies lack of
convergence in the iterative fluid saturation model. In those
instances, method 203 iteratively repeats blocks 230, 250, 270, and
280. For example method 203 might iterate until the rock type
classification at block 230 converges. For example, with
convergence, there is no change in the rock type classification
from one iteration to the next. As another example, with
convergence, the inverted porosity and Vclay would not change given
the same facies and input data. In some embodiments, a specified
number of iterations are performed before proceeding to block
290.
[0054] In some embodiments, a more sophisticated machine learning
approach, such as deep learning, may be utilized to identify
hydrocarbon sands when inverting under a brine flood assumption.
Such an approach might include constraints on 3-D distributions of
hydrocarbon sands, and further constraining where hydrocarbon sand
can be found. For example, once there is a transition from
hydrocarbon to wet for a given reservoir, there should be no
hydrocarbon further downdip. In other words there should be a
single fluid contact. Instead of using a simple classification as
used in the example in a 1-D sense (trace by trace), a 3-D neural
network may be trained to identify hydrocarbon sands in a brine
flood based on the anomalous porosity and Vclay values. As before,
the misfit-based approach and the classification-during-inversion
approach may be utilized in conjunction to lessen or remove
ambiguity between an actual fluid response and an unexpected
lithology variation.
[0055] In practical applications, several of the embodiments
described herein must be used in conjunction with, and/or carried
out using, a seismic data analysis system (e.g., a high-speed
computer) programmed in accordance with the disclosures herein. For
example, any of the petrophysical or other inversion techniques
will in various of these embodiments be carried out using such a
system. Likewise, generating the various models (e.g., trial fluid
saturation models and/or fluid saturation models) will be carried
out using such as system, according to various of these
embodiments. Identification of misfits may also be carried out
using such a system (e.g., automated or semi-automated
identification), although it will be appreciated that such
identification may be carried out in whole or in part by user
input. Such a seismic data analysis system may be referred to in
generic shorthand simply as a "computer." The same or a different
computer (and/or seismic data analysis system) may be used to carry
out different inversions, and/or different steps of generating a
model and/or displaying an image of a subsurface region.
[0056] Preferably, in order to effectively perform petrophysical
inversion according to various embodiments herein, the seismic data
analysis system is a high performance computer (HPC), as known to
those skilled in the art. Such high performance computers typically
involve clusters of nodes, each node having multiple central
processing units (CPUs) and computer memory that allow parallel
computation. The models may be visualized and edited using any
interactive visualization programs and associated hardware, such as
monitors and projectors. The architecture of the system may vary
and may be composed of any number of suitable hardware structures
capable of executing logical operations and displaying the output
according to the present technological advancement. Those of
ordinary skill in the art are aware of suitable supercomputers
available from Cray or IBM.
[0057] As will be appreciated from the above discussion, in certain
embodiments of the present approach, expert inputs are elicited
that will have the most impact on the efficacy of a learning
algorithm employed in the analysis, such as a classification or
ranking algorithm, and which may involve eliciting a judgment or
evaluation of classification or rank (e.g., right or wrong, good or
bad) by the reviewer with respect to a presented query. Such inputs
may be incorporated in real time in the analysis of seismic data,
either in a distributed or non-distributed computing framework. In
certain implementations, queries to elicit such input are generated
based on a seismic data set undergoing automated evaluation, and
the queries are sent to a workstation for an expert to review.
[0058] FIG. 5 illustrates a block diagram of a seismic data
analysis system 9900 upon which the present technological
advancement may be embodied. A central processing unit (CPU) 9902
is coupled to system bus 9904. The CPU 9902 may be any
general-purpose CPU, although other types of architectures of CPU
9902 (or other components of exemplary system 9900) may be used as
long as CPU 9902 (and other components of system 9900) supports the
operations as described herein. Those of ordinary skill in the art
will appreciate that, while only a single CPU 9902 is shown in FIG.
5, additional CPUs may be present. Moreover, the system 9900 may
comprise a networked, multi-processor computer system that may
include a hybrid parallel CPU/GPU system. The CPU 9902 may execute
the various logical instructions according to various teachings
disclosed herein. For example, the CPU 9902 may execute
machine-level instructions for performing processing according to
the operational flow described.
[0059] The seismic data analysis system 9900 may also include
computer components such as non-transitory, computer-readable
media. Examples of computer-readable media include a random access
memory (RAM) 9906, which may be SRAM, DRAM, SDRAM, or the like. The
system 9900 may also include additional non-transitory,
computer-readable media such as a read-only memory (ROM) 9908,
which may be PROM, EPROM, EEPROM, or the like. RAM 9906 and ROM
9908 hold user and system data and programs, as is known in the
art. The system 9900 may also include an input/output (I/O) adapter
9910, a communications adapter 9922, a user interface adapter 9924,
and a display adapter 9918; the system 9900 may potentially also
include one or more graphics processor units (GPUs) 9914, and one
or more display drivers 9916.
[0060] The I/O adapter 9910 may connect additional non-transitory,
computer-readable media such as storage device(s) 9912, including,
for example, a hard drive, a compact disc (CD) drive, a floppy disk
drive, a tape drive, and the like to seismic data analysis system
9900. The storage device(s) may be used when RAM 9906 is
insufficient for the memory requirements associated with storing
data for operations of the present techniques. The data storage of
the system 9900 may be used for storing information and/or other
data used or generated as disclosed herein. For example, storage
device(s) 9912 may be used to store configuration information or
additional plug-ins in accordance with the present techniques.
Further, user interface adapter 9924 couples user input devices,
such as a keyboard 9928, a pointing device 9926 and/or output
devices to the system 9900. The display adapter 9918 is driven by
the CPU 9902 to control the display on a display device 9920 to,
for example, present information to the user. For instance, the
display device may be configured to display visual or graphical
representations of any or all of the models discussed herein (e.g.,
fluid saturation models, porosity models, Vclay models, rock type
models, seismic images, feature probability maps, feature objects,
predicted labels of geologic features in seismic data, etc.). As
the models themselves are representations of geophysical data, such
a display device may also be said more generically to be configured
to display graphical representations of a geophysical data set,
which geophysical data set may include the models and data
representations (including models and representations labeled with
features predicted by a trained ML model) described herein, as well
as any other geophysical data set those skilled in the art will
recognize and appreciate with the benefit of this disclosure.
[0061] The architecture of seismic data analysis system 9900 may be
varied as desired. For example, any suitable processor-based device
may be used, including without limitation personal computers,
laptop computers, computer workstations, and multi-processor
servers. Moreover, the present technological advancement may be
implemented on application specific integrated circuits (ASICs) or
very large scale integrated (VLSI) circuits. In fact, persons of
ordinary skill in the art may use any number of suitable hardware
structures capable of executing logical operations according to the
present technological advancement. The term "processing circuit"
encompasses a hardware processor (such as those found in the
hardware devices noted above), ASICs, and VLSI circuits. Input data
to the system 9900 may include various plug-ins and library files.
Input data may additionally include configuration information.
[0062] Seismic data analysis system 9900 may include one or more
machine learning architectures. The machine learning architectures
may be trained on various training data sets, e.g., as described in
connection with various methods herein. The machine learning
architectures may be applied to analysis and/or problem solving
related to various unanalyzed data sets (e.g., test data such as
acquired seismic or other geophysical data, as described herein).
It should be appreciated that the machine learning architectures
perform training and/or analysis that exceed human capabilities and
mental processes. The machine learning architectures, in many
instances, function outside of any preprogrammed routines (e.g.,
varying functioning dependent upon dynamic factors, such as data
input time, data processing time, data set input or processing
order, and/or a random number seed). Thus, the training and/or
analysis performed by machine learning architectures is not
performed by predefined computer algorithms and extends well beyond
mental processes and abstract ideas.
[0063] The above-described techniques, and/or systems implementing
such techniques, can further include hydrocarbon management based
at least in part upon the above techniques. For instance, methods
according to various embodiments may include managing hydrocarbons
based at least in part upon fluid saturation models constructed
according to the above-described methods. In particular, such
methods may include drilling a well, and/or causing a well to be
drilled, based at least in part upon the fluid saturation models
(e.g., such that the well is located based at least in part upon a
location determined from the fluid saturation models, which
location may optionally be informed by other inputs, data, and/or
analyses, as well) and further prospecting for and/or producing
hydrocarbons using the well.
[0064] The foregoing description is directed to particular example
embodiments of the present technological advancement. It will be
apparent, however, to one skilled in the art, that many
modifications and variations to the embodiments described herein
are possible. All such modifications and variations are intended to
be within the scope of the present disclosure, as defined in the
appended claims. Persons skilled in the art will readily recognize
that in preferred embodiments of the invention, some or all of the
steps in the present inventive method are performed using a
computer, i.e., the invention is computer implemented. In such
cases, the fluid saturation models (and/or images generated of a
subsurface region based on such models) may be downloaded or saved
to computer storage, and/or displayed using a computer and/or
associated display.
* * * * *