U.S. patent application number 16/344462 was filed with the patent office on 2019-10-24 for automated mutual improvement of oilfield models.
The applicant listed for this patent is LANDMARK GRAPHICS CORPORATION. Invention is credited to Brent Charles HOUCHENS, Kaiji LU, Ethan MYERS, Michael Keith REDMAN, Avinash WESLEY, Joseph Blake WINSTON, Feifei ZHANG.
Application Number | 20190323323 16/344462 |
Document ID | / |
Family ID | 62491650 |
Filed Date | 2019-10-24 |
![](/patent/app/20190323323/US20190323323A1-20191024-D00000.png)
![](/patent/app/20190323323/US20190323323A1-20191024-D00001.png)
![](/patent/app/20190323323/US20190323323A1-20191024-D00002.png)
![](/patent/app/20190323323/US20190323323A1-20191024-D00003.png)
![](/patent/app/20190323323/US20190323323A1-20191024-D00004.png)
![](/patent/app/20190323323/US20190323323A1-20191024-D00005.png)
![](/patent/app/20190323323/US20190323323A1-20191024-D00006.png)
![](/patent/app/20190323323/US20190323323A1-20191024-D00007.png)
![](/patent/app/20190323323/US20190323323A1-20191024-D00008.png)
![](/patent/app/20190323323/US20190323323A1-20191024-D00009.png)
![](/patent/app/20190323323/US20190323323A1-20191024-D00010.png)
View All Diagrams
United States Patent
Application |
20190323323 |
Kind Code |
A1 |
ZHANG; Feifei ; et
al. |
October 24, 2019 |
AUTOMATED MUTUAL IMPROVEMENT OF OILFIELD MODELS
Abstract
Systems, methods, and computer-readable media are described for
the mutual improvement of physics-based and data-driven models
related to an oilfield. These may involve generating, via a
processor, with an oilfield related condition as a first input, a
first output based on one of a physics-based model or a data-based
model; generating, using the first input or a second input, a
second output based on the other of the physics-based model or the
data-based model not used to generate the first output; and
modifying, automatically, at least one of the physics-based model,
data-driven model, the first input or the second input, based on
the first output or second output.
Inventors: |
ZHANG; Feifei; (Spring,
TX) ; HOUCHENS; Brent Charles; (Houston, TX) ;
WINSTON; Joseph Blake; (Houston, TX) ; REDMAN;
Michael Keith; (Houston, TX) ; WESLEY; Avinash;
(New Caney, TX) ; MYERS; Ethan; (Houston, TX)
; LU; Kaiji; (Houston, TX) |
|
Applicant: |
Name |
City |
State |
Country |
Type |
LANDMARK GRAPHICS CORPORATION |
Houston |
TX |
US |
|
|
Family ID: |
62491650 |
Appl. No.: |
16/344462 |
Filed: |
April 27, 2017 |
PCT Filed: |
April 27, 2017 |
PCT NO: |
PCT/US2017/029765 |
371 Date: |
April 24, 2019 |
Related U.S. Patent Documents
|
|
|
|
|
|
Application
Number |
Filing Date |
Patent Number |
|
|
62431359 |
Dec 7, 2016 |
|
|
|
Current U.S.
Class: |
1/1 |
Current CPC
Class: |
E21B 49/00 20130101;
E21B 41/00 20130101; E21B 41/0092 20130101; E21B 47/00
20130101 |
International
Class: |
E21B 41/00 20060101
E21B041/00 |
Claims
1. A method comprising: generating, via a processor, with an
oilfield related condition as a first input, a first output based
on one of a physics-based model or a data-based model; generating,
using the first input or a second input, a second output based on
the other of the physics-based model or the data-based model not
used to generate the first output; and modifying, automatically, at
least one of the physics-based model, data-driven model, the first
input or the second input, based on the first output or second
output.
2. The method of claim 1 wherein the first output and second output
are generated in parallel.
3. The method of claim 1 wherein the first output and second output
are generated in series.
4. The method of claim 3, further comprising, subsequent generating
the first output, obtaining measured data from the oilfield related
to the first output.
5. The method of claim 4, wherein the second output is generated
based on the second input, and wherein the second input is at least
one of the measured data, the first output, or a difference between
the measured data and the first output.
6. The method of claim 5, wherein the second input is at least one
of the measured data, or the difference between the measured data
and the first output, and the measured data is measured in
real-time.
7. The method of claim 1, wherein modifying at least one of the
physics-based model or the data-driven model comprises replacement
with a different respective physics-based model or data-driven
model.
8. The method of claim 1, wherein modifying the physic-based model
comprises altering a variable within the model.
9. The method of claim 1, further comprising determining a normal
range for the first output based on the second output.
10. The method of claim 1, wherein the first output is generated
based on the physics-based model, and the second output is
generated based on the data-driven model.
11. The method of claim 1, further comprising assigning a
confidence value to the first and second outputs.
12. Then method of claim 1, wherein the method is conducted
continuously in real-time.
13. A system comprising: one or more processors; and at least one
computer-readable storage medium having stored therein instructions
which, when executed by the one or more processors, cause the one
or more processors to: generate, via a processor, with an oilfield
related condition as a first input, a first output based on one of
a physics-based model or a data-based model; generate, using the
first input or a second input, a second output based on the other
of the physics-based model or the data-based model not used to
generate the first output; and modify automatically at least one of
the physics-based model, data-driven model, the first input or the
second input, based on the first output or second output.
14. The system of claim 13, the at least one computer-readable
storage medium storing additional instructions which, when executed
by the one or more processors, cause the one or more processors to:
subsequent generating the first output, obtain measured data from
the oilfield related to the first output.
15. The system of claim 14, wherein the second output is generated
based on the second input, and wherein the second input is at least
one of the measured data, the first output, or a difference between
the measured data and the first output.
16. The system of claim 13, wherein modifying at least one of the
physics-based model or the data-driven model comprises replacement
with a different respective physics-based model or data-driven
model.
17. The system of claim 13, wherein modifying the physic-based
model comprises altering a variable within the model.
18. A non-transitory computer-readable storage medium comprising:
instructions stored therein which, when executed by one or more
processors, cause the one or more processors to: generate, via a
processor, with an oilfield related condition as a first input, a
first output based on one of a physics-based model or a data-based
model; generate, using the first input or a second input, a second
output based on the other of the physics-based model or the
data-based model not used to generate the first output; and modify
automatically at least one of the physics-based model, data-driven
model, the first input or the second input, based on the first
output or second output.
19. The non-transitory computer-readable storage medium of claim
18, comprising additional instructions which, when executed by the
one or more processors, cause the one or more processors to:
subsequent generating the first output, obtain measured data from
the oilfield related to the first output.
20. The non-transitory computer-readable storage medium of claim
19, wherein the second output is generated based on the second
input, and wherein the second input is at least one of the measured
condition, the first output, or a difference between the measured
data and the first output.
Description
[0001] This application claims priority to U.S. Provisional
Application No. 62/431,359, entitled "AUTOMATED MUTUAL IMPROVEMENT
OF OILFIELD MODELS" filed on Dec. 7, 2016, which is incorporated
herein by reference in its entirety.
TECHNICAL FIELD
[0002] The present technology pertains to the use and improvement
oilfield related modeling for hydrocarbon exploration, drilling,
and production. In particular, the present disclosure relates to
the mutual improvement of physics-based models and data-driven
models for improved accuracy and expedited oilfield solutions.
BACKGROUND
[0003] During various phases of hydrocarbon exploration and
production, it may be necessary to characterize and model the
various aspects of an oilfield. The models assist in planning,
prediction, and understanding the various variables and how they
may affect outcomes. These can include anything from physical
factors such has formation type, fluid flow, to determining causes
for events. Models that are developed may vary in accuracy and use
depending on the known and unknown variables present and what
metrics are desired. With improved modeling costs can be reduced,
potential problems avoided, and improved hydrocarbon production can
be achieved.
BRIEF DESCRIPTION OF THE DRAWINGS
[0004] In order to describe the manner in which the above-recited
and other advantages and features of the disclosure can be
obtained, a more particular description of the principles briefly
described above will be rendered by reference to specific
embodiments thereof which are illustrated in the appended drawings.
Understanding that these drawings depict only exemplary embodiments
of the disclosure and are not therefore to be considered to be
limiting of its scope, the principles herein are described and
explained with additional specificity and detail through the use of
the accompanying drawings in which:
[0005] FIG. 1A illustrates an exemplary oilfield environment for
implementation of the disclosure herein;
[0006] FIG. 1B illustrates an exemplary oilfield environment for
implementation of the disclosure herein;
[0007] FIG. 1C illustrates an exemplary oilfield environment with
production tubing for implementation of the disclosure herein;
[0008] FIG. 1D illustrates an exemplary oilfield environment with a
drilling device for implementation of the disclosure herein;
[0009] FIG. 1E illustrates an exemplary oilfield environment with a
wireline device for implementation of the disclosure herein;
[0010] FIG. 2 illustrates a flow diagram of one implementation of
the improvement model disclosed herein;
[0011] FIG. 3 illustrates a flow diagram of one implementation of
the improvement model disclosed herein;
[0012] FIG. 4 illustrates a flow diagram of one implementation of
the improvement model disclosed herein;
[0013] FIG. 5 illustrates a flow diagram of one implementation of a
parallelized weighted model disclosed herein;
[0014] FIGS. 6A and 6B illustrates schematic diagram of example
computing device.
DETAILED DESCRIPTION
[0015] Various embodiments of the disclosure are discussed in
detail below. While specific implementations are discussed, it
should be understood that this is done for illustration purposes
only. A person skilled in the relevant art will recognize that
other components and configurations may be used without parting
from the spirit and scope of the disclosure.
[0016] Additional features and advantages of the disclosure will be
set forth in the description which follows, and in part will be
obvious from the description, or can be learned by practice of the
herein disclosed principles. The features and advantages of the
disclosure can be realized and obtained by means of the instruments
and combinations particularly pointed out in the appended claims.
These and other features of the disclosure will become more fully
apparent from the following description and appended claims, or can
be learned by the practice of the principles set forth herein.
[0017] It will be appreciated that for simplicity and clarity of
illustration, where appropriate, reference numerals have been
repeated among the different figures to indicate corresponding or
analogous elements. In addition, numerous specific details are set
forth in order to provide a thorough understanding of the
embodiments described herein. However, it will be understood by
those of ordinary skill in the art that the embodiments described
herein can be practiced without these specific details. In other
instances, methods, procedures and components have not been
described in detail so as not to obscure the related relevant
feature being described. The drawings are not necessarily to scale
and the proportions of certain parts may be exaggerated to better
illustrate details and features. The description is not to be
considered as limiting the scope of the embodiments described
herein.
[0018] The term "coupled" is defined as connected, whether directly
or indirectly through intervening components, and is not
necessarily limited to physical connections. The term
"substantially" is defined to be essentially conforming to the
particular dimension, shape or other word that substantially
modifies, such that the component need not be exact. In the
following discussion and in the claims, the terms "including" and
"comprising" are used in an open-ended fashion, and thus should be
interpreted to mean "including, but not limited to . . . ". The
term "oilfield" should be interpreted to mean any area, including
its surface and subsurface regions, having a reservoir of
hydrocarbons, be they oil, gas or a combination, with or without
water and other non-hydrocarbon components, and may include any
number of wellbores in various phases of development, rigs,
stimulation or production equipment or other equipment.
[0019] Hyper-physical parameters may be defined herein as physical
parameters which have high uncertainty or are difficult to obtain
(examples: viscosity, density, surface tension, conductivity,
friction factor, porosity, etc.). Physics-based models are defined
herein as models built on first-principles and laws of nature and
may include unknown parameters and closure relations. A
physics-based model may encompass a number of sub-models. Examples
of physics-based models or sub-models include conservation of mass,
conservation of momentum, 1st and 2nd laws of thermodynamics,
Maxwell's equations, and the like.
[0020] Data-driven models may be defined herein as models that are
not based on first-principles. Data-driven models attempt to model
actual real-world data via various analysis techniques, and involve
post hoc modeling of obtained data. A data-driven model may
encompass a number of sub-models. Examples of data-driven models or
sub-models include numerical analysis, mathematical analysis, curve
fitting, clustering, and rules-based decisions, with variables not
necessarily related to a physical variable or parameter. Primary
data may be defined herein as direct observations or measurements.
Secondary data may be defined herein as indirect measurements,
including data from complex tests, such as formation permeability,
skin factor. Hybrid models may be defined herein as combination of
physical-models with data-driven models. When referring to a model
herein, such as a physics-based model or data-driven model, the
term "model" encompasses the singular and plural. In particular, a
physics-based model or a data-driven model may be made up of, or
otherwise encompass, multiple models, which are referred to herein
as sub-models. Accordingly, unless otherwise noted, the term
"model," whether physics-based or data-driven, may be made up of a
single model or multiple models. Further the plural form "models"
may refer to multiple sub-models which make up or are encompassed
by a model, or may refer to multiple models each differing from one
another in some way, such as differing in the particular sub-models
used, or using the same kind of sub-models but with the sub-models
differing in one or more variables, constant or other parameters,
or the multiple models otherwise differing in the variables,
constants, or other parameter while still each being physics-based
or data-driven.
[0021] Causes, when related to an oilfield event, may be defined
herein as symptom/potential scenario/potential events (e.g., what
can go wrong). Cause-control (referred to herein also as control
node) may be defined herein as "how an event may have occurred"
tests to verify if cause happened or is happening (via model(s) or
limits). Central node as defined herein determines that the
potential event (cause) has happened and why (control).
Overview
[0022] Disclosed herein are systems, methods and computer readable
storage media for automatic modification of one or both of a
physics-based model and a data-driven model based on the results of
the other, i.e., the mutual improvement of the combined use of at
least one of a physics-based model and at least one of a
data-driven model. Real-world data is collected related to an
oilfield and used along with physics-based and data-driven models
as inputs as well as to compare outcomes in order to automatically
improve one or both of the physics-based and data-driven
modeling.
[0023] Both the physics-based models and data-driven models each
have particular strengths which may be used to complement and
correct the other to produce more accurate modeling. Inputs related
to an oilfield condition, including estimated physical (including
hyper-physical) parameters and/or measured data from the oilfield,
may be provided to one or both of the physics-based model and
data-driven models in parallel or in series to generate a
prediction regarding various conditions or parameters related to an
oilfield. Real world measured data related to the oilfield can then
be collected from downhole sources, surface sources, and historical
data, and then compared to the outputs of the models, such as a
predicted result, or modeled parameter or condition. The difference
between the outputs of the models and the measured data can then be
the basis for the other of the models, or the next iteration of the
models' processing. Inputs to one or both of the models may include
outputs from the other of the models, the conditions related to an
oilfield, differences between the condition and/or the output from
one or both of the models, as well as the original inputs related
to the oilfield, and/or adjusted or estimated hyper-physical
parameters.
[0024] The outputs of the models provide a basis upon which to
analyze the quality of the results of both the physics-based model
and data-driven model. For example, the data-driven output may give
a different result than the physics-based model or may aid in
explaining why the physics-based model may have been incorrect or
inaccurate. Alternatively, it may be that the physics-based model
provided a more accurate modeling than the data-driven model, in
which case the data-driven model may be modified.
[0025] In view of the different outputs, one or both of the models
may be improved in some way. For instance, the inputs may be
modified or the models modified in some way. Modifying the models
may include modifying one or more of the variables of the model,
one or more constants, or replacing the physics-based model with a
new physics-based model, or the data-driven model with a new
data-driven model, which may be more pertinent for modeling the
desired oilfield condition.
[0026] The physics-based models and data-driven models may be run
in parallel. In such case, estimated and/or collected data related
to an oilfield condition, whether via sensors or historical data,
may be provided as inputs to each of the physics-based models and
data-driven models. The results of each of the physics-based models
and data-driven models can be compared to the other or to other
collected data. One or both of the physics-based models and
data-driven models can be modified in view of the results of the
other. When run in series, the physics-based model may be processed
first, with the output, either directly or indirectly serving as a
basis an input into the data-driven model. For example, the output
itself may be used, or the differences of the output with measured
data (real-time or historical) from the oilfield. Additionally, or
alternatively, the original input or the measured data may be
provided as an input to the second processed model. This can be
carried out in reverse where the data-driven model is followed by
the physics-based model. The processing of the models and
collection of the measured data can be carried out repeatedly, in
real-time, substantially in real-time and automatically.
[0027] Furthermore, the results from each of the physics-based
model and the data-driven model can be assigned a confidence
weightage value. For instance, during operations in an oilfield
incidents or accidents may occur, or in response to a set of
circumstances an operator may choose to carry out an operation.
Accordingly, during operations there may be a "cause" or various
"causes" for an "event." The physics-based model and the
data-driven model can be used to evaluate whether something is
cause for an event or which cause is most likely for a particular
event, or whether an event actually occurred. Each of the
physics-based model and the data-driven model can be weighted with
a confidence weightage value for determining which cause is most
likely.
Description
[0028] The disclosure herein can be implemented in the context of
an oilfield environment having one or more boreholes for the
production of hydrocarbons. An exemplary oilfield in which the
present disclosure may be implemented is illustrated in FIG. 1A.
The oilfield 100 can include multiple wells 110A-F which may have
tools 102A-D for data acquisition. The multiple wells 110A-F may
target one or more hydrocarbon reservoirs. Moreover, the oilfield
100 has sensors and computing devices positioned at various
locations for sensing, collecting, analyzing, and/or reporting
data. For instance, well 110A illustrates a drilled well having a
wireline data acquisition tool 102A suspended from a rig at the
surface for sensing and collecting data, generating well logs, and
performing downhole tests which are provided to the surface. Well
110B is currently being drilled with drilling tool 102B which may
incorporate subs and additional tools for logging while drilling
(LWD) and/or measuring while drilling (MWD). Well 110C is a
producing well having a production tool 102C. The tool 102C is
deployed from a Christmas tree 120 at the surface (having valves,
spools, and fittings). Fluid flows through perforations in the
casing (not shown) and into the production tool 102C in the
wellbore to the surface. Well 110D illustrates a well having
blowout event of fluid from an underground reservoir. The tool 102D
may permit data acquisition by a geophysicist to determine
characteristics of a subterranean formation and features, including
seismic data. Well 110E is undergoing fracturing and having initial
fractures 115, with producing equipment 122 at the surface. Well
110F is an abandoned well which had been previously drilled and
produced.
[0029] The oilfield 100 can include a subterranean formation 104,
which can have multiple geological formations 106A-D, such as a
shale layer 106A, a carbonate layer 106B, a shale layer 106C, and a
sand layer 106D. In some cases, a fault line 108 can extend through
one or more of the layers 106A-D.
[0030] Sensors and data acquisition tools may be provided around
the oilfield 100, multiple wells 110A-E and associated with tools
102A-D. The data may be collected to a central aggregating unit and
then provided to a processing unit. The data collected by such
sensors and tools 102A-D can include oilfield parameters, values,
graphs, models, predictions, monitor conditions and/or operations,
describe properties or characteristics of components and/or
conditions below ground or on the surface, manage conditions and/or
operations in the oilfield 100, analyze and adapt to changes in the
oilfield 100, etc. The data can include, for example, properties of
formations or geological features, physical conditions in the
oilfield 100, events in the oilfield 100, parameters of devices or
components in the oilfield 100, etc.
[0031] The general structure of the method is shown in FIG. 1B with
flow 100B having an oilfield 101 with multiple producing wells 125.
Downhole measurements 45 may be taken by sensors and tools
underground within or around wells 125, along with surface
measurements 55 taken at the surface, with such measurements
including flow rates, temperature, pressure, fluid composition,
hydrocarbon composition, and other various parameters of interest.
The downhole measurements 45 and surface measurements 55 may be
provided to the data aggregator unit 50 (referred to also herein as
the "aggregator"). The data received by the aggregator 50 may be
filtered to remove or reduce noise, or otherwise treat the measured
data. The collected data from aggregator 50 may then be fed to a
control unit 65 having a processor. The data aggregator unit 50 and
control unit 65 may be a single unit or multiple units. Historical
data 60 of an oilfield condition may also be provided to the data
aggregator unit 50 or directly to the control unit 65.
[0032] The control unit 65 may process both physics-based models 70
and data-driven models 75 (one or more of each of a physics-based
model 70 or data-driven model 75). The initial inputs to the
physics-based models 70 and data-driven models 75 may be related to
an oilfield condition, including collected measured data from
downhole measurements 45, surface measurements 55 as well as
historical data 60 about an oilfield, or other oilfield related
parameters necessary for processing the models, and/or estimated
physical and hyper-physical parameters. The inputs can be the same
to each model (each having a first input), or may be different (a
first input to one model, a second input into the other of the
models). After the initial processing by either of the models, the
input parameters may include outputs from one or both of the
physics-based models 70 and data-driven models 75 to the other of
the models. Furthermore, after an output is obtained from one or
both of the models, the output can be compared to the measured data
from the oilfield. The collected measured data may be from downhole
measurements 45, surface measurements 55 as well as historical data
60 about an oilfield. This difference, and/or the measured data can
be provided as an input to the other or both of the physics-based
models 70 and data-driven models 75. In view of the output results
from each of the physics-based models 70 and data-driven models 75,
one or both of the physics-based model and data-driven models may
be modified, or the input to either of the models may be modified.
Modifying the models may include updated constants of models,
variables, algorithms, equations, or may also include replacement
of the models with another of the physics-based models 70 or
data-driven models 75. This process can be carried out in parallel,
where physics-based models 70 and data-driven models 75 are being
processed simultaneously, or in series where one is processed
first, followed by the other. In both cases, the method steps can
be carried out in real-time and repeated continuously and
automatically, with each iteration intended to provide a more
accurate overall model of the oilfield conditions or parameters of
interest.
[0033] For example, the control unit 65 can process the
physics-based models 70 and data-driven models 75 in parallel such
that results of either can be compared one to the other, or to
collected data, and one or both of the physics-based models 70 and
data-driven models 75 in view of the other. The initial inputs to
either of the physics-based models 70 and data-driven models 75
maybe an oilfield related condition, and may include collected
measured data, or estimated hyper-physical parameters, and the
inputs into either model may be the same or different. The results
of one or both of the physics-based models 70 and data-driven
models 75 can be provided to the other of the models, and/or the
results compared one to the other or to the collected measured
data, and/or modifying the physics-based models 70 and data-driven
models 75 and/or inputs to either of the models. This can be
repeated to further update models, inputs and evaluate models to
further refine and obtain more accurate models and outputs.
[0034] Additionally, or alternatively, the physics-based models 70
and data-driven models 75 can also be processed in series. For
example, the physics-based models 70 can be provided with an input
related to an oilfield condition (from collected measured data,
estimations, estimated hyper-physical parameters, or outputs or a
calculating of the data-driven models 75) to obtain an output, such
as a predicted downhole parameter, which may be compared to
collected measured data from the oilfield. The collected measured
data and/or the differences between the output and the collected
measured data, and/or a physical parameter can then be provided as
data-based input parameter to the data-driven model. The
data-driven models 75 can then generate a data-driven output
parameter. In view of this result, at least one of the
physics-based models 70, data-driven models 75, or an input to the
physics-based models 70 can be modified.
[0035] The order of the physics-based models 70 and data-driven
models 75 may also be reversed when processed in series. Rather
than processing the physics-based models 70 first as described
above, collected measured data can first be provided to the
data-driven models 75. The output from the data-driven models 75
can be provided to physics-based models 70 and/or compared to
collected measured data and the difference and/or the collected
measured data provided as an input to the physics-based models 70.
Based on the output from the physics-based model 70, at least one
of the physics-based models 70, data-driven models 75, or an input
can be modified.
[0036] Based on a single or numerous iterations, reports 80 can be
can be generated related to the results of the models. Such reports
80 may include for example surveillance reports, abnormal
activities reports, or optimized solutions to resolve problems.
[0037] Whereas FIG. 1A and FIG. 1B show multiple wells, FIGS. 1C-1E
illustrate various types of wells which may be part of an oilfield
as in FIG. 1A or FIG. 1B, along with a flow diagram of how such
measured data may be collected and processing physics-based and
data-driven models. Such are exemplary and as described previously,
data may be obtained from various parts of the oilfield along with
various types of wells at various stages of life. As illustrated in
FIG. 1C, there is shown a producing well flow 100C having a surface
5 with a borehole 25 extending down through subterranean earth 20.
Within the borehole 25 are production tubulars 30 extending from
the surface 5. The borehole 25 extends to a target production zone
35 having fractures 40 for extraction of hydrocarbons 30 and/or
injection of fracturing or flow control fluids. Provided downhole
are downhole sensors 37 to obtain measurements of various downhole
parameters. At the surface 5 is a pump 10 with surface valves 15
and surface sensors 17. The downhole sensors 37 and the surface
valves 15 may be actuated for various purposes including
controlling the flow of fluids. The downhole sensors 37 and surface
sensors 17 may be used for collecting downhole measurements 45 and
surface measurements 55 such as flow rates, temperature, pressure,
fluid composition, hydrocarbon composition, and other various
parameters of interest. Such measured data is provided to the data
aggregator unit 50. The data aggregator unit 50 may be on the
surface or provided within the borehole 25. The data may be
processed with control unit 65, having a processor, and used with
the physics-based models 70 and data-driven models 75 as discussed
above
[0038] While FIG. 1C includes a stimulation environment with
production tubing, FIG. 1D includes a downhole drilling environment
flow 100D with a diagrammatic view of a logging while drilling
(LWD) wellbore operating environment 127. As depicted in FIG. 1D, a
drilling platform 160 is equipped with a derrick 140 that supports
a hoist 126 for raising and lowering a drill string 142. The hoist
126 suspends a top drive 138 suitable for rotating the drill string
142 and lowering the drill string 142 through the well head 132.
Connected to the lower end of the drill string 142 is a drill bit
148. As the drill bit 148 rotates, the drill bit 148 creates a
wellbore 144 that passes through various formations 146. A pump 136
circulates drilling fluid through a supply pipe 134 to top drive
138, down through the interior of drill string 142, through
orifices in drill bit 148, back to the surface via the annulus
around drill string 142, and into a retention pit 124. The drilling
fluid transports cuttings from the wellbore 144 into the pit 124
and aids in maintaining the integrity of the wellbore 144. Various
materials can be used for drilling fluid, including oil-based
fluids and water-based fluids.
[0039] The well head 132 or derrick 140 may include sensors and
valves to for collecting surface measurements such as flow rate,
temperature, pressure, composition of drilling fluids etc. With
respect to downhole measurements, logging tools 156 can be
integrated into the bottom-hole assembly 152 near the drill bit
148. As the drill bit 148 extends the wellbore 144 through the
formations 146, logging tools 156 collect measurements relating to
various formation properties as well as the orientation of the tool
and various other drilling conditions. The bottom-hole assembly 152
may also include a telemetry sub 154 to transfer measurement data
to a surface receiver 130 and to receive commands from the surface.
In at least some cases, the telemetry sub 154 communicates with a
surface receiver 130 using mud pulse telemetry. In some instances,
the telemetry sub 154 does not communicate with the surface, but
rather stores logging data for later retrieval at the surface when
the logging assembly is recovered.
[0040] Each of the logging tools 156 may include a plurality of
tool components, spaced apart from each other, and communicatively
coupled with one or more wires. The logging tools 156 may also
include one or more computing devices 150 communicatively coupled
with one or more of the plurality of tool components by one or more
wires. The computing device 150 may be configured to control or
monitor the performance of the tool, process logging data, and/or
carry out the methods of the present disclosure. The computing
device 150 may also receive data regarding the drilling device
including drilling rate, orientation, etc. and transmitted to the
surface.
[0041] In at least some instances, one or more of the logging tools
156 may communicate with a surface receiver 130 via acoustics,
wirelessly, fiber optics, or via a wire, such as wired drillpipe.
In other cases, the one or more of the logging tools 156 may
communicate with a surface receiver 130 by wireless signal
transmission. In at least some cases, one or more of the logging
tools 156 may receive electrical power from a wire that extends to
the surface, including wires extending through a wired
drillpipe.
[0042] Downhole measurements 45 such as from the logging tools 156,
performance of the tool or drilling device, data collected by
computing device 150, along with surface measurements 55 may be
provided to the data aggregator unit 50 along with any surface
measurements. The data may be processed with control unit 65 and
used with the physics-based and data-driven models as discussed
above
[0043] Whereas FIG. 1C refers to a stimulation environment and FIG.
1D a drilling environment, FIG. 1E illustrates a wireline oilfield
environment flow 100E. As shown in FIG. 1E, a tool having tool body
192 can be employed with "wireline" systems, in order to carry out
logging or other operations. For example, instead of using the
drill string 142 of FIG. 1D to lower tool body 192, which may
contain sensors or other instrumentation for detecting and logging
nearby characteristics and conditions of the wellbore and
surrounding formation, a conveyance 182 can be used. The tool body
192 can be lowered into the wellbore 194 by wireline conveyance
182. The conveyance 182 can be anchored in the drill rig 180 or
portable means such as a truck. The conveyance 182 can be wireline
(having one or more wires), slicklines, cables, or the like, as
well as tubular conveyances such as coiled tubing, joint tubing, or
other tubulars.
[0044] The illustrated conveyance 182 provides support for the
tool, as well as enabling communication between the tool processors
on the surface and providing a power supply. The wireline
conveyance 182 can include fiber optic cabling for carrying out
communications. The wireline conveyance 182 is sufficiently strong
and flexible to tether the tool body 192 through the wellbore 194,
while also permitting communication through the wireline conveyance
182 to local processor 188 and/or remote processors 184, 186.
Additionally, power can be supplied via the wireline conveyance 182
to meet power requirements of the tool. For slickline or coiled
tubing configurations, power can be supplied downhole with a
battery or via a downhole generator. Accordingly downhole
measurements 45 from the wireline logging too may be transmitted to
the surface processors 184, 186. Further, surface sensors 190 may
be provided which obtain surface measurements 55, which may relate
to speed of the logging tool, conveyance 182, temperature,
pressure, or other parameters of interest at the surface. The data
aggregator unit 50 may be on the surface or provided within the
wellbore 194. The data may be processed with control unit 65 and
used with the physics-based and data-driven models as discussed
above
[0045] With respect to the flows as shown in FIGS. 1B-E the inputs
may be initially based on estimates of parameters, and then the
models and inputs improved as more data is collected and fed into
the system. Additionally, for situations where the physical process
is fully or sufficiently understood and the confidence about the
physics-based model is high, the model is set and only the
hyper-physical input parameters need to be adjusted by the
data-driven model, which may be illustrated in FIG. 2.
[0046] Referring now to FIG. 2, an exemplary flow diagram 200 is
illustrated which provides for a mutual improvement flow between
the physics-based model 70 and data-driven model 75. As disclosed
therein, estimated hyper-physical parameters 205 may be determined,
such as permeability, skin factor, friction factor, porosity, etc.
One or more of these can be provided as a first input to a
physics-based model 210, which can output predicted parameter
result 215. Examples of a physics-based model 210 may include
Buckley-Leverett displacement model for immiscible displacement or
the well inflow equations based on Darcy's law, among others, to
make an oilfield prediction, such as predict the water
break-through or well production rate. Further, as shown, collected
data regarding an oilfield condition, such as real-time measured
data 220, may be collected via surface and/or downhole sensors.
These may include direct measurement of the predicted parameters by
the physics-based model or indirect measurements which allow
calculation of the same. Further, historical data can be employed
additionally or alternatively. These measurements can be filtered
to remove noise thereby producing filtered data 225 which may be
more effectively used for accurate modeling.
[0047] Next, the trend of differences between the results 215
generated by the physics-based model 210 is compared to the
filtered data 225 to obtain the difference 230. The differences 230
are then provided as an input (referred to herein also as a second
input) to the data-driven model 235. Alternatively, or
additionally, the measured data 220 can be provided as an input
into the data-driven model 235. Still further, the output of the
physics-based model 210 may be provided as an input into the
data-driven model 235. Still further, the first input which was
provided to the physics-based model may be provided as an input to
the data-driven model 235. Based on the output of the data-driven
model 235, the adjusted estimated physical hyper-parameters 240 can
be generated. The adjusted physical hyper-parameters 240 can then
be provided as updated inputs into the physics-based model 210. The
steps of 210-240 can then be repeated any number of iterations.
With each iteration, the physical hyper-parameters as input into
the physics-based model 210 and the predicted-results 215 may be
improved by the data-driven model 235. Each of the steps in flow
200 can be carried out automatically by a processor.
[0048] The physics-based model and data-driven model of flow
diagram 200 may be particular types of models, where although each
may be made up of, or otherwise encompass, multiple models, are
each made up of one kind or type of a physics-based model or a
data-driven model respectively. For the situations where the
physical process is not fully understood, the physical system may
not be modeled correctly. Accordingly, multiple physics-based
models which differ from one another may be used in parallel to
obtain optimal results. These multiple physic-based models may each
differ from one another, such as differing in the particular
sub-models used, or using the same sub-models but with different
variables, constant or other parameters, or otherwise differing in
the variables, constants, or other parameter between the models or
sub-models while still each being physics-based. In particular, a
plurality of different types of physics-based models may be run for
a given time iteration. The data-driven model then uses the
performance history to determine the best physics-based model in
each scenario. An illustrative flow for such method is shown in
FIG. 3.
[0049] FIG. 3 illustrates an exemplary flow diagram 300, which may
be a modification of flow 200 in FIG. 2. In the flow diagram 300,
several physics-models may be evaluated and the best performing in
view of the output of the data-driven model, including data-driven
sub-models, may be selected for modeling. To begin the flow,
estimated hyper-physical parameters 305 may be estimated or
determined, such as permeability, skin factor, porosity, etc. One
or more of these can be provided as a first input to a
physics-based model A 310A. The physics-based model A 310A in the
first iteration may be a best guess at which model may best be
appropriate. On subsequent iterations, it may be the best
performing model of the previous iteration. Simultaneously, or on
subsequent iterations of the method in flow diagram 300, other
physics-based models may be processed with the first input as well,
such as physics-based model B 310B, or any number n of a plurality
of physics-based models 310n. The physics-based models 310A-n may
each provide an output such as predicted parameter results 315.
Further, real-time measured data 320 may be collected via surface
and/or downhole sensors, which may include direct measurement of
the predicted parameters by the physics-based models or indirect
measurements which allow calculation of the same. The real-time
measurements can be filtered to remove noise thereby producing
filtered data 325 which may be more effectively used for accurate
modeling. Historical data can be employed additionally or
alternatively provided along with the real-time measured data
320.
[0050] Next, the trend of differences between the predicted results
315 generated by the physics-based models 310A-n are compared to
the filtered data 325 to obtain the difference 330. The differences
330 may also include the differences resulting from a comparison to
one or more physics-based models 310A-n from the current and/or one
or more previous iterations of the flow. Accordingly, with each
iteration, the best performing model may be recorded to form a
historical data set of best performing models for comparison in
each iteration. Alternatively, or additionally, the measured data
320 can be provided as an input into the data-driven model 335.
Still further, the output of the physics-based models 310A-n or
previous physics-based models may be provided as an input into the
data-driven model 335. Still further, the first input which was
provided to the physics-based models may be provided as an input to
the data-driven model 335.
[0051] Based on the output of the data-driven model 335, adjusted
physical parameters 340 may be generated. Additionally, the best
performing physics-based model 345 may be determined. For example,
the output of the data-driven model 335 may indicate which of the
physics-based models 310A-n produced the most accurate results.
Alternatively, or additionally, the outputs of the physics-based
models 310A-n may be compared to the filtered data 325 which may
include an oilfield condition which the physics-based models are
attempting to model. Alternatively, or additionally, the output of
the physics-based models 310A-n may be compared to the output of
the data-driven model 335 and/or oilfield conditions measured and
collected in filtered data 325. Accordingly, any number of metrics
may be employed to determine the best performing physics-based
model of the plurality of physics-based models 310A-n. The method
may then proceed again with another iteration employing the best
performing physics-based model as physics-based model A 310A. Any
number of new or previously used physics-based models 310B-n can
also be employed for comparison to physics-based model A 310A in
the next iteration to determine the best performing model.
[0052] Each of the steps in flow 300 can be carried out
automatically by a processor. The method may be carried out
continuously in real-time with each iteration of the method
intended to produce a more accurate modeling of various oilfield
conditions and parameters.
[0053] The present disclosure provides for detecting abnormal
measured parameters to monitor the status of sensors and equipment.
One illustrated embodiment is provided for in flow diagram 400
shown in FIG. 4. In this embodiment, a physics-based model, which
may contain sub-models, sets a range for the values. The
data-driven model, which may contain sub-models, analyzes the
frequency, trend and severity of un-physical real-time measured
values and detect the possible failures. The physics-based model
determines "in-range" correct values of the real-time measurements
and feeds it to a secondary data-based model to improve that model,
including improvements to any encompassed sub-models.
[0054] Accordingly, the method begins with processing of a
physics-based model 405. In this instance, the input into the
physics-based model and any sub-models may include estimated
hyper-physical parameters, and/or may include measured oilfield
conditions or parameters, and/or be outputs from previous
physics-based or data-driven models, or other inputs. Real-time
measurements 410 in an oilfield are made of various oilfield
conditions via various sensors, tools and other data gathering
instruments. A physics-based model 405 defines an acceptable range
415 of values for these collected real-time measurements 410.
[0055] Out of range values 420 may be considered as incorrect, as
not representative of measurements or data that are correct or able
to occur in the real world, and so may be indicative of some type
of problem. These out of range values 420 are fed to a data-driven
model 425. The data driven model 425 may process the frequency,
trend and severity of the out of range values 420 for various real
world event failures 430 which may result from such values being
out of range. These real world event failures may include sensor
failures, equipment failures, process failures, among other
failures.
[0056] For those in range values 435, these may be considered as
correct values and so may serve as a basis for modeling oilfield
conditions or other type of modeling. Accordingly, for values
in-range, the flow proceeds to the next step 440, wherein the
in-range values 435 may be provided to improved data-driven model
445, which, due to the correct range, may provide faster and/or
more accurate results 450, interpretations and extrapolations. The
flow diagram 400 may also be incorporated into the flow diagrams
200 and/or 300 to provide correct range values to the data-driven
processing steps 225 and 335 to improve the data-driven models
therein. The flow diagram may be incorporated into the data filter
steps 225 and 325 of flow diagrams 200 and/or 300 respectively.
[0057] Additionally, or alternatively the data-driven models may be
employed in the data filter steps 225 and 325 of flow diagrams 200
and/or 300. The data-driven models can be used as virtual sensors
to filter the noise out of the real-time data and then input the
clean data to the physics-based models 210 or 310A-n and/or the
data-driven models 235 and/or 335 from FIG. 2 or 3, respectively.
For example, the data-driven models may be chosen so as to
determine which measured values is noise and which is true, or
provide an acceptable value range, or provide trends or other
fitting models for more accurate use of measured data.
[0058] As previously discussed the present disclosure encompasses
the processing of the data-driven models and physics-based models
in parallel. These can be employed with weighted confidence values
to reduce the number of false alarms: process physics-based and
data-driven models simultaneously and determine which generates
more accurate alarms. Depending on the performance of each model,
the system assigns different confidence values to weight different
models. The final weights for the physics-based models and
data-driven models are different. Trained multiple data-driven
models and the plurality of votes can be used to assign weights.
One exemplary embodiment is provided in FIG. 5, related to
determining causes for an event, such a as a failure or other
occurrence.
[0059] Illustrated in FIG. 5 is a flow diagram 500 illustrating the
use of confidence values with physics-based and data-driven models
to determine causes for an event. As shown therein, a plurality of
causes are considered, for example cause node 505A, cause node
505B, or any n number of causes 505n. The cause nodes 505A-n are
possible causes for an event, represented by main event node 590.
Accordingly, if the main event is a stuck pipe, the possible cause
nodes 505A-n may be divided up for example amongst the various
possible causes such as wellbore collapse, key seating, poor hole
cleaning, etc. Each of the cause nodes 505A-n have a particular
positive and nonzero confidence value weightage 506A-n
respectively, the entirety of which are normalized, for example
such that they all sums to 1. For example, cause node 505A may be
assigned a confidence weightage value 506A of 0.4, cause node 505B
may be assigned a confidence weightage value 506A of 0.3 as a
confidence weightage value, and the n number of additional cause
nodes 505n may be assigned a confidence weightage value 506n of
0.3. Each of these cause nodes 505A and cause node 506B add
together, and n number of cause nodes 505n sum to 1 (0.4+0.3+0.3).
With n number of cause nodes 505n, they similarly are
normalized.
[0060] Each cause node 505A-n has its own assigned respective
control node 510A-n. The control nodes of 510A-n contain various
models for evaluating the likelihood of the cause nodes 505A-n and
assigning them the confidence weightage value. Further, each
control node may each have a plurality of sub-control nodes. As
shown in FIG. 5, cause node 505A has a control node 510A. Control
node 510A contains a number of sub-control nodes. As shown in FIG.
5, it has sub-control node 515A, sub-control node 515B, and any
number of other sub-control nodes 515n. Further, within each
sub-control nodes various models are processed, and as shown in
FIG. 5, each of sub-control nodes 515A-n have a data-driven model,
physics-based model, and a rules-based model. These are each
assigned a normalized confidence value weightage, for example
between 0 and 1 and such that they together sum to 1. For example,
sub-control node 515A may have data-driven model 520A,
physics-based model 525A and rules-based model 530A. Additionally,
sub-control node 515B may have data-driven model 520B,
physics-based model 525B and rules-based model 530B. Further, each
of the n number of sub-control nodes 515n may have data-driven
model 520n, physics-based model 525n and rules-based model 530n.
The data-driven modules contain the logic to detect the anomalies
using statistical and machine learning techniques and the
physics-based modules contain logic to detect the anomalies using
first principle concepts. The anomalies are then monitored using
clustering algorithms. They detect any emerging clusters of rising
anomalies, e.g., if too many incidences of anomalies have occurred
in a short time period, the clustering algorithms define
probabilities based on the size of emerging cluster. The likelihood
calculations are performed each time new data arrives.
[0061] These are then processed according to their confidence
weightage values 516A-n providing a confidence value output 540A-n
regarding a cause. Each of the sub-control nodes 515A, 515B, and
515n are assigned a confidence weightage value 516A, 516B, 516n,
each normalized for example between 0 and 1, and which together sum
to 1. The sub-control nodes 515A-n are processed according to their
respective confidence weightage values 516A-n to obtain confidence
value outputs 540A-n. For example, the sub-control node 515A may
output 540A. Sub-control node 515B may have an output 540B and any
number of sub-control nodes n may have n number of outputs,
represented by 540n.
[0062] Similarly cause node 505B and n number of cause nodes 505n
each have assigned a control node 510B and n number of control
nodes represented by 510n respectively. Similarly to 510A, the
control nodes 510B and 510n may have a plurality of sub-control
nodes. Each of the sub-control nodes may have a plurality of
models, such as a data-driven model, a physics-based model, and a
rules-based model. Each of the sub-control nodes also have
confidence weightage values and provide a confidence value output.
The output of each of the control nodes 510A, 510B, and 510n for
each cause are then fed into main event node 590. The main event
node 590 then itself processes the various control node outputs and
cause node outputs to determine if any one of the causes is the
cause for an event. Additionally, or alternatively, the main event
node 590 may determine if an event has occurred. For instance,
based on the outputs and confidence value weightages whether an
alarm is a false alarm or real alarm can be determined.
[0063] The process of FIG. 5 can be carried out continuously and in
real-time, including substantially in real-time and each of the
confidence value weightages can be updated continuously, as can the
various models employed, control nodes and various causes.
[0064] FIG. 6A illustrates an example computing device which can be
employed to perform various steps, methods, and techniques
disclosed above. The more appropriate embodiment will be apparent
to those of ordinary skill in the art when practicing the present
technology. Persons of ordinary skill in the art will also readily
appreciate that other system embodiments are possible.
[0065] Example system and/or computing device 1000 includes a
processing unit (CPU or processor) 1010 and a system bus 1005 that
couples various system components including the system memory 1015
such as read only memory (ROM) 1020 and random access memory (RAM)
1035 to the processor 1010. The processors disclosed herein can all
be forms of this processor 1010. The system 1000 can include a
cache 1012 of high-speed memory connected directly with, in close
proximity to, or integrated as part of the processor 1010. The
system 1000 copies data from the memory 1015 and/or the storage
device 1030 to the cache 1012 for quick access by the processor
1010. In this way, the cache provides a performance boost that
avoids processor 1010 delays while waiting for data. These and
other modules can control or be configured to control the processor
1010 to perform various operations or actions. Other system memory
1015 may be available for use as well. The memory 1015 can include
multiple different types of memory with different performance
characteristics. It can be appreciated that the disclosure may
operate on a computing device 1000 with more than one processor
1010 or on a group or cluster of computing devices networked
together to provide greater processing capability. The processor
1010 can include any general purpose processor and a hardware
module or software module, such as module 1 1032, module 2 1034,
and module 3 1036 stored in storage device 1030, configured to
control the processor 1010 as well as a special-purpose processor
where software instructions are incorporated into the processor.
The processor 1010 may be a self-contained computing system,
containing multiple cores or processors, a bus, memory controller,
cache, etc. A multi-core processor may be symmetric or asymmetric.
The processor 1010 can include multiple processors, such as a
system having multiple, physically separate processors in different
sockets, or a system having multiple processor cores on a single
physical chip. Similarly, the processor 1010 can include multiple
distributed processors located in multiple separate computing
devices, but working together such as via a communications network.
Multiple processors or processor cores can share resources such as
memory 1015 or the cache 1012, or can operate using independent
resources. The processor 1010 can include one or more of a state
machine, an application specific integrated circuit (ASIC), or a
programmable gate array (PGA) including a field PGA (FPGA).
[0066] The system bus 1005 may be any of several types of bus
structures including a memory bus or memory controller, a
peripheral bus, and a local bus using any of a variety of bus
architectures. A basic input/output (BIOS) stored in ROM 1020 or
the like, may provide the basic routine that helps to transfer
information between elements within the computing device 1000, such
as during start-up. The computing device 1000 further includes
storage devices 1030 or computer-readable storage media such as a
hard disk drive, a magnetic disk drive, an optical disk drive, tape
drive, solid-state drive, RAM drive, removable storage devices, a
redundant array of inexpensive disks (RAID), hybrid storage device,
or the like. The storage device 1030 can include software modules
1032, 1034, 1036 for controlling the processor 1010. The system
1000 can include other hardware or software modules. The storage
device 1030 is connected to the system bus 1005 by a drive
interface. The drives and the associated computer-readable storage
devices provide nonvolatile storage of computer-readable
instructions, data structures, program modules and other data for
the computing device 1000. In one aspect, a hardware module that
performs a particular function includes the software component
stored in a tangible computer-readable storage device in connection
with the necessary hardware components, such as the processor 1010,
bus 1005, and so forth, to carry out a particular function. In
another aspect, the system can use a processor and
computer-readable storage device to store instructions which, when
executed by the processor, cause the processor to perform
operations, a method or other specific actions. The basic
components and appropriate variations can be modified depending on
the type of device, such as whether the device 1000 is a small,
handheld computing device, a desktop computer, or a computer
server. When the processor 1010 executes instructions to perform
"operations", the processor 1010 can perform the operations
directly and/or facilitate, direct, or cooperate with another
device or component to perform the operations.
[0067] Although the exemplary embodiment(s) described herein
employs the hard disk 1030, other types of computer-readable
storage devices which can store data that are accessible by a
computer, such as magnetic cassettes, flash memory cards, digital
versatile disks (DVDs), cartridges, random access memories (RAMs)
1035, read only memory (ROM) 1020, a cable containing a bit stream
and the like, may also be used in the exemplary operating
environment. Tangible computer-readable storage media,
computer-readable storage devices, or computer-readable memory
devices, expressly exclude media such as transitory waves, energy,
carrier signals, electromagnetic waves, and signals per se.
[0068] To enable user interaction with the computing device 1000,
an input device 1045 represents any number of input mechanisms,
such as a microphone for speech, a touch-sensitive screen for
gesture or graphical input, keyboard, mouse, motion input, speech
and so forth. An output device 1035 can also be one or more of a
number of output mechanisms known to those of skill in the art. In
some instances, multimodal systems enable a user to provide
multiple types of input to communicate with the computing device
1000. The communications interface 1040 generally governs and
manages the user input and system output. There is no restriction
on operating on any particular hardware arrangement and therefore
the basic hardware depicted may easily be substituted for improved
hardware or firmware arrangements as they are developed.
[0069] For clarity of explanation, the illustrative system
embodiment is presented as including individual functional blocks
including functional blocks labeled as a "processor" or processor
1010. The functions these blocks represent may be provided through
the use of either shared or dedicated hardware, including, but not
limited to, hardware capable of executing software and hardware,
such as a processor 1010, that is purpose-built to operate as an
equivalent to software executing on a general purpose processor.
For example the functions of one or more processors presented in
FIG. 6A may be provided by a single shared processor or multiple
processors. (Use of the term "processor" should not be construed to
refer exclusively to hardware capable of executing software.)
Illustrative embodiments may include microprocessor and/or digital
signal processor (DSP) hardware, read-only memory (ROM) 1020 for
storing software performing the operations described below, and
random access memory (RAM) 1035 for storing results. Very large
scale integration (VLSI) hardware embodiments, as well as custom
VLSI circuitry in combination with a general purpose DSP circuit,
may also be provided.
[0070] The logical operations of the various embodiments are
implemented as: (1) a sequence of computer implemented steps,
operations, or procedures running on a programmable circuit within
a general use computer, (2) a sequence of computer implemented
steps, operations, or procedures running on a specific-use
programmable circuit; and/or (3) interconnected machine modules or
program engines within the programmable circuits. The system 1000
shown in FIG. 6A can practice all or part of the recited methods,
can be a part of the recited systems, and/or can operate according
to instructions in the recited tangible computer-readable storage
devices. Such logical operations can be implemented as modules
configured to control the processor 1010 to perform particular
functions according to the programming of the module. For example,
FIG. 6A illustrates three modules Mod1 1032, Mod2 1034 and Mod3
1036 which are modules configured to control the processor 1010.
These modules may be stored on the storage device 1030 and loaded
into RAM 1035 or memory 1015 at runtime or may be stored in other
computer-readable memory locations.
[0071] One or more parts of the example computing device 1000, up
to and including the entire computing device 1000, can be
virtualized. For example, a virtual processor can be a software
object that executes according to a particular instruction set,
even when a physical processor of the same type as the virtual
processor is unavailable. A virtualization layer or a virtual
"host" can enable virtualized components of one or more different
computing devices or device types by translating virtualized
operations to actual operations. Ultimately however, virtualized
hardware of every type is implemented or executed by some
underlying physical hardware. Thus, a virtualization compute layer
can operate on top of a physical compute layer. The virtualization
compute layer can include one or more of a virtual machine, an
overlay network, a hypervisor, virtual switching, and any other
virtualization application.
[0072] The processor 1010 can include all types of processors
disclosed herein, including a virtual processor. However, when
referring to a virtual processor, the processor 1010 includes the
software components associated with executing the virtual processor
in a virtualization layer and underlying hardware necessary to
execute the virtualization layer. The system 1000 can include a
physical or virtual processor 1010 that receive instructions stored
in a computer-readable storage device, which cause the processor
1010 to perform certain operations. When referring to a virtual
processor 1010, the system also includes the underlying physical
hardware executing the virtual processor 1010.
[0073] FIG. 6B illustrates an example computer system 1050 having a
chipset architecture that can be used in executing the described
method and generating and displaying a graphical user interface
(GUI). Computer system 1050 is an example of computer hardware,
software, and firmware that can be used to implement the disclosed
technology. System 1050 can include a processor 1052,
representative of any number of physically and/or logically
distinct resources capable of executing software, firmware, and
hardware configured to perform identified computations. Processor
1052 can communicate with a chipset 1054 that can control input to
and output from processor 1052. In this example, chipset 1054
outputs information to output 1062, such as a display, and can read
and write information to storage device 1064, which can include
magnetic media, and solid state media, for example. Chipset 1054
can also read data from and write data to RAM 1066. A bridge 1056
for interfacing with a variety of user interface components 1085
can be provided for interfacing with chipset 1054. Such user
interface components 1085 can include a keyboard, a microphone,
touch detection and processing circuitry, a pointing device, such
as a mouse, and so on. In general, inputs to system 1050 can come
from any of a variety of sources, machine generated and/or human
generated.
[0074] Chipset 1054 can also interface with one or more
communication interfaces 1060 that can have different physical
interfaces. Such communication interfaces can include interfaces
for wired and wireless local area networks, for broadband wireless
networks, as well as personal area networks. Some applications of
the methods for generating, displaying, and using the GUI disclosed
herein can include receiving ordered datasets over the physical
interface or be generated by the machine itself by processor 1052
analyzing data stored in storage 1064 or 1066. Further, the machine
can receive inputs from a user via user interface components 1085
and execute appropriate functions, such as browsing functions by
interpreting these inputs using processor 1052.
[0075] It can be appreciated that example systems 1000 and 1050 can
have more than one processor 1010/1052 or be part of a group or
cluster of computing devices networked together to provide greater
processing capability.
[0076] Embodiments within the scope of the present disclosure may
also include tangible and/or non-transitory computer-readable
storage devices for carrying or having computer-executable
instructions or data structures stored thereon. Such tangible
computer-readable storage devices can be any available device that
can be accessed by a general purpose or special purpose computer,
including the functional design of any special purpose processor as
described above. By way of example, and not limitation, such
tangible computer-readable devices can include RAM, ROM, EEPROM,
CD-ROM or other optical disk storage, magnetic disk storage or
other magnetic storage devices, or any other device which can be
used to carry or store desired program code in the form of
computer-executable instructions, data structures, or processor
chip design. When information or instructions are provided via a
network or another communications connection (either hardwired,
wireless, or combination thereof) to a computer, the computer
properly views the connection as a computer-readable medium. Thus,
any such connection is properly termed a computer-readable medium.
Combinations of the above should also be included within the scope
of the computer-readable storage devices.
[0077] Computer-executable instructions include, for example,
instructions and data which cause a general purpose computer,
special purpose computer, or special purpose processing device to
perform a certain function or group of functions.
Computer-executable instructions also include program modules that
are executed by computers in stand-alone or network environments.
Generally, program modules include routines, programs, components,
data structures, objects, and the functions inherent in the design
of special-purpose processors, etc. that perform particular tasks
or implement particular abstract data types. Computer-executable
instructions, associated data structures, and program modules
represent examples of the program code means for executing steps of
the methods disclosed herein. The particular sequence of such
executable instructions or associated data structures represents
examples of corresponding acts for implementing the functions
described in such steps.
[0078] Other embodiments of the disclosure may be practiced in
network computing environments with many types of computer system
configurations, including personal computers, hand-held devices,
multi-processor systems, microprocessor-based or programmable
consumer electronics, network PCs, minicomputers, mainframe
computers, and the like. Embodiments may also be practiced in
distributed computing environments where tasks are performed by
local and remote processing devices that are linked (either by
hardwired links, wireless links, or by a combination thereof)
through a communications network. In a distributed computing
environment, program modules may be located in both local and
remote memory storage devices.
[0079] STATEMENTS OF THE DISCLOSURE INCLUDE:
[0080] Statement 1: A method including generating, via a processor,
with an oilfield related condition as a first input, a first output
based on one of a physics-based model or a data-based model;
generating, using the first input or a second input, a second
output based on the other of the physics-based model or the
data-based model not used to generate the first output; and
modifying, automatically, at least one of the physics-based model,
data-driven model, the first input or the second input, based on
the first output or second output.
[0081] Statement 2: The method according to Statement 1, wherein
the first output and second output are generated in parallel.
[0082] Statement 3: The method according to Statement 1, wherein
the first output and second output are generated in series.
[0083] Statement 4: The method according to any one of Statements
1-3, further including, subsequent generating the first output,
obtaining measured data from the oilfield related to the first
output.
[0084] Statement 5: The method according to any one of Statements
1-4, wherein the second output is generated based on the second
input, and wherein the second input is at least one of the measured
data, the first output, or a difference between the measured data
and the first output.
[0085] Statement 6: The method according to any one of Statements
1-5, wherein the second input is at least one of the measured data,
or the difference between the measured data and the first output,
and the measured data is measured in real-time.
[0086] Statement 7: The method according to any one of Statements
1-6, wherein modifying at least one of the physics-based model or
the data-driven model includes replacement with a different
respective physics-based model or data-driven model.
[0087] Statement 8: The method according to any one of Statements
1-7, wherein modifying the physic-based model includes altering a
variable within the model.
[0088] Statement 9: The method according to any one of Statements
1-8, further including determining a normal range for the first
output based on the second output.
[0089] Statement 10: The method according to any one of Statements
1-9, wherein the first output is generated based on the
physics-based model, and the second output is generated based on
the data-driven model.
[0090] Statement 11: The method according to any one of Statements
1-10, further including assigning a confidence value to the first
and second outputs.
[0091] Statement 12: The method according to any one of Statements
1-11, wherein the method is conducted continuously in
real-time.
[0092] Statement 13: A system including one or more processors; and
at least one computer-readable storage medium having stored therein
instructions which, when executed by the one or more processors,
cause the one or more processors to: generate, via a processor,
with an oilfield related condition as a first input, a first output
based on one of a physics-based model or a data-based model;
generate, using the first input or a second input, a second output
based on the other of the physics-based model or the data-based
model not used to generate the first output; and modify
automatically at least one of the physics-based model, data-driven
model, the first input or the second input, based on the first
output or second output.
[0093] Statement 14: The system according to Statement 13, the at
least one computer-readable storage medium storing additional
instructions which, when executed by the one or more processors,
cause the one or more processors to: subsequent generating the
first output, obtain measured data from the oilfield related to the
first output.
[0094] Statement 15: The system according to any one of Statements
13-14, wherein the second output is generated based on the second
input, and wherein the second input is at least one of the measured
data, the first output, or a difference between the measured data
and the first output.
[0095] Statement 16: The system according to any one of Statements
13-15, wherein modifying at least one of the physics-based model or
the data-driven model comprises replacement with a different
respective physics-based model or data-driven model.
[0096] Statement 17: The system according to any one of Statements
13-16, wherein modifying the physic-based model comprises altering
a variable within the model.
[0097] Statement 18: A non-transitory computer-readable storage
medium including: instructions stored therein which, when executed
by one or more processors, cause the one or more processors to:
generate, via a processor, with an oilfield related condition as a
first input, a first output based on one of a physics-based model
or a data-based model; generate, using the first input or a second
input, a second output based on the other of the physics-based
model or the data-based model not used to generate the first
output; and modify automatically at least one of the physics-based
model, data-driven model, the first input or the second input,
based on the first output or second output.
[0098] Statement 19: The non-transitory computer-readable storage
medium according to Statement 18, including additional instructions
which, when executed by the one or more processors, cause the one
or more processors to: subsequent generating the first output,
obtain measured data from the oilfield related to the first
output.
[0099] Statement 20: The non-transitory computer-readable storage
medium according to Statements 18 or 19, the second output is
generated based on the second input, and wherein the second input
is at least one of the measured condition, the first output, or a
difference between the measured data and the first output.
[0100] Although a variety of information was used to explain
aspects within the scope of the appended claims, no limitation of
the claims should be implied based on particular features or
arrangements, as one of ordinary skill would be able to derive a
wide variety of implementations. Further and although some subject
matter may have been described in language specific to structural
features and/or method steps, it is to be understood that the
subject matter defined in the appended claims is not necessarily
limited to these described features or acts. Such functionality can
be distributed differently or performed in components other than
those identified herein. Rather, the described features and steps
are disclosed as possible components of systems and methods within
the scope of the appended claims.
* * * * *