DETAILED ACTION
Notice of Pre-AIA or AIA Status
The present application, filed on or after March 16, 2013, is being examined under the first inventor to file provisions of the AIA .
This communication is responsive to application filed on 05/05/2023.
Claims 1-20 are presented for examination.
Information Disclosure Statement
The information disclosure statements (IDSs) submitted on 06/21/2023, 01/08/2026 and 01/20/2026 are in compliance with the provisions of 37 CFR 1.97. Accordingly, the information disclosure statement is being considered by the examiner.
Claim Rejections - 35 USC § 103
The following is a quotation of 35 U.S.C. 103 which forms the basis for all obviousness rejections set forth in this Office action:
A patent for a claimed invention may not be obtained, notwithstanding that the claimed invention is not identically disclosed as set forth in section 102, if the differences between the claimed invention and the prior art are such that the claimed invention as a whole would have been obvious before the effective filing date of the claimed invention to a person having ordinary skill in the art to which the claimed invention pertains. Patentability shall not be negated by the manner in which the invention was made.
The factual inquiries for establishing a background for determining obviousness under 35 U.S.C. 103 are summarized as follows:
1. Determining the scope and contents of the prior art.
2. Ascertaining the differences between the prior art and the claims at issue.
3. Resolving the level of ordinary skill in the pertinent art.
4. Considering objective evidence present in the application indicating obviousness or nonobviousness.
Claims 1-20 are rejected under 35 U.S.C. 103 as being unpatentable over US Publication No. 2016/0145977 A1 issued to Chugunov et al in view of US Patent No. 6, 980, 940 B1 issued to Gurpinar et al.
1. Chugunov et al discloses a method comprising:
receiving a location from a process guided by an agent, wherein the process intends to reach a target (See: [0043] Results of the analysis that identify the largest contributors to the total uncertainty allow an identification of gaps in available data about the uncertain properties β and therefore permit development of a targeted measurement program to reduce uncertainty in those parameters. A targeted measurement program may include additional downhole measurements, lab measurements, and pilot projects that include injection of a limited amount of EOR agent at preselected locations of a reservoir utilizing a log-inject-log sequence);
assigning uncertainty to the process (See: [0006] In one embodiment, a method for adaptive optimization of an EOR project under uncertainty involves using a predictive physics-based reservoir simulation (model) to estimate performance of an EOR project; [0019] Before turning to the Figures, it is useful to understand the scientific basis of the disclosed methods. Consider a general case when an underlying physical process is modeled by a function y=f(α, β), where α={α.sub.1 . . . α.sub.N} and β={β.sub.1 . . . β.sub.M} are two sets of parameters. Here, α represents a set of control parameters (to be used in optimization), and β denotes a set of uncertain parameters. Mathematically, β are considered to be random variables represented by a joint probability density function (pdf)).
Chugunov et al does not disclose but Gurpinar et al discloses performing multiple simulation runs, guided by agent output, from the location with an intent to reach the target, wherein the multiple simulation runs account for the uncertainty (See: Col. 28 lines 38-52, The activity in the `Update Reservoir Model . . . ` block 65, of updating the reservoir model and associated uncertainties, may alternatively be entered directly from the `Consider New Data` decision triangle 49 as shown. The fluid flow simulator model is altered to reproduce the acquired reservoir production data by history matching as is taught in the cited Guerillot, Stein and Wason references. The degree of uncertainty in the reservoir simulator parameters is re-computed to account for the new reservoir measurements; Col. 34 lines 58-61, Those uncertainties should be examined with the model and have an impact on the design criteria in the `reservoir model design criteria` block 41d3.); and generating output based on the multiple runs that characterizes an ability of the agent to reach the target in view of the uncertainty (See: Abstract, A method of managing a fluid or gas reservoir is disclosed which assimilates diverse data having different acquisition time scales and spatial scales of coverage for iteratively producing a reservoir development plan that is used for optimizing an overall performance of a reservoir. The method includes: (a) generating an initial reservoir characterization, (b) from the initial reservoir characterization, generating an initial reservoir development plan, (c) when the reservoir development plan is generated, incrementally advancing and generating a capital spending program, (d) when the capital spending program is generated, monitoring a performance of the reservoir by acquiring high rate monitor data from a first set of data measurements taken in the reservoir and using the high rate monitor data to perform well-regional and field-reservoir evaluations, (e) further monitoring the performance of the reservoir by acquiring low rate monitor data from a second set of data measurements taken in the reservoir, (f) assimilating together the high rate monitor data and the low rate monitor data, (g) from the high rate monitor data and the low rate monitor data, determining when it is necessary to update the initial reservoir development plan to produce a newly updated reservoir development plan, (h) when necessary, updating the initial reservoir development plan to produce the newly updated reservoir development plan, and (i) when the newly updated reservoir development plan is produced, repeating steps (c) through (h). A detailed disclosure is provided herein relating to the step (a) for generating the initial reservoir characterization and the step (b) for generating the initial reservoir development plan).
It would have been obvious before the effective filing date to combine integrated reservoir optimization as taught by Gurpinar et al et al to adaptive optimization of enhanced oil recovery performance under uncertainty method of Chugunov et al would be to improve our understanding of the particular reservoir, and produce a continuously updated development plan corresponding to the particular reservoir (Gurpinar et al).
2. Gurpinar et al discloses the method of claim 1, wherein the target is within a physical environment (See: Col. 1 lines 14-22, monitoring and controlling the extraction of fluid and gas deposits from subsurface geological formations. This includes a method for monitoring the status of fluid and gas deposits in subsurface geological formations and controlling the location and use of physical resources and extraction rates to maximize the extraction of such deposits from the subsurface geological formations).
3. Gurpinar et al discloses the method of claim 2, wherein the physical environment comprises a subsurface environment (See: Col. 1 lines 14-22, monitoring and controlling the extraction of fluid and gas deposits from subsurface geological formations. This includes a method for monitoring the status of fluid and gas deposits in subsurface geological formations and controlling the location and use of physical resources and extraction rates to maximize the extraction of such deposits from the subsurface geological formations).
4. Gurpinar et al discloses the method of claim 2, wherein the physical environment comprises a surface environment (See: Col. 1 lines 14-22, monitoring and controlling the extraction of fluid and gas deposits from subsurface geological formations. This includes a method for monitoring the status of fluid and gas deposits in subsurface geological formations and controlling the location and use of physical resources and extraction rates to maximize the extraction of such deposits from the subsurface geological formations).
5. Chugunov et al the method of claim 1, wherein the process utilizes equipment (See: par [0049] The measurement tool may also be a wellbore tool, such as a wireline logging tool or a logging-while-drilling tool. FIG. 7 shows one example of a wireline logging system 700 at a well site. The system includes a wireline logging tool 702 that is lowered into a wellbore 704 and that traverses the formation 706 using a cable 708 and a winch 710. The wireline tool 702 makes a number of measurements of the adjacent formation 706. The data from these measurements is communicated through the cable 708 to surface equipment 712, which may include a processing system for storing and processing the data obtained by the wireline tool 702. The surface equipment 712 includes a truck that supports the wireline tool 702. In other embodiments, the surface equipment may be located in other locations, such as within a cabin on an off-shore platform. The wellbore tool may include various different modules for performing measurements on the formation).
6. Chugunov et al discloses the method of claim 5, wherein the equipment comprises drilling equipment (See: par [0049] The measurement tool may also be a wellbore tool, such as a wireline logging tool or a logging-while-drilling tool. FIG. 7 shows one example of a wireline logging system 700 at a well site. The system includes a wireline logging tool 702 that is lowered into a wellbore 704 and that traverses the formation 706 using a cable 708 and a winch 710. The wireline tool 702 makes a number of measurements of the adjacent formation 706. The data from these measurements is communicated through the cable 708 to surface equipment 712, which may include a processing system for storing and processing the data obtained by the wireline tool 702. The surface equipment 712 includes a truck that supports the wireline tool 702. In other embodiments, the surface equipment may be located in other locations, such as within a cabin on an off-shore platform. The wellbore tool may include various different modules for performing measurements on the formation).
7. Chugunov et al discloses the method of claim 5, wherein the equipment comprises a vehicle (See: par [0049] The system includes a wireline logging tool 702 that is lowered into a wellbore 704 and that traverses the formation 706 using a cable 708 and a winch 710. The wireline tool 702 makes a number of measurements of the adjacent formation 706. The data from these measurements is communicated through the cable 708 to surface equipment 712, which may include a processing system for storing and processing the data obtained by the wireline tool 702. The surface equipment 712 includes a truck that supports the wireline tool 702. In other embodiments, the surface equipment may be located in other locations, such as within a cabin on an off-shore platform. The wellbore tool may include various different modules for performing measurements on the formation).
8. Chugunov et al discloses the method of claim 1, wherein the uncertainty comprises uncertainty in input to the agent (See: [0023] From the operational perspective, the goal is to reduce this risk while maintaining the same level of expected performance (represented by μ). In order to reduce the uncertainty, it is useful to understand where it is coming from. Therefore, a quantitative link between uncertainties in input parameters (β) and uncertainty in the output can be desirable. According to one aspect, this link can be quantified using Global Sensitivity Analysis (GSA) based on variance decomposition).
9. Chugunov et al discloses the method of claim 1, wherein the uncertainty comprises uncertainty in output of the agent (See: [0023] From the operational perspective, the goal is to reduce this risk while maintaining the same level of expected performance (represented by μ). In order to reduce the uncertainty, it is useful to understand where it is coming from. Therefore, a quantitative link between uncertainties in input parameters (β) and uncertainty in the output can be desirable. According to one aspect, this link can be quantified using Global Sensitivity Analysis (GSA) based on variance decomposition).
10. Chugunov et al discloses the method of claim 1, wherein the uncertainty comprises uncertainty in an environment in which the target is located (See: [0052] There have been described and illustrated herein several embodiments of methods for adaptive optimization of enhanced oil recovery project performance under uncertainty. While particular embodiments and aspects have been described, it is not intended that the disclosure be limited thereto, and it is intended that the claims be as broad in scope as the art will allow and that the specification be read likewise. Thus, while particular control variables and uncertain variables were described, it will be appreciated that other control variables and/or other uncertain variables could be utilized. Thus, by way of example only, control variables may include target production and injection rates, injector/production well spacing, chemical composition and concentration of an EOR agent, etc. Also by way of example only, uncertain variables may include porosity, permeability, parametric dependence of relative permeability curves, viscosity as a function of an EOR agent concentration, an EOR agent adsorption by the rock, etc.).
11. Chugunov et al discloses the method of claim 1, comprising, responsive to characterization of the ability of the agent, adjusting the process (See: [0046] As seen in FIG. 5, the updated efficient frontier (“after GSA”) moves to the left (desired reduction in uncertainty) relative to the initial analysis (“before GSA”). The vertical direction of the shift in efficient frontier depends on underlying values in the physical quantity of interest (cumulative oil production) in the updated range of the uncertain parameter).
12. Gurpinar et al discloses the method of claim 11, wherein adjusting the process comprises adjusting a level of automation of the process as guided by the agent (See: Col. 4 lines 4-15, obtain a new and more comprehensive method for managing an oil and/or gas reservoir, there is a further need to provide a more organized, efficient, and automated process for automatically updating on a periodic basis the original development plan for the first reservoir field property when the resultant activity or results obtained from the first property are initially received).
13. Chugunov et al discloses the method of claim 11, wherein adjusting the process comprises selecting a different agent or calling for retraining of the agent (See: [0006] In one embodiment, a method for adaptive optimization of an EOR project under uncertainty involves using a predictive physics-based reservoir simulation (model) to estimate performance of an EOR project. Performance may be measured with respect to one or more quantities such as total or incremental oil production, recovery factor, displacement efficiency, net present value (NPV), etc. The input parameters of the model are divided into control variables such as target well production, injection rates, injector/production well spacing, chemical composition of an EOR agent, etc., and uncertain variables related to uncertain formation and fluid properties such as porosity, permeability, parametric dependence of relative permeability curves, viscosity as a function of an EOR agent, EOR agent adsorption by the formation rock, etc.).
14. Chugunov et al discloses the method of claim 1, comprising rendering a graphic to a display based at least in part on the output (See: Fig. 6 and corresponding texts).
15. Chugunov et al discloses the method of claim 1, wherein the output indicates fidelity of the agent (See: [0023] From the operational perspective, the goal is to reduce this risk while maintaining the same level of expected performance (represented by μ). In order to reduce the uncertainty, it is useful to understand where it is coming from. Therefore, a quantitative link between uncertainties in input parameters (β) and uncertainty in the output can be desirable. According to one aspect, this link can be quantified using Global Sensitivity Analysis (GSA) based on variance decomposition).
16. Chugunov et al discloses the method of claim 1, wherein the output comprises chance of success (See: [0023] From the operational perspective, the goal is to reduce this risk while maintaining the same level of expected performance (represented by μ). In order to reduce the uncertainty, it is useful to understand where it is coming from. Therefore, a quantitative link between uncertainties in input parameters (β) and uncertainty in the output can be desirable. According to one aspect, this link can be quantified using Global Sensitivity Analysis (GSA) based on variance decomposition).
17. Chugunov et al discloses the method of claim 1, wherein generating the output comprises generating statistics based at least in part on the multiple simulation runs (See: Abstract, Methods are provided for adaptive optimization of enhanced oil recovery project performance under uncertainty. Predictive physics-based reservoir simulation is used to estimate performance of the project. Input parameters of the model are divided into control variables and uncertain variables. The reservoir model is optimized to obtain values of control variables maximizing mean value of a chosen performance metric under initial uncertainty of formation and fluid properties. An efficient frontier can characterize dependence between the optimized mean value of the performance metric and its uncertainty expressed by the standard deviation).
18. Chugunov et al discloses the method of claim 1, wherein the process and the performing occur simultaneously (See: [0026] Similarly, V.sub.ij=Var[E(Y|β.sub.i, β.sub.j)]−V.sub.i−V.sub.j is the second-order contribution to the total variance Var(Y) due to interaction between β.sub.i and β.sub.j. It should be noted that the estimate of variance Var[E(Y|β.sub.i, β.sub.j)] when both β.sub.i and β.sub.j are fixed simultaneously should be corrected for individual contributions V.sub.i and V.sub.j).
As per Claims 19-20: The instant claims recite substantially same limitation as the above rejected claim 1, and therefore rejected under the same rationale.
Conclusion
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KIBROM K. GEBRESILASSIE
Primary Examiner
Art Unit 2189
/KIBROM K GEBRESILASSIE/Primary Examiner, Art Unit 2189 07/20/2026
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