DETAILED ACTION
The present application, filed on or after March 16, 2013, is being examined under the first inventor to file provisions of the AIA .
A request for continued examination under 37 CFR 1.114, including the fee set forth in 37 CFR 1.17(e), was filed in this application after final rejection. Since this application is eligible for continued examination under 37 CFR 1.114, and the fee set forth in 37 CFR 1.17(e) has been timely paid, the finality of the previous Office action has been withdrawn pursuant to 37 CFR 1.114. Applicant's submission filed on 3/18/25 has been entered.
Response to Arguments
Response: 35 U.S.C. § 101
Applicants argue:
The applicant argues that the recent amendment of claim 1 that states “automatically identifying, using simulation engine, a minimum number of the simulation resources to perform the reservoir simulation and satisfy the constraint by comparing the reservoir data with performance data from previous reservoir simulations” cannot be performed in the human mind or with pencil and paper, because the process of identifying is being done using a simulation engine.
The applicant also argues that the previous office action doesn’t explain or specify what the broadest reasonable interpretations are in or in any way provide any support for drawing the conclusion that, even under the broadest reasonable interpretation, the limitations are mental processes. (Remarks: page 11)
2. Examiner Response:
The examiner respectfully disagrees. The examiner notes that the simulation engine that is added to the claim language of claim 1 in the recent amendment, is applying an instruction where it’s functioning as a tool, see MPEP 2106.05(f) (2) Whether the claim invokes computers or other machinery merely as a tool to perform an existing process. Use of a computer or other machinery in its ordinary capacity for economic or other tasks (e.g., to receive, store, or transmit data) or simply adding a general purpose computer or computer components after the fact to an abstract idea (e.g., a fundamental economic practice or mathematical equation) does not integrate a judicial exception into a practical application or provide significantly more.
Also, the recent amendment to the identifying limitation shown above in section 1 of the current office action, doesn’t distinguish itself where it cannot be conducted in the human mind or with pencil and paper. This limitation is identifying a minimum number of the simulation resources to perform the reservoir simulation and satisfy the constraint by doing a comparison. Therefore, under the broadest reasonable interpretation, this limitation is a process step that covers performance in the human mind or with the aid of pencil and paper. As such, this limitation falls within the “Mental Process” grouping of abstract ideas.
Applicants argue:
The applicant argues that the with the recent amendment, the claims integrate the alleged abstract idea into a practical application. The applicant argues that the claims provide a technological solution, where the computing system is improved by being able to automatically provide a user with simulation results without requiring the user to have to determine how many and which computer resources to use and how to configure their parameters for interacting to be able to perform the simulation. The applicant points to the court cases of Amdocs and Visual Memory, where the Federal Circuit held that a distributed network architecture operating in an unconventional fashion to reduce network congestion while generating networking accounting data records is patent eligible and held that a memory system having programmable operational characteristics that are configurable based on the type of processor, which can be used with different types of processors without a tradeoff in processor performance is patent eligible. (Remarks: pages 11-14)
4. Examiner Response:
The examiner notes that from the recent amendment, the examiner doesn’t see where there’s an improvement to the computing system. The computing system is still functioning the same, however, it just reading less information. In MPEP 2106.05(f) (2) that states “claiming the improved speed or efficiency inherent with applying the abstract idea on a computer” does not “provide a sufficient inventive concept.” Intellectual Ventures | LLC v. Capital One Bank (USA) (“ Intellectual Ventures v. Capital One Bank”), 792 F.3d 1363, 1367 (Fed. Cir. 2015). This demonstrates why the claim limitations are not improving the functionality of the computing system. Also, regarding the Amdocs court case, the examiner notes that while the computing system does have more than one location to store information, the claims as written do not integrate an abstract idea into a practical application. Further, regarding the Visual Memory court case, the amended claims perform a simulation using simulation resources, the claims as written do not integrate an abstract idea into a practical application.
Applicants argue:
The applicant argues that the recent amendment that states “and controlling, using the injection treatment control subsystem, the injection system to perform a physical reservoir operation according to the injection treatment plan, including controlling the injection equipment to flow a fluid through the reservoir.” integrates the judicial exception into a practical application by using the simulation results to ultimately control a physical operation. The applicant argues that the practical application would include using the reservoir simulation data to create an injection treatment plan, communicating that plan to an injection control system, and then using an injection treatment control subsystem to control the physical operation of injection equipment of the injection treatment system to perform the injection treatment plan. (Remarks: pages 14-15)
6. Examiner Response:
The examiner notes that the controlling limitation shown above in section 5 of the current office action is controlling the injection system according to the injection treatment plan. This limitation is viewed as merely reciting the words “apply it” or an equivalent, see MPEP 2106.05(f) “(3) The particularity or generality of the application of the judicial exception. A claim having broad applicability across many fields of endeavor may not provide meaningful limitations that integrate a judicial exception into a practical application or amount to significantly more. For instance, a claim that generically recites an effect of the judicial exception or claims every mode of accomplishing that effect, amounts to a claim that is merely adding the words "apply it" to the judicial exception.”.
Also, the injection treatment control subsystem, amounts to mere instructions to apply an exception, where it’s applying the instruction, see MPEP 2106.05(f) (2) Whether the claim invokes computers or other machinery merely as a tool to perform an existing process. Use of a computer or other machinery in its ordinary capacity for economic or other tasks (e.g., to receive, store, or transmit data) or simply adding a general purpose computer or computer components after the fact to an abstract idea (e.g., a fundamental economic practice or mathematical equation) does not integrate a judicial exception into a practical application or provide significantly more.
Applicants argue:
The applicant argues that the amended claims recite technological improvements at a specific level such that the methods and processes involve unconventional steps that confine the claim to a particular useful application. The claimed subject matter thus recites specific limitations other than what is well-understood, routine, and conventional in the field because it specifically recites unconventional steps that confine the claimed subject matter to a particular useful application of the judicial exception. (Remarks: pages 15-16)
8. Examiner Response:
The examiner respectfully disagrees. The examiner notes that the amended receiving limitation that states “receiving input from an operator into a computing system”, amounts to insignificant extra-solution activity of receiving data i.e. pre-solution activity of gathering data for use in the claimed process, see MPEP 2106.05(g).
For Step 2B, the limitation is also shown to reflect the court decisions of Versata Dev. Group, Inc. v. SAP Am., Inc. iv. Storing and retrieving information in memory, shown in MPEP 2106.05(d) (II).
Also, the controlling limitation mentioned above in sections 5 and 6 is viewed as merely reciting the words “apply it” or an equivalent, see MPEP 2106.05(f) “(3) The particularity or generality of the application of the judicial exception. A claim having broad applicability across many fields of endeavor may not provide meaningful limitations that integrate a judicial exception into a practical application or amount to significantly more. For instance, a claim that generically recites an effect of the judicial exception or claims every mode of accomplishing that effect, amounts to a claim that is merely adding the words "apply it" to the judicial exception.”.
Further, the claim(s) does/do not include additional elements that are sufficient to amount to significantly more than the judicial exception. As discussed above with respect to integration of the abstract idea into a practical application, the additional element of the computing system amounts no more than mere instructions to apply the exception using a generic computer component that does not impose any meaningful limits on practicing the abstract idea and therefore cannot provide an inventive concept, see MPEP 2106.05(b).
Response: 35 U.S.C. § 103
9. The examiner’s response regarding the applicant’s arguments to the newly added limitations are shown below.
Claim Rejections - 35 USC § 101
35 U.S.C. 101 reads as follows:
Whoever invents or discovers any new and useful process, machine, manufacture, or composition of matter, or any new and useful improvement thereof, may obtain a patent therefor, subject to the conditions and requirements of this title.
Claims 1-4, 6-9, 11-14, 18-19 and 22-24 are rejected under 35 U.S.C. 101 because the claimed invention is directed to an abstract idea without significantly more. Under the broadest reasonable interpretation, the claims covers performance of the limitation in the mind or by pencil and paper as well as a mathematical concept.
Claims 1 and 11
Regarding step 1, claims 1 and 11 are directed towards a method and system, which has the claims fall within the eligible statutory categories of processes, machines, manufactures and composition of matter under 35 U.S.C. 101.
Claim 1
Regarding step 2A, prong 1, claim 1 recites “the input comprising at least one of a cost objective or a time objective as a constraint on a cost or time of performing a reservoir simulation of a subterranean reservoir with the computing system”. Under the broadest reasonable interpretation, this limitation is a process step that covers performance in the human mind or with the aid of pencil and paper. As such, this limitation falls within the “Mental Process” grouping of abstract ideas.
Claim 1 recites “the constraint being independent of the type of reservoir simulation”. Under the broadest reasonable interpretation, this limitation is a process step that covers performance in the human mind or with the aid of pencil and paper. As such, this limitation falls within the “Mental Process” grouping of abstract ideas.
Claim 1 recites “and the computing system comprising a simulation engine and simulation resources comprising virtual machines and a data storage device”. Under the broadest reasonable interpretation, this limitation is a process step that covers performance in the human mind or with the aid of pencil and paper. As such, this limitation falls within the “Mental Process” grouping of abstract ideas.
Claim 1 recites “automatically identifying, using simulation engine, a minimum number of the simulation resources to perform the reservoir simulation and satisfy the constraint by comparing the reservoir data with performance data from previous reservoir simulations”. This limitation doesn’t distinguish itself where it cannot be conducted in the human mind or with pencil and paper. Therefore, under the broadest reasonable interpretation, this limitation is a process step that covers performance in the human mind or with the aid of pencil and paper. As such, this limitation falls within the “Mental Process” grouping of abstract ideas.
Claim 1 recites “automatically identifying, using the simulation engine, simulation parameters used to configure the identified number of simulation resources based on the performance data”. This limitation doesn’t distinguish itself where it cannot be conducted in the human mind or with pencil and paper, where simulation parameters are being identified based on the performance data. Therefore, under the broadest reasonable interpretation, this limitation is a process step that covers performance in the human mind or with the aid of pencil and paper. As such, this limitation falls within the “Mental Process” grouping of abstract ideas.
Claim 1 recites “automatically configuring, using the simulation engine, the identified simulation resource resources with the identified simulation parameters”. This limitation doesn’t distinguish itself where it cannot be conducted in the human mind or with pencil and paper. Therefore, under the broadest reasonable interpretation, this limitation is a process step that covers performance in the human mind or with the aid of pencil and paper. As such, this limitation falls within the “Mental Process” grouping of abstract ideas.
Claim 1 recites “automatically performing, using the processor, a pilot run of a portion of the reservoir simulation using the configured simulation resources and the reservoir data”. In this limitation, “performing a pilot run” encompasses the user mentally estimating a preliminary result based on the configured simulation resource and the reservoir data. Therefore, under the broadest reasonable interpretation, this limitation is a process step that covers performance in the human mind or with the aid of pencil and paper. As such, this limitation falls within the “Mental Process” grouping of abstract ideas.
Claim 1 recites “automatically re-configuring, using the simulation engine, the adjusted number of simulation resources with the adjusted simulation parameters”. This limitation doesn’t distinguish itself where it cannot be conducted in the human mind or with pencil and paper. Therefore, under the broadest reasonable interpretation, this limitation is a process step that covers performance in the human mind or with the aid of pencil and paper. As such, this limitation falls within the “Mental Process” grouping of abstract ideas.
Claim 1 recites “automatically performing, the reservoir simulation using the re-configured simulation resources to produce reservoir simulation data”. In this limitation, “performing the reservoir simulation” encompasses the user mentally estimating a result based on the re-configured simulation and the reservoir data. Therefore, under the broadest reasonable interpretation, this limitation is a process step that covers performance in the human mind or with the aid of pencil and paper. As such, this limitation falls within the “Mental Process” grouping of abstract ideas.
Claim 1 recites “automatically producing, using the computing system, an injection treatment plan based on the reservoir simulation data”. Under the broadest reasonable interpretation, this limitation is a process step that covers performance in the human mind or with the aid of pencil and paper. As such, this limitation falls within the “Mental Process” grouping of abstract ideas.
Claim 1 recites “communicating the injection treatment plan to an injection treatment control subsystem of an injection system comprising injection equipment”. This limitation doesn’t distinguish itself where it cannot be conducted in the human mind or with pencil and paper. Therefore, under the broadest reasonable interpretation, this limitation is a process step that covers performance in the human mind or with the aid of pencil and paper. As such, this limitation falls within the “Mental Process” grouping of abstract ideas.
Regarding step 2A, prong 2, the limitations of “receiving input from an operator into a computing system” and “receiving, at the computing system, reservoir data associated with the reservoir” amounts to insignificant extra-solution activity of receiving data i.e. pre-solution activity of gathering data for use in the claimed process, see MPEP 2106.05(g).
Also, the limitation of “automatically adjusting, using the simulation engine, the number of simulation resources and the simulation parameters based on how the pilot run satisfied the cost constraint” is viewed as merely reciting the words “apply it” or an equivalent, see MPEP 2106.05(f) “(3) The particularity or generality of the application of the judicial exception. A claim having broad applicability across many fields of endeavor may not provide meaningful limitations that integrate a judicial exception into a practical application or amount to significantly more. For instance, a claim that generically recites an effect of the judicial exception or claims every mode of accomplishing that effect, amounts to a claim that is merely adding the words "apply it" to the judicial exception.”.
Also, the limitation of automatically adjusting, using the simulation engine, the number of simulation resources and the simulation parameters based on how the pilot run satisfied the cost constraint” amounts to mere instructions to apply an exception, where an simulation engine is applying the instruction, see MPEP 2106.05(f) (2) Whether the claim invokes computers or other machinery merely as a tool to perform an existing process. Use of a computer or other machinery in its ordinary capacity for economic or other tasks (e.g., to receive, store, or transmit data) or simply adding a general purpose computer or computer components after the fact to an abstract idea (e.g., a fundamental economic practice or mathematical equation) does not integrate a judicial exception into a practical application or provide significantly more.
Also, the limitation of “and controlling, using the injection treatment control subsystem, the injection system to perform a physical reservoir operation according to the injection treatment plan, including controlling the injection equipment to flow a fluid through the reservoir” is viewed as merely reciting the words “apply it” or an equivalent, see MPEP 2106.05(f) “(3) The particularity or generality of the application of the judicial exception. A claim having broad applicability across many fields of endeavor may not provide meaningful limitations that integrate a judicial exception into a practical application or amount to significantly more. For instance, a claim that generically recites an effect of the judicial exception or claims every mode of accomplishing that effect, amounts to a claim that is merely adding the words "apply it" to the judicial exception.”.
Further, the claim recites the additional elements of a computing system and simulation engine. The computing system and simulation engine are recited at a high level of generality such that it amounts no more than mere instructions to apply the exception using a computer and/or a generic computer component. Accordingly, this additional element does not integrate the abstract idea into a practical application because it does not impose any meaningful limits on practicing the abstract idea, see MPEP 2106.05(b).
Regarding Step 2B, the limitations of receiving input from an operator into a computing system” and “receiving, at the computing system, reservoir data associated with the reservoir” are also shown to reflect the court decisions of Versata Dev. Group, Inc. v. SAP Am., Inc. iv. Storing and retrieving information in memory, shown in MPEP 2106.05(d) (II).
Also, the limitation of “automatically adjusting, using the simulation engine, the number of simulation resources and the simulation parameters based on how the pilot run satisfied the cost constraint” is viewed as merely reciting the words “apply it” or an equivalent, see MPEP 2106.05(f) “(3) The particularity or generality of the application of the judicial exception. A claim having broad applicability across many fields of endeavor may not provide meaningful limitations that integrate a judicial exception into a practical application or amount to significantly more. For instance, a claim that generically recites an effect of the judicial exception or claims every mode of accomplishing that effect, amounts to a claim that is merely adding the words "apply it" to the judicial exception.”.
Also, the limitation of automatically adjusting, using the simulation engine, the number of simulation resources and the simulation parameters based on how the pilot run satisfied the cost constraint” amounts to mere instructions to apply an exception, where an simulation engine is applying the instruction, see MPEP 2106.05(f) (2) Whether the claim invokes computers or other machinery merely as a tool to perform an existing process. Use of a computer or other machinery in its ordinary capacity for economic or other tasks (e.g., to receive, store, or transmit data) or simply adding a general purpose computer or computer components after the fact to an abstract idea (e.g., a fundamental economic practice or mathematical equation) does not integrate a judicial exception into a practical application or provide significantly more.
Also, the limitation of “and controlling, using the injection treatment control subsystem, the injection system to perform a physical reservoir operation according to the injection treatment plan, including controlling the injection equipment to flow a fluid through the reservoir” is viewed as merely reciting the words “apply it” or an equivalent, see MPEP 2106.05(f) “(3) The particularity or generality of the application of the judicial exception. A claim having broad applicability across many fields of endeavor may not provide meaningful limitations that integrate a judicial exception into a practical application or amount to significantly more. For instance, a claim that generically recites an effect of the judicial exception or claims every mode of accomplishing that effect, amounts to a claim that is merely adding the words "apply it" to the judicial exception.”.
Further, the claim(s) does/do not include additional elements that are sufficient to amount to significantly more than the judicial exception. As discussed above with respect to integration of the abstract idea into a practical application, the additional element of the computing system and simulation engine amounts no more than mere instructions to apply the exception using a generic computer component that does not impose any meaningful limits on practicing the abstract idea and therefore cannot provide an inventive concept, see MPEP 2106.05(b).
Claim 11
Regarding step 2A, prong 1, claim 11 recites “an injection system comprising injection equipment and an injection treatment control subsystem operable to control an injection treatment of a subterranean reservoir”. This limitation doesn’t distinguish itself where it cannot be conducted in the human mind or with pencil and paper. Therefore, under the broadest reasonable interpretation, this limitation is a process step that covers performance in the human mind or with the aid of pencil and paper. As such, this limitation falls within the “Mental Process” grouping of abstract ideas.
Claim 11 recites “a computing system comprising a simulation engine comprising a processor and simulation resources comprising virtual machines and a data storage device”. This limitation doesn’t distinguish itself where it cannot be conducted in the human mind or with pencil and paper. Therefore, under the broadest reasonable interpretation, this limitation is a process step that covers performance in the human mind or with the aid of pencil and paper. As such, this limitation falls within the “Mental Process” grouping of abstract ideas.
Claim 11 recites “wherein the injection treatment control subsystem is operable to control the injection treatment system to perform a physical reservoir operation according to the injection treatment plan, including controlling the injection equipment to flow a fluid through the reservoir”. This limitation doesn’t distinguish itself where it cannot be conducted in the human mind or with pencil and paper. Therefore, under the broadest reasonable interpretation, this limitation is a process step that covers performance in the human mind or with the aid of pencil and paper. As such, this limitation falls within the “Mental Process” grouping of abstract ideas.
The other limitations of the claim recite the same substantive limitations as claim 1 above, and are rejected using the same teachings.
Regarding step 2A, prong 2, the limitations of “receive input from an operator into a computing system” and “receive, at the computing system, reservoir data associated with the reservoir” amounts to insignificant extra-solution activity of receiving data i.e. pre-solution activity of gathering data for use in the claimed process, see MPEP 2106.05(g).
Also, the limitation of “automatically adjusting, using the simulation engine, the number of simulation resources and the simulation parameters based on the pilot run satisfied the cost constraint” is viewed as merely reciting the words “apply it” or an equivalent, see MPEP 2106.05(f) “(3) The particularity or generality of the application of the judicial exception. A claim having broad applicability across many fields of endeavor may not provide meaningful limitations that integrate a judicial exception into a practical application or amount to significantly more. For instance, a claim that generically recites an effect of the judicial exception or claims every mode of accomplishing that effect, amounts to a claim that is merely adding the words "apply it" to the judicial exception.”.
Also, the limitation of automatically adjusting, using the simulation engine, the number of simulation resources and the simulation parameters based on the pilot run satisfied the cost constraint” amounts to mere instructions to apply an exception, where an simulation engine is applying the instruction, see MPEP 2106.05(f) (2) Whether the claim invokes computers or other machinery merely as a tool to perform an existing process. Use of a computer or other machinery in its ordinary capacity for economic or other tasks (e.g., to receive, store, or transmit data) or simply adding a general purpose computer or computer components after the fact to an abstract idea (e.g., a fundamental economic practice or mathematical equation) does not integrate a judicial exception into a practical application or provide significantly more.
Further, the claim recites the additional element of a processor. The processor is recited at a high level of generality such that it amounts no more than mere instructions to apply the exception using a computer and/or a generic computer component. Accordingly, this additional element does not integrate the abstract idea into a practical application because it does not impose any meaningful limits on practicing the abstract idea, see MPEP 2106.05(b).
Regarding Step 2B, the limitations of receive input from an operator into a computing system” and “receive, at the computing system, reservoir data associated with the reservoir” are also shown to reflect the court decisions of Versata Dev. Group, Inc. v. SAP Am., Inc. iv. Storing and retrieving information in memory, shown in MPEP 2106.05(d) (II).
Also, the limitation of “automatically adjusting, using the simulation engine, the number of simulation resources and the simulation parameters based on the pilot run satisfied the cost constraint” is viewed as merely reciting the words “apply it” or an equivalent, see MPEP 2106.05(f) “(3) The particularity or generality of the application of the judicial exception. A claim having broad applicability across many fields of endeavor may not provide meaningful limitations that integrate a judicial exception into a practical application or amount to significantly more. For instance, a claim that generically recites an effect of the judicial exception or claims every mode of accomplishing that effect, amounts to a claim that is merely adding the words "apply it" to the judicial exception.”.
Also, the limitation of automatically adjusting, using the simulation engine, the number of simulation resources and the simulation parameters based on the pilot run satisfied the cost constraint” amounts to mere instructions to apply an exception, where an simulation engine is applying the instruction, see MPEP 2106.05(f) (2) Whether the claim invokes computers or other machinery merely as a tool to perform an existing process. Use of a computer or other machinery in its ordinary capacity for economic or other tasks (e.g., to receive, store, or transmit data) or simply adding a general purpose computer or computer components after the fact to an abstract idea (e.g., a fundamental economic practice or mathematical equation) does not integrate a judicial exception into a practical application or provide significantly more.
Further, the claim(s) does/do not include additional elements that are sufficient to amount to significantly more than the judicial exception. As discussed above with respect to integration of the abstract idea into a practical application, the additional element of the processor amounts no more than mere instructions to apply the exception using a generic computer component that does not impose any meaningful limits on practicing the abstract idea and therefore cannot provide an inventive concept, see MPEP 2106.05(b).
Claim 2
Dependent claim 2 recites “wherein automatically identifying the simulation parameters comprises identifying an amount of memory of the data storage device used by t necessary for running the reservoir simulation”. Under the broadest reasonable interpretation, this limitation is a process step that covers performance in the human mind or with the aid of pencil and paper. As such, this limitation falls within the “Mental Process” grouping of abstract ideas.
Claim 3
Dependent claim 3 recites “wherein the virtual machines each comprise a processor and a memory”. Under the broadest reasonable interpretation, this limitation is a process step that covers performance in the human mind or with the aid of pencil and paper. As such, this limitation falls within the “Mental Process” grouping of abstract ideas.
Claim 4
Dependent claim 4 recites “wherein identifying the simulation parameters comprises identifying at least one of a network parameter, a data storage parameter, a processor parameter, and a memory parameter to perform the reservoir simulation”. Under the broadest reasonable interpretation, this limitation is a process step that covers performance in the human mind or with the aid of pencil and paper. As such, this limitation falls within the “Mental Process” grouping of abstract ideas.
Claims 6 and 22
Dependent claim 6 recites “wherein identifying the minimum number of simulation resources further comprises applying a regression model to the reservoir data and the performance data”. Under the broadest reasonable interpretation, this limitation is a process step that covers performance in the human mind or with the aid of pencil and paper. As such, this limitation falls within the “Mental Process” grouping of abstract ideas.
Claims 7 and 23
Dependent claim 7 recites “wherein identifying the simulation parameters comprises reducing the reservoir data to a reservoir signature and comparing the reservoir signature with the performance data”. Under the broadest reasonable interpretation, this limitation is a process step that covers performance in the human mind or with the aid of pencil and paper. As such, this limitation falls within the “Mental Process” grouping of abstract ideas.
Claims 8 and 24
Dependent claim 8 recites “wherein identifying the simulation parameters comprises using pattern recognition of reservoir signatures and performance data from completed reservoir simulations”. Under the broadest reasonable interpretation, this limitation is a process step that covers performance in the human mind or with the aid of pencil and paper. As such, this limitation falls within the “Mental Process” grouping of abstract ideas.
Claim 9
Dependent claim 9 recites “configuring the simulation resources using the simulation engine to minimize the cost to perform the reservoir simulation”. Under the broadest reasonable interpretation, this limitation is a process step that covers performance in the human mind or with the aid of pencil and paper. As such, this limitation falls within the “Mental Process” grouping of abstract ideas.
Claim 12
Dependent claim 12 recites “wherein the simulation resources and the simulation engine each comprise a virtual machine comprising a processor and a memory”. Under the broadest reasonable interpretation, this limitation is a process step that covers performance in the human mind or with the aid of pencil and paper. As such, this limitation falls within the “Mental Process” grouping of abstract ideas.
Claim 13
Dependent claim 13 recites “wherein the simulation parameters comprise any one or combination of a network parameter, a data storage parameter, a processor parameter, and a memory parameter to perform the reservoir simulation”. Under the broadest reasonable interpretation, this limitation is a process step that covers performance in the human mind or with the aid of pencil and paper. As such, this limitation falls within the “Mental Process” grouping of abstract ideas.
Claim 14
Dependent claim 14 recites “wherein the simulation engine is further operable to automatically identify an amount of memory used by the simulation resource of the data storage for running the reservoir simulation”. Under the broadest reasonable interpretation, this limitation is a process step that covers performance in the human mind or with the aid of pencil and paper. As such, this limitation falls within the “Mental Process” grouping of abstract ideas.
Claim 18
Dependent claim 18 recites “wherein the simulation engine is further operable to determine a runtime of the reservoir simulation using the identified simulation resources”. Under the broadest reasonable interpretation, this limitation is a process step that covers performance in the human mind or with the aid of pencil and paper. As such, this limitation falls within the “Mental Process” grouping of abstract ideas.
Claim 19
Dependent claim 19 recites “wherein the simulation engine is further operable to configure the simulation resources to minimize the cost to perform the reservoir simulation”. Under the broadest reasonable interpretation, this limitation is a process step that covers performance in the human mind or with the aid of pencil and paper. As such, this limitation falls within the “Mental Process” grouping of abstract ideas.
Claims 1-4, 6-9, 11-14, 18-19 and 22-24 are therefore not drawn to eligible subject matter as they are directed to an abstract idea without significantly more.
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.
Claim(s) 1-4, 7, 9, 11-14, 18-19 and 23 is/are rejected under 35 U.S.C. 103 as being
unpatentable over Rocha et al. (EP 3086229) (from IDS dated 1/5/22) in view of Guyaguler et al. (U.S. PGPub 2007/0299643) in further view of Shetty et al. (U.S. PGPub 2018/0210980).
With respect to claim 1, Rocha et al. discloses “A method” as [Rocha et al. (paragraph [0010] “The present invention relates to a system, method and program product for managing hydrocarbon energy production.”)];
“and the computing system comprising a simulation engine and simulation resources comprising virtual machines and a data storage device” as [Rocha et al. (paragraph [0048] “Referring now to Figure , a set of functional abstraction layers provided by cloud computing environment 50 ( Figure 2 ) is shown”, Rocha et al. paragraph [0050] “Virtualization layer 62 provides an abstraction layer from which the following examples of virtual entities may be provided: virtual servers; virtual storage; virtual networks, including virtual private networks; virtual applications and operating systems; and virtual clients.”)];
“automatically identifying, using simulation engine, a minimum number of the simulation resources to perform the reservoir simulation” as [Rocha et al. (paragraph [0058] “Preferably, the preferred predictor 106 applies a preliminary balancing strategy to data 110 from, and the current state of, ongoing simulation 116 to predict potential imbalances from dynamic sources. As a preliminary balancing strategy the preferred predictor 106 may use lightweight models or a coarse prediction strategy, for example. Suitable such a preliminary balancing strategy may be based on, for example, machine learning, reduced order models (ROM), external events, mechanical failure prediction, and/or stress path prediction. The
predictor 106 uses the prediction results to predict computational loads to determine an optimal workload distribution 114 for automatically rebalancing during ongoing, parallel simulation 116.”)];
“automatically identifying, using the simulation engine, simulation parameters used to configure the identified number of simulation resources based on the performance data” as [Rocha et al. (paragraph [0058] “The predictor 106 uses the prediction results to predict computational loads to determine an optimal workload distribution 114 for automatically rebalancing during ongoing, parallel simulation 116. Thus, the predictor 106 estimates future work load 108 on each processor or processing unit to predict dynamic source effects for proactively redistributing modeling loads for an optimal load redistribution.”, Rocha et al. paragraph [0060] “Typical training parameters include, for example, application/frontend parameters and backend/control parameters. Examples of typical application/frontend parameters may include material strength parameters, e.g., yield strength, cohesion, angle of internal friction, and dilation angle; and rock-mass mechanical properties, e.g., Young's modulus, the Poisson ratio, the friction/dilation angle.”)];
“automatically configuring, using the simulation engine, the identified simulation resource according to the value using a simulation engine to include the simulation parameter for performing the reservoir simulation resources with the identified simulation parameters” as [Rocha et al. (paragraph [0058] “The predictor 106 uses the prediction results to predict computational loads to determine an optimal workload distribution 114 for automatically rebalancing during ongoing, parallel simulation 116. Thus, the predictor 106 estimates future work load 108 on each processor or processing unit to predict dynamic source effects for proactively redistributing modeling loads for an optimal load redistribution.”, Rocha et al. paragraph [0069] “Thus advantageously, load balancing 100 according to the present invention is predictive, using physical knowledge of the processes involved (e.g., potential failures) to proactively predict dynamic sources for redistributing modeling loads optimally to limit rebalancing”)];
“automatically performing, using the processor, a pilot run of a portion of the reservoir simulation using the configured simulation resources and the reservoir data” as [Rocha et al. (paragraph [0053] “A load balancing predictor 106 estimates future work load 108 for each work unit segment, based on historical and current simulation state and results 110”, Rocha et al. paragraph [0055] “Further, the processors 16 may simulate 116 iteratively for a given number of iterations, a given length of time or over a given time horizon.”, Rocha et al. paragraph [0062] “After modeling the reservoir 102 the predictor 106 generates and uses a lightweight coarser model 142 to identify dynamic regions 144 in the simulation state/data 110 and, once identified, marks 146 those regions.”, The examiner considers the estimations from the predictor to be the pilot run, since the estimates are from a simulation for each work unit segment)];
“automatically producing, using the computing system, an injection treatment plan based on the reservoir simulation data” as [Rocha et al. (paragraph [0002] “The development plan provides production guidelines for a given planning horizon on a drilling schedule, selected to maximize field production for the reservoir. Arriving at a good comprehensive development plan requires accurate computer models modeling the reservoir. A reservoir development engineer extracts information from the model(s) for decision makers. Decision makers select the best development plan for economically committing available resources to achieve an optimum return.”)];
While Rocha et al. teaches receiving reservoir data for a reservoir that is simulated, Rocha et al. doesn’t explicitly disclose “receiving input from an operator into a computing system; the input comprising at least one of a cost objective or a time objective as a constraint on a cost or time of performing a reservoir simulation of a subterranean reservoir with the computing system, the constraint being independent of the type of reservoir simulation; receiving, at the computing system, reservoir data associated with the reservoir; and satisfy the constraint by comparing the reservoir data with performance data from previous reservoir simulations; automatically adjusting, using the simulation engine, the number of simulation resources and the simulation parameters based on how the pilot run satisfied the cost constraint; automatically re-configuring, using the simulation engine, the adjusted number of simulation resources with the adjusted simulation parameters; automatically performing, the reservoir simulation using the re-configured simulation resources to produce reservoir simulation data”
Guyaguler et al. discloses “receiving input from an operator into a computing system” as [Guyaguler et al. (paragraph [0042] “The computer system 10 is responsive to a set of `input data` 25 which will be discussed in greater detail later in this specification.”, Fig. 1)];
“the input comprising at least one of a cost objective or a time objective as a constraint on a cost or time of performing a reservoir simulation of a subterranean reservoir with the computing system, the constraint being independent of the type of reservoir simulation” as [Guyaguler et al. (paragraph [0385] “A user-input prediction of oil prices in the future will be used as a weighing factor of oil production rate within the objective of an optimization problem that is set up to maximize the cash-flow obtained from the field at a given time”, Guyaguler et al. paragraph [0386] – [0392] “An optimization problem is set up as follows:….. All the functionality, except the means to define a customized oil price prediction, is available in the Field Management framework. To add this extra functionality, the reservoir engineer sets up f(t) in the form of a time versus oil price table and creates a custom variable in the form of a function that returns the value of the oil price and takes the simulation time as the argument.”, The examiner considers the prediction of the oil prices to be the cost objective, since the prediction of the oil prices are from a user that are used as a weighing factor for the oil production)];
“receiving, at the computing system, reservoir data associated with the reservoir” as [Guyaguler et al. (paragraph [0401] “Sensors are located about the wellsite to collect data, preferably in real time, concerning the operation of the wellsite, as well as conditions at the wellsite. For example, monitors, such as cameras 147, may be provided to provide pictures of the operation. Surface sensors or gauges 149 are disposed about the surface systems to provide information about the surface unit, such as standpipe pressure, hookload, depth, surface torque, rotary rpm, among others. Downhole sensors or gauges 151 are disposed about the drilling tool and/or wellbore to provide information about downhole conditions, such as wellbore pressure, weight on bit, torque on bit, direction, inclination, collar rpm, tool temperature, annular temperature and toolface, among others. The information collected by the sensors and cameras is conveyed to the surface system, the downhole system and/or the surface control unit.”)];
“and satisfy the constraint by comparing the reservoir data with performance data from previous reservoir simulations” as [Guyaguler et al. (paragraph [0336] “The currently operational reservoir model utilizes the conventional if-then logic of conditions and actions in the effort to bring the operating conditions of the model to the desired state. The conventional logic has 15 sequential steps”, Guyaguler et al. paragraph [0356] “10. Adjust gas injection rates to match produced reservoir volume”, Guyaguler et al. paragraph [0363] “15. Adjust water injection to match field injection target”
“automatically adjusting, using the simulation engine, the number of simulation resources and the simulation parameters based on how the pilot run satisfied the cost constraint” as [Guyaguler et al. (paragraph [0005] “the method comprising: modifying one or more of the simulators such that the simulators adhere to the interface characteristics of the open interfaces of the one or more adaptors which are operatively connected to the Field Management framework”, Guyaguler et al. paragraph [0392] ““To add this extra functionality, the reservoir engineer sets up f(t) in the form of a time versus oil price table and creates a custom variable in the form of a function that returns the value of the oil price and takes the simulation time as the argument. The engineer then uses this custom variable within the objective expression. This setup will basically result in an optimization problem with a changing objective (based on f(t)) being solved at every time step.”)];
“automatically re-configuring, using the simulation engine, the adjusted number of simulation resources with the adjusted simulation parameters” as [Guyaguler et al. (paragraph [0392] “To add this extra functionality, the reservoir engineer sets up f(t) in the form of a time versus oil price table and creates a custom variable in the form of a function that returns the value of the oil price and takes the simulation time as the argument. The engineer then uses this custom variable within the objective expression. This setup will basically result in an optimization problem with a changing objective (based on f(t)) being solved at every time step.”, The examiner considers the changing objective to be the re-configuring, since the objective is changing at every time step)];
“automatically performing, the reservoir simulation using the re-configured simulation resources to produce reservoir simulation data” as [Guyaguler et al. (paragraph [0417] “(4) Solving the surface facility network model is basically needed after every single incremental choking in any well, which might be prohibitively expensive in terms of CPU time, step 54 in FIG. 4, and (5) Depending on the speed/accuracy requirements, the `FM framework` 12 provides the engineer the options to balance the surface facility network (balancing action) at any of the following levels: (5a) After every single incremental choking back of any well at every reservoir simulator's Newton iteration, (5b) After choking back the well to have its velocity below the limit given the current flow conditions of the well at every reservoir simulator's Newton iteration, (5c) After choking back all wells to have them obey the erosional velocity limit at every reservoir simulator's Newton iteration”, The examiner considers the choking back of the well incrementally at every reservoir simulator’s Newton iteration to be performing a reservoir simulation, since the choking back of the well is conducted to reduce the fluid flow velocity in the well. The simulation is conducted again after the choking back of the well.)];
Rocha et al. and Guyaguler et al. are analogous art because they are from the same field endeavor of analyzing a reservoir.
Before the effective filing date of the invention, it would have been obvious to a person of ordinary skill in the art to modify the teachings of Rocha et al. of receiving reservoir data for a reservoir that is simulated by incorporating receiving input from an operator into a computing system; the input comprising at least one of a cost objective or a time objective as a constraint on a cost or time of performing a reservoir simulation of a subterranean reservoir with the computing system, the constraint being independent of the type of reservoir simulation; receiving, at the computing system, reservoir data associated with the reservoir; and satisfy the constraint by comparing the reservoir data with performance data from previous reservoir simulations; automatically adjusting, using the simulation engine, the number of simulation resources and the simulation parameters based on how the pilot run satisfied the cost constraint; automatically re-configuring, using the simulation engine, the adjusted number of simulation resources with the adjusted simulation parameters; automatically performing, the reservoir simulation using the re-configured simulation resources to produce reservoir simulation data as taught by Guyaguler et al. for the purpose of controlling Field Management logic of the Field Management of a reservoir.
Rocha et al. in view of Guyaguler et al. teaches receiving input from an operator into a computing system; the input comprising at least one of a cost objective or a time objective as a constraint on a cost or time of performing a reservoir simulation of a subterranean reservoir with the computing system, the constraint being independent of the type of reservoir simulation; receiving, at the computing system, reservoir data associated with the reservoir; and satisfy the constraint by comparing the reservoir data with performance data from previous reservoir simulations; automatically adjusting, using the simulation engine, the number of simulation resources and the simulation parameters based on how the pilot run satisfied the cost constraint; automatically re-configuring, using the simulation engine, the adjusted number of simulation resources with the adjusted simulation parameters; automatically performing, the reservoir simulation using the re-configured simulation resources to produce reservoir simulation data.
The motivation for doing so would have been because Guyaguler et al. teaches that by having Field Management tools that doesn’t lack flexibility and extensibility, the ability for users to controlling Field Management logic of the Field Management of a reservoir, can be accomplished, where real field situations can be accommodated (Guyaguler et al. paragraph [0003]).
While the combination of Rocha et al. and Guyaguler et al. teaches receiving reservoir data and performing a pilot run of the reservoir simulation using the re-configured simulation resource and the reservoir data, Rocha et al. and Guyaguler et al. doesn’t explicitly disclose “communicating the injection treatment plan to an injection treatment control subsystem of an injection system comprising injection equipment; and controlling, using the injection treatment control subsystem, the injection system to perform a physical reservoir operation according to the injection treatment plan, including controlling the injection equipment to flow a fluid through the reservoir.”
Shetty et al. discloses “communicating the injection treatment plan to an injection treatment control subsystem of an injection system comprising injection equipment” as [Shetty et al. (paragraph [0017] “The injection treatment control subsystem 111 may receive, generate or modify an injection treatment plan (e.g., a pumping schedule) that specifies properties of an injection treatment to be applied to the subterranean region 104.”)];
“and controlling, using the injection treatment control subsystem, the injection system to perform a physical reservoir operation according to the injection treatment plan, including controlling the injection equipment to flow a fluid through the reservoir.” as [Shetty et al. (paragraph [0012] “The injection assembly 108 includes instrument trucks 114 and pump trucks 116 that operate to inject fluid via the conduit 112 into the subterranean region 104, thereby opening existing fractures and creating new fractures.”, Shetty et al. paragraph [0017] ““The injection treatment control subsystem 111 may receive, generate or modify an injection treatment plan (e.g., a pumping schedule) that specifies properties of an injection treatment to be applied to the subterranean region 104”, Shetty et al. paragraph [0020] “These physical characteristics of the fracture network make its computational simulation computationally complex in space and time. The computing subsystem 110 can perform simulations before, during, or after the injection treatment. In some implementations, the injection treatment control subsystem 111 controls the injection treatment based on simulations performed by the computing subsystem 110.”)];
Rocha et al., Guyaguler et al. and Shetty et al. are analogous art because they are from the same field endeavor of analyzing a reservoir.
Before the effective filing date of the invention, it would have been obvious to a person of ordinary skill in the art to modify the teachings of Rocha et al. and Guyaguler et al. of receiving reservoir data and performing a pilot run of the reservoir simulation using the re-configured simulation resource and the reservoir data by incorporating communicating the injection treatment plan to an injection treatment control subsystem of an injection system comprising injection equipment; and controlling, using the injection treatment control subsystem, the injection system to perform a physical reservoir operation according to the injection treatment plan, including controlling the injection equipment to flow a fluid through the reservoir as taught by Shetty et al. for the purpose of providing fracturing operation simulators that provide accurate representations of the system’s behavior.
Rocha et al. in view of Guyaguler et al. in further view of Shetty et al. teaches communicating the injection treatment plan to an injection treatment control subsystem of an injection system comprising injection equipment; and controlling, using the injection treatment control subsystem, the injection system to perform a physical reservoir operation according to the injection treatment plan, including controlling the injection equipment to flow a fluid through the reservoir.
The motivation for doing so would have been because Shetty et al. teaches that by providing fracturing operation simulators that provide accurate representations of the system’s behavior in a timeframe, the operators can make realistic planning control the stimulation process (Shetty et al. paragraph [0001]).
With respect to claim 2, the combination of Rocha et al., Guyaguler et al. and Shetty et al. discloses the method of claim 1 above, and Rocha et al. further discloses “wherein automatically identifying the simulation parameters comprises identifying an amount of memory of the data storage device used by t necessary for running the reservoir simulation” as [Rocha et al. (paragraph [0021] “Cloud computing is a model of service delivery for enabling convenient, on-demand network access to a shared pool of configurable computing resources (e.g. networks, network bandwidth, servers, processing, memory, storage, applications, virtual machines, and services) that can be rapidly provisioned and released with minimal management effort or interaction with a provider of the service.”)];
With respect to claim 3, the combination of Rocha et al., Guyaguler et al. and Shetty et al. discloses the method of claim 1 above, and Rocha et al. further discloses “wherein the virtual machines each comprise a processor and a memory” as [Rocha et al. (paragraph [0021] “Cloud computing is a model of service delivery for enabling convenient, on-demand network access to a shared pool of configurable computing resources (e.g. networks, network bandwidth, servers, processing, memory, storage, applications, virtual machines, and services) that can be rapidly provisioned and released with minimal management effort or interaction with a provider of the service.”, Rocha et al. paragraph [0050] “Virtualization layer 62 provides an abstraction layer from which the following examples of virtual entities may be provided: virtual servers; virtual storage; virtual networks, including virtual private networks; virtual applications and operating systems; and virtual clients”)];
With respect to claim 4, the combination of Rocha et al., Guyaguler et al. and Shetty et al. discloses the method of claim 1 above, and Rocha et al. further discloses “wherein identifying the simulation parameters comprises identifying at least one of a network parameter, a data storage parameter, a processor parameter, and a memory parameter to perform the reservoir simulation” as [Rocha et al. (paragraph [0004] “Thus, to keep the computer resource load at a manageable level during field analysis, the simulation may be spread over multiple processors, e.g., parallel processors or, even on several independent cloud computers. With the simulation spread across multiple processors, each processor carries out some aspect, portion of the reservoir model to arrive at a geomechanical and fluidic response relatively quickly”, Rocha et al. paragraph [0021] “Cloud computing is a model of service delivery for enabling convenient, on-demand network access to a shared pool of configurable computing resources (e.g. networks, network bandwidth, servers, processing, memory, storage, applications, virtual machines, and services) that can be rapidly provisioned and released with minimal management effort or interaction with a provider of the service”)];
With respect to claim 7, the combination of Rocha et al., Guyaguler et al. and Shetty et al. discloses the method of claim 1 above, and Rocha et al. further discloses “wherein identifying the simulation parameters comprises reducing the reservoir data to a reservoir signature and comparing the reservoir signature with the performance data” as [Rocha et al. (paragraph [0058] “Suitable such a preliminary balancing strategy may be based on, for example, machine learning, reduced order models (ROM), external events, mechanical failure prediction, and/or stress path prediction”, Rocha et al. paragraph [0059] “Subsequent to training the machine learning predictor 106 runs simultaneously with the simulation 116 or, optionally, in conjunction with a reduced-order model simulation.”, Rocha et al. paragraph [0061] “A singular value decomposition (SVD) applied to the resulting data matrix provides a reduced model for projecting a solution into a low-dimensional (the lighter model) subspace”)];
With respect to claim 9, the combination of Rocha et al., Guyaguler et al. and Shetty et al. discloses the method of claim 1 above, and Rocha et al. further discloses “configuring the simulation resources using the simulation engine to minimize the cost to perform the reservoir simulation” as [Rocha et al. (paragraph [0006] “and more particularly, for optimally balancing processor load prospectively on an optimal number of processors running a reservoir model.”)];
What respect to claim 11, Rocha et al. discloses “A system” as [Rocha et al. paragraph [0010] “The present invention relates to a system, method and program product for managing hydrocarbon energy production.”)];
“a simulation engine comprising a processor” as [Rocha et al. paragraph [0010] “A load predictor predicts processing workload in modeling the hydrocarbon energy field, and identifies a balanced modeling unit distribution across multiple processors simulating field production. A load distribution unit distributes the modeling units across the processors for a balanced modeling unit distribution.”
“simulation resources comprising virtual machines and a data storage device” as [Rocha et al. (paragraph [0048] “Referring now to Figure , a set of functional abstraction layers provided by cloud computing environment 50 ( Figure 2 ) is shown”, Rocha et al. paragraph [0050] “Virtualization layer 62 provides an abstraction layer from which the following examples of virtual entities may be provided: virtual servers; virtual storage; virtual networks, including virtual private networks; virtual applications and operating systems; and virtual clients.”)];
Shetty et al discloses “an injection system comprising injection equipment and an injection treatment control subsystem operable to control an injection treatment of a subterranean reservoir” as [Shetty et al. (paragraph [0012] “The injection assembly 108 includes instrument trucks 114 and pump trucks 116 that operate to inject fluid via the conduit 112 into the subterranean region 104, thereby opening existing fractures and creating new fractures.”, Shetty et al. paragraph [0017] “The injection treatment control subsystem 111 may receive, generate or modify an injection treatment plan (e.g., a pumping schedule) that specifies properties of an injection treatment to be applied to the subterranean region 104”, Shetty et al. paragraph [0020] “These physical characteristics of the fracture network make its computational simulation computationally complex in space and time. The computing subsystem 110 can perform simulations before, during, or after the injection treatment. In some implementations, the injection treatment control subsystem 111 controls the injection treatment based on simulations performed by the computing subsystem 110.”)];
Rocha et al., Guyaguler et al. and Shetty et al. are analogous art because they are from the same field endeavor of analyzing a reservoir.
Before the effective filing date of the invention, it would have been obvious to a person of ordinary skill in the art to modify the teachings of Rocha et al. and Guyaguler et al. of receiving reservoir data and performing a pilot run of the reservoir simulation using the re-configured simulation resource and the reservoir data by incorporating an injection system comprising injection equipment and an injection treatment control subsystem operable to control an injection treatment of a subterranean reservoir as taught by Shetty et al. for the purpose of providing fracturing operation simulators that provide accurate representations of the system’s behavior.
Rocha et al. in view of Guyaguler et al. in further view of Shetty et al. teaches an injection system comprising injection equipment and an injection treatment control subsystem operable to control an injection treatment of a subterranean reservoir.
The motivation for doing so would have been because Shetty et al. teaches that by providing fracturing operation simulators that provide accurate representations of the system’s behavior in a timeframe, the operators can make realistic planning control the stimulation process (Shetty et al. paragraph [0001]).
The other limitations of the claim recite the same substantive limitations as claim 1 above, and are rejected using the same teachings.
With respect to claim 12, the claims recite the same substantive limitations as claim 3 above, and are rejected using the same teachings.
With respect to claim 13, the claims recite the same substantive limitations as claim 4 above, and are rejected using the same teachings.
With respect to claim 14, the claims recite the same substantive limitations as claim 2 above, and are rejected using the same teachings.
With respect to claim 18, the combination of Rocha et al., Guyaguler et al. and Shetty et al. discloses the system of claim 11 above, and Rocha et al. further discloses “wherein the simulation engine is further operable to determine a runtime of the reservoir simulation using the identified simulation resources” as [Rocha et al. (paragraph [0050] “Virtualization layer 62 provides an abstraction layer from which the following examples of virtual entities may be provided: virtual servers; virtual storage; virtual networks, including virtual private networks; virtual applications and operating systems; and virtual clients.”, Rocha et al. (paragraph [0055] “Optionally, a user can choose a predictor approach to estimate future loads 108, or the predictor 106 may select an approach automatically. Thus, the predictor 106 can use a heuristic, an optimization algorithm, or machine learning strategy, e.g., reinforcement learning, to automatically decide the best specific simulation distribution approach. Further, the processors 16 may simulate 116 iteratively for a given number of iterations, a given length of time or over a given time horizon”)];
With respect to claim 19, the claims recite the same substantive limitations as claim 9 above, and are rejected using the same teachings.
With respect to claim 23, the claims recite the same substantive limitations as claim 7 above, and is rejected using the same teachings.
Claim(s) 6 and 22 is/are rejected under 35 U.S.C. 103 as being unpatentable over Rocha
et al. in view of Guyaguler et al. in further view of Shetty et al. in further view of Sarma (U.S. PGPub 2012/0215512).
With respect to claim 6, the combination of Rocha et al., Guyaguler et al. and Shetty et al. discloses the method of claim 1 above.
While the combination of Rocha et al., Guyaguler et al. and Shetty et al. teaches identifying, using the processor, a value for a simulation parameter associated with a simulation resource comprising a virtual machine to perform the reservoir simulation using the first reservoir data based on the cost objective, Rocha et al., Guyaguler et al. and Shetty et al. do not explicitly disclose “wherein identifying the minimum number of simulation resources further comprises applying a regression model to the reservoir data and the performance data”
Sarma discloses “wherein identifying the minimum number of simulation resources further comprises applying a regression model to the reservoir data and the performance data” as [Sarma (paragrpah [0005] “Another approach for uncertainty quantification that has recently been introduced to the petroleum industry is the probabilistic collocation method (PCM). This approach has been applied to uncertainty quantification in the context of optimization of petroleum reservoir production and for quantification of uncertainty for flow in porous media in hydrogeology and petroleum engineering. In the probabilistic collocation method, dependent random variables are represented by employing bi-orthogonal polynomial functions, or polynomial chaos expansions, as the bases of the random space. The polynomial chaos expansions (PCEs) are orthogonal to each other and also with respect to the specific PDFs of the input random variables. They are capable of encapsulating the possibly nonlinear relationships between input and output random variables, and therefore can be used as proxies to the simulation model for efficient uncertainty quantification. PCEs have a significant advantage over other proxies or response surfaces as they converge to the true distribution of the output random variable of interest, such as cumulative oil production, as the order of the PCE and number of simulations used to calculate the PCE coefficients is increased. In the PCM method, the coefficients of the PCE are calculated via collocation or regression. Like most response surface methods, the simulator is used as a black-box in PCM, and is thus very easy to implement.”, The examiner considers the coefficients of the PCE to be the performance parameters, since the coefficients are used in determining the optimization of petroleum reservoir production)];
Rocha et al., Guyaguler et al., Shetty et al. and Sarma are analogous art because they are from the same field endeavor of analyzing a reservoir.
Before the effective filing date of the invention, it would have been obvious to a person of ordinary skill in the art to modify the teachings of Rocha et al., Guyaguler et al. and Shetty et al. of identifying, using the processor, a value for a simulation parameter associated with a simulation resource comprising a virtual machine to perform the reservoir simulation using the first reservoir data based on the cost objective by incorporating wherein identifying the minimum number of simulation resources further comprises applying a regression model to the reservoir data and the performance data as taught by Sarma for the purpose of quantifying uncertainty and evaluating production performance of a subterranean reservoir.
Rocha et al. in view of Guyaguler et al. in further view of Shetty et al. in further view of Sarma teaches wherein identifying the minimum number of simulation resources further comprises applying a regression model to the reservoir data and the performance data.
The motivation for doing so would have been because Sarma teaches that by quantifying uncertainty and evaluating production performance of a subterranean reservoir, the ability to manage a reservoir can be accomplished, where large simulations can be conducted to obtain accurate results of the reservoir (Sarma paragraph [0003] – [0004]).
With respect to claim 22, the claims recite the same substantive limitations as claim 6 above, and is rejected using the same teachings.
Claim(s) 8 and 24 is/are rejected under 35 U.S.C. 103 as being unpatentable over Rocha
et al. in view of Guyaguler et al. in further view of Shetty et al. in further view of Wu et al. (U.S. PGPub 2014/0114632).
With respect to claim 8, the combination of Rocha et al., Guyaguler et al. and Shetty et al. discloses the method of claim 1 above.
While the combination of Rocha et al., Guyaguler et al. and Shetty et al. teaches identifying, using the processor, a value for a simulation parameter associated with a simulation resource comprising a virtual machine to perform the reservoir simulation using the first reservoir data based on the cost objective, Rocha et al., Guyaguler et al. and Shetty et al. do not explicitly disclose wherein identifying the simulation parameters comprises using pattern recognition of reservoir signatures and performance data from completed reservoir simulations”
Wu et al. discloses “wherein identifying the simulation parameters comprises using pattern recognition of reservoir signatures and performance data from completed reservoir simulations” as [Wu et al. (paragraph [0043] “The FILTERSIM algorithm (Zhang, 2006) first extracts all of the patterns from the given training image (TI) using a predefined template. As previously discussed, the training image is a geological concept model depicting geological patterns. The geological patterns are then grouped into different classes based on the filters. Finally the algorithm performs stochastic simulation using pattern recognition techniques. The simulated realization can be conditioned to various types of data, such as well hard data, soft probability or trend data, and azimuth and scaling factors. In general, the FILTERSIM algorithm requires a 3D TI for 3D simulations.”)];
Rocha et al., Guyaguler et al., Shetty et al. and Wu et al. are analogous art because they are from the same field endeavor of analyzing a reservoir.
Before the effective filing date of the invention, it would have been obvious to a person of ordinary skill in the art to modify the teachings of Rocha et al., Guyaguler et al. and Shetty et al. of identifying, using the processor, a value for a simulation parameter associated with a simulation resource comprising a virtual machine to perform the reservoir simulation using the first reservoir data based on the cost objective by incorporating wherein identifying the simulation parameters comprises using pattern recognition of reservoir signatures and performance data from completed reservoir simulations as taught by Wu et al. for the purpose of generating complex geological features, such as lobes and channels.
Rocha et al. in view of Guyaguler et al. in further view of Shetty et al. in further view of Wu et al. teaches wherein identifying the simulation parameters comprises using pattern recognition of reservoir signatures and performance data from completed reservoir simulations.
The motivation for doing so would have been because Wu et al. teaches that by generating a macro scale multiple-point geostatistical simulations (mps), the ability to generate 3D reservoir distributions (facies, porosity, permeability) with 2D training images can be accomplished, so that the performance of the reservoir can be predicted more efficiency (Wu et al. paragraph [0003] – [0004], paragraph [0014] – [0015]).
With respect to claim 24, the claims recite the same substantive limitations as claim 8 above, and is rejected using the same teachings.
Conclusion
Any inquiry concerning this communication or earlier communications from the examiner should be directed to BERNARD E COTHRAN whose telephone number is (571)270-5594. The examiner can normally be reached 9AM -5:30PM EST M-F.
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/BERNARD E COTHRAN/Examiner, Art Unit 2188
/RYAN F PITARO/Supervisory Patent Examiner, Art Unit 2188