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 .
DETAILED ACTION
Continued Examination Under 37 CFR 1.114
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and the fee set forth in 37 CFR 1.17(e) has been timely paid, the finality of the previous Office action has been withdrawn pursuant to 37 CFR 1.114.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 06 March 2026 has been entered.
This Continued Examination Office Action is in reply to the Request for Continued Examination filed on 06 March 2026.
Claims 1, 11, 19 have been amended.
Claims 1-20 are currently pending and have been examined.
Response to Amendment
In the previous office action, Claims 1-20 were rejected under 35 U.S.C. 101 because the claimed invention is directed to non-statutory subject matter. Applicants have not amended Claims 1-20 to provide statutory support and the rejection is maintained.
Claims 1-20 were rejected were rejected under 35 U.S.C. 112(a) or 35 U.S.C. 112 (pre-AIA ), first paragraph, as failing to comply with the written description requirement and rejected under 35 USC 112(b) or 35 USC 112(pre-AIA ), second paragraph as being indefinite. Applicants have amended these claims to now recite definite subject matter and these rejections are withdrawn.
Response to Arguments
Applicants’ arguments filed 06 March 20206 have been fully considered but they are not persuasive.
In the remarks regarding the 35 USC § 101 rejection for Claims 1-20, Applicants argue that: (1) the claims are not directed to an abstract idea, and even if they were, they would amount to significantly more than the abstract idea. Examiner respectfully disagrees. Commensurate with the 2019 revised patent subject matter eligibility guidance (2019 PEG), the October 2019 Update: Subject Matter Eligibility (“October 2019 Update”) and updated with the addition of new Examples 47-49 published July 2024, the claims are continued analyzed based on these new guidelines and is detailed below in the maintained rejection under 35 USC 101.
In the previous Final Office action, Claims 1-20 were rejected under 35 U.S.C. 112(a) or 35 U.S.C. 112 (pre-AIA ), first paragraph, as failing to comply with the written description requirement. The claim(s) contains subject matter which was not described in the specification in such a way as to reasonably convey to one skilled in the relevant art that the inventor or a joint inventor, or for applications subject to pre-AIA 35 U.S.C. 112, the inventor(s), at the time the application was filed, had possession of the claimed invention. As now amended for Claims 1-20, the rejection is withdrawn.
Claims 1-20 were rejected under 35 U.S.C. 112(b) or 35 U.S.C. 112 (pre-AIA ), second paragraph, as being indefinite for failing to particularly point out and distinctly claim the subject matter which the inventor or a joint inventor (or for applications subject to pre-AIA 35 U.S.C. 112, the applicant), regards as the invention. As now amended for Claims 1-20, the rejection is withdrawn.
In the remarks regarding the 35 USC § 103 rejection for Claims 1-20, Applicants argue that: 2) Bisht et al. (Bisht) (US 2024/0062124) in view of Shelley et al. (Shelley) (US 2014/0067353) does not teach or suggest in amended claim 1: “constraining, using the computer processor, the plurality of top-ranked individually trained ML models using one or multiple known physical rules and selecting a subset of the top-ranked individually trained ML models that are based on a sensitivity analysis including a perforated well length rule”. However this broadly recited limitation is further clarified as seen below in the maintained rejection. It is noted that any citations to specific, pages, columns, paragraphs, lines, or figures in the prior art references and any interpretation of the reference should not be considered to be limiting in any way. A reference is relevant for all it contains and may be relied upon for all that it would have reasonably suggested to one having ordinary skill in the art. See MPEP 2123. The Examiner has a duty and responsibility to the public and to Applicant to interpret the claims as broadly as reasonably possible during prosecution. In re Prater, 415 F.2d 1 393, 1404-05, 162 USPQ 541, 550-51 (CCPA 1969). For at least these reasons, the rejection is maintained.
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 1-20 are rejected under 35 U.S.C. § 101 because the claimed invention is directed to a judicial exception (i.e., a law of nature, natural phenomenon, or an abstract idea) because the claimed invention is directed to a judicial exception (i.e., a law of nature, natural phenomenon, or an abstract idea) without significantly more. The claims as a whole recite certain grouping of an abstract idea and are analyzed in the following step process:
Step 1: Claims 1-20 are each focused to a statutory category of invention, namely “method; non-transitory computer readable medium; system” sets.
Step 2A: Prong One: Claims 1-20 recite limitations that set forth the abstract ideas, namely, the claims as a whole recite the claimed invention is directed to an abstract idea without significantly more. The claims recite steps for:
“obtaining, using a computer processor, a training data set for training a machine learning (ML) model, wherein the ML model generates predicted well production data based on geological, completion, and petrophysical data of interest, wherein the training data set comprises historical well production data and corresponding geological, completion, and petrophysical data; selecting, using the computer processor, an artificial neural network (ANN) model structure, the model structure including a number of layers and a number of nodes of each layer; generating, using an ML algorithm applied to the training data set, a plurality of individually trained ML models, wherein each individually trained ML model is generated based on one of a plurality sets of initial model parameters and selecting the plurality of individually trained ML models based on loss values of the training data set; calculating, using the computer processor, a model performance of each trained model by evaluating a difference between a model prediction and a well performance data, determining, based on the model performance, an order of individually trained ML models based on both the loss value of the training data set and the loss values of a validation data set and selecting, based on the order, a plurality of top-ranked individually trained ML models; constraining, using the computer processor, the plurality of top-ranked individually trained ML models using one or multiple known physical rules and selecting a subset of the top-ranked individually trained ML models that are based on a sensitivity analysis including a perforated well length rule; generating, using the computer processor and the geological, the completion, and the petrophysical data of interest as input to the constrained models, a plurality of individual predicted well production data; and generating, using the computer processor and based on the plurality of individual predicted well production data, a final predicted well production data, wherein a candidate location to drill is determined based, at least in part, on the final predicted well production data, and wherein the wellbore is drilled at the candidate location using a drilling system”
These abstract idea limitations identified above under their broadest reasonable interpretation of the claims as a whole, cover performance of their limitations as for, generally, “predicting well production of a reservoir” and falls under the abstract ideas of:
(a) Mathematical concepts – mathematical relationships, mathematical formulas or equations, mathematical calculations. This is the primary category. The method utilizes mathematical relationships, formulas (physical rules), and calculations (training ANN, evaluating differences, sensitivity analysis) to generate predicted well production data. Obtaining/Selecting Data/Structures: Obtaining geological, completion, and petrophysical data, and selecting ANN structures is generally treated as gathering data or fundamental computer activity, often falling under mathematical concepts (data analysis). "generating, using an ML algorithm... a plurality of individually trained ML models" is a core mathematical process. Training a neural network using loss values and validation sets is a mathematical optimization calculation (mathematical concept) i.e., ML models; ML algorithm; value(s); calculating a model; weights, etc..
(b) Certain methods of organizing human activity –managing personal behavior or relationships or interactions between people (including social activities, teaching, and following rules or instructions) (using a(the) computer processor; obtaining; selecting; calculating; evaluating; selecting; constraining; generating; is determined; predicted, etc. (since it is not specifically how these step limitations are being accomplished and is considered a person or user organizing human activity and following rules or instructions); using one or multiple known physical rules and selecting a subset of the top-ranked individually trained ML models that are based on a sensitivity analysis including a perforated well length rule; the wellbore path is drilled using a drilling system);
(c) Mental processes – concepts performed in the human mind (including an observation, evaluation, judgment, opinion). The steps of analyzing data, selecting top-ranked models based on performance, and applying physical constraints can be construed as steps that could be performed in the human mind, or by a person using pen and paper, e.g., obtaining; selecting; calculating; evaluating; selecting, etc..
See MPEP § 2106.04(a) II C. Hence, the claims are ineligible under Step 2A Prong one. Furthermore, the dependent claims are merely directed to the particulars of the abstract idea and likewise do not add significantly more to the above-identified judicial exception.
Prong Two: Claims 1-20: With regard to this step of the analysis (as explained in MPEP § 2106.04(d)), the judicial exception is not integrated into a practical application. Independent Claim 11 recites additional elements directed to “computer processor; non-transitory computer readable medium; artificial neural network (ANN) model network” (see at least Applicants published Specification ¶’s 17-20). Therefore, the claims contain computer components that are cited at a high level of generality and are merely invoked as a tool to perform the abstract idea. Simply implementing an abstract idea on a computer is not a practical application of the abstract idea. Furthermore, the dependent claims are merely directed to the particulars of the abstract idea and likewise do not add significantly more to the above-identified judicial exception. The limitations of the claims do not transform the abstract idea that they recite into patent-eligible subject matter because the claims simply instruct the practitioner to implement the abstract idea using generally-recited computer components, and furthermore do not amount to an improvement to a computer or any other technology, and thus are ineligible.
Step 2B: (As explained in MPEP § 2106.05), Claims 1-20 do not include additional elements that are sufficient to amount to significantly more than the judicial exception because the additional elements when considered both individually and as an ordered combination do not amount to significantly more than the abstract idea nor recites additional elements that integrate the judicial exception into a practical application. The additional elements “computer processor; non-transitory computer readable medium; artificial neural network (ANN) model network” (see at least Applicants published Specification ¶’s 17-20) are generically-recited computer-related elements that amount to a mere instruction to “apply it” (the abstract idea) on the computer-related elements (see MPEP § 2106.05 (f) – Mere Instructions to Apply an Exception). These additional elements in the claims are recited at a high level of generality and are merely limiting the field of use of the judicial exception (see MPEP §2106.05 (h) – Field of Use and Technological Environment). There is no indication that the combination of elements improves the function of a computer or improves any other technology. Furthermore, the dependent claims are merely directed to the particulars of the abstract idea and likewise do not add significantly more to the above-identified judicial exception. The limitations of the claims do not transform the abstract idea that they recite into patent-eligible subject matter because the claims simply instruct the practitioner to implement the abstract idea using generally-recited computer components, and furthermore do not amount to an improvement to a computer or any other technology, and thus are ineligible.
The Examiner interprets that the steps of the claimed invention both individually and as an ordered combination result in Mere Instructions to Apply a Judicial Exception (see MPEP §2106.05 (f)). These claims recite only the idea of a solution or outcome with no restriction on how the result is accomplished and no description of the mechanism used for accomplishing the result. Here, the claims utilize a computer or other machinery (e.g., see Applicants’ published Specification ¶’s 5, 16) regarding using existing computer processors as well as program products comprising machine-readable media for carrying or having machine-executable instructions or data structures stored. “well environment (100)” in its ordinary capacity for performing tasks (e.g., to receive, analyze, transmit and display data) and/or use computer components after the fact to an abstract idea (e.g., a fundamental economic practice and certain methods of organization human activities) and does not provide significantly more. See Affinity Labs v. DirecTV, 838 F.3d 1253, 1262, 120 USPQ2d 1201, 1207 (Fed. Cir. 2016)). Software implementations are accomplished with standard programming techniques with logic to perform connection steps, processing steps, comparison steps and decisions steps. These claims are directed to being a commonplace business method being applied on a general-purpose computer (see Alice Corp. Pty, Ltd. V. CLS Bank Int’l, 134 S. Ct. 2347, 1357, 110 USPQ2d 1976, 1983 (2014)); Versata Dev. Group, Inc., v. SAP Am., Inc., 793 D.3d 1306, 1334, 115 USPQ2d 1681, 1701 (Fed. Cir. 2015)) and require the use of software such as via a server to tailor information and provide it to the user on a generic computer. Examiner finds that when viewed either individually or in combination, these additional claim element(s) do not provide meaningful limitation(s) that raise to the high standards of eligibility to transform the abstract idea(s) into a patent eligible application of the abstract idea(s) such that the claim(s) amounts to significantly more than the abstract idea(s) itself. Accordingly, Claims 1-20 are rejected under 35 U.S.C. §101 because the claimed invention is directed to a judicial exception (i.e. abstract idea exception) 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.
Claims 1-20 are rejected under 35 U.S.C. 103 as being unpatentable over Bisht et al. (Bisht) (US 2024/0062124) in view of Shelley et al. (Shelley) (US 2014/0067353).
With regard to Claims 1-3, 11-13, 19, 20, Bisht teaches a method/non-transitory/system/ non-transitory computer readable medium storing instructions executable by a computer processor, the instructions comprising functionality for: (A method can include receiving a request for field equipment data; responsive to the request, automatically processing the field equipment data using a trained machine learning model to generate a quality score for the field equipment data; and outputting the quality score. A system can include a processor; memory accessible to the processor; and processor-executable instructions stored in the memory to instruct the system to: receive a request for field equipment data; responsive to the request, automatically process the field equipment data using a trained machine learning model to generate a quality score for the field equipment data; and output the quality score. One or more computer-readable storage media can include processor-executable instructions to instruct a computing system to: receive a request for field equipment data; responsive to the request, automatically process the field equipment data using a trained machine learning model to generate a quality score for the field equipment data; and output the quality score. Various other apparatuses, systems, methods, etc.) for predicting well production of a reservoir (The PETREL framework can be part of the DELFI cognitive exploration and production (E&P) environment (Schlumberger Limited, Houston, Texas, referred to as the DELFI environment) for utilization in geosciences and geoengineering, for example, to analyze subsurface data from exploration to production of fluid from a reservoir) (see at least paragraphs 2, 41-55), comprising:
a tight reservoir (to analyze subsurface data from exploration to production of fluid from a reservoir); a data repository storing a training data set for training a machine learning (ML) model (artificial intelligence and machine learning), wherein the training data set comprises historical well production data (A process known as history matching can involve comparing simulation results to actual field data acquired during production of fluid from a field. Information gleaned from history matching, can provide for adjustments to a model, data, etc., which can help to increase accuracy of simulation) and corresponding geological, completion, and petrophysical data (petrophysical; The DRILLPLAN framework provides for digital well construction planning and includes features for automation of repetitive tasks and validation workflows, enabling improved quality drilling programs (e.g., digital drilling plans, etc.) to be produced quickly with assured coherency; The PETREL framework can be part of the DELFI cognitive exploration and production (E&P) environment (Schlumberger Limited, Houston, Texas, referred to as the DELFI environment) for utilization in geosciences and geoengineering, for example, to analyze subsurface data from exploration to production of fluid from a reservoir; One or more types of frameworks may be implemented within or in a manner operatively coupled to the DELFI environment, which is a secure, cognitive, cloud-based collaborative environment that integrates data and workflows with digital technologies, such as artificial intelligence (AI) and machine learning (ML). As an example, such an environment can provide for operations that involve one or more frameworks. The DELFI environment may be referred to as the DELFI framework, which may be a framework of frameworks. As an example, the DELFI environment can include various other frameworks, which can include, for example, one or more types of models (e.g., simulation models, etc.).; The TECHLOG framework can handle and process field and laboratory data for a variety of geologic environments (e.g., deepwater exploration, shale, etc.). The TECHLOG framework can structure wellbore data for analyses, planning, etc.; The PIPESIM simulator includes solvers that may provide simulation results such as, for example, multiphase flow results (e.g., from a reservoir to a wellhead and beyond, etc.), flowline and surface facility performance, etc. The PIPESIM simulator may be integrated, for example, with the AVOCET production operations framework (Schlumberger Limited, Houston Texas). As an example, a reservoir or reservoirs may be simulated with respect to one or more enhanced recovery techniques (e.g., consider a thermal process such as steam-assisted gravity drainage (SAGD), etc.). As an example, the PIPESIM simulator may be an optimizer that can optimize one or more operational scenarios at least in part via simulation of physical phenomena; The ECLIPSE framework provides a reservoir simulator (e.g., as a computational framework) with numerical solutions for fast and accurate prediction of dynamic behavior for various types of reservoirs and development schemes) (see at least paragraphs 41-45, 56-85);
obtaining, using a computer processor, a training data set for training a machine learning (ML) model (a trained ML model can be a classifier that can classify data. For example, consider a classification score as to acceptable or unacceptable status, which may be accompanied by a regression value such as a quality score. In such an example, training can involve reward-based feedback for a DNN model, for example, to retrain the DNN model in dynamic manner, for example, if a given classification does not match user expectation), wherein the ML model generates predicted well production data based on geological, completion, and petrophysical data of interest, wherein the training data set comprises historical well production data and corresponding geological, completion, and petrophysical data (While several simulators are illustrated in the example of FIG. 1, one or more other simulators may be utilized, additionally or alternatively. For example, consider the VISAGE geomechanics simulator (Schlumberger Limited, Houston Texas) or the PETROMOD simulator (Schlumberger Limited, Houston Texas), etc. The VISAGE simulator includes finite element numerical solvers that may provide simulation results such as, for example, results as to compaction and subsidence of a geologic environment, well and completion integrity in a geologic environment, cap-rock and fault-seal integrity in a geologic environment, fracture behavior in a geologic environment, thermal recovery in a geologic environment, CO 2 disposal, etc. The PETROMOD framework provides petroleum systems modeling capabilities that can combine one or more of seismic, well, and geological information to model the evolution of a sedimentary basin. The PETROMOD framework can predict if, and how, a reservoir has been charged with hydrocarbons, including the source and timing of hydrocarbon generation, migration routes, quantities, and hydrocarbon type in the subsurface or at surface conditions. The MANGROVE simulator (Schlumberger Limited, Houston, Texas) provides for optimization of stimulation design (e.g., stimulation treatment operations such as hydraulic fracturing) in a reservoir-centric environment. The MANGROVE framework can combine scientific and experimental work to predict geomechanical propagation of hydraulic fractures, reactivation of natural fractures, etc., along with production forecasts within 3D reservoir models (e.g., production from a drainage area of a reservoir where fluid moves via one or more types of fractures to a well and/or from a well). The MANGROVE framework can provide results pertaining to heterogeneous interactions between hydraulic and natural fracture networks, which may assist with optimization of the number and location of fracture treatment stages (e.g., stimulation treatment(s)), for example, to increased perforation efficiency and recovery) (see at least paragraphs 55-59, 155);
selecting, using the computer processor, an artificial neural network (ANN) model structure, the model structure including a number of layers (a data engine can assess data quality, which can be useful for performing further analysis and/or decision making. As an example, a ML model-based approach can be data driven. For example, deep neural networks (DNNs) can include multiple layers (e.g., deep layers) that can be trained using data in a supervised, semi-supervised or unsupervised manner. As an example, a trained ML model can be a classifier and/or a predictor to facilitate enterprise data management (EDM). As an example of an unsupervised ML process, consider utilization of principal component analysis (PCA) for one or more purposes such as exploratory data analysis, dimensionality reduction, information compression, data de-noising, etc. A PCA approach may provide for both identification and quality assessment. As an example, a PCA approach may be combined with one or more other techniques. For example, consider PCA and clustering where PCA may be applied one or more times to data. In such an example, identification and/or quality assessment may occur in a PCA space, a cluster space, or other space) and a number of nodes of each layer (a system can provide for real-time monitoring. For example, consider a backend AI engine that can be integrated with a real-time distributed framework. In such an example, a workflow may include big data streaming that internally distributes computational loads to various provisioned nodes where the nodes process data for the AI engine in a manner that may provide for continuous feedback (e.g., training, re-training, etc.)) (see at least paragraphs 145, 232);
generating, using the computer processor and an ML algorithm applied to the training data set, a plurality of individually trained ML models, wherein each individually trained ML model is generated based on one of a plurality sets of initial model parameters and selecting the plurality of individually trained ML models based on loss values (a data engine may implement clustering or grouping, which can be a problem of recognition of similarities. As an example, a combined regression (prediction) and classification ML model may be constructed. For example, consider an architecture with an input layer, hidden layers and multiple output layers. In such an example, regression and classification output layers can be connected to a common last hidden layer of the model. Given two output layers, a model may be trained using two loss functions, for example, consider a mean squared error (MSE) loss for the regression output layer and a sparse categorical cross-entropy for the classification output layer) of the training data set (a system can provide for real-time monitoring. For example, consider a backend AI engine that can be integrated with a real-time distributed framework. In such an example, a workflow may include big data streaming that internally distributes computational loads to various provisioned nodes where the nodes process data for the AI engine in a manner that may provide for continuous feedback (e.g., training, re-training, etc.)) (see at least paragraphs 145, 156, 232);
calculating, using the computer processor, a model performance of each trained model by evaluating a difference between a model prediction and a well performance data (As to training and building a ML model, training can be perform on appropriate split data where one or more of various technique can be utilized to fine tune performance, for example, if a given threshold is not met. As mentioned tuning techniques can include VIF, p value, etc.) (see at least paragraphs 156, 188, 232, 323),
determining, using the computer processor, based on the model performance, an order of individually trained ML models based on both the loss value of the training data set and the loss values of a validation data set ((a system can provide for real-time monitoring. For example, consider a backend AI engine that can be integrated with a real-time distributed framework. In such an example, a workflow may include big data streaming that internally distributes computational loads to various provisioned nodes where the nodes process data for the AI engine in a manner that may provide for continuous feedback (e.g., training, re-training, etc.))) and selecting, based on the order, a plurality of top-ranked individually trained ML models (selecting a trained machine learning model from an ensemble of trained machine learning models based at least in part on an accuracy metric. For example, consider utilizing a number of ML models to process data and to generate accuracy metrics based on such processing. As explained with respect to the example tables 1410 and 1420 of FIG. 14, accuracy metrics can be compared to rank models based on accuracy) (see at least paragraphs 156, 188, 232, 323);
constraining, using the computer processor, (As to the facies and petrophysical property interpolation 253, it may include an assessment of type of rocks and of their petrophysical properties (e.g. porosity, permeability), for example, optionally in areas not sampled by well logs or coring. As an example, such an interpolation may be constrained by interpretations from log and core data, and by prior geological knowledge) the plurality of top-ranked individually trained ML models using one or multiple known physical rules (to identify data issues and changes by applying user-defined assessment rules to an area of interest, to locate data changes or issues and automatically adjust and/synchronize data through a combination of techniques) and selecting a subset of the top-ranked individually trained ML models that are based on a sensitivity (may help to create and enrich data assets in an efficient, user-friendly way; may help to maintain high-quality data by supporting proactive and reactive data maintenance as well as for data unification; may help to manage a data life-cycle, especially when it comes to sensitive data and retiring data; may help to increase the use of data by improving data discovery for users (e.g., data scientists for model training, etc.). The system 2200 may be implemented in a manner that helps to reduce the impact of bad data) analysis (FIG. 13 also shows an example table 1380 that can be output that includes data quality metrics per a ML model-based data engine as may be implemented using the APIGEE developer portal. In the example of FIG. 13, a status is shown as “good”, which can be an acceptable data quality status indicator. Additionally, as shown in the example table 1380, a model name can be received such as “decision tree”. As mentioned, a data engine may employ more than one ML model, for example, in an ensemble manner. In such an example, various ML models may generate output where output of one of the ML models can be ranked or otherwise selected for output. As an example, a call may be made to a data engine for output from a specific ML model and/or for output from more than one ML model) including a rule (The INNERLOGIX data engine provide components for automated techniques to identify data issues and changes by applying user-defined assessment rules to an area of interest, to locate data changes or issues and automatically adjust and/synchronize data through a combination of techniques. Results can include results of automated assessment runs where adjusted and/or synchronized data can be displayed in GIS, chart, or spreadsheet form, and stored in a database. The INNERLOGIX data engine can provide a specialized set of rules that includes features such as a wellbore digital elevation checker, deviation survey outliner method, and log curve stratigraphic range verification; graphs, GIS, and reports to expose underlying data quality issues; a manual quality control tool for analyzing, comparing, and correcting data; and plug-and-play adapters for reading, inserting, and updating data from various applications into common and proprietary data stores) (see at least paragraphs 70, 173, 303);
generating, using the computer processor and the geological, the completion, and the petrophysical data of interest as input to the constrained models, a plurality of individual predicted well production data (While several simulators are illustrated in the example of FIG. 1, one or more other simulators may be utilized, additionally or alternatively. For example, consider the VISAGE geomechanics simulator (Schlumberger Limited, Houston Texas) or the PETROMOD simulator (Schlumberger Limited, Houston Texas), etc. The VISAGE simulator includes finite element numerical solvers that may provide simulation results such as, for example, results as to compaction and subsidence of a geologic environment, well and completion integrity in a geologic environment, cap-rock and fault-seal integrity in a geologic environment, fracture behavior in a geologic environment, thermal recovery in a geologic environment, CO 2 disposal, etc. The PETROMOD framework provides petroleum systems modeling capabilities that can combine one or more of seismic, well, and geological information to model the evolution of a sedimentary basin. The PETROMOD framework can predict if, and how, a reservoir has been charged with hydrocarbons, including the source and timing of hydrocarbon generation, migration routes, quantities, and hydrocarbon type in the subsurface or at surface conditions. The MANGROVE simulator (Schlumberger Limited, Houston, Texas) provides for optimization of stimulation design (e.g., stimulation treatment operations such as hydraulic fracturing) in a reservoir-centric environment. The MANGROVE framework can combine scientific and experimental work to predict geomechanical propagation of hydraulic fractures, reactivation of natural fractures, etc., along with production forecasts within 3D reservoir models (e.g., production from a drainage area of a reservoir where fluid moves via one or more types of fractures to a well and/or from a well). The MANGROVE framework can provide results pertaining to heterogeneous interactions between hydraulic and natural fracture networks, which may assist with optimization of the number and location of fracture treatment stages (e.g., stimulation treatment(s)), for example, to increased perforation efficiency and recovery) (see at least paragraphs 55-59, 155);
generating, using the computer processor and based on the plurality of individual predicted well production data, a final predicted well production data (a type of data metric can be, for example, a completeness data metric, which, for example, may be computed on a real-time data stream of one or more types of data such as, for example, production data) (see at least paragraphs 55-59, 142, 152-155),
wherein a candidate location to drill (the well prognosis application 242 may include predicting type and characteristics of geological formations that may be encountered by a drill-bit, and location where such rocks may be encountered (e.g., before a well is drilled); the reserve calculations application 244 may include assessing total amount of hydrocarbons or ore material present in a subsurface environment (e.g., and estimates of which proportion can be recovered, given a set of economic and technical constraints); and the well stability assessment application 246 may include estimating risk that a well, already drilled or to-be-drilled, will collapse or be damaged due underground stress; one or more locations in a well; A deviation survey can include measurements of inclination and azimuth of one or more locations in a well (e.g., total depth at time of measurement). In both directional and straight boreholes, knowing positions along a borehole with reasonable accuracy can help to assess a borehole trajectory with respect to a plan and, for example, to allow for appropriate drilling of a relief well if warranted. Measurements of a deviation survey can include inclination from vertical and azimuth (or compass heading) of a borehole) is determined based (Measurements of a deviation survey can include inclination from vertical and azimuth (or compass heading) of a borehole. Such measurements can be made at a plurality of discrete points in the well, and the approximate path of the wellbore computed from the discrete points), at least in part, on the final predicted well production data (A deviation survey can include measurements of inclination and azimuth of one or more locations in a well (e.g., total depth at time of measurement). In both directional and straight boreholes, knowing positions along a borehole with reasonable accuracy can help to assess a borehole trajectory with respect to a plan and, for example, to allow for appropriate drilling of a relief well if warranted. Measurements of a deviation survey can include inclination from vertical and azimuth (or compass heading) of a borehole) (see at least paragraphs 54, 96), and
wherein the wellbore is drilled at the candidate location ( the well prognosis application 242 may include predicting type and characteristics of geological formations that may be encountered by a drill-bit, and location where such rocks may be encountered (e.g., before a well is drilled); the reserve calculations application 244 may include assessing total amount of hydrocarbons or ore material present in a subsurface environment (e.g., and estimates of which proportion can be recovered, given a set of economic and technical constraints); and the well stability assessment application 246 may include estimating risk that a well, already drilled or to-be-drilled, will collapse or be damaged due underground stress; A deviation survey can include measurements of inclination and azimuth of one or more locations in a well (e.g., total depth at time of measurement). In both directional and straight boreholes, knowing positions along a borehole with reasonable accuracy can help to assess a borehole trajectory with respect to a plan and, for example, to allow for appropriate drilling of a relief well if warranted. Measurements of a deviation survey can include inclination from vertical and azimuth (or compass heading) of a borehole. ) using a drilling system (the well prognosis application 242 may include predicting type and characteristics of geological formations that may be encountered by a drill-bit, and location where such rocks may be encountered (e.g., before a well is drilled); the reserve calculations application 244 may include assessing total amount of hydrocarbons or ore material present in a subsurface environment (e.g., and estimates of which proportion can be recovered, given a set of economic and technical constraints); and the well stability assessment application 246 may include estimating risk that a well, already drilled or to-be-drilled, will collapse or be damaged due underground stress) (see at least paragraphs 74, 96);
Bisht does not specifically recite: including a perforated well length/perforated; proppant per foot, and a proppant size ratio.
Shelley teaches: a perforated well length/perforated (the data gathering step 122 includes gathering or obtaining completion information 140, such as perforation clusters, phasing, perforation interval depth and thickness, cluster per fracture stages, tubing size, completion time/date, and/or other information regarding completion);
proppant per foot, and a proppant size ratio (the data gathering step 122 includes gathering or obtaining stimulation information 142, such as treatment volumes and rates, fluid types, proppant size and mass, acid volume, fracture stages, initial and final instantaneous shut-in pressure (ISIP), breakdown pressure, pressure trend, closure pressure, conductivity, fracture gradient, and/or other information regarding stimulation. In this regard, the stimulation design parameters from completed wells are defined and described by the stimulation information 142. Accordingly, in some instances the stimulation information 142 is utilized in a predictive model to estimate the effects of various stimulation options (e.g., hydraulic fracturing parameters) on well production) in analogous art of well production for the purposes of: “a predictive model identified as the "best" predictive model for the field is utilized to estimate well production for a plurality of different completion parameters in order to identify a combination of completion parameters that optimizes well production; hydrocarbon production is correlated to the completion information 140. In some instances, the completion information is derived from a plurality of data sources having various file formats; in some instances representative values of such data types are found for the entire fractured region. In some implementations, this is done by analyzing the wellbore directional survey, extracting perforation schemes and locating the hydraulic fracture intervals. Once the fracture intervals are identified, average representative values of well logs and digital mud logs are calculated” (see at least paragraphs 28-45, 68).
It would have been obvious to one of ordinary skill in the art at the time of the invention to include the wellbore completion and hydraulic fracturing optimization methods and associated systems as taught by Shelley in the system of Bisht, since the claimed invention is merely a combination of old elements, and in the combination each element merely would have performed the same function as it did separately, and one of ordinary skill in the art would have recognized that the results of the combination were predictable.
With regard to Claims 2, 12, 20, Bisht teaches wherein fixed parameters examined in the sensitivity analysis are a pressure/volume/temperature window, a resource density, a total organic carbon (TOC), a water saturation, and wherein a single variable parameter is well length (see at least paragraphs 54-79, 263).
With regard to Claims 3, 13, Bisht teaches, wherein only a plurality of selected models that predict increase in well performance with the increase length are used to generate the plurality of individual predicted well production data (see at least paragraphs 41-45, 56-85).
With regard to Claims 4, 14, Bisht teaches wherein the set of initial model parameters correspond to weights associated with connections between neural nodes of the ANN (see at least paragraphs 125, 145);
wherein the initial model parameters correspond to weights associated with connections between neural nodes of the ANN (see at least paragraphs 125, 145).
With regard to Claims 5, 15, Bisht teaches wherein the set of initial model parameters comprises randomly generated model parameter values (see at least paragraph 157).
With regard to Claims 6, 16, Bisht teaches wherein the reservoir is a tight reservoir (see at least paragraphs 1, 37);
wherein the training data set comprises the historical well production data and corresponding geological, completion, and petrophysical data that are obtained from less than 100 production wells of the reservoir (see at least paragraphs 85, 78-83).
With regard to Claims 7, 17, Bisht teaches wherein generating the final predicted well production data comprises averaging the plurality of individual predicted well production data (see at least paragraphs 55-59, 142, 152-155).
With regard to Claims 8, 18, Bisht teaches wherein the ML algorithm is applied to the training data set to generate a set of trained model parameters for each of the plurality of individually trained ML models (see at least paragraphs 41-45, 56-85).
With regard to Claim 9, Bisht teaches wherein generating the order of the plurality of individually trained ML models is based on a loss function representing a mean squared error (MSE) for the validation data set of the plurality of individually trained ML models (see at least paragraphs 41-45, 56-165).
With regard to Claim 10, Bisht teaches wherein constraining the plurality of individually trained ML models is based on a physics rule or multiple rules of the plurality of individually trained ML models (see at least paragraphs 41-45, 56-85).
Conclusion
The prior art made of record and not relied upon is considered pertinent to Applicant's disclosure:
Memarzadeh et al. (US 2020/0202047)
Roberts et al. (WO 2019/055653 A1)
Kong, Xiangming, et al. "A hybrid oil production prediction model based on artificial intelligence technology." Energies 16.3 (2023): 1027.
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THOMAS L. MANSFIELD
Examiner
Art Unit 3623
/THOMAS L MANSFIELD/Primary Examiner, Art Unit 3624
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