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 .
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and the fee set forth in 37 CFR 1.17(e) has been timely paid, the finality of the previous Office action has been withdrawn pursuant to 37 CFR 1.114.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 13 July 2026 has been entered.
Response to Arguments
Regarding the 35 USC 101 rejection, Examiner has fully considered Applicant’s arguments and amendments.
Regarding Applicant’s assertion of “The claims recite a specific improvement to the technical field of unconventional reservoir development, namely generating a well development sequence for a field based on a combination of production forecasting, economic performance modeling, and verification of defensive refract candidates to identify simultaneous operations events, such that a potential defensive refract candidate is removed from the sequence based on the identified simultaneous operations event.,” and “Thus, the technical problem is a technical reservoir-development problem arising from the physical characteristics and interdependencies of unconventional reservoirs.” Examiner respectfully asserts that generating an improved sequence, or plan, for a field based on abstract information is not sufficient to prove integration into a practical application or anything significantly more. This type of improvement would be an improvement related to the abstract limitations for consideration under Step 2A, Prong 1. MPEP 2106.05(a): “It is important to note, the judicial exception alone cannot provide the improvement. The improvement can be provided by one or more additional elements...” Additionally, as discussed in 2106.05(a)(II) improvements to technology or technical fields, “an improvement in the abstract idea itself … is not an improvement in technology”
Regarding Applicant’s assertion of “The August 4, 2025, USPTO memorandum explains that the improvement inquiry asks whether the claim reflects "a technological solution to a technological problem" and whether the claim covers a particular solution to a problem rather than merely claiming the idea of a desired result. Here, the claims recite a particular solution to the unconventional-reservoir development problem. For instance, claim 1 requires generating a production forecast for a plurality of wells of the field, modeling economic performance of the field using specific well-development parameters including location, lateral length, and operational expenditures estimated based on the production forecast, verifying potential defensive refract candidates to identify a simultaneous operations event, and generating the well development sequence to remove the potential defensive refract candidate from the sequence based on the identified simultaneous operations event. These limitations define how the well development is technically improved, by integrating forecasted reservoir production, field economics, defensive refract screening, and simultaneous operations constraints into the sequence generation process.,” Examiner respectfully asserts that generating an improved well development sequence, as drafted, under considerations of the broadest reasonable interpretation, does not improve any of the additional elements of the claim. This type of improvement would be reflected in the abstract limitations for consideration under Step 2A, Prong 1. MPEP 2106.05(a): “It is important to note, the judicial exception alone cannot provide the improvement. The improvement can be provided by one or more additional elements...” Additionally, as discussed in 2106.05(a)(II) improvements to technology or technical fields, “an improvement in the abstract idea itself … is not an improvement in technology”
Regarding Applicant’s assertion of “Applicant respectfully asserts the Office improperly treats the "well development sequence" as part of the alleged abstract idea and therefore discounts the improvement. The improvement is a technical improvement to the technical field of unconventional reservoir management through a specific sequencing architecture that accounts for physical and operational reservoir-development constraints that are unique to unconventional fields, including defensive refract candidates and simultaneous operations events. The specification explains that the development optimization platform integrates key elements of unconventional development planning, such as production forecast, lease obligations, surface facilities, economic metrics, and analysis associated with past and future field development and production. The claim reflects this improvement by requiring the recited production forecast, economic performance modeling, defensive refract verification, and removal of a candidate from the well development sequence based on the identified simultaneous operations event.,” Examiner respectfully disagrees. The sequence of the claims, under considerations of the broadest reasonable interpretation, is merely the plan outlining the drilling sequence of the plurality of wells. This does not include any additional elements or particular details regarding the refracturing or drilling of the well itself, such as what equipment is used or how this is performed in particular. The claim merely generates the sequence for a plurality of wells including one well at that is “drillable,” which is nothing more than merely a feature or characteristic of the well and is not an additional element for consideration under Step 2A, Prong 2. Examiner further notes that developing an improved sequence of events based on information including “production forecast, economic performance modeling, defensive refract verification, and removal of a candidate from the well development sequence based on the identified simultaneous operations event” would be an improvement reflected in the abstract limitations for consideration under Step 2A, Prong 1. MPEP 2106.05(a): “It is important to note, the judicial exception alone cannot provide the improvement. The improvement can be provided by one or more additional elements...” Additionally, as discussed in 2106.05(a)(II) improvements to technology or technical fields, “an improvement in the abstract idea itself … is not an improvement in technology”
Regarding Applicant’s assertion of “The claim is analogous to eligible USPTO Example 47, claim 3. Claim 3 was deemed eligible because the claim used the detection results in a particular way that improved the technical field of network intrusion detection. The USPTO explained that the claim reflected the improvement described in the specification and therefore integrated the judicial exception into a practical application under MPEP § 2106.04(d)(1) and § 2106.05(a). Here, claim 1 uses the forecast, modeled economic performance, and verified defensive refract candidates in a particular way to generate an improved well development sequence that removes a defensive refract candidate based on an identified simultaneous operations event, thereby improving efficiency of well sequencing and reservoir management of unconventional reservoirs.,” Examiner respectfully disagrees. The claims of Example 47 are directed to monitoring network traffic data in such a way that takes proactive measures to remediate danger, which results in a specific improvement over existing systems in the field of network intrusion detection. The present claims do not provide an analogous improvement. Furthermore, the sequence generation of the claims, as drafted, is not sufficient to prove integration into a practical application or anything significantly more because it is part of the abstract limitations for consideration under Step 2A, Prong 1. The enhancement of a “target field” is not akin to a specific improvement to a technical field, such as network intrusion detection. Therefore, Examiner respectfully disagrees with Applicant’s assertion.
Accordingly, the present claims are rejected under 35 USC 101.
Regarding the 35 USC 103 rejection, Examiner has fully considered Applicant’s arguments and amendments.
Regarding Applicant’s assertion of “Therefore, for at least this reason, Perecharla does not disclose the features of amended claim 1 such that the claim is allowable over the cited reference. The Office action does not assert that either Ba and Roussel teach the features of amended claim 1 discussed above,” Applicant’s arguments with respect to the previous prior art combination of the record have been considered but are moot because the new grounds of rejection does not rely on any reference applied in the prior art rejection for any teachings or matter specifically challenged in the argument. The claims are rejected under a new grounds of rejection. Examiner has introduced the combination of Asala and Perecharla. See the detailed rejection below.
Accordingly, the present claims are rejected under 35 USC 103.
Claim Objections
Claim 17 is objected to because of the following informalities: Examiner suggests amending the claim to incorporate a semicolon as follows: and an operational expenditures estimated based on the production forecast for the plurality of wells;
Appropriate correction is required.
Claim Rejections - 35 USC § 112
The following is a quotation of 35 U.S.C. 112(b):
(b) CONCLUSION.—The specification shall conclude with one or more claims particularly pointing out and distinctly claiming the subject matter which the inventor or a joint inventor regards as the invention.
The following is a quotation of 35 U.S.C. 112 (pre-AIA ), second paragraph:
The specification shall conclude with one or more claims particularly pointing out and distinctly claiming the subject matter which the applicant regards as his invention.
Claims 17, 18, and 20 are 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.
Regarding claim 17, the metes and bounds of the claim are rendered unclear due to the combination of limitations of “model, based on the production forecast for the plurality of wells of the field, an economic performance of the field, the economic performance at least partially based on a location of the plurality of wells, a lateral length of the plurality of wells, and an operational expenditures estimated based on the production forecast for the plurality of wells” and “model, based on the production forecast for the plurality of wells of the field, an economic performance of the field, the economic performance at least partially based on a location and lateral length of the plurality of wells.” The metes and bounds of the claim are rendered unclear because the claim initially recites modeling “an economic performance” based at least partially on “a location of the plurality of wells, a lateral length of the plurality of wells.” The claim then further recites modeling “an economic performance,” which does not antecedently rely upon the initial recitation of modeling an economic performance. It is unclear if this is the same model and economic performance, or if there is a second step involving additional modeling of the economic performance. Furthermore, the second limitation related to modeling further recites modeling based at least in part on “a location” and “a lateral length.” The initial recitation of modeling is the first recitation of “a location” and “a lateral length.” The metes and bounds of the claim are rendered unclear because it is unclear if the second recitation of modeling comprises the same location and lateral length, or if there is a second, new location and lateral length introduced in the subsequent modeling limitation. Therefore, the metes and bounds of the claim are rendered unclear because the claim initially recites “modeling.. an economic performance.. based on a location.. and a lateral length” and then recites “modeling.. an economic performance… based on a location and lateral length” with no antecedent basis to the previously recited limitation.
The metes and bounds of the claim are rendered further unclear due to the recitation of “generate, based on the modeled economic performance, the modeled economic performance of the field, and the verified potential defensive refract candidates, a well development sequence…” The metes and bounds of the claim are rendered further unclear because it is unclear if the “modeled economic performance” of this limitation is referring to the first recitation or the second recitation of “model.. an economic performance.” Therefore, the metes and bounds of the claim are rendered further unclear due to the lack of clear antecedent basis.
For the sake of compact prosecution, Examiner is interpreting the claim as though the second recitation of modeling relies antecedently on the first recitation of modeling. Examiner suggests amending the claim to clarify the antecedent basis.
Dependent claims 18 and 20 are rejected due to dependency on rejected base claim 17.
Accordingly, claims 17, 18, and 20 are rejected under 35 USC 112(b).
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 1-2, 4, 6-11, 13, 15-18, and 20 are rejected under 35 USC 101 because the claimed invention is directed to a judicial exception (i.e. abstract idea) without anything significantly more
Step 1: Claims 1-2, 4, and 6-9 are directed to a method, claims 10-11, 13, and 15-16 are directed to one or more non-transitory computer readable medium, and claims 17-18 and 20 are directed to a system. Therefore, the claims are directed to patent eligible categories of invention.
Step 2A, Prong 1: Independent claims 1, 10, and 17 are related to generate a well development sequence for the field, constituting an abstract idea based on “Mental Processes” related to concepts performed in the human mind including observation, evaluation, judgment, and opinion. Claim 1 recites limitations including “receiving, field data corresponding to a plurality of wells of the field; generating, based on the received field data, a production forecast for the plurality of wells of the field; modeling, based on the production forecast for the plurality of wells of the field, an economic performance of the field, the economic performance at least partially based on a location of the plurality of wells, a lateral length of the plurality of wells, and an operational expenditures estimated based on the production forecast for the plurality of wells; verifying potential defensive refract candidates of the plurality of wells of the field to identify a simultaneous operations event; and generating, based on the production forecast, the modeled economic performance of the field, and the verified potential defensive refract candidates, a well development sequence for the field to remove the potential defensive refract candidate from the well development sequence based on the identified simultaneous operations event, a well of the plurality of wells in the field of the unconventional reservoir being drillable according to at least one drilling operation of the well development sequence.” Claim 10 recites limitations including “receiving, field data corresponding to a plurality of wells of a field of an unconventional reservoir; generating, based on the received field data, a production forecast for the plurality of wells of the field; modeling, based on the production forecast for the plurality of wells of the field, an economic performance of the field, the economic performance at least partially based on a location of the plurality of wells, a lateral length of the plurality of wells, and an operational expenditures estimated based on the production forecast for the plurality of wells; verifying potential defensive refract candidates of the plurality of wells of the field to identify a simultaneous operations event; and generating, based on the production forecast, the modeled economic performance of the field, and the verified potential defensive refract candidates, a well development sequence for the field to remove the potential defensive refract candidate from the well development sequence based on the identified simultaneous operations event, a well of the plurality of wells in the field of the unconventional reservoir being drillable according to at least one drilling operation of the well development sequence.” Claim 17 recites limitations including “receiving, field data corresponding to a plurality of wells of the field; and generate, based on the received field data, a production forecast for the plurality of wells of the field; model, based on the production forecast for the plurality of wells of the field, an economic performance of the field, the economic performance at least partially based on a location of the plurality of wells, a lateral length of the plurality of wells, and an operational expenditures estimated based on the production forecast for the plurality of wells verify potential defensive refract candidates of the plurality of wells of the field to identify a simultaneous operations event; model, based on the production forecast for the plurality of wells of the field, an economic performance of the field, the economic performance at least partially based on a location and lateral length of the plurality of wells; and generate, based on the modeled economic performance, the modeled economic performance of the field, and the verified potential defensive refract candidates, a well development sequence for the field to remove the potential defensive refract candidate from the well development sequence based on the identified simultaneous operations event, a well of the plurality of wells in the field of the unconventional reservoir being drillable according to at least one drilling operation of the well development sequence.” These limitations, as drafted, but for the recitation of “processor,” is a process that covers performance of the limitations in the mind but for the recitation of generic computer components. That is, but for the “processor” language, nothing in the claim elements preclude the steps from practically being performed in the human mind. For example, with the exception of the “processor” language, the claim steps in the context of the claim encompass a user mentally or manually performing the steps of the claim.
Dependent claims 2, 4, 6-8, 11, 13, 15-16, and 18 further narrow the abstract idea identified in the independent claims and do not introduce further additional elements for consideration.
Dependent claims 9 and 20 will be evaluated under Step 2A, Prong 2 below.
Step 2A, Prong 2: Claims 1, 10, and 17 do not integrate the judicial exception into a practical application. Claim 1 recites the additional element of “receiving, at a processor and from a plurality of databases, field data corresponding to a plurality of wells of the field.” Claim 10 recites “one or more tangible non-transitory computer-readable storage media storing computer- executable instructions for performing a computer process on a computing system, the computer process comprising” within the preamble of the claim. Claim 10 recites the additional element of “receiving, at a processor of the computing system and from a plurality of databases, field data corresponding to a plurality of wells of a field of an unconventional reservoir.” Claim 17 is a system that recites “a processor; a communication port receiving, from a plurality of databases, field data corresponding to a plurality of wells of the field; and a non-transitory computer-readable medium encoded with instructions, which when executed by the processor, cause the processor to.” Use of a computer or other machinery in its ordinary capacity for 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., mental processes) does not integrate a judicial exception into a practical application. See MPEP 2106.05(f).
Therefore, the additional elements of the independent claims, when considered both individually and in combination, are not sufficient to prove integration into a practical application.
Dependent claims 2, 4, 6-8, 11, 13, 15-16, and 18 further narrow the abstract idea identified in the independent claims and do not introduce further additional elements for consideration, which does not integrate the judicial exception into a practical application.
Dependent claim 9 recites the additional element of “further comprising: displaying, in a user interface, the generated well development sequence for the field.” Use of a computer or other machinery in its ordinary capacity for 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., mental processes) does not integrate a judicial exception into a practical application. See MPEP 2106.05(f).
Dependent claim 20 recites the additional element of “a display device in communication with the processor, the instructions causing the processor to display, on the display device, the generated well development sequence for the field.” Use of a computer or other machinery in its ordinary capacity for 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., mental processes) does not integrate a judicial exception into a practical application. See MPEP 2106.05(f).
Therefore, the additional elements of the dependent claims, when considered both individually and in the context of the independent claims, are not sufficient to prove integration into a practical application.
Step 2B: Claims 1, 10, and 17 do not comprise anything significantly more than the judicial exception. Claim 1 recites the additional element of “receiving, at a processor and from a plurality of databases, field data corresponding to a plurality of wells of the field.” Claim 10 recites “one or more tangible non-transitory computer-readable storage media storing computer- executable instructions for performing a computer process on a computing system, the computer process comprising” within the preamble of the claim. Claim 10 recites the additional element of “receiving, at a processor of the computing system and from a plurality of databases, field data corresponding to a plurality of wells of a field of an unconventional reservoir.” Claim 17 is a system that recites “a processor; a communication port receiving, from a plurality of databases, field data corresponding to a plurality of wells of the field; and a non-transitory computer-readable medium encoded with instructions, which when executed by the processor, cause the processor to.” Use of a computer or other machinery in its ordinary capacity for 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., mental processes) is not anything significantly more. See MPEP 2106.05(f).
Therefore, the additional elements of the independent claims, when considered both individually and in combination, are not anything significantly more than the judicial exception.
Dependent claims 2, 4, 6-8, 11, 13, 15-16, and 18 further narrow the abstract idea identified in the independent claims and do not introduce further additional elements for consideration, which does not integrate the judicial exception into a practical application.
Dependent claim 9 recites the additional element of “further comprising: displaying, in a user interface, the generated well development sequence for the field.” Use of a computer or other machinery in its ordinary capacity for 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., mental processes) is not anything significantly more. See MPEP 2106.05(f).
Dependent claim 20 recites the additional element of “a display device in communication with the processor, the instructions causing the processor to display, on the display device, the generated well development sequence for the field.” Use of a computer or other machinery in its ordinary capacity for 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., mental processes) is not anything significantly more. See MPEP 2106.05(f).
Therefore, the additional elements of the dependent claims, when considered both individually and in the context of the independent claims, are not anything significantly more than the judicial exception.
Accordingly, claims 1-2, 4, 6-11, 13, 15-18, and 20 are rejected under 35 USC 101.
Claim Rejections - 35 USC § 103
In the event the determination of the status of the application as subject to AIA 35 U.S.C. 102 and 103 (or as subject to pre-AIA 35 U.S.C. 102 and 103) is incorrect, any correction of the statutory basis (i.e., changing from AIA to pre-AIA ) for the rejection will not be considered a new ground of rejection if the prior art relied upon, and the rationale supporting the rejection, would be the same under either status.
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.
This application currently names joint inventors. In considering patentability of the claims the examiner presumes that the subject matter of the various claims was commonly owned as of the effective filing date of the claimed invention(s) absent any evidence to the contrary. Applicant is advised of the obligation under 37 CFR 1.56 to point out the inventor and effective filing dates of each claim that was not commonly owned as of the effective filing date of the later invention in order for the examiner to consider the applicability of 35 U.S.C. 102(b)(2)(C) for any potential 35 U.S.C. 102(a)(2) prior art against the later invention.
Claim(s) 1-2, 4, 6, 9-11, 13, 15, 17-18, and 20 are rejected under 35 U.S.C. 103 as being unpatentable over Asala et al. (“An Integrated Machine-Learning Approach to Shale-Gas Supply-Chain Optimization and Refrac Candidate Identification,” 2019) in view of Perecharla et al. (US 20250117864 A1).
Regarding claim 1, Asala teaches a method for optimizing production of a field of an unconventional reservoir (Fig. 3), the method comprising:
receiving, at a processor and from a plurality of databases, field data corresponding to a plurality of wells of the field (Pg. 1205 teaches mining 300 wells in the area of interest using operator data and public-domain databases, wherein input features for 202 to 300 wells were used, as well as in Pgs. 1208-1209 teach deriving major parameters necessary for identifying potentially successful refrac candidates using data from a plurality of wells, wherein the parameters were derived from databases and operator data, wherein data preprocessing was performed to clean up the data, wherein Pg. 1205 teaches modeling a plurality of wells, and wherein Pg. 1222 teaches the modeling is performed on a computer; see also: Pgs. 1210; Examiner’s Note: A computer comprises a processor.);
generating, based on the received field data, a production forecast for the plurality of wells of the field (Pg. 1201 teaches that before reservoir simulation, a machine learning model is used to predict successful refracturing candidates using a feed-forward neural network and other models, wherein Pgs. 1208-1209 teach deriving major parameters necessary for identifying potentially successful refrac candidates using data from a plurality of wells, wherein the parameters were derived from databases and operator data, wherein data preprocessing was performed to clean up the data, wherein the neural network comprises 21 parameters for input including production data, wherein the metrics used to filter out successful refractured wells from the 300-well data set include a 9-month post-refrac to pre-refrac cumulative gas production and remaining recoverable gas-in-place/gross perforated lateral length, wherein the non-lease allocated production decline curve behavior of each well was approximated using DCA by computing EUR and the remaining gas in place, wherein Pg. 1205-1206 teach the decline curve analysis, or DCA, determines an estimated ultimate recovery for a 10 year period (i.e. production forecast), as well as in wherein Pg. 1210 teaches after NN training/testing, correlations were used to suggest candidates for techno-economic review, wherein fractured-well-class probability outcomes are obtained as NN output, wherein consequently, refracturing candidates are selected, wherein the refract candidates that pass the techno-economic review, and are explicitly modeled in CMG, are used for field-development planning, wherein predictions for refracturing planned infill wells are derived from the results of the preceding analysis, wherein refracturing probabilities associated with the well class are generated; see also: Pgs. 1204);
modeling, based on the production forecast for the plurality of wells of the field, an economic performance of the field (Pg. 1201 teaches that before reservoir simulation, a machine learning model is used to predict successful refracturing candidates using a feed-forward neural network and other models, wherein the obtained results are then used to identify the best-practice field development strategies using reservoir simulation, wherein successful refracturing candidates can be proposed based on frac/re-frac well classification, wherein Pgs. 1208-1209 teach deriving major parameters necessary for identifying potentially successful refrac candidates using data from a plurality of wells, wherein the parameters were derived from databases and operator data, wherein data preprocessing was performed to clean up the data, wherein Pg. 1210 teaches after NN training/testing, correlations were used to suggest candidates for techno-economic review, wherein fractured-well-class probability outcomes are obtained as NN output, wherein consequently, refracturing candidates are selected, wherein the refract candidates that pass the techno-economic review, and are explicitly modeled in CMG, are used for field-development planning, wherein predictions for refracturing planned infill wells are derived from the results of the preceding analysis, wherein refracturing probabilities associated with the well class are generated, wherein Pgs. 1212-1213 teach the heart of the integrated optimization framework is the strategic planning, or SP, model, wherein the SP model comprises variable relating to operating expenditure including projected cash flow, cost of drilling and stimulation, and total capital expenditure, respectively, wherein the SP model further takes into account economic constraints that are imposed to avoid paying taxes when losses occur, as well as considering the profit, wherein Pg. 1220 teaches the taxes are related to costs associated with taxes and royalties, wherein the short and long term state-tax regulation and legislation associated with subsurface mineral-rights acquisition must be carefully considered; see also Pgs. 1202-1203),
the economic performance at least partially based on a location of the plurality of wells (Pgs. 1208-1209 teach deriving major parameters necessary for identifying potentially successful refrac candidates using data from a plurality of wells, wherein the parameters were derived from databases and operator data, wherein data preprocessing was performed to clean up the data, wherein Pg. 1210 teaches after NN training/testing, correlations were used to suggest candidates for techno-economic review, wherein fractured-well-class probability outcomes are obtained as NN output, wherein consequently, refracturing candidates are selected, wherein the refract candidates that pass the techno-economic review, and are explicitly modeled in CMG, are used for field-development planning, wherein predictions for refracturing planned infill wells are derived from the results of the preceding analysis, wherein refracturing probabilities associated with the well class are generated, wherein Pgs. 1212-1213 teach the heart of the integrated optimization framework is the strategic planning, or SP, model, wherein the SP model comprises variable relating to operating expenditure including projected cash flow, cost of drilling and stimulation, and total capital expenditure, respectively, wherein the SP model further takes into account economic constraints that are imposed to avoid paying taxes when losses occur, as well as considering the profit, wherein Pg. 1220 teaches the taxes are related to costs associated with taxes and royalties, wherein the short and long term state-tax regulation and legislation associated with subsurface mineral-rights acquisition must be carefully considered; see also Pgs. 1201-1203; Examiner’s Note: State specific taxes are an economic performance consideration that is based on the location of the plurality of wells.),
an operational expenditures estimated based on the production forecast for the plurality of wells (Pgs. 1208-1209 teach deriving major parameters necessary for identifying potentially successful refrac candidates using data from a plurality of wells, wherein the parameters were derived from databases and operator data, wherein data preprocessing was performed to clean up the data, wherein Pg. 1210 teaches after NN training/testing, correlations were used to suggest candidates for techno-economic review, wherein fractured-well-class probability outcomes are obtained as NN output, wherein consequently, refracturing candidates are selected, wherein the refract candidates that pass the techno-economic review, and are explicitly modeled in CMG, are used for field-development planning, wherein predictions for refracturing planned infill wells are derived from the results of the preceding analysis, wherein refracturing probabilities associated with the well class are generated, wherein Pgs. 1212-1213 teach the heart of the integrated optimization framework is the strategic planning, or SP, model, wherein the SP model comprises variable relating to operating expenditure including projected cash flow, cost of drilling and stimulation, and total capital expenditure, respectively, wherein the SP model further takes into account economic constraints that are imposed to avoid paying taxes when losses occur, as well as considering the profit, wherein Pg. 1210 teaches the capital expenditures costs are disaggregated into well-pad, pipeline construction, impoundments, processing-plant, and compression station costs; see also Pgs. 1201-1203);
verifying potential defensive refract candidates of the plurality of wells of the field to identify a simultaneous operations event (Pg. 1201 teaches that before reservoir simulation, a machine learning model is used to predict successful refracturing candidates using a feed-forward neural network and other models, wherein the obtained results are then used to identify the best-practice field development strategies using reservoir simulation, wherein successful refracturing candidates can be proposed based on frac/re-frac well classification, wherein Pg. 1202 teaches refracturing-candidate selection should be distinguished from refracturing execution, wherein production and fracture-growth models are used to analyze the benefit of protection fractures or deleterious effects of stress shadows, before final decisions are made on the field-development strategies, such as refracturing, wherein the mechanical integrity of the well must be assessed before refracturing execution, wherein Pgs. 1208-1209 teach deriving major parameters necessary for identifying potentially successful refrac candidates using data from a plurality of wells, wherein the parameters were derived from databases and operator data, wherein data preprocessing was performed to clean up the data, wherein there are a plurality of various strategies identified for refracturing the wells, wherein the strategies include both sequential hydraulic fracturing of the well pads, as well as batch-concurrent hydraulic fracturing, wherein well completion can be performed using batch-concurrent, batch-sequential, and single categories alone, wherein Pg. 1210 teaches after NN training/testing, correlations were used to suggest candidates for techno-economic review, wherein fractured-well-class probability outcomes are obtained as NN output, wherein consequently, refracturing candidates are selected, wherein the refract candidates that pass the techno-economic review, and are explicitly modeled in CMG, are used for field-development planning, wherein predictions for refracturing planned infill wells are derived from the results of the preceding analysis, wherein refracturing probabilities associated with the well class are generated; see also: Pg. 1216; Examiner’s Note: Examiner is interpreting the “simultaneous operation event” of the present claims, under broadest reasonable interpretation, to include batch-concurrent hydraulic fracturing strategies, which is a drilling technique known in the art that includes multiple drilling rigs operating simultaneously to drill a series of wells int eh same section or phase before moving to the next.); and
generating, based on the production forecast, the modeled economic performance of the field, and the verified potential defensive refract candidates, a well development sequence for the field to remove the potential defensive refract candidate from the well development sequence based on the identified simultaneous operations event (Pg. 1201 teaches that before reservoir simulation, a machine learning model is used to predict successful refracturing candidates using a feed-forward neural network and other models, wherein the obtained results are then used to identify the best-practice field development strategies using reservoir simulation, wherein successful refracturing candidates can be proposed based on frac/re-frac well classification, wherein Pg. 1210 teaches after NN training/testing, correlations were used to suggest candidates for techno-economic review, wherein fractured-well-class probability outcomes are obtained as NN output, wherein consequently, refracturing candidates are selected, wherein the refract candidates that pass the techno-economic review, and are explicitly modeled in CMG, are used for field-development planning, wherein predictions for refracturing planned infill wells are derived from the results of the preceding analysis, wherein refracturing probabilities associated with the well class are generated, wherein Pg. 1208 teaches four development strategies in each reservoir model include strategies of hydraulic fracturing of six new well pads and strategies of batch-concurrent hydraulic fracturing, and wherein Pg. 1216 teaches the drilling, stimulation, and completion strategies were modeled over the planning horizon, wherein planned well pads A and C, D and E, and G and I were batch drilled sequentially depending on proximity to well pads and other logistical considerations, wherein to project refract candidature for planned infill wells, estimated are made using the explicitly modeled wells, including 18 wells and six wells with refrac-candidate status, which were selected, wherein 12 planned infill wells can be scheduled for refracturing during the 10 year planning horizon, wherein refract times were varied between strategies 1 and 3 and strategies 2 and 4; see also: Pgs. 1217-1218),
a well of the plurality of wells in the field of the unconventional reservoir being drillable according to at least one drilling operation of the well development sequence (Pg. 1201 teaches that before reservoir simulation, a machine learning model is used to predict successful refracturing candidates using a feed-forward neural network and other models, wherein the obtained results are then used to identify the best-practice field development strategies using reservoir simulation, wherein successful refracturing candidates can be proposed based on frac/re-frac well classification, wherein Pg. 1202 teaches predicting future well performance for unconventional wells, wherein Pg. 1210 teaches after NN training/testing, correlations were used to suggest candidates for techno-economic review, wherein fractured-well-class probability outcomes are obtained as NN output, wherein consequently, refracturing candidates are selected, wherein the refract candidates that pass the techno-economic review, and are explicitly modeled in CMG, are used for field-development planning, wherein predictions for refracturing planned infill wells are derived from the results of the preceding analysis, wherein refracturing probabilities associated with the well class are generated, wherein Pg. 1208 teaches four development strategies in each reservoir model include strategies of hydraulic fracturing of six new well pads and strategies of batch-concurrent hydraulic fracturing, and wherein Pg. 1216 teaches the drilling, stimulation, and completion strategies were modeled over the planning horizon, wherein planned well pads A and C, D and E, and G and I were batch drilled sequentially depending on proximity to well pads and other logistical considerations, wherein to project refract candidature for planned infill wells, estimated are made using the explicitly modeled wells, including 18 wells and six wells with refrac-candidate status, which were selected, wherein 12 planned infill wells can be scheduled for refracturing during the 10 year planning horizon, wherein refract times were varied between strategies 1 and 3 and strategies 2 and 4; see also: Pgs. 1217-1218).
While Asala evaluates both the economic performance of the plurality of wells and the lateral length of the wells, Asala fails to explicitly evaluate the economic performance related to the lateral length of the plurality of wells. In particular, Asala fails to explicitly teach the economic performance at least partially based on a lateral length of the plurality of wells.
From the same or similar field of endeavor, Perecharla teaches the economic performance at least partially based on a lateral length of the plurality of wells ([0086] teaches the completion refinement process can include identifying locations in the asset and generate completion parameters to be used, wherein economic calculations can be performed to identify how long it will take for these wells to turn profitable, wherein [0087] teaches the completion refinement process uses data from wells to create a regression model to establish relationship between three month production and completion and geological parameters, wherein the inputs into the model include lateral length, proppant mass per foot of lateral length, fluid in gals per foot of lateral length, and well latitude and longitude location, wherein the objective of the model is to predict the best three month production from the well based on the inputs, wherein [0092] teaches the for potential locations of a well, completions can be refined using the completion refinement ML workflow which can provide the ML based production forecast and economic forecasts, wherein [0090] teaches the obtained completion design and location are used in the DCA ML to forecast production behavior and the obtained forecasted production rate is used to conduct economic analysis of the well, wherein the economic analysis enables long-term cashflow for existing and not yet drilled wells and time to break even; see also: [0003-0004, 0049, 0082-0084]).
It would have been obvious to a person having ordinary skill in the art before the effective filing date of the claimed invention to modify Asala to incorporate the teachings of Perecharla to include the economic performance at least partially based on a lateral length of the plurality of wells. One would have been motivated to do so in order to allow asset teams to quickly come up with informed operational decisions related to impact of drilling factors like lateral length on well performance and identification of refract well candidates (Percecharla, [0083-0084]). By incorporating the teachings of Perecharla, one would have been able to predict the best three month production based on the lateral length and identify how long it will take for the well to turn profitable (Perecharla, [0086-0087]).
Regarding claim 10, the claims recite limitations already addressed by the rejection of claim 1. Regarding claim 10, Asala teaches one or more tangible non-transitory computer-readable storage media storing computer- executable instructions for performing a computer process on a computing system (Pg. 1222 teaches the modeling is performed on a computer; see also: Pgs. 1210), the computer process comprising. receiving, at a processor of the computing system and from a plurality of databases, field data corresponding to a plurality of wells of a field of an unconventional reservoir (Pg. 1205 teaches mining 300 wells in the area of interest using operator data and public-domain databases, wherein input features for 202 to 300 wells were used, as well as in Pgs. 1208-1209 teach deriving major parameters necessary for identifying potentially successful refrac candidates using data from a plurality of wells, wherein the parameters were derived from databases and operator data, wherein data preprocessing was performed to clean up the data, wherein Pg. 1202 teaches predicting future well performance for unconventional wells, and wherein Pg. 1222 teaches the modeling is performed on a computer; see also: Pgs. 1210; Examiner’s Note: A computer comprises a processor.). Accordingly, claim 10 is rejected as being unpatentable over Asala in view of Perecharla.
Regarding claims 2 and 11, the combination of Asala and Perecharla teaches all the limitations of claims 1 and 10 above.
However, Asala does not explicitly teach wherein the production forecast is generated through a type-curve workflow of the field data.
From the same or similar field of endeavor, Perecharla further teaches wherein the production forecast is generated through a type-curve workflow of the field data ([0092] teaches type curves for production per generation can tell if a particular generation of wells for a given spacing performs good or poorly, wherein these type curves can be extrapolated with decline curve analysis to estimate long term production forecasts for any generation of planned well, as well as in [0079] teaches fitting the predicted time series from neural networks in traditional decline curve type model that diminishes the effect of the outliers in the systems and improves prediction behavior; see also: [0003, 0048, 0076]).
It would have been obvious to a person having ordinary skill in the art before the effective filing date of the claimed invention to modify the combination of Asala and Perecharla to incorporate the further teachings of Perecharla to include wherein the production forecast is generated through a type-curve workflow of the field data. One would have been motivated to do so in order to allow asset teams to quickly come up with informed operational decisions related to impact of drilling factors like lateral length on well performance and identification of refract well candidates (Percecharla, [0083-0084]). By incorporating the teachings of Perecharla, one would have been able to predict the best three month production based on the lateral length and identify how long it will take for the well to turn profitable (Perecharla, [0086-0087]).
Regarding claims 4, 13, and 18, the combination of Asala and Perecharla teaches all the limitations of claims 1, 10, and 17 above.
Asala further teaches wherein verifying the potential defensive refract candidates is based on one of a well design (Pg. 1201 teaches that before reservoir simulation, a machine learning model is used to predict successful refracturing candidates using a feed-forward neural network and other models, wherein the obtained results are then used to identify the best-practice field development strategies using reservoir simulation, wherein successful refracturing candidates can be proposed based on frac/re-frac well classification, wherein Pg. 1210 teaches after NN training/testing, correlations were used to suggest candidates for techno-economic review, wherein fractured-well-class probability outcomes are obtained as NN output, wherein consequently, refracturing candidates are selected, wherein the refract candidates that pass the techno-economic review, and are explicitly modeled in CMG, are used for field-development planning, wherein predictions for refracturing planned infill wells are derived from the results of the preceding analysis, wherein refracturing probabilities associated with the well class are generated, wherein Pg. 1208 teaches four development strategies in each reservoir model include strategies of hydraulic fracturing of six new well pads and strategies of batch-concurrent hydraulic fracturing, and wherein Pg. 1216 teaches the drilling, stimulation, and completion strategies were modeled over the planning horizon, wherein planned well pads A and C, D and E, and G and I were batch drilled sequentially depending on proximity to well pads and other logistical considerations, wherein to project refract candidature for planned infill wells, estimated are made using the explicitly modeled wells, including 18 wells and six wells with refrac-candidate status, which were selected, wherein 12 planned infill wells can be scheduled for refracturing during the 10 year planning horizon, wherein refract times were varied between strategies 1 and 3 and strategies 2 and 4, wherein strategies 2 and 4 indicate the only selected candidate parent wells are refractured; see also: Pgs. 1217-1218).
Regarding claims 6 and 15, the combination of Asala and Perecharla teaches all the limitations of claims 1, 10, and 17 above.
Asala further teaches wherein the field data comprises at least one of geology data, drilled or undrilled inventory data, lease data, or economic data (Pg. 1205 teaches mining 300 wells in the area of interest using operator data and public-domain databases, wherein input features for 202 to 300 wells were used, as well as in Pgs. 1208-1209 teach deriving major parameters necessary for identifying potentially successful refrac candidates using data from a plurality of wells, wherein the parameters were derived from databases and operator data, wherein data preprocessing was performed to clean up the data, wherein the time logistics are taken into consideration when approaching lease expiration, wherein the metrics include remaining recoverable gas in place, wherein Pg. 1205 teaches modeling a plurality of wells, wherein the data includes public-domain geology data and proxy well logs, as well as in Pg. 1206 teaches input data includes rock density, well radius, and other well parameters, wherein Pg. 1216 teaches the drilling, stimulation, and completion strategies were modeled over the planning horizon, wherein planned well pads A and C, D and E, and G and I were batch drilled sequentially depending on proximity to well pads and other logistical considerations, wherein to project refract candidature for planned infill wells, estimated are made using the explicitly modeled wells, including 18 wells and six wells with refrac-candidate status, which were selected, wherein 12 planned infill wells can be scheduled for refracturing during the 10 year planning horizon, wherein refract times were varied between strategies 1 and 3 and strategies 2 and 4, wherein strategies 2 and 4 indicate the only selected candidate parent wells are refractured, wherein Pg. 1220 teaches the taxes are related to costs associated with taxes and royalties, wherein the short and long term state-tax regulation and legislation associated with subsurface mineral-rights acquisition must be carefully considered, and wherein Pg. 1222 teaches the modeling is performed on a computer; see also: Pgs. 1210).
Regarding claim 9, the combination of Asala and Perecharla teaches all the limitations of claim 1 above.
Asala further teaches further comprising: displaying, in a user interface, the generated well development sequence for the field (Pgs. 1219-1220 teach a display of the optimal shale-gas supply-chain network after SP model optimization, wherein the wells in the well pads are restimulated based on what is both operationally and economically optimal for the shale-gas venture, and wherein Pg. 1222 teaches the modeling is performed on a computer; see also: Pgs. 1221).
Regarding claim 17, Asala teaches a system optimizing production of a field of an unconventional reservoir (Fig. 3, Pg. 1205 teaches mining 300 wells in the area of interest (i.e. a plurality of wells of the field) using operator data and public-domain databases, wherein input features for 202 to 300 wells were used, as well as in Pgs. 1208-1209 teach deriving major parameters necessary for identifying potentially successful refrac candidates using data from a plurality of wells, wherein the parameters were derived from databases and operator data, wherein data preprocessing was performed to clean up the data, wherein Pg. 1202 teaches predicting future well performance for unconventional wells, and wherein Pg. 1222 teaches the modeling is performed on a computer; see also: Pgs. 1210), the system comprising:
a processor (Fig. 3, Pg. 1205 teaches mining 300 wells in the area of interest (i.e. a plurality of wells of the field) using operator data and public-domain databases, wherein input features for 202 to 300 wells were used, as well as in Pgs. 1208-1209 teach deriving major parameters necessary for identifying potentially successful refrac candidates using data from a plurality of wells, wherein the parameters were derived from databases and operator data, wherein data preprocessing was performed to clean up the data, wherein Pg. 1202 teaches predicting future well performance for unconventional wells, wherein Pg. 1205 teaches modeling a plurality of wells, and wherein Pg. 1222 teaches the modeling is performed on a computer; see also: Pgs. 1210; Examiner’s Note: A computer comprises a processor);
a communication port receiving, from a plurality of databases, field data corresponding to a plurality of wells of the field (Pg. 1205 teaches mining 300 wells in the area of interest (i.e. a plurality of wells of the field) using operator data and public-domain databases, wherein input features for 202 to 300 wells were used, as well as in Pgs. 1208-1209 teach deriving major parameters necessary for identifying potentially successful refrac candidates using data from a plurality of wells, wherein the parameters were derived from databases and operator data, wherein data preprocessing was performed to clean up the data, and wherein Pg. 1222 teaches the modeling is performed on a computer; see also: Pgs. 1210; Examiner’s Note: See the 35 USC 103 combination below for teachings pertaining to the unbolded claim language.); and
a non-transitory computer-readable medium encoded with instructions, which when executed by the processor (Fig. 3, Pg. 1205 teaches mining 300 wells in the area of interest (i.e. a plurality of wells of the field) using operator data and public-domain databases, wherein input features for 202 to 300 wells were used, as well as in Pgs. 1208-1209 teach deriving major parameters necessary for identifying potentially successful refrac candidates using data from a plurality of wells, wherein the parameters were derived from databases and operator data, wherein data preprocessing was performed to clean up the data, wherein Pg. 1202 teaches predicting future well performance for unconventional wells, and wherein Pg. 1222 teaches the modeling is performed on a computer; see also: Pgs. 1210), cause the processor to:
generate, based on the received field data, a production forecast for the plurality of wells of the field (Pg. 1201 teaches that before reservoir simulation, a machine learning model is used to predict successful refracturing candidates using a feed-forward neural network and other models, wherein Pgs. 1208-1209 teach deriving major parameters necessary for identifying potentially successful refrac candidates using data from a plurality of wells, wherein the parameters were derived from databases and operator data, wherein data preprocessing was performed to clean up the data, wherein the neural network comprises 21 parameters for input including production data, wherein the metrics used to filter out successful refractured wells from the 300-well data set include a 9-month post-refrac to pre-refrac cumulative gas production and remaining recoverable gas-in-place/gross perforated lateral length, wherein the non-lease allocated production decline curve behavior of each well was approximated using DCA by computing EUR and the remaining gas in place, wherein Pg. 1205-1206 teach the decline curve analysis, or DCA, determines an estimated ultimate recovery for a 10 year period (i.e. production forecast), as well as in wherein Pg. 1210 teaches after NN training/testing, correlations were used to suggest candidates for techno-economic review, wherein fractured-well-class probability outcomes are obtained as NN output, wherein consequently, refracturing candidates are selected, wherein the refract candidates that pass the techno-economic review, and are explicitly modeled in CMG, are used for field-development planning, wherein predictions for refracturing planned infill wells are derived from the results of the preceding analysis, wherein refracturing probabilities associated with the well class are generated; see also: Pgs. 1204);
model, based on the production forecast for the plurality of wells of the field, an economic performance of the field (Pg. 1201 teaches that before reservoir simulation, a machine learning model is used to predict successful refracturing candidates using a feed-forward neural network and other models, wherein the obtained results are then used to identify the best-practice field development strategies using reservoir simulation, wherein successful refracturing candidates can be proposed based on frac/re-frac well classification, wherein Pgs. 1208-1209 teach deriving major parameters necessary for identifying potentially successful refrac candidates using data from a plurality of wells, wherein the parameters were derived from databases and operator data, wherein data preprocessing was performed to clean up the data, wherein Pg. 1210 teaches after NN training/testing, correlations were used to suggest candidates for techno-economic review, wherein fractured-well-class probability outcomes are obtained as NN output, wherein consequently, refracturing candidates are selected, wherein the refract candidates that pass the techno-economic review, and are explicitly modeled in CMG, are used for field-development planning, wherein predictions for refracturing planned infill wells are derived from the results of the preceding analysis, wherein refracturing probabilities associated with the well class are generated, wherein Pgs. 1212-1213 teach the heart of the integrated optimization framework is the strategic planning, or SP, model, wherein the SP model comprises variable relating to operating expenditure including projected cash flow, cost of drilling and stimulation, and total capital expenditure, respectively, wherein the SP model further takes into account economic constraints that are imposed to avoid paying taxes when losses occur, as well as considering the profit, wherein Pg. 1220 teaches the taxes are related to costs associated with taxes and royalties, wherein the short and long term state-tax regulation and legislation associated with subsurface mineral-rights acquisition must be carefully considered; see also Pgs. 1202-1203),
the economic performance at least partially based on a location of the plurality of wells (Pgs. 1208-1209 teach deriving major parameters necessary for identifying potentially successful refrac candidates using data from a plurality of wells, wherein the parameters were derived from databases and operator data, wherein data preprocessing was performed to clean up the data, wherein Pg. 1210 teaches after NN training/testing, correlations were used to suggest candidates for techno-economic review, wherein fractured-well-class probability outcomes are obtained as NN output, wherein consequently, refracturing candidates are selected, wherein the refract candidates that pass the techno-economic review, and are explicitly modeled in CMG, are used for field-development planning, wherein predictions for refracturing planned infill wells are derived from the results of the preceding analysis, wherein refracturing probabilities associated with the well class are generated, wherein Pgs. 1212-1213 teach the heart of the integrated optimization framework is the strategic planning, or SP, model, wherein the SP model comprises variable relating to operating expenditure including projected cash flow, cost of drilling and stimulation, and total capital expenditure, respectively, wherein the SP model further takes into account economic constraints that are imposed to avoid paying taxes when losses occur, as well as considering the profit, wherein Pg. 1220 teaches the taxes are related to costs associated with taxes and royalties, wherein the short and long term state-tax regulation and legislation associated with subsurface mineral-rights acquisition must be carefully considered; see also Pgs. 1201-1203; Examiner’s Note: State specific taxes are an economic performance consideration that is based on the location of the plurality of wells.),
and an operational expenditures estimated based on the production forecast for the plurality of wells (Pgs. 1208-1209 teach deriving major parameters necessary for identifying potentially successful refrac candidates using data from a plurality of wells, wherein the parameters were derived from databases and operator data, wherein data preprocessing was performed to clean up the data, wherein Pg. 1210 teaches after NN training/testing, correlations were used to suggest candidates for techno-economic review, wherein fractured-well-class probability outcomes are obtained as NN output, wherein consequently, refracturing candidates are selected, wherein the refract candidates that pass the techno-economic review, and are explicitly modeled in CMG, are used for field-development planning, wherein predictions for refracturing planned infill wells are derived from the results of the preceding analysis, wherein refracturing probabilities associated with the well class are generated, wherein Pgs. 1212-1213 teach the heart of the integrated optimization framework is the strategic planning, or SP, model, wherein the SP model comprises variable relating to operating expenditure including projected cash flow, cost of drilling and stimulation, and total capital expenditure, respectively, wherein the SP model further takes into account economic constraints that are imposed to avoid paying taxes when losses occur, as well as considering the profit, wherein Pg. 1210 teaches the capital expenditures costs are disaggregated into well-pad, pipeline construction, impoundments, processing-plant, and compression station costs; see also Pgs. 1201-1203)
verify potential defensive refract candidates of the plurality of wells of the field to identify a simultaneous operations event (Pg. 1201 teaches that before reservoir simulation, a machine learning model is used to predict successful refracturing candidates using a feed-forward neural network and other models, wherein the obtained results are then used to identify the best-practice field development strategies using reservoir simulation, wherein successful refracturing candidates can be proposed based on frac/re-frac well classification, wherein Pg. 1202 teaches refracturing-candidate selection should be distinguished from refracturing execution, wherein production and fracture-growth models are used to analyze the benefit of protection fractures or deleterious effects of stress shadows, before final decisions are made on the field-development strategies, such as refracturing, wherein the mechanical integrity of the well must be assessed before refracturing execution, wherein Pgs. 1208-1209 teach deriving major parameters necessary for identifying potentially successful refrac candidates using data from a plurality of wells, wherein the parameters were derived from databases and operator data, wherein data preprocessing was performed to clean up the data, wherein there are a plurality of various strategies identified for refracturing the wells, wherein the strategies include both sequential hydraulic fracturing of the well pads, as well as batch-concurrent hydraulic fracturing, wherein well completion can be performed using batch-concurrent, batch-sequential, and single categories alone, wherein Pg. 1210 teaches after NN training/testing, correlations were used to suggest candidates for techno-economic review, wherein fractured-well-class probability outcomes are obtained as NN output, wherein consequently, refracturing candidates are selected, wherein the refract candidates that pass the techno-economic review, and are explicitly modeled in CMG, are used for field-development planning, wherein predictions for refracturing planned infill wells are derived from the results of the preceding analysis, wherein refracturing probabilities associated with the well class are generated; see also: Pg. 1216; Examiner’s Note: Examiner is interpreting the “simultaneous operation event” of the present claims, under broadest reasonable interpretation, to include batch-concurrent hydraulic fracturing strategies, which is a drilling technique known in the art that includes multiple drilling rigs operating simultaneously to drill a series of wells int eh same section or phase before moving to the next.);
model, based on the production forecast for the plurality of wells of the field, an economic performance of the field (Pg. 1201 teaches that before reservoir simulation, a machine learning model is used to predict successful refracturing candidates using a feed-forward neural network and other models, wherein the obtained results are then used to identify the best-practice field development strategies using reservoir simulation, wherein successful refracturing candidates can be proposed based on frac/re-frac well classification, wherein Pgs. 1208-1209 teach deriving major parameters necessary for identifying potentially successful refrac candidates using data from a plurality of wells, wherein the parameters were derived from databases and operator data, wherein data preprocessing was performed to clean up the data, wherein Pg. 1210 teaches after NN training/testing, correlations were used to suggest candidates for techno-economic review, wherein fractured-well-class probability outcomes are obtained as NN output, wherein consequently, refracturing candidates are selected, wherein the refract candidates that pass the techno-economic review, and are explicitly modeled in CMG, are used for field-development planning, wherein predictions for refracturing planned infill wells are derived from the results of the preceding analysis, wherein refracturing probabilities associated with the well class are generated, wherein Pgs. 1212-1213 teach the heart of the integrated optimization framework is the strategic planning, or SP, model, wherein the SP model comprises variable relating to operating expenditure including projected cash flow, cost of drilling and stimulation, and total capital expenditure, respectively, wherein the SP model further takes into account economic constraints that are imposed to avoid paying taxes when losses occur, as well as considering the profit, wherein Pg. 1220 teaches the taxes are related to costs associated with taxes and royalties, wherein the short and long term state-tax regulation and legislation associated with subsurface mineral-rights acquisition must be carefully considered; see also Pgs. 1202-1203),
the economic performance at least partially based on a location of the plurality of wells (Pgs. 1208-1209 teach deriving major parameters necessary for identifying potentially successful refrac candidates using data from a plurality of wells, wherein the parameters were derived from databases and operator data, wherein data preprocessing was performed to clean up the data, wherein Pg. 1210 teaches after NN training/testing, correlations were used to suggest candidates for techno-economic review, wherein fractured-well-class probability outcomes are obtained as NN output, wherein consequently, refracturing candidates are selected, wherein the refract candidates that pass the techno-economic review, and are explicitly modeled in CMG, are used for field-development planning, wherein predictions for refracturing planned infill wells are derived from the results of the preceding analysis, wherein refracturing probabilities associated with the well class are generated, wherein Pgs. 1212-1213 teach the heart of the integrated optimization framework is the strategic planning, or SP, model, wherein the SP model comprises variable relating to operating expenditure including projected cash flow, cost of drilling and stimulation, and total capital expenditure, respectively, wherein the SP model further takes into account economic constraints that are imposed to avoid paying taxes when losses occur, as well as considering the profit, wherein Pg. 1220 teaches the taxes are related to costs associated with taxes and royalties, wherein the short and long term state-tax regulation and legislation associated with subsurface mineral-rights acquisition must be carefully considered; see also Pgs. 1201-1203; Examiner’s Note: State specific taxes are an economic performance consideration that is based on the location of the plurality of wells.); and
generate, based on the modeled economic performance, the modeled economic performance of the field, and the verified potential defensive refract candidates, a well development sequence for the field to remove the potential defensive refract candidate from the well development sequence based on the identified simultaneous operations event (Pg. 1201 teaches that before reservoir simulation, a machine learning model is used to predict successful refracturing candidates using a feed-forward neural network and other models, wherein the obtained results are then used to identify the best-practice field development strategies using reservoir simulation, wherein successful refracturing candidates can be proposed based on frac/re-frac well classification, wherein Pg. 1210 teaches after NN training/testing, correlations were used to suggest candidates for techno-economic review, wherein fractured-well-class probability outcomes are obtained as NN output, wherein consequently, refracturing candidates are selected, wherein the refract candidates that pass the techno-economic review, and are explicitly modeled in CMG, are used for field-development planning, wherein predictions for refracturing planned infill wells are derived from the results of the preceding analysis, wherein refracturing probabilities associated with the well class are generated, wherein Pg. 1208 teaches four development strategies in each reservoir model include strategies of hydraulic fracturing of six new well pads and strategies of batch-concurrent hydraulic fracturing, and wherein Pg. 1216 teaches the drilling, stimulation, and completion strategies were modeled over the planning horizon, wherein planned well pads A and C, D and E, and G and I were batch drilled sequentially depending on proximity to well pads and other logistical considerations, wherein to project refract candidature for planned infill wells, estimated are made using the explicitly modeled wells, including 18 wells and six wells with refrac-candidate status, which were selected, wherein 12 planned infill wells can be scheduled for refracturing during the 10 year planning horizon, wherein refract times were varied between strategies 1 and 3 and strategies 2 and 4; see also: Pgs. 1217-1218),
a well of the plurality of wells in the field of the unconventional reservoir being drillable according to at least one drilling operation of the well development sequence (Pg. 1201 teaches that before reservoir simulation, a machine learning model is used to predict successful refracturing candidates using a feed-forward neural network and other models, wherein the obtained results are then used to identify the best-practice field development strategies using reservoir simulation, wherein successful refracturing candidates can be proposed based on frac/re-frac well classification, wherein Pg. 1202 teaches predicting future well performance for unconventional wells, wherein Pg. 1210 teaches after NN training/testing, correlations were used to suggest candidates for techno-economic review, wherein fractured-well-class probability outcomes are obtained as NN output, wherein consequently, refracturing candidates are selected, wherein the refract candidates that pass the techno-economic review, and are explicitly modeled in CMG, are used for field-development planning, wherein predictions for refracturing planned infill wells are derived from the results of the preceding analysis, wherein refracturing probabilities associated with the well class are generated, wherein Pg. 1208 teaches four development strategies in each reservoir model include strategies of hydraulic fracturing of six new well pads and strategies of batch-concurrent hydraulic fracturing, and wherein Pg. 1216 teaches the drilling, stimulation, and completion strategies were modeled over the planning horizon, wherein planned well pads A and C, D and E, and G and I were batch drilled sequentially depending on proximity to well pads and other logistical considerations, wherein to project refract candidature for planned infill wells, estimated are made using the explicitly modeled wells, including 18 wells and six wells with refrac-candidate status, which were selected, wherein 12 planned infill wells can be scheduled for refracturing during the 10 year planning horizon, wherein refract times were varied between strategies 1 and 3 and strategies 2 and 4; see also: Pgs. 1217-1218).
However, Asala does not explicitly teach a communication port receiving, from a plurality of databases, field data corresponding to a plurality of wells of the field; and the economic performance at least partially based on a lateral length of the plurality of wells, the economic performance at least partially based on lateral length of the plurality of wells.
From the same or similar field of endeavor, Perecharla teaches a communication port receiving, from a plurality of databases, field data corresponding to a plurality of wells of the field (Fig. 17 and [0098] teach a computing system comprising a processor and a storage media, wherein the processor is connected to a network interface that allows the computer system to communicate over a data network with additional computing systems, wherein Fig. 5 and [0068] teach receiving oilfield input, wherein the input classes include the input shown in Fig. 4, wherein Fig. 4 and [0065] teach the data to perform data analytics includes well attributes, production data, pressure data, and other reservoir data collected from available sources, wherein the platform can store the data; see also: [0005, 0063, 0066, 0075]); and
the economic performance at least partially based on a lateral length of the plurality of wells ([0086] teaches the completion refinement process can include identifying locations in the asset and generate completion parameters to be used, wherein economic calculations can be performed to identify how long it will take for these wells to turn profitable, wherein [0087] teaches the completion refinement process uses data from wells to create a regression model to establish relationship between three month production and completion and geological parameters, wherein the inputs into the model include lateral length, proppant mass per foot of lateral length, fluid in gals per foot of lateral length, and well latitude and longitude location, wherein the objective of the model is to predict the best three month production from the well based on the inputs, wherein [0092] teaches the for potential locations of a well, completions can be refined using the completion refinement ML workflow which can provide the ML based production forecast and economic forecasts, wherein [0090] teaches the obtained completion design and location are used in the DCA ML to forecast production behavior and the obtained forecasted production rate is used to conduct economic analysis of the well, wherein the economic analysis enables long-term cashflow for existing and not yet drilled wells and time to break even; see also: [0003-0004, 0049, 0082-0084]),
the economic performance at least partially based on lateral length of the plurality of wells ([0086] teaches the completion refinement process can include identifying locations in the asset and generate completion parameters to be used, wherein economic calculations can be performed to identify how long it will take for these wells to turn profitable, wherein [0087] teaches the completion refinement process uses data from wells to create a regression model to establish relationship between three month production and completion and geological parameters, wherein the inputs into the model include lateral length, proppant mass per foot of lateral length, fluid in gals per foot of lateral length, and well latitude and longitude location, wherein the objective of the model is to predict the best three month production from the well based on the inputs, wherein [0092] teaches the for potential locations of a well, completions can be refined using the completion refinement ML workflow which can provide the ML based production forecast and economic forecasts, wherein [0090] teaches the obtained completion design and location are used in the DCA ML to forecast production behavior and the obtained forecasted production rate is used to conduct economic analysis of the well, wherein the economic analysis enables long-term cashflow for existing and not yet drilled wells and time to break even; see also: [0003-0004, 0049, 0082-0084]).
It would have been obvious to a person having ordinary skill in the art before the effective filing date of the claimed invention to modify Asala to incorporate the teachings of Perecharla to include a communication port receiving, from a plurality of databases, field data corresponding to a plurality of wells of the field; and the economic performance at least partially based on a lateral length of the plurality of wells, the economic performance at least partially based on lateral length of the plurality of wells. One would have been motivated to do so in order to allow asset teams to quickly come up with informed operational decisions related to impact of drilling factors like lateral length on well performance and identification of refract well candidates (Percecharla, [0083-0084]). By incorporating the teachings of Perecharla, one would have been able to predict the best three month production based on the lateral length and identify how long it will take for the well to turn profitable (Perecharla, [0086-0087]).
Regarding claim 20, the combination of Asala and Perecharla teaches all the limitations of claim 1 above.
Asala further teaches display, on the display device, the generated well development sequence for the field (Pgs. 1219-1220 teach a display of the optimal shale-gas supply-chain network after SP model optimization, wherein the wells in the well pads are restimulated based on what is both operationally and economically optimal for the shale-gas venture, and wherein Pg. 1222 teaches the modeling is performed on a computer; see also: Pgs. 1221).
However, Asala does not explicitly teach further comprising: a display device in communication with the processor, the instructions causing the processor to display, on the display device, the generated well development sequence for the field.
From the same or similar field of endeavor, Perecharla further teaches further comprising: a display device in communication with the processor, the instructions causing the processor to display, on the display device, the generated well development sequence for the field (Fig. 5 and [0070] teach determining completion operations to conduct for individual wells in the field using machine-learning based workflows, wherein the determinations take into account connectivity of proximal wells, forecasting results of completion operations, as well as determining whether to conduct one or more hydraulic fracturing operations, wherein [0071] teaches if/when these completion operations are conducted, as well as simulated, the projections at 504 may be impacted and adjusted through a return loop, wherein Fig. 12 and [0081-0083] teach generating a dashboard for monitoring well performance and identifying potential areas further to exploit, wherein the dashboard can display operational decisions including the asset forecast and the well ranking decision dashboard, wherein [0084-0085] teaches the operational insights include operational insights for completion decisions through the completion refinement workflow, as well as in [0007] teaches determining one or more completion operations to conduct for the one or more identified wells and displaying the identified wells, analytics, and recommendations using a display dashboard; see also: [0005, 0080])
It would have been obvious to a person having ordinary skill in the art before the effective filing date of the claimed invention to modify the combination of Asala and Perecharla to incorporate the further teachings of Perecharla to include further comprising: a display device in communication with the processor, the instructions causing the processor to display, on the display device, the generated well development sequence for the field. One would have been motivated to do so in order to allow asset teams to quickly come up with informed operational decisions related to impact of drilling factors like lateral length on well performance and identification of refract well candidates (Percecharla, [0083-0084]). By incorporating the teachings of Perecharla, one would have been able to predict the best three month production based on the lateral length and identify how long it will take for the well to turn profitable (Perecharla, [0086-0087]).
Claim(s) 7 and 16 are rejected under 35 U.S.C. 103 as being unpatentable over Asala et al. (“An Integrated Machine-Learning Approach to Shale-Gas Supply-Chain Optimization and Refrac Candidate Identification,” 2019) in view of Perecharla et al. (US 20250117864 A1) in view of Allen et al. (US 20220372860 A1).
Regarding claims 7 and 16, the combination of Asala and Perecharla teaches all the limitations of claims 1, 10, and 17 above.
However, Asala does not explicitly teach wherein generating the well development sequence for the field is further based on data associated with one or more pilot wells.
From the same or similar field of endeavor, Allen teaches wherein generating the well development sequence for the field is further based on data associated with one or more pilot wells ([0147] teaches the workflow can commence to an evaluation stage that takes into consideration a variety of different inputs including pilot well data, wherein [0148] teaches this data can be modeled and utilized in order to determine the well trajectory and plans, wherein [0157] teaches well planning can determine a path of well, or trajectory, that can extend a reservoir to produce hydrocarbons, and wherein [0079] teaches planning for full-field development; see also: [0045, 0055, 0146]).
It would have been obvious to a person having ordinary skill in the art before the effective filing date of the claimed invention to modify the combination of Asala and Perecharla to incorporate the teachings of Allen to include wherein generating the well development sequence for the field is further based on data associated with one or more pilot wells. One would have been motivated to do so in order to establish a safe and efficient drilling plan based real-life drilling parameters (Allen, [0153]). By incorporating the teachings of Allen, one would have been able to utilize an automated planning workflow that includes a plurality of inputs including pilot well data (Allen, [0146-0147]).
Claim(s) 8 is rejected under 35 U.S.C. 103 as being unpatentable over Asala et al. (“An Integrated Machine-Learning Approach to Shale-Gas Supply-Chain Optimization and Refrac Candidate Identification,” 2019) in view of Perecharla et al. (US 20250117864 A1) in view of Ba et al. (US 20220290550 A1).
Regarding claim 8, the combination of Asala and Perecharla teaches all the limitations of claim 1 above.
However, Asala fails to explicitly disclose wherein generating the well development sequence for the field is further based on ranking of one or more key performance indicators of the plurality of wells of the field.
From the same or similar field of endeavor, Ba teaches wherein generating the well development sequence for the field is further based on ranking of one or more key performance indicators of the plurality of wells of the field (Fig. 1 and [0071] teach assessing one or more fractures at a wellsite, wherein [0151-0152] teach generating a working plan involving generating a range of trajectory candidates that satisfy a number of different conditions and evaluate the candidates based on the trajectory context and rank, wherein the trajectories may be ranked using candidate properties including key performance indicators; see also: [0004, 0091, 0243]).
It would have been obvious to a person having ordinary skill in the art before the effective filing date of the claimed invention to modify the combination of Asala and Perecharla to incorporate the teachings of Ba to include wherein generating the well development sequence for the field is further based on ranking of one or more key performance indicators of the plurality of wells of the field. One would have been motivated to do so in order to utilize a plan generation system that can be quite extensible to consider various different properties for the purposes of ranking, which can be used for real-time execution Ba, [0243]). By incorporating the teachings of Perecharla, one would have been able to generate a real-time ranking of the candidate trajectories based on constraint information and context (Ba, [0419]).
Conclusion
The prior art made of record and not relied upon is considered pertinent to applicant's disclosure:
Lindsay et al. (“Understanding the Applicability and Economic Viability of Refracturing Horizontal Wells in Unconventional Plays,” 2016) discloses analyzing the economic viability of refracturing horizonal wells in unconventional plays, wherein the refracturing of horizonal lateral lengths can be evaluated in order to identify the best refracturing candidates, which is related to the independent claim limitations of at least “optimizing production of a field of an unconventional reservoir” and “modeling, based on the production forecast for the plurality of wells of the field, an economic performance of the field, the economic performance at least partially based on a location of the plurality of wells, a lateral length of the plurality of wells”
Crews et al. (US 10253598 B2) discloses the use of multiple diagnostic lateral wellbores can assist in locating economical horizons, wherein these lateral wellbores can help identify and land in sweet-spot horizons and help determine the primary lateral wellbore location and length, placement, and purpose, which is related to the independent claim limitations of at least “modeling, based on the production forecast for the plurality of wells of the field, an economic performance of the field, the economic performance at least partially based on a location of the plurality of wells, a lateral length of the plurality of wells”
Sarduy et al. (CA 3001146 C) discloses calculating the predicted decline curve based on lateral length of the well, as well as performing cost-effective optimization for each well, placement, and purpose, which is related to the independent claim limitations of at least “modeling, based on the production forecast for the plurality of wells of the field, an economic performance of the field, the economic performance at least partially based on a location of the plurality of wells, a lateral length of the plurality of wells”
Pradhan et al. (“Evaluating Underperforming Wells on Permian Basin University Lands,” July 23, 2018) discloses developing numerical models that forecast what each well could produce at a given lateral length and identifying the potential maximum net present value for each well, which is related to the independent claim limitations of at least “modeling, based on the production forecast for the plurality of wells of the field, an economic performance of the field, the economic performance at least partially based on a location of the plurality of wells, a lateral length of the plurality of wells”
Meehan (US 20180047117 A1) discloses a workflow model that takes into consideration pilot well data, as well as generating a statistical breakdown which can be incorporated into KPI reports, which is related to dependent claims 7, which recites generating the well development sequence based on pilot wells, as well as dependent claim 8, which recites ranking key performance indicators for each well
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/SARA GRACE BROWN/Primary Examiner, Art Unit 3625
