DETAILED ACTION
Notice of Pre-AIA or AIA Status
The present application, filed on or after March 16, 2013, is being examined under the first inventor to file provisions of the AIA .
Status
Claims 1, 3-12, and 14-22 are pending. Claims 1, 3-4, 12, and 14-15 are amended. Claims 2, 13, and 23 are cancelled. No new claims are added.
Claims 1, 3-12, and 14-22 are rejected under 35 USC 103.
Response to Amendment
In light of the amendments, the objection to the specification is withdrawn, the claim objections are withdrawn, and the rejection under 35 USC 112(b) is withdrawn.
Response to Arguments
Applicant’s arguments with respect to claim(s) 11, 3-12, and 14-22 have been considered but are moot because the new ground of rejection does not rely on any reference applied in the prior rejection of record for any teaching or matter specifically challenged in the argument. The new grounds of rejection include the newly cited reference Zhang et al as necessitated by the claim amendments.
The dependent claims are argued to be allowable because the independent claims are supposedly allowable. However, the independent claims are not allowable; therefore, the dependent claims are not allowable since they do not add any further allowable limitations.
Claim Rejections - 35 USC § 103
The following is a quotation of 35 U.S.C. 103 which forms the basis for all obviousness rejections set forth in this Office action:
A patent for a claimed invention may not be obtained, notwithstanding that the claimed invention is not identically disclosed as set forth in section 102, if the differences between the claimed invention and the prior art are such that the claimed invention as a whole would have been obvious before the effective filing date of the claimed invention to a person having ordinary skill in the art to which the claimed invention pertains. Patentability shall not be negated by the manner in which the invention was made.
The factual inquiries for establishing a background for determining obviousness under 35 U.S.C. 103 are summarized as follows:
1. Determining the scope and contents of the prior art.
2. Ascertaining the differences between the prior art and the claims at issue.
3. Resolving the level of ordinary skill in the pertinent art.
4. Considering objective evidence present in the application indicating obviousness or nonobviousness.
Claim(s) 1, 3-10, 12, and 14-21 are rejected under 35 U.S.C. 103 as being unpatentable over Vallabhaneni et al. (US 2022/0075915 A1) in view of Bakulin et al. (US 2021/0140298 A1) in view of Zhang et al. (US 2022/0129788 A1).
Regarding claim 1, Vallabhaneni discloses a method for managing operations involving a well in a subsurface region using a neural network implemented on a hardware integrated circuit, the method comprising: ([0028] “At block 154, machine learning begins to train the first ML model 159 using default values and then updating the default values iteratively using the enhanced seismic data and matched well log data as inputs.” Abstract “Methods and apparatus for generating one or more reservoir 3D models are provided.”)
deriving a plurality of inputs from one or more first wireline logs ([0057] “In one or more embodiments, the borehole seismic receivers 302 can be part of a logging-while-drilling (LWD) tool string or wireline logging tool string.”);
accessing a predictive model comprising a neural network trained to generate one or more data predictions ([0069] “The computer system 600 also includes a first neural network processor 613. The first neural network processor 613 can perform one or more operations to train the first ML model 159 and generate one or more integrated enhanced logs from input data that includes seismic data and well log data, e.g., the integrated data set 110 described above.”);
processing, by the predictive model, the plurality of inputs derived from the one or more first wireline logs through one or more layers of the neural network ([0069] “Further, based on updated inputs, e.g., real-time data 130, the first neural network processor 613 can generate updated integrated enhanced logs to provide the update 3D model 162.” The wireline data is input to the neural network), the one or more first wireline logs comprising at least one gamma ray log ([0061] “For example, well data 116 from the downhole tool 402 can include, but is not limited to, temperature, pressure, caliper, density, porosity, acoustic, gamma, pulsed neutron, resistivity, nuclear magnetic resonance (NMR), distributed acoustic sensing (DAS), distributed temperature sensing (DTS), or a combination thereof.”);
generating, by the predictive model, a prediction identifying a plurality of second wireline logs for a reservoir in the subsurface region based on the processing of the plurality of inputs ([0069] “Further, based on updated inputs, e.g., real-time data 130, the first neural network processor 613 can generate updated integrated enhanced logs to provide the update 3D model 162.” The neural network is used to generate enhanced logs.), the plurality of second wireline logs comprising a bulk-density wireline log ([0061] “In examples, downhole tool 402 may operate with additional equipment (not illustrated) on surface 408 and/or disposed in a separate well measurement system (not illustrated) to record measurements and/or values from formation 432 to render a measurement or log of formation 432. Processing of information measured or logged may occur downhole and/or on surface 408. These measurements, either raw or processed can form the well data 116. For example, well data 116 from the downhole tool 402 can include, but is not limited to, temperature, pressure, caliper, density,”); and
controlling, based on the plurality of second wireline logs, well drilling operations that simulate hydrocarbon production at the reservoir ([0059] “The processing may be performed real-time during data acquisition or after recovery of downhole tool 402. Processing may alternatively occur downhole or may occur both downhole and at surface. In some examples, signals recorded by downhole tool 402 may be conducted to information handling system 414 by way of conveyance 410. Information handling system 414 may process the signals, and the information contained therein may be displayed for an operator to observe and stored for future processing and reference. Information handling system 414 may also contain an apparatus for supplying control signals and power to downhole tool 402.”).
Vallabhaneni does not disclose the one or more first wireline logs comprising at least one compressional slowness log or the plurality of second wireline logs comprising a shear-slowness wireline log.
Bakulin teaches generating the plurality of second wireline logs comprising a shear-slowness wireline log ([0024] “In step 120, the computer system uses the selected machine learning regression algorithm to determine the elastic properties of the geological formation based on the second feature vector. The elastic properties include the compressional velocity, the shear velocity, the density, and the unconfined compressive strength.”);
Vallabhaneni and Bakulin are analogous because they are from the “same field of endeavor” well modelling.
Before the effective filing date of the claimed invention, it would have been obvious to one of the ordinary skill in the art, having the teachings of Vallabhaneni and Bakulin before him or her, to modify Vallabhaneni to include well properties as taught by Bakulin.
The suggestion/motivation for doing so would have been Bakulin [0015] “Among other benefits and advantages, the implementations disclosed provide a flexible and integrated framework for determining elastic properties of a geological formation. The implementations provide improved analysis of data that is routinely acquired while drilling.”
While Bakulin teaches wireline logs as model inputs in [0049], teaches compressional velocity as an elastic property in [0024], and teaches compressional velocity based on the past compressional velocity logs in [0036], Vallabhaneni and Bakulin do not teach the one or more first wireline logs comprising at least one compressional slowness log.
Zhang teaches the one or more first wireline logs comprising at least one compressional slowness log ([0035] “[0035] Machine learning model training can be flexible in
terms of the type of data that is input and output. Certain embodiments involve a workflow that is based on traces extracted along well bores where control data (density and compressional slowness logs) have been recorded or derived synthetically.”).
Vallabhaneni, Bakulin, and Zhang are analogous because they are from the “same field of endeavor” well modelling.
Before the effective filing date of the claimed invention, it would have been obvious to one of the ordinary skill in the art, having the teachings of Vallabhaneni, Bakulin, and Zhang before him or her, to modify Vallabhaneni and Bakulin to include compressional slowness as a model input as taught by Zhang.
The suggestion/motivation for doing so would have been Zhang [0036] “Another additional benefit of techniques described herein is the dramatic simplification of a standard earth modeling workflow. The operational outcome of techniques described herein includes a shorter and less labor-intensive project lifespan, with reduced need for specialized software.”
Regarding claim 3. Vallabhaneni in view of Bakulin and Zhang teaches the method of claim 1, and Vallabhaneni discloses computing, using the predictive model, characterizations of the reservoir in the subsurface region ([0040] “In one or more embodiments, the dynamic ML model 179 can use the updated 3D model 162 or at least values therefrom as the starting point for the training process.”) based on the predicted bulk-density wireline log included among the plurality of second wireline logs ([0061] “For example, well data 116 from the downhole tool 402 can include, but is not limited to, temperature, pressure, caliper, density,” [0069] “Further, based on updated inputs, e.g., real-time data 130, the first neural network processor 613 can generate updated integrated enhanced logs to provide the update 3D model 162.” The first neural network is used to generate enhanced logs for the 3D model; the 3D model including the enhanced logs is used as input for the second neural network.).
Vallabhaneni does not disclose computing characterizations of the reservoir based on the predicted shear-slowness wireline log.
Bakulin teaches computing characterizations of the reservoir based on the predicted shear-slowness wireline log ([0024] “In step 120, the computer system uses the selected machine learning regression algorithm to determine the elastic properties of the geological formation based on the second feature vector. The elastic properties include the compressional velocity, the shear velocity, the density, and the unconfined compressive strength.”);
Vallabhaneni and Bakulin are analogous because they are from the “same field of endeavor” well modelling.
Before the effective filing date of the claimed invention, it would have been obvious to one of the ordinary skill in the art, having the teachings of Vallabhaneni and Bakulin before him or her, to modify Vallabhaneni to include well properties as taught by Bakulin.
The suggestion/motivation for doing so would have been Bakulin [0015] “Among other benefits and advantages, the implementations disclosed provide a flexible and integrated framework for determining elastic properties of a geological formation. The implementations provide improved analysis of data that is routinely acquired while drilling.”
Regarding claim 4, Vallabhaneni in view of Bakulin and Zhang teaches the method of claim 1, and Vallabhaneni discloses determining, by the predictive model, a plurality of earth properties for an area of the subsurface region that includes the reservoir ([0032] “Integrated enhanced logs are logs of reservoir properties that have been generated through the machine learning approach integrating seismic data, drilling data, production data, core data, micro-seismic data, well log data, and/or the like. The integrated enhanced logs can be machine learning generated logs of the 2D properties of the reservoir.“); and determining, by the predictive model, a characteristic of the reservoir in the subsurface region based on the plurality of earth properties (0033] The static reservoir 3D model 158 is formed from the ensemble of integrated enhanced logs 156. The static reservoir 3D model 158 thus includes the ensembles of 2D properties ( e.g., in the form of the integrated enhanced logs) for the whole 3D space. In one or more embodiments, the static reservoir 3D model 158 can be a multi-resolution geocellular model that represents the size, shape, orientation, composition, and internal arrangement of a reservoir.”).
Regarding claim 5, Vallabhaneni in view of Bakulin and Zhang teaches the method of claim 4, and Vallabhaneni discloses wherein determining the plurality of earth properties comprises: calculating a set of mechanical earth properties based on at least one of the plurality of second wireline logs ([0037] “In the second process 170, the updated 3D model 162 and additional data for dynamic modeling, i.e., dynamic modeling data 174, are used as inputs to further machine learning at block 172. The dynamic modeling data 174 can include data used to predict flow properties, such as porosity, pore pressure, pressure, choke, lithology, one or more velocity models, production rates, production history, flow properties, and other data related to flow properties obtained from production logging, formation evaluation, completion data, e.g., from an intelligent completion system, or any combination thereof.”);
Vallabhaneni does not disclose calculating a set of elastic earth properties based on at least one of the plurality of second wireline logs.
Bakulin teaches calculating a set of elastic earth properties based on at least one of the plurality of second wireline logs ([0022] “In some implementations, an optimal machine learning regression algorithm and the parameters of the machine learning regression algorithm are determined by training using wireline logs, drilling parameters, bit vibrations, and logging while drilling logs obtained from offset wells.” [0023] “In step 116, the computer system extracts a second feature vector from the data acquired during drilling based on the drilling environment classification and the selected machine learning regression algorithm. The second feature vector is indicative of elastic properties of a geological formation.” ).
Vallabhaneni and Bakulin are analogous because they are from the “same field of endeavor” well modelling.
Before the effective filing date of the claimed invention, it would have been obvious to one of the ordinary skill in the art, having the teachings of Vallabhaneni and Bakulin before him or her, to modify Vallabhaneni to include well properties as taught by Bakulin.
The suggestion/motivation for doing so would have been Bakulin [0015] “Among other benefits and advantages, the implementations disclosed provide a flexible and integrated framework for determining elastic properties of a geological formation. The implementations provide improved analysis of data that is routinely acquired while drilling.”
Regarding claim 6, Vallabhaneni in view of Bakulin and Zhang teaches the method of claim 5, and Vallabhaneni does not disclose wherein the set of mechanical earth properties and the set of elastic earth properties comprises one or more of: a Young's modulus, a bulk modulus, a shear modulus, and a Poisons ratio.
Bakulin teaches wherein the set of mechanical earth properties and the set of elastic earth properties comprises one or more of: a Young's modulus and a Poisons ratio (Under the broadest reasonable interpretation, only one of the listed alternatives must be taught. [0024] “In step 120, the computer system uses the selected machine learning regression algorithm to determine the elastic properties of the geological formation based on the second feature vector.” “The elastic properties also include derivative properties, such as the Young's modulus and the Poisson's ratio.”).
Vallabhaneni and Bakulin are analogous because they are from the “same field of endeavor” well modelling.
Before the effective filing date of the claimed invention, it would have been obvious to one of the ordinary skill in the art, having the teachings of Vallabhaneni and Bakulin before him or her, to modify Vallabhaneni to include well properties as taught by Bakulin.
The suggestion/motivation for doing so would have been Bakulin [0015] “Among other benefits and advantages, the implementations disclosed provide a flexible and integrated framework for determining elastic properties of a geological formation. The implementations provide improved analysis of data that is routinely acquired while drilling.”
Regarding claim 7, Vallabhaneni in view of Bakulin and Zhang teaches the method of claim 5, and Vallabhaneni discloses further comprising: computing, from computed outputs of the predictive model, characterizations of the reservoir in the subsurface region based on at least one of: the set of mechanical earth properties (Under the broadest reasonable interpretation, only one of the listed alternatives must be taught. [0037] “In the second process 170, the updated 3D model 162 and additional data for dynamic modeling, i.e., dynamic modeling data 174, are used as inputs to further machine learning at block 172. The dynamic modeling data 174 can include data used to predict flow properties, such as porosity, pore pressure, pressure, choke, lithology, one or more velocity models, production rates, production history, flow properties, and other data related to flow properties obtained from production logging, formation evaluation, completion data, e.g., from an intelligent completion system, or any combination thereof.”).
Regarding claim 8, Vallabhaneni in view of Bakulin and Zhang teaches the method of claim 7, and Vallabhaneni does not disclose wherein computing characterizations of the reservoir comprises: identifying a stiffness of porous fluid saturated rocks at the reservoir based on the set of mechanical earth properties and the set of elastic earth properties.
Bakulin teaches wherein computing characterizations of the reservoir comprises: identifying a stiffness of porous fluid saturated rocks at the reservoir based on the set of mechanical earth properties and the set of elastic earth properties ([0024] “In step 120, the computer system uses the selected machine learning regression algorithm to determine the elastic properties of the geological formation based on the second feature vector.” “The elastic properties also include derivative properties, such as the Young's modulus and the Poisson's ratio.” The Young’s modulus is a measurement of rock stiffness and is in the set of mechanical properties and the set of elastic properties. [0024] Porosity is also derived. ).
Vallabhaneni and Bakulin are analogous because they are from the “same field of endeavor” well modelling.
Before the effective filing date of the claimed invention, it would have been obvious to one of the ordinary skill in the art, having the teachings of Vallabhaneni and Bakulin before him or her, to modify Vallabhaneni to include well properties as taught by Bakulin.
The suggestion/motivation for doing so would have been Bakulin [0015] “Among other benefits and advantages, the implementations disclosed provide a flexible and integrated framework for determining elastic properties of a geological formation. The implementations provide improved analysis of data that is routinely acquired while drilling.”
Regarding claim 9, Vallabhaneni in view of Bakulin and Zhang teaches the method of claim 8, and Vallabhaneni does not disclose wherein identifying a stiffness of porous fluid saturated rocks at the reservoir comprises: identifying the stiffness based on elastic moduli that identify stiffer rocks in unconventional oil and gas reservoirs.
Bakulin teaches wherein identifying a stiffness of porous fluid saturated rocks at the reservoir comprises: identifying the stiffness based on elastic moduli that identify stiffer rocks in unconventional oil and gas reservoirs.([0024] “In step 120, the computer system uses the selected machine learning regression algorithm to determine the elastic properties of the geological formation based on the second feature vector.” “The elastic properties also include derivative properties, such as the Young's modulus and the Poisson's ratio.” The Young’s modulus is a measurement of rock stiffness and is in the set of mechanical properties and the set of elastic properties. [0024] Porosity is also derived.).
Vallabhaneni and Bakulin are analogous because they are from the “same field of endeavor” well modelling.
Before the effective filing date of the claimed invention, it would have been obvious to one of the ordinary skill in the art, having the teachings of Vallabhaneni and Bakulin before him or her, to modify Vallabhaneni to include well properties as taught by Bakulin.
The suggestion/motivation for doing so would have been Bakulin [0015] “Among other benefits and advantages, the implementations disclosed provide a flexible and integrated framework for determining elastic properties of a geological formation. The implementations provide improved analysis of data that is routinely acquired while drilling.”
Regarding claim 10, Vallabhaneni in view of Bakulin and Zhang teaches the method of claim 7, and Vallabhaneni in view of Bakulin does not disclose further comprising: determining, using the predictive model, a placement location for a well drilling operation based on the computed characterizations of the reservoir.
Zhang teaches determining, using the predictive model, a placement location for a well drilling operation based on the computed characterizations of the reservoir ([0029] “[0029] Use of techniques described herein can help enhance and "operationalize" earth models for any organization. In certain reservoirs, the earth model can be used to optimize well placement,”).
Vallabhaneni, Bakulin, and Zhang are analogous because they are from the “same field of endeavor” well modelling.
Before the effective filing date of the claimed invention, it would have been obvious to one of the ordinary skill in the art, having the teachings of Vallabhaneni, Bakulin, and Zhang before him or her, to modify Vallabhaneni and Bakulin to include compressional slowness as a model input as taught by Zhang.
The suggestion/motivation for doing so would have been Zhang [0036] “Another additional benefit of techniques described herein is the dramatic simplification of a standard earth modeling workflow. The operational outcome of techniques described herein includes a shorter and less labor-intensive project lifespan, with reduced need for specialized software.”
Regarding claim 12, Vallabhaneni discloses a system for managing operations involving a well in a subsurface region using a neural network implemented on a hardware integrated circuit of the system, the system comprising a processor and a non-transitory machine-readable storage device storing instructions that are executable by the processor to perform operations comprising: ([0028] “At block 154, machine learning begins to train the first ML model 159 using default values and then updating the default values iteratively using the enhanced seismic data and matched well log data as inputs.” Abstract “Methods and apparatus for generating one or more reservoir 3D models are provided.” [0069] “Any one of the previously described functionalities may be partially (or entirely) implemented in hardware and/or on the processor 601. For example, the functionality may be implemented with an application specific integrated circuit, in logic implemented in the processor 601, in a co-processor on a peripheral device or card, etc.”)
The remainder of claim 12 is rejected in the same way as claim 1.
Claims 14-21 are rejected in the same way as claims 3-10.
Claim(s) 11 and 22 are rejected under 35 U.S.C. 103 as being unpatentable over Vallabhaneni et al. (US 2022/0075915 A1) in view of Bakulin et al. (US 2021/0140298 A1) in view of Zhang et al. (US 2022/0129788 A1) in view of Ore et al. “Supervised machine learning to predict brittleness using well logs and seismic signal attributes: Methods and application in an unconventional reservoir” 2021.
Regarding claim 11, Vallabhaneni in view of Bakulin and Zhang teaches the method of claim 10, and while the claim limitation is well known, Vallabhaneni in view of Bakulin and Zhang do not explicitly disclose wherein controlling the well drilling operations comprises: causing a hydraulic fracture at the placement location; and stimulating a particular type of hydrocarbon production at the reservoir in response to causing the hydraulic fracture at the placement location.
Ore teaches wherein controlling the well drilling operations comprises: causing a hydraulic fracture at the placement location; and stimulating a particular type of hydrocarbon production at the reservoir in response to causing the hydraulic fracture at the placement location (¶ 1 “In unconventional reservoir sweet-spot identification, brittleness is an important proxy used as a measure of easiness for oil and gas production. Production from this low permeability reservoir is realized by hydraulic fracturing, which depends on how brittle the rock is—as it opens natural fractures and also creates new fractures.” Formations are hydraulicly fractured for hydrocarbon extraction.).
Vallabhaneni, Bakulin, Zhang, and Ore are analogous because they are from the “same field of endeavor” well modelling.
Before the effective filing date of the claimed invention, it would have been obvious to one of the ordinary skill in the art, having the teachings of Vallabhaneni, Bakulin, Zhang, and Ore before him or her, to modify Vallabhaneni, Bakulin, and Zhang to include fracturing as taught by Ore.
The suggestion/motivation for doing so would have been Ore Col. 1 ¶ 2 “Horizontal drilling and multistage hydraulic fracturing (stimulation) have made the exploitation of previously untapped resources not only possible but profitable. Hydraulic fracturing is a technique in which fluids are injected at high pressure into the target formation to activate or create a fracture network, thereby increasing the ability of fluids to flow.”
Claim 22 is rejected in the same way as claim 11.
Conclusion
Applicant's amendment necessitated the new ground(s) of rejection presented in this Office action. Accordingly, THIS ACTION IS MADE FINAL. See MPEP § 706.07(a). Applicant is reminded of the extension of time policy as set forth in 37 CFR 1.136(a).
A shortened statutory period for reply to this final action is set to expire THREE MONTHS from the mailing date of this action. In the event a first reply is filed within TWO MONTHS of the mailing date of this final action and the advisory action is not mailed until after the end of the THREE-MONTH shortened statutory period, then the shortened statutory period will expire on the date the advisory action is mailed, and any nonprovisional extension fee (37 CFR 1.17(a)) pursuant to 37 CFR 1.136(a) will be calculated from the mailing date of the advisory action. In no event, however, will the statutory period for reply expire later than SIX MONTHS from the mailing date of this final action.
Any inquiry concerning this communication or earlier communications from the examiner should be directed to TROY A MAUST whose telephone number is (571)272-1931. The examiner can normally be reached on Monday-Friday from 8AM to 4PM.
If attempts to reach the examiner by telephone are unsuccessful, the examiner’s supervisor, Rehana Perveen, can be reached at telephone number (571) 272-3676. The fax phone number for the organization where this application or proceeding is assigned is 571-273-8300.
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/T.A.M./Examiner, Art Unit 2189
/REHANA PERVEEN/Supervisory Patent Examiner, Art Unit 2189