DETAILED ACTION
The present application, filed on or after March 16, 2013, is being examined under the first inventor to file provisions of the AIA .
Responsive to the communication dated 12/21/2023.
Claims 1 – 20 are presented for examination.
Priority
ADS dated 11/02/2022 does not claim any domestic or international priority. The effective filing date of the application is 11/02/2022.
Information Disclosure Statement
IDS dated 11/02/2022 and 12/21/2023 have been reviewed. See attached.
Drawings
The drawing dated 11/02/2022 have been reviewed. They are accepted.
Specification
The abstract dated 11/02/2022 has 158 words, 12 lines, and no legal phraseology. The abstract is objected to because it has more 158 words.
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.
Claims 1-20 are rejected under 35 U.S.C. 103 as being unpatentable over Kulkarni_2021 (US 2021/0404334 A1) in view of Jamison_2012 (US 2012/0094876 A1).
Claim 1. Kulkarni_2021 makes obvious “A system comprising: A processing device; and
A memory device that includes instructions executable by the processing device for causing the processing device to:” (par 19 - 21: “FIG. 2 is a block diagram of a drilling fluid analysis and control system according to at least one aspect of the disclosure… device 204 can include one or more processors 208 coupled to memory 216 through a bus 212. Memory 216 may be a non-transitory computer-readable medium… memory 216 stores instructions 220 and one or more interfaces 224 such as application programming interfaces and data interfaces that enable receiving or exporting data… can include one or more sets of instructions that execute using one or more processors 208…”; par 63: “in some aspects, systems and methods for analyzing and controlling drilling fluids are provided…”; par 87: “… embodiments may be implemented by hardware, software, scripting languages, firmware, middleware, microcode, hardware description languages, or any combination thereof, the program code or code segments to perform the necessary tasks may be stored in a machine readable medium…”; par 88: “… implementations in firmware, software, or combinations thereof, the methodologies may be implemented… any machine-readable medium tangibly embodying instructions may be used in implementing the methodologies described herein…”) Receive a set of target fluid properties for a downhole drilling fluid as input from a user (FIG. 5 block 520: “receiving a target property value corresponding to an optimal value of a property of the water-based drilling fluid”; par 12: “… the fluid analyzer receives a target property value that corresponds to an optimal value of a property of the water-based drilling fluid…”; par 31: “… drilling system 248 may receive an indication of the particular composition of drilling fluid that is to be used in drilling operations or a request to modify the drilling fluid that is currently in use from drilling system control 244…”; par 56: “… at block 520, a target property value is received. The target property value may be an optimal value for a property of the drill fluid…”; par 57: “… in some instances, the new target property value may be received automatically upon one or more sensors in the wellbore detecting a change in the drilling operations or subterranean environment. In other instances, the new target property value may be continuously derived by a field engineer…”; par 61: “… block 520, may include receiving a target value for multiple properties at once…”); Execute an iterative optimization process configured to determine an optimized composition of the downhole drilling fluid that satisfies at least one objective function and matches the set of target fluid properties, wherein the iterative optimization process is configured to iterate until a stopping condition is satisfied (FIG. 5 block 528: “property value = target value Yes or NO” if No iterate block 532: “modify water-based drilling fluid”; par 1: “… analysis and optimization of wellbore drilling fluids…”; par 10: “… optimization of the drilling fluid…”; par 12: “… receives a target property value that corresponds to an optimal value of a property… determine a composition of the drilling fluid and whether the value of the property of the drilling fluid is approximately equal to the target property value…”), each iteration of the iterative optimization process involving:
Selecting a mixture of fluid components for the downhole drilling fluid that includes a plurality of different mixtures of fluid components; Providinginput to a trained machine-learning model, the trained machine-learning model being configured to determine a set of predicted fluid properties for the mixture of fluid components; Receiving the set of predicted fluid properties for the mixture of fluid components as output from the trained machine-learning model (par 24: “… properties of the drilling fluid may be obtained from one or more machine-learning models 232. One or more machine-learning models 232 may process sensor measurements to derive an output indicating or representing properties of the drilling fluid that may may not be directly measured using sensors 236…”; par 26: “the machine-learning models may be trained… the machine-learning models 232 may be trained… the feature set may indicate that drilling fluid with a particular volume of brine, low-gravity solids, and high-gravity solids is made up of a particular percentage of water. The machine learning model may use the feature set, as input, and the labels, as expected output, to define one or more functions that will output the expected additional one or more properties of the drilling fluid…”) ; and determining whether the set of predicted fluid properties matches the set of target fluid properties (FIG. 5; Page 58: “… to determine if the value of the property of the modified water-based drilling fluid now approximately equals the target property value…”); and transmit a control signal to a mixing subsystem for causing the mixing subsystem to produce the optimized composition of the downhole drilling fluid” (FIG. 2 block 244 drilling system control, block 248 drilling system; FIG. 5 block 536: “pump water-based drilling fluid into wellbore”; page 30: “… drilling system control 244 can control the operation of drilling system 248. In some instances, the output from fluid analysis device 204 may be used to reformulate the drilling fluid that is in operation. For instance, drilling system control 244 may receive the output and determine that the properties of the current drilling fluid may not be capable of producing an intended hydrostatic pressure on the formation surrounding the wellbore. Drilling system control 244 may generate a request to drilling system 248 to increase the density of the drilling fluid…”).
Kulkarni_2021 does not explicitly teach Selecting a mixture of fluid components for the downhole drilling fluid “from a search space” that includes a plurality of different mixtures of fluid components nor providing ”the selected mixture of fluid components as.”
Jamison_2012, however, makes obvious selecting “from a search space” and providing “the selected mixture of fluid component” (abstract: “… determining a Design space comprising specified ranges for one or more drilling fluid properties…” FIG. 3 determine design space and fluid formulation sets; FIG. 4 design space and formulation set; Par 23: “in order to design a drilling fluid which accounts for many of the significant performance criterion, a complex mathematical model may be utilized… in some embodiments, artificial neural networks (“ANNs”) may utilize specified parameters as inputs in steps throughout the overall design process to result in an optimal drilling fluid…”)
Kulkarni_2021 and Jamison_2012 are analogous art because they are from the same field of endeavor called oil wells. Before the effective filing date, it would have been obvious to a person of ordinary skill in the art to combine Kulkarni_2021 and Jamison_2012. The rationale for doing so would have been that Kulkarni_2021 teaches to make a drilling fluid that has target properties. Jamison_2012 teaches to use a neural network (i.e., machine learning model) to design drilling fluids. Therefore, it would have been obvious to combine Kulkarni_2021 and Jamison_2012 for the benefit of deriving the target properties of the drilling fluid to obtain the invention as specified in the claims.
Claim 8. The limitations of claim 8 are substantially the same as those of claim 1 and are therefore rejected due to the same reasons as outlined above for claim 1. Kulkarni_2021 makes obvious the further limitations of “a method comprising:” and “by a processing device” and “by the processing device” (par 19 - 21: “FIG. 2 is a block diagram of a drilling fluid analysis and control system according to at least one aspect of the disclosure… device 204 can include one or more processors 208 coupled to memory 216 through a bus 212. Memory 216 may be a non-transitory computer-readable medium… memory 216 stores instructions 220 and one or more interfaces 224 such as application programming interfaces and data interfaces that enable receiving or exporting data… can include one or more sets of instructions that execute using one or more processors 208…”; par 63: “in some aspects, systems and methods for analyzing and controlling drilling fluids are provided…”; par 87: “… embodiments may be implemented by hardware, software, scripting languages, firmware, middleware, microcode, hardware description languages, or any combination thereof, the program code or code segments to perform the necessary tasks may be stored in a machine readable medium…”; par 88: “… implementations in firmware, software, or combinations thereof, the methodologies may be implemented… any machine-readable medium tangibly embodying instructions may be used in implementing the methodologies described herein…”).
Claim 15. The limitations of claim 15 are substantially the same as those of claim 1 and are therefore rejected due to the same reasons as outlined above for claim 1. Kulkarni_2021 makes obvious the further limitations of “a non-transitory computer-readable medium comprising instructions that are executable by a processor device for causing the processing device to perform operations comprising:” (par 19 - 21: “FIG. 2 is a block diagram of a drilling fluid analysis and control system according to at least one aspect of the disclosure… device 204 can include one or more processors 208 coupled to memory 216 through a bus 212. Memory 216 may be a non-transitory computer-readable medium… memory 216 stores instructions 220 and one or more interfaces 224 such as application programming interfaces and data interfaces that enable receiving or exporting data… can include one or more sets of instructions that execute using one or more processors 208…”; par 63: “in some aspects, systems and methods for analyzing and controlling drilling fluids are provided…”; par 87: “… embodiments may be implemented by hardware, software, scripting languages, firmware, middleware, microcode, hardware description languages, or any combination thereof, the program code or code segments to perform the necessary tasks may be stored in a machine readable medium…”; par 88: “… implementations in firmware, software, or combinations thereof, the methodologies may be implemented… any machine-readable medium tangibly embodying instructions may be used in implementing the methodologies described herein…”).
Claim 2, 9, 16. Kulkarni_2021 makes obvious “Wherein the memory device further includes instructions executable by the processing device for causing the processing device to execute the interative optimization process by, for a current iteration of the iterative optimization process: determining that the set of predicted fluid properties does not match the set of target fluid properties; determining a difference between the set of predicted fluid properties and the set of target fluid properties; and performing a subsequent iteration of the iterative optimization process based on the difference, such that the subsequent iteration is informed by the difference determined in the current iteration (FIG. 5).
Claim 3, 10, 17. Jamison_2012 makes obvious “Wherein the at least one objective function is configured to optimize for drilling speed downhole based on the optimized composition of the downhole drilling fluid” (Par 25: “… design space input parameters specifying potential ranges for drilling fluid properties may be determined to comply with a variety of parameters… to comply with given operational design parameters… operational design parameters may include, but are not limited to… rate of penetration…”).
Claim 4, 11, 18. Kulkarni_2021 makes obvious “Wherein the memory device further includes instructions executable by the processing device for causing the processing device to: generate the trained machine-learning model by training a machine-learning model using historical data, the historical data indicating fluid properties of candidate fluid components” (Par 23: “… historical records corresponding to one or more variations of drilling fluid… historical data associated each variation of drilling fluid that was in use at a particular time…”; par 26: “the machine-learning models may be trained using stored feature sets from contemporaneously collected sensor data, historical data, or generated data…”).
Claim 5, 12, 19. Kulkarni_2021 makes obvious “Wherein the memory device further includes instructions executable by the processing device for causing the processing device to generate the trained machine-learning model by: identifying a subset of fluid components, from among the candidate fluid components listed in the historical data, that perform a same function in the downhole drilling fluid; modifying the historical data to replace the subset of fluid components with a single fluid component that is representative of the subset of fluid components; and training the machine-learning model using the modified historical data to generate the trained machine-learning model” (par 26: “… supervised or unsupervised learning…”; par 28: “… machine-learning models may be trained… until the machine-learning model reaches a predetermined accuracy value…”; par 29: “… training or re-training of machine-learning models 232…”; par 54: “… principle components can include training a machine-learning model based using historical drilling fluids or generated data… one or more sensor measurements…”).
Claim 6, 13, 20. Kulkarni_2021 makes obvious “Wherein the memory device further includes instructions executable by the processing device for causing the processing device to: receive a set of measured fluid properties for the mixture of fluid components output from the trained machine-learning model; modifying the historical data to include the set of measured fluid properties for the mixture of fluid components; and train the trained machine-learning model using the modified historical data” (par 29: “… training or re-training, machine-learning models 232 may continue to analyze drilling fluid… new machine-learning models may be instanced and trained using historical measurements, previously captured measurements stored in stored data 228…”).
Claim 7, 14. Kulkarni_2021 makes obvious “Wherein the iterative optimization process is implemented using an optimization algorithm, and wherein the optimization algorithm includes a Bayesian optimization algorithm, a genetic algorithm, or a Latin hypercube algorithm” (par 26 - 27: “… random forest, linear and non-linear; Bayesian statistics; neural networks; decision trees; Gaussian process regression, nearest neighbor, long short-term memory; deep learning algorithms; combinations thereof; and the like…”).
Conclusion
Any inquiry concerning this communication or earlier communications from the examiner should be directed to BRIAN S COOK whose telephone number is (571)272-4276. The examiner can normally be reached 8:00 AM - 5:00 PM.
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/BRIAN S COOK/Primary Examiner, Art Unit 2187
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