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 .
Claims 1-20 are pending.
Information Disclosure Statement
The information disclosure statements (IDS) submitted on 03/20/2024 is in compliance with the provisions of 37 CFR 1.97. Accordingly, the information disclosure statement is being considered by the examiner.
Claim Rejections - 35 USC § 102
The following is a quotation of the appropriate paragraphs of 35 U.S.C. 102 that form the basis for the rejections under this section made in this Office action:
A person shall be entitled to a patent unless –
(a)(2) the claimed invention was described in a patent issued under section 151, or in an application for patent published or deemed published under section 122(b), in which the patent or application, as the case may be, names another inventor and was effectively filed before the effective filing date of the claimed invention.
Claims 1, 3-5, and 18 are rejected under 35 U.S.C. 102(a)(2) as being anticipated by Alharbi et al. USPGPUB 2023/0003113 (hereinafter “Alharbi”).
Regarding claim 1, Alharbi teaches a method, comprising: obtaining a first well data for a well operation described by an operating condition (Paragraph [0029] “For example, a well intervention manager (160) may include hardware and/or software to collect well operation data (e.g., well operation data (150)) from one or more well sites”);
determining, using an artificial intelligence (AI) model with the first well data as input, a first operating condition for the well operation (Paragraph [0032] “artificial intelligence (AI) techniques may assist a well intervention manager in linking contributions of different service entities to a well delivery process (i.e., a well delivery process that includes one or more well operations) to evaluate efficiency and service quality”, Paragraph [0030] “well intervention operations may include various operations carried out by one or more service entities for an oil or gas well during its productive life (e.g., fracking operations, CT, flow back, separator, pumping, wellhead and Christmas tree maintenance, slickline, wireline, well maintenance, stimulation, braded line, coiled tubing, snubbing, workover, subsea well intervention, etc.)… With respect to service entities, a service entity may be a company or other actor that performs one or more types of oil field services, such as well operations, at a well site”, wherein examiner interpreted well intervention manager linking contribution of different service entities to a well delivery process to evaluate efficiency and service quality which uses AI as determining first operating condition for the well operation using AI model with the first well data as input);
obtaining a plurality of well interventions that can be performed on the well operation (Paragraph [0037] “the well intervention manager (e.g., well intervention manager X (260)) may include hardware and/or software to generate one or more well intervention plans within the well management network (e.g., well management network A (200)) using one or more ML algorithms (e.g., ML algorithms X (264)) based on the obtained scheduling criterion and well intervention activities”, Paragraph [0030], wherein examiner interpreted generating one or more well intervention plans as obtaining a plurality of well interventions that can be performed on the well operation);
determining, using a reinforcement learning (RL) policy, an optimum well intervention sequence that optimizes a performance of the well operation (Paragraph [0043] “a well intervention manager is used to generate a well intervention plan within a well management network for planning, synchronizing and optimizing the logistics a well intervention plan using one or more ML algorithms (e.g., an unsupervised ML algorithm, a reinforcement ML algorithm, a self-supervised ML algorithm) to maximize operational efficiency, availing hydrocarbon and minimizing budget expenditure”, Paragraph [0038], and Paragraph [0041], wherein examiner interpreted generating intervention plan that is optimizing the logistics a well intervention plan using one or more algorithms that includes reinforcement ML algorithm to maximize operational efficiency, availing hydrocarbon and minimizing budget expenditure as determining an optimum well intervention sequence that optimizes a performance of the well operation); and
performing the optimum well intervention sequence on the well operation (Paragraph [0031] “a well intervention manager (160) may include functionality for coordinating various oilfield services, such as well intervention using various commands (e.g., command (155)), e.g., by transmitting commands to various network devices (e.g., control system (144)) in a drilling system as well as various user devices at the well site. In some embodiments, for example, a command is a network message that automatically assigns or reassigns tasks or operations to various service entities at a well site”, and Paragraph [0059] “In Block 350, one or more commands are transmitted to adjust well operations based on a well adjustment plan in accordance with one or more embodiments. For example, a well intervention manager may communicate with one or more control systems at a well site in order to implement various sub-tasks in a well intervention plan according to a desired time line”, wherein examiner interpreted transmitting commands to adjust well operations according to well intervention plan as performing the optimum well intervention sequence on the well operation).
Regarding claim 3, Alharbi teaches wherein the plurality of well interventions comprises one or more of: hydraulic fracturing; acidizing; a perforation; inserting coil tubing; cementing; water flooding; a gas lift; and an artificial lift (Paragraph [0030] “well intervention operations may include various operations carried out by one or more service entities for an oil or gas well during its productive life (e.g., fracking operations, CT, flow back, separator, pumping, wellhead and Christmas tree maintenance, slickline, wireline, well maintenance, stimulation, braded line, coiled tubing, snubbing, workover, subsea well intervention, etc.). For example, well intervention activities may be similar to well completion operations, well delivery operations, and/or drilling operations in order to modify the state of a well or well geometry. In some embodiments, well intervention operations provide well diagnostics, and/or manage the production of the well. With respect to service entities, a service entity may be a company or other actor that performs one or more types of oil field services, such as well operations, at a well site. For example, one or more service entities may be responsible for performing a cementing operation in the wellbore (116) prior to delivering the well to a producing entity”, Paragraph [0023]).
Regarding claim 4, Alharbi teaches wherein the AI model comprises a convolutional neural network (CNN) performing one or more of: a classification; a regression; and an encoding (Paragraph [0039] “With respect to ML models, different types of ML models may be used, such as convolutional neural networks, deep neural networks, recurrent neural networks, support vector machines, decision trees, inductive learning models, deductive learning models, unsupervised learning models, supervised learning models, reinforcement learning models, self-supervised learning models, etc.”, Paragraph [0045] “The objective of an unsupervised learning algorithm may force the neural network to learn the compact internal representation (e.g., natural clusters) of data inputs based on multiple attributes to solve the task of interest. For example, an unsupervised learning algorithm builds a well intervention plan and classifies the pattern of well intervention activities based on an obtained scheduling criterion. These natural clusters are given various priorities to solve the task. For example, a natural cluster “1” is given a “high” priority when the well intervention activity is urgent and all the required tools and materials are available. For another example, a natural cluster “5” is given a “low” priority when the well intervention activity is not urgent and all the required tools and materials are unavailable. The unsupervised learning algorithm then classifies the list of well intervention activities by using multiple natural clusters for the compact internal representation based on one or more qualities of data inputs (e.g., scheduling criteria X (261), well data X (263), inventory availability data (268), and/or service provider data (266)) for a particular well (e.g., well B (220)) and convergence items for recommendation based on the analysis of the pattern of natural clusters that characterize the list of well intervention activities”, wherein examiner interpreted convolutional neural network used to classify pattern of well intervention activities as convolution neural network performing classification).
Regarding claim 5, Alharbi teaches wherein the RL policy comprises a neural network (Paragraph [0039] “With respect to ML models, different types of ML models may be used, such as convolutional neural networks, deep neural networks, recurrent neural networks, support vector machines, decision trees, inductive learning models, deductive learning models, unsupervised learning models, supervised learning models, reinforcement learning models, self-supervised learning models, etc.”).
Regarding claim 18, Alharbi teaches a non-transitory computer-readable memory comprising computer-executable instructions stored thereon that, when executed on a processor, cause the processor to perform steps comprising (Paragraph [0005] “a non-transitory computer readable medium storing instructions executable by a computer processor. The instructions include generating a first well intervention plan for a well site automatically based on a predetermined scheduling criterion”):
obtaining a first well data for a well operation described by an operating condition (Paragraph [0029] “For example, a well intervention manager (160) may include hardware and/or software to collect well operation data (e.g., well operation data (150)) from one or more well sites”);
determining, using an artificial intelligence (AI) model with the first well data as input, a first operating condition for the well operation (Paragraph [0032] “artificial intelligence (AI) techniques may assist a well intervention manager in linking contributions of different service entities to a well delivery process (i.e., a well delivery process that includes one or more well operations) to evaluate efficiency and service quality”, Paragraph [0030] “well intervention operations may include various operations carried out by one or more service entities for an oil or gas well during its productive life (e.g., fracking operations, CT, flow back, separator, pumping, wellhead and Christmas tree maintenance, slickline, wireline, well maintenance, stimulation, braded line, coiled tubing, snubbing, workover, subsea well intervention, etc.)… With respect to service entities, a service entity may be a company or other actor that performs one or more types of oil field services, such as well operations, at a well site”, wherein examiner interpreted well intervention manager linking contribution of different service entities to a well delivery process to evaluate efficiency and service quality which uses AI as determining first operating condition for the well operation using AI model with the first well data as input);
obtaining a plurality of well interventions that can be performed on the well operation (Paragraph [0037] “the well intervention manager (e.g., well intervention manager X (260)) may include hardware and/or software to generate one or more well intervention plans within the well management network (e.g., well management network A (200)) using one or more ML algorithms (e.g., ML algorithms X (264)) based on the obtained scheduling criterion and well intervention activities”, Paragraph [0030], wherein examiner interpreted generating one or more well intervention plans as obtaining a plurality of well interventions that can be performed on the well operation);
determining, using a reinforcement learning (RL) policy, an optimum well intervention sequence that optimizes a performance of the well operation (Paragraph [0043] “a well intervention manager is used to generate a well intervention plan within a well management network for planning, synchronizing and optimizing the logistics a well intervention plan using one or more ML algorithms (e.g., an unsupervised ML algorithm, a reinforcement ML algorithm, a self-supervised ML algorithm) to maximize operational efficiency, availing hydrocarbon and minimizing budget expenditure”, Paragraph [0038], and Paragraph [0041], wherein examiner interpreted generating intervention plan that is optimizing the logistics a well intervention plan using one or more algorithms that includes reinforcement ML algorithm to maximize operational efficiency, availing hydrocarbon and minimizing budget expenditure as determining an optimum well intervention sequence that optimizes a performance of the well operation); and
sending a command to perform the optimum well intervention sequence on the well operation (Paragraph [0031] “a well intervention manager (160) may include functionality for coordinating various oilfield services, such as well intervention using various commands (e.g., command (155)), e.g., by transmitting commands to various network devices (e.g., control system (144)) in a drilling system as well as various user devices at the well site. In some embodiments, for example, a command is a network message that automatically assigns or reassigns tasks or operations to various service entities at a well site”, and Paragraph [0059] “In Block 350, one or more commands are transmitted to adjust well operations based on a well adjustment plan in accordance with one or more embodiments. For example, a well intervention manager may communicate with one or more control systems at a well site in order to implement various sub-tasks in a well intervention plan according to a desired time line”, wherein examiner interpreted transmitting commands to adjust well operations according to well intervention plan as sending a command to perform the optimum well intervention sequence on the well operation).
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 11, and 13 are rejected under 35 U.S.C. 103 as being unpatentable over Alharbi et al. USPGPUB 2023/0003113 (hereinafter “Alharbi”) as applied to claims 1, 3-5, and 18 above, in view of Saidutta et al. USPGPUB 2023/0116456 (hereinafter “Saidutta”).
Regarding claim 11, Alharbi teaches a system, comprising: a well on which a well operation is performed, the well operation described by an operating condition (Paragraph [0004] “The system further includes a well intervention manager including a computer processor. The well intervention manager is coupled to the servers and the well site. The well intervention manager generates a first well intervention plan for a well site automatically based on a predetermined scheduling criterion. The first well intervention plan is generated using a first set of data inputs regarding one or more well intervention providers and one or more well conditions”);
a plurality of sensors connected to the well (Paragraph [0019] “ the control system (144) may be coupled to the sensor assembly (123) in order to perform various program functions for up-down steering and left-right steering of the drill bit (124) through the wellbore (116)”, Paragraph [0031] “the well intervention manager (160) may also collect other well operation data, such as sensor data from the drilling system (100), service provider data, resource data (including rig and rigless site daily reports), feedback through a human machine interface from other personnel at the well site, and/or data from a historian operating at the well site”, and Paragraph [0021], and [FIG. 1]);
equipment to perform a plurality of well interventions on the well operation (Paragraph [0019] “While one control system is shown in FIG. 1, the drilling system (100) may include multiple control systems for managing various well drilling operations, maintenance operations, well completion operations, and/or well intervention operations”, Paragraph [0029] “a well intervention manager (160) is coupled to one or more control systems (e.g., control system (144)) at a wellsite”, Paragraph [0030] “well intervention operations may include various operations carried out by one or more service entities for an oil or gas well during its productive life (e.g., fracking operations, CT, flow back, separator, pumping, wellhead and Christmas tree maintenance, slickline, wireline, well maintenance, stimulation, braded line, coiled tubing, snubbing, workover, subsea well intervention, etc.). For example, well intervention activities may be similar to well completion operations, well delivery operations, and/or drilling operations in order to modify the state of a well or well geometry. In some embodiments, well intervention operations provide well diagnostics, and/or manage the production of the well”, Paragraph [0031] “a well intervention manager (160) may include functionality for coordinating various oilfield services, such as well intervention using various commands (e.g., command (155)), e.g., by transmitting commands to various network devices (e.g., control system (144)) in a drilling system as well as various user devices at the well site”, Paragraph [0059] “a well intervention manager may communicate with one or more control systems at a well site in order to implement various sub-tasks in a well intervention plan according to a desired time line”, wherein examiner interpreted well intervention manager and control systems as equipment to perform a plurality of well interventions on the well operation);
a computer comprising one or more computer processors (Paragraph [0004] “The system further includes a well intervention manager including a computer processor”), configured to:
receive from, at least, the sensors, a first well data for the well operation (Paragraph [0029] “For example, a well intervention manager (160) may include hardware and/or software to collect well operation data (e.g., well operation data (150)) from one or more well sites”, Paragraph [0019] “ the control system (144) may be coupled to the sensor assembly (123) in order to perform various program functions for up-down steering and left-right steering of the drill bit (124) through the wellbore (116)”, Paragraph [0031] “the well intervention manager (160) may also collect other well operation data, such as sensor data from the drilling system (100), service provider data, resource data (including rig and rigless site daily reports), feedback through a human machine interface from other personnel at the well site, and/or data from a historian operating at the well site”, and Paragraph [0021]),
determine, using an artificial intelligence (AI) model with the first well data as input, a first operating condition for the well operation (Paragraph [0032] “artificial intelligence (AI) techniques may assist a well intervention manager in linking contributions of different service entities to a well delivery process (i.e., a well delivery process that includes one or more well operations) to evaluate efficiency and service quality”, Paragraph [0030] “well intervention operations may include various operations carried out by one or more service entities for an oil or gas well during its productive life (e.g., fracking operations, CT, flow back, separator, pumping, wellhead and Christmas tree maintenance, slickline, wireline, well maintenance, stimulation, braded line, coiled tubing, snubbing, workover, subsea well intervention, etc.)… With respect to service entities, a service entity may be a company or other actor that performs one or more types of oil field services, such as well operations, at a well site”, wherein examiner interpreted well intervention manager linking contribution of different service entities to a well delivery process to evaluate efficiency and service quality which uses AI as determining first operating condition for the well operation using AI model with the first well data as input),
determine, using a reinforcement learning (RL) policy, an optimum well intervention sequence that optimizes a performance of the well operation (Paragraph [0043] “a well intervention manager is used to generate a well intervention plan within a well management network for planning, synchronizing and optimizing the logistics a well intervention plan using one or more ML algorithms (e.g., an unsupervised ML algorithm, a reinforcement ML algorithm, a self-supervised ML algorithm) to maximize operational efficiency, availing hydrocarbon and minimizing budget expenditure”, Paragraph [0038], and Paragraph [0041], wherein examiner interpreted generating intervention plan that is optimizing the logistics a well intervention plan using one or more algorithms that includes reinforcement ML algorithm to maximize operational efficiency, availing hydrocarbon and minimizing budget expenditure as determining an optimum well intervention sequence that optimizes a performance of the well operation); and
a command system configured to send a command to perform the optimum well intervention sequence on the well operation (Paragraph [0031] “a well intervention manager (160) may include functionality for coordinating various oilfield services, such as well intervention using various commands (e.g., command (155)), e.g., by transmitting commands to various network devices (e.g., control system (144)) in a drilling system as well as various user devices at the well site. In some embodiments, for example, a command is a network message that automatically assigns or reassigns tasks or operations to various service entities at a well site”, and Paragraph [0059] “In Block 350, one or more commands are transmitted to adjust well operations based on a well adjustment plan in accordance with one or more embodiments. For example, a well intervention manager may communicate with one or more control systems at a well site in order to implement various sub-tasks in a well intervention plan according to a desired time line”, wherein examiner interpreted well intervention manager transmitting commands to a controller to adjust well operations according to well intervention plan as a command system configured to send a command to perform the optimum well intervention sequence on the well operation).
Alharbi does not explicitly teach a simulator configured to simulate the well interventions within the plurality of well interventions.
However, Saidutta teaches a simulator configured to simulate the well interventions within the plurality of well interventions (Paragraph [0052] “As shown in FIG. 5, the historical wellsite data 502 and expert data 504 may be provided to a learning component 510 (or “learned control component” of FIG. 5), which may provide the data to a simulation environment 512. Using the historical wellsite data 502 and/or expert data 504, the simulation environment 512 may determine one or more formation model parameters (or “earth model parameters”) 514. The formation model may be, for example, a geophysical model of the subsurface formation of a prospective or current wellsite. The formation model parameters 514 are input into a “PATH and ROP Optimizer” 520, similar to process 400 of FIG. 4, described above. For instance, the PATH and ROP Optimizer 520 may estimate an optimal well path and associated drilling parameters 522 needed to drill the wellbore along the path. The optimal well path and associated drilling parameters 522 may then be fed back into the learning component 510. In some embodiments, the drilling parameters 522 may represent a current state of a drilling environment 530, e.g., drilling environment 406 of FIG. 4, as described above, which may be fed into the learning component 510”, wherein examiner interpreted simulation environment determining formation model parameters and estimating an optimal well path as a simulator configured to simulate the well interventions within the plurality of well interventions).
Alharbi, and Saidutta are analogous art because they are from the same field of endeavor and contain overlapping structural and functional similarities. They relate to well operations.
Therefore, before the time of effective filing date, it would have been obvious to a person of ordinary skill in the art to modify the above system for well operation, as taught by Alharbi, and incorporating a simulator, as taught by Saidutta.
One of ordinary skill in the art would have been motivated to improve determining optimal well path to drill the wellbore, as suggested by Saidutta (see Paragraph [0052]).
Regarding claim 13, Alharbi, and Saidutta teaches all of the features with respect to claim 11 as outlined above.
Alharbi further teaches wherein the plurality of well interventions comprises one or more of: hydraulic fracturing; acidizing; a perforation; inserting coil tubing; cementing; water flooding; a gas lift; and an artificial lift (Paragraph [0030] “well intervention operations may include various operations carried out by one or more service entities for an oil or gas well during its productive life (e.g., fracking operations, CT, flow back, separator, pumping, wellhead and Christmas tree maintenance, slickline, wireline, well maintenance, stimulation, braded line, coiled tubing, snubbing, workover, subsea well intervention, etc.). For example, well intervention activities may be similar to well completion operations, well delivery operations, and/or drilling operations in order to modify the state of a well or well geometry. In some embodiments, well intervention operations provide well diagnostics, and/or manage the production of the well. With respect to service entities, a service entity may be a company or other actor that performs one or more types of oil field services, such as well operations, at a well site. For example, one or more service entities may be responsible for performing a cementing operation in the wellbore (116) prior to delivering the well to a producing entity”, Paragraph [0023]).
Allowable Subject Matter
Claims 2, 6-10, 12, 14-17, and 19-20 are objected to as being dependent upon a rejected base claim, but would be allowable if rewritten in independent form including all of the limitations of the base claim and any intervening claims.
Citation of Pertinent Prior Art
The prior art made of record and on the attached PTO Form 892 but not relied upon is considered pertinent to applicant's disclosure.
Stolyarov et al. [USPGPUB 2019/0003298] teaches methods and systems for generating intervention programs for a downhole formation.
NISTALA et al. [USPGPUB 2022/0214474] teaches a system configured to automatically compose a well performance optimization problem based on the current performance of the wells and health of well assets and solve the problem to identify optimal process settings for improving the operation of connected oil and gas wells.
Dande et al. [USPGPUB 2021/0372259] teaches a system for real-time drilling using automated data quality control can include a computing device, a drilling tool, sensors, and a message bus.
BROUWER et al. [USPGPUB 2026/0093214] teaches systems and methods presented herein facilitate ensuring the integrity of oil and gas well intervention operations using blockchain technologies.
Conclusion
Any inquiry concerning this communication or earlier communications from the examiner should be directed to DHRUVKUMAR PATEL whose telephone number is (571)272-5814. The examiner can normally be reached 7:30 AM to 5:30 AM.
Examiner interviews are available via telephone, in-person, and video conferencing using a USPTO supplied web-based collaboration tool. To schedule an interview, applicant is encouraged to use the USPTO Automated Interview Request (AIR) at http://www.uspto.gov/interviewpractice.
If attempts to reach the examiner by telephone are unsuccessful, the examiner’s supervisor, Mohammad Ali can be reached at (571)272-4105. The fax phone number for the organization where this application or proceeding is assigned is 571-273-8300.
Information regarding the status of published or unpublished applications may be obtained from Patent Center. Unpublished application information in Patent Center is available to registered users. To file and manage patent submissions in Patent Center, visit: https://patentcenter.uspto.gov. Visit https://www.uspto.gov/patents/apply/patent-center for more information about Patent Center and https://www.uspto.gov/patents/docx for information about filing in DOCX format. For additional questions, contact the Electronic Business Center (EBC) at 866-217-9197 (toll-free). If you would like assistance from a USPTO Customer Service Representative, call 800-786-9199 (IN USA OR CANADA) or 571-272-1000.
/D.P./ Examiner, Art Unit 2119
/MOHAMMAD ALI/ Supervisory Patent Examiner, Art Unit 2119
Prosecution Insights