DETAILED ACTION
This Office Action is in response to Applicant's Communication received on 10/21/2022 for application number 17/970,865.
Claims 1-20 are presented for examination. Claims 1, 15, and 18 are independent claims.
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 .
Information Disclosure Statement
The information disclosure statement (IDS) submitted on 02/29/2024 has been considered by the Examiner.
Claim Objection
Claim 4 is objected to because of the following informalities: in Claim 4, line 2, “the two or more drilling advisory model” should be “the two or more drilling advisory models”. Appropriate correction is required.
Claim 6 is objected to because of the following informalities: in Claim 6, line 4, “connection model” should be “connections model”. Appropriate correction is required.
Claim 6 is objected to because of the following informalities: Claim 6 is repeated twice. The second Claim 6 should be renumbered to “Claim 21”. Appropriate correction is required.
Claim 12 is objected to because of the following informalities: in Claim 12, “associated a hookload” should be “associated with a hookload”. Appropriate correction is required.
Claim Rejections - 35 USC § 101
35 U.S.C. 101 reads as follows:
Whoever invents or discovers any new and useful process, machine, manufacture, or composition of matter, or any new and useful improvement thereof, may obtain a patent therefor, subject to the conditions and requirements of this title.
Claims 1-20 are rejected under 35 U.S.C. 101 because the claimed invention is directed to an abstract idea without significantly more.
Step 1
Claims 1-14 are directed to a system, Claims 15-17 are directed to a medium, and Claims 18-20 are directed to a method. Thus, the claims fall within one of the statutory categories (machine, articles of manufacture, process) and are eligible under Step 1.
Step 2A Prong 1
Independent Claims
Claims 1, 15, and 18 recite:
analyzing the input data using two or more drilling advisory models, wherein the two or more drilling advisory models are associated with drilling operations features that support generating drilling advisory recommendations that identify prescriptions for drilling operations at drilling sites; based on analyzing the input data using the two or more drilling advisory models, generating a drilling advisory recommendation for the drilling operations of the drilling site - these limitations encompass a mental process of analyzing data using models and determining/ writing down drilling recommendations based on the analysis using the models, which is observing, evaluating and judging that is practically capable of being performed in the human mind or by a human using a pen and paper.
Accordingly, these claims recite an abstract idea that falls under the “mental process” grouping.
Step 2A Prong 2
Independent Claims
Additional elements
Claims 1, 15, and 18:
accessing, at a drilling advisory engine, input data comprising real time sensor data of drilling operations, historical drilling information, and contextual drilling information associated with a drilling site; communicating the drilling advisory recommendation - these limitations amount to insignificant extra-solution activity of mere data gathering and outputting (see MPEP § 2106.05(g)).
Claim 1:
a computerized system comprising: one or more computer processors; and
computer memory storing computer-useable instructions that, when used by the one or more computer processors, cause the one or more computer processors to perform operations - these limitations are recited at a high-level of generality such that it amounts to no more than mere instructions to apply the abstract idea on a generic computer (see MPEP § 2106.05(f)). These limitations can also be viewed as generally linking the use of a judicial exception to the field of generic computer (see MPEP § 2106.05(h)).
Claim 15:
one or more computer-storage media having computer-executable instructions embodied thereon that, when executed by a computing system having a processor and memory, cause the processor to perform operations - these limitations are recited at a high-level of generality such that it amounts to no more than mere instructions to apply the abstract idea on a generic computer (see MPEP § 2106.05(f)). These limitations can also be viewed as generally linking the use of a judicial exception to the field of generic computer (see MPEP § 2106.05(h)).
Claim 18:
computer-implemented method - these limitations are recited at a high-level of generality such that it amounts to no more than mere instructions to apply the abstract idea on a generic computer (see MPEP § 2106.05(f)). These limitations can also be viewed as generally linking the use of a judicial exception to the field of generic computer (see MPEP § 2106.05(h)).
Accordingly, these additional elements do not integrate the judicial exception into a practical application because they do not impose any meaningful limits on practicing the abstract idea. These claims are directed to the abstract idea.
Step 2B
Independent Claims
Additional elements
Claims 1, 15, and 18:
accessing, at a drilling advisory engine, input data comprising real time sensor data of drilling operations, historical drilling information, and contextual drilling information associated with a drilling site; communicating the drilling advisory recommendation - these limitations amount to insignificant extra-solution activity of mere data gathering and outputting, which is well-understood, routine, and conventional activity (see MPEP § 2106.05(d), “receiving/ transmitting data”, “presenting offers”).
Claim 1:
a computerized system comprising: one or more computer processors; and
computer memory storing computer-useable instructions that, when used by the one or more computer processors, cause the one or more computer processors to perform operations - these limitations are recited at a high-level of generality such that it amounts to no more than mere instructions to apply the abstract idea on a generic computer (see MPEP § 2106.05(f)). These limitations can also be viewed as generally linking the use of a judicial exception to the field of generic computer (see MPEP § 2106.05(h)).
Claim 15:
one or more computer-storage media having computer-executable instructions embodied thereon that, when executed by a computing system having a processor and memory, cause the processor to perform operations - these limitations are recited at a high-level of generality such that it amounts to no more than mere instructions to apply the abstract idea on a generic computer (see MPEP § 2106.05(f)). These limitations can also be viewed as generally linking the use of a judicial exception to the field of generic computer (see MPEP § 2106.05(h)).
Claim 18:
computer-implemented method - these limitations are recited at a high-level of generality such that it amounts to no more than mere instructions to apply the abstract idea on a generic computer (see MPEP § 2106.05(f)). These limitations can also be viewed as generally linking the use of a judicial exception to the field of generic computer (see MPEP § 2106.05(h)).
Accordingly, these additional elements do not amount to significantly more than the judicial exception. As such, these claims are patent ineligible.
Step 2A Prong 1
Dependent Claims
Claim 3:
the data-based operations comprise data ingestion, data processing, and activity recognition associated with the real time sensor data of the drilling operations, the historical drilling information, and the contextual drilling information - these limitations encompass a mental process of interpreting and analyzing observed data to determine activity associated with the data, which is observing, evaluating and judging that is practically capable of being performed in the human mind or by a human using a pen and paper.
Claim 4:
the model-based operations comprise continuously updating the two or more drilling advisory model, wherein the two or more drilling advisory models include any of the following: a statistical model, or a physical model that are employed to reduce Invisible Lost Time (ILT) in drilling operations - these limitations encompass updating models that are employed to reduce ILT, which is observing, evaluating and judging that is practically capable of being performed in the human mind or by a human using a pen and paper.
Claim 5:
the analysis-based operations comprise contextualization and generating the drilling advisory recommendation, wherein the drilling advisory recommendations comprise dynamically targeted prescriptions for routine operations of the drilling operations - these limitations encompass a mental process of analyzing data to determine drilling recommendations, which is observing, evaluating and judging that is practically capable of being performed in the human mind or by a human using a pen and paper.
Claim 6:
the two more drilling advisory models comprise a Rate of Penetration (ROP) optimization model and a connections model, wherein the ROP optimization model support optimizing rate of penetration as a measure of drilling speed; and where connection model provides prescriptions associated with durations of routine operations of the drilling operations - these limitations merely furthers the mental process of determining drilling recommendations by specifying the models used.
Claim 13:
the two or more drilling advisory models are configured to operate in continuous competition mode, wherein a first drilling advisory model is trained with litholgic formations from previous well, a second drilling advisory model is trained with recent drilling data, and a third drilling model is trained with lithologic formations of the drilling a current well of the drilling site - these limitations merely furthers the mental process of determining drilling recommendations by specifying the models.
Thus, the claims recite the abstract idea.
Step 2A Prong 2
Dependent Claims
Additional elements
Claims 2 and 19:
the drilling advisory engine supports a plurality of drilling advisory operations comprising data-based operations, model-based operations, and analysis-based operations for drilling advisory recommendations associated with prescriptions for changing conditions and activities of drilling operations - these limitations are recited at a high-level of generality such that it amounts to no more than merely using computer as a tool, by using it in its ordinary capacity to perform tasks/ support various operations (see MPEP § 2106.05(f)).
Claim 4:
the two or more drilling advisory models include any of the following: a machine learning model - these limitations are recited at a high-level of generality such that it amounts to no more than generally linking the use of a judicial exception to the field of machine learning models (see MPEP § 2106.05(h)) and merely using machine learning as a tool, by using it in its ordinary capacity to perform tasks (see MPEP § 2106.05(f)).
Claim 6:
a material processing engine is configured to train two or more drilling advisory models that are associated with drilling operations features, wherein the two or more drilling advisory models include any of the following: a machine learning model, a statistical model, or a physical model that are employed to reduce Invisible Lost Time (ILT) in drilling operations - this is a high level training step such that it merely recites the idea of training models that are associated with drilling operations features and are employed to reduce Invisible Lost Time (ILT) in drilling operations, without providing the details of how the training is accomplished (see MPEP § 2106.05(f)).
Claims 7 and 20:
the drilling advisory recommendation is associated with a plurality of drilling advisory recommendation interfaces, the drilling advisory recommendation interfaces comprising a drilling screen, a connection screen, a tripping screen, a casing screen, and a cleaning screen - these limitations amount to generally linking the use of judicial exception to the technological environment of user interfaces (see MPEP § 2106.05(h)) and insignificant extra-solution activity of mere data gathering and outputting (see MPEP § 2106.05(g)).
Claim 8:
the drilling screen comprises drilling advisory interface recommendation data associated with rotations per minute, weight-on-bit, flow, and rate of penetration - these limitations amount to generally linking the use of judicial exception to the technological environment of user interfaces (see MPEP § 2106.05(h)) and insignificant extra-solution activity of mere data gathering and outputting (see MPEP § 2106.05(g)).
Claim 9:
the connection screen comprises drilling advisory interface recommendation data associated with time left to complete connections and fastest connection - these limitations amount to generally linking the use of judicial exception to the technological environment of user interfaces (see MPEP § 2106.05(h)) and insignificant extra-solution activity of mere data gathering and outputting (see MPEP § 2106.05(g)).
Claim 10:
the tripping screen comprises drilling advisory recommendation data associated with a minimum target speed, an actual speed, a maximum target speed, and a current trip performance - these limitations amount to generally linking the use of judicial exception to the technological environment of user interfaces (see MPEP § 2106.05(h)) and insignificant extra-solution activity of mere data gathering and outputting (see MPEP § 2106.05(g)).
Claim 11:
the casing screen comprises drilling advisory recommendation data associated with a minimum target speed, an actual speed, a maximum target speed, and a current trip performance - these limitations amount to generally linking the use of judicial exception to the technological environment of user interfaces (see MPEP § 2106.05(h)) and insignificant extra-solution activity of mere data gathering and outputting (see MPEP § 2106.05(g)).
Claim 12:
the cleaning screen comprises drilling advisory recommendation data associated a hookload, a free rotating TOR, a standard pipe pressure, a measured depth, and one or more alerts - these limitations amount to generally linking the use of judicial exception to the technological environment of user interfaces (see MPEP § 2106.05(h)) and insignificant extra-solution activity of mere data gathering and outputting (see MPEP § 2106.05(g)).
Claims 14 and 16:
communicating, from a material processing engine client, a request for the drilling advisory recommendation; receive the drilling advisory recommendation for the drilling operations; and cause generation of a drilling advisory recommendation associated with the drilling advisory recommendation - these limitations amount to insignificant extra-solution activity of mere data gathering and outputting (see MPEP § 2106.05(g)).
Claim 17:
The drilling advisory engine is configured to train two or more drilling advisory models that are associated with drilling operations features, wherein the two or more drilling advisory models include any of the following: a machine learning model, a statistical model, or a physical model - this is a high level training step such that it merely recites the idea of training models that are associated with drilling operations features and are employed to reduce Invisible Lost Time (ILT) in drilling operations, without providing the details of how the training is accomplished (see MPEP § 2106.05(f)).
Accordingly, these additional elements do not integrate the judicial exception into a practical application because they do not impose any meaningful limits on practicing the abstract idea. The claims are directed to the abstract idea.
Step 2B
Dependent Claims
Additional elements
Claims 2 and 19:
the drilling advisory engine supports a plurality of drilling advisory operations comprising data-based operations, model-based operations, and analysis-based operations for drilling advisory recommendations associated with prescriptions for changing conditions and activities of drilling operations - these limitations are recited at a high-level of generality such that it amounts to no more than merely using computer as a tool, by using it in its ordinary capacity to perform tasks/ support various operations (see MPEP § 2106.05(f)).
Claim 4:
the two or more drilling advisory models include any of the following: a machine learning model - these limitations are recited at a high-level of generality such that it amounts to no more than generally linking the use of a judicial exception to the field of machine learning models (see MPEP § 2106.05(h)) and merely using machine learning as a tool, by using it in its ordinary capacity to perform tasks (see MPEP § 2106.05(f)).
Claim 6:
a material processing engine is configured to train two or more drilling advisory models that are associated with drilling operations features, wherein the two or more drilling advisory models include any of the following: a machine learning model, a statistical model, or a physical model that are employed to reduce Invisible Lost Time (ILT) in drilling operations - this is a high level training step such that it merely recites the idea of training models that are associated with drilling operations features and are employed to reduce Invisible Lost Time (ILT) in drilling operations, without providing the details of how the training is accomplished (see MPEP § 2106.05(f)).
Claims 7 and 20:
the drilling advisory recommendation is associated with a plurality of drilling advisory recommendation interfaces, the drilling advisory recommendation interfaces comprising a drilling screen, a connection screen, a tripping screen, a casing screen, and a cleaning screen - these limitations amount to generally linking the use of judicial exception to the technological environment of user interfaces (see MPEP § 2106.05(h)) and insignificant extra-solution activity of mere data gathering and outputting, which is well-understood, routine, and conventional activity (see MPEP § 2106.05(d), “receiving/ transmitting data” and “presenting offers”).
Claim 8:
the drilling screen comprises drilling advisory interface recommendation data associated with rotations per minute, weight-on-bit, flow, and rate of penetration - these limitations amount to generally linking the use of judicial exception to the technological environment of user interfaces (see MPEP § 2106.05(h)) and insignificant extra-solution activity of mere data gathering and outputting, which is well-understood, routine, and conventional activity (see MPEP § 2106.05(d), “receiving/ transmitting data” and “presenting offers”).
Claim 9:
the connection screen comprises drilling advisory interface recommendation data associated with time left to complete connections and fastest connection - these limitations amount to generally linking the use of judicial exception to the technological environment of user interfaces (see MPEP § 2106.05(h)) and insignificant extra-solution activity of mere data gathering and outputting, which is well-understood, routine, and conventional activity (see MPEP § 2106.05(d), “receiving/ transmitting data” and “presenting offers”).
Claim 10:
the tripping screen comprises drilling advisory recommendation data associated with a minimum target speed, an actual speed, a maximum target speed, and a current trip performance - these limitations amount to generally linking the use of judicial exception to the technological environment of user interfaces (see MPEP § 2106.05(h)) and insignificant extra-solution activity of mere data gathering and outputting, which is well-understood, routine, and conventional activity (see MPEP § 2106.05(d), “receiving/ transmitting data” and “presenting offers”).
Claim 11:
the casing screen comprises drilling advisory recommendation data associated with a minimum target speed, an actual speed, a maximum target speed, and a current trip performance - these limitations amount to generally linking the use of judicial exception to the technological environment of user interfaces (see MPEP § 2106.05(h)) and insignificant extra-solution activity of mere data gathering and outputting, which is well-understood, routine, and conventional activity (see MPEP § 2106.05(d), “receiving/ transmitting data” and “presenting offers”).
Claim 12:
the cleaning screen comprises drilling advisory recommendation data associated a hookload, a free rotating TOR, a standard pipe pressure, a measured depth, and one or more alerts - these limitations amount to generally linking the use of judicial exception to the technological environment of user interfaces (see MPEP § 2106.05(h)) and insignificant extra-solution activity of mere data gathering and outputting, which is well-understood, routine, and conventional activity (see MPEP § 2106.05(d), “receiving/ transmitting data” and “presenting offers”).
Claims 14 and 16:
communicating, from a material processing engine client, a request for the drilling advisory recommendation; receive the drilling advisory recommendation for the drilling operations; and cause generation of a drilling advisory recommendation associated with the drilling advisory recommendation - these limitations amount to insignificant extra-solution activity of mere data gathering and outputting, which is well-understood, routine, and conventional activity (see MPEP § 2106.05(d), “receiving/ transmitting data” and “presenting offers”).
Claim 17:
The drilling advisory engine is configured to train two or more drilling advisory models that are associated with drilling operations features, wherein the two or more drilling advisory models include any of the following: a machine learning model, a statistical model, or a physical model - this is a high level training step such that it merely recites the idea of training models that are associated with drilling operations features and are employed to reduce Invisible Lost Time (ILT) in drilling operations, without providing the details of how the training is accomplished (see MPEP § 2106.05(f)).
Accordingly, these additional elements do not amount to significantly more than the judicial exception. As such, the claims are patent ineligible.
Claim Rejections - 35 USC § 102
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 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 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)(1) the claimed invention was patented, described in a printed publication, or in public use, on sale or otherwise available to the public before the effective filing date of the claimed invention.
Claims 1-3, 5-6, 15, and 18-19 are rejected under 35 U.S.C. 102(a)(1) as being anticipated by Chang et al. (US 2014/0277752 A1 hereinafter Chang).
Regarding Claim 1, Chang teaches a computerized system ([0035] systems and methods used in connection with any drilling operation; [0058] the systems comprise a computer-based system for use in association with drilling operations) comprising:
one or more computer processors ([0058] computer-based systems comprises a processor; the processor is adapted to execute instructions and include one or more processors); and
computer memory storing computer-useable instructions that, when used by the one or more computer processors, cause the one or more computer processors to perform operations ([0058] computer-based systems comprises a processor, a storage medium, and at least one instruction set; the processor is adapted to execute instructions and include one or more processors; the storage medium (i.e., memory) is adapted to communicate with the processor and to store data and other information, including the at least one instruction set) comprising:
accessing, at a drilling advisory engine, input data comprising real time sensor data of drilling operations, historical drilling information, and contextual drilling information associated with a drilling site ([0059] the computer-based system (i.e., drilling advisory engine) receives data at data input; [0035] drilling conditions (i.e., contextual drilling information associated with a drilling site) refer to the conditions in the wellbore during the drilling operation; the drilling conditions are comprised of a variety of drilling parameters; [0036] receiving data regarding ongoing drilling operations (i.e., real-time sensor data), specifically data regarding drilling parameters that characterize the drilling operations (i.e., contextual drilling information associated with a drilling site), at 202; [0044] compute changes to past or current controllable drilling parameters (i.e., historical drilling information) to improve the objective function value; [0058] the present systems and methods may refer to historical data (i.e., historical drilling information) from other wells, which may be obtained from a centralized server);
analyzing the input data using two or more drilling advisory models, wherein the two or more drilling advisory models are associated with drilling operations features that support generating drilling advisory recommendations that identify prescriptions for drilling operations at drilling sites ([0036] executing a local search engine 203 and a global search engine 204 either in serial or in parallel mode; generating operational recommendations to optimize drilling performance (i.e., generating a drilling advisory recommendation) based on a data fusion method, at 206; [0046] at 203, a local search engine that utilizes a statistical model to identify at least one controllable drilling parameter having significant correlation to an objective function, or one or more objective functions, incorporating two or more drilling performance measurements, such as ROP, MSE, vibration measurements, etc., and mathematical combinations thereof (i.e., analyzing input data); two or more statistical models (i.e., two or more drilling advisory models associated with drilling operations features) may be used in cooperation, synchronously, iteratively, or in other arrangements to identify the significantly correlated and controllable drilling parameters; the statistical model may be utilized in substantially real-time utilizing the received data; [0051] the local and global search engines generate recommendations separately for the controllable drilling parameters in serial and/or parallel mode (i.e., drilling advisory models that support generating drilling advisory recommendations that identify prescriptions for drilling operations at drilling sites));
based on analyzing the input data using the two or more drilling advisory models, generating a drilling advisory recommendation for the drilling operations of the drilling site ([0036] executing a local search engine 203 and a global search engine 204 either in serial or in parallel mode; generating operational recommendations to optimize drilling performance based on a data fusion method, at 206; [0046] at 203, a local search engine that utilizes a statistical model to identify at least one controllable drilling parameter having significant correlation to an objective function, or one or more objective functions; two or more statistical models (i.e., two or more drilling advisory models associated with drilling operations features) may be used in cooperation, synchronously, iteratively, or in other arrangements to identify the significantly correlated and controllable drilling parameters; [0051] the local and global search engines generate recommendations separately for the controllable drilling parameters in serial and/or parallel mode; then at 206, a method is used to fuse the recommendations from the two engines or select between the two engines based on whether specified criteria are met for the response score and objective score; [0054] the drilling equipment and computer-based systems associated with the present methods may be adapted to present the operational recommendations to a user, such as an operator, who determines the operational updates based at least in part on the operational recommendations; [0045] an output system adapted to communicate the generated operational recommendations for consideration in controlling drilling operations); and
communicating the drilling advisory recommendation ([0054] the drilling equipment and computer-based systems associated with the present methods may be adapted to present the operational recommendations to a user, such as an operator, who determines the operational updates based at least in part on the operational recommendations).
As to dependent Claim 2, Chang teaches all the limitations of claim 1. Chang further teaches wherein the drilling advisory engine supports a plurality of drilling advisory operations comprising data-based operations, model-based operations, and analysis-based operations for drilling advisory recommendations associated with prescriptions for changing conditions and activities of drilling operations ([0059] the computer-based system (i.e., drilling advisory engine) receives data at data input; the at least one instruction set is adapted to export the generated operational recommendations for consideration in controlling drilling operations; [0036] receiving data regarding ongoing drilling operations (i.e., real-time sensor data), specifically data regarding drilling parameters that characterize the drilling operations (i.e., contextual drilling information associated with a drilling site), at 202; executing a local search engine 203 and a global search engine 204 either in serial or in parallel mode; generating operational recommendations to optimize drilling performance (i.e., drilling advisory recommendations) based on a data fusion method, at 206; [0045] an output system adapted to communicate the generated operational recommendations for consideration in controlling drilling operations (i.e., prescriptions for changing conditions and activities of drilling operations);[0046] at 203, a local search engine that utilizes a statistical model to identify at least one controllable drilling parameter having significant correlation to an objective function, or one or more objective functions (i.e., analyzing input data); two or more statistical models (i.e., two or more drilling advisory models associated with drilling operations features) may be used in cooperation, synchronously, iteratively, or in other arrangements to identify the significantly correlated and controllable drilling parameters - thus, the computer-based system/drilling advisory engine supports advisory operations for advisory recommendations by analyzing data regarding ongoing drilling operations and providing operational recommendations using statistical models (i.e., data-based operations, model-based operations, and analysis-based operations)).
As to dependent Claim 3, Chang teaches all the limitations of claim 2. Chang further teaches wherein the data-based operations comprise data ingestion, data processing, and activity recognition associated with the real time sensor data of the drilling operations, the historical drilling information, and the contextual drilling information ([0035] drilling conditions (i.e., contextual drilling information) refer to the conditions in the wellbore during the drilling operation; the drilling conditions are comprised of a variety of drilling parameters; [0036] receiving data regarding ongoing drilling operations (i.e., real-time sensor data), specifically data regarding drilling parameters that characterize the drilling operations (i.e., contextual drilling information associated with a drilling site), at 202; [0044] compute changes to past (i.e., historical drilling information) or current controllable drilling parameters to improve the objective function value; [0058] the present systems and methods may refer to historical data (i.e., historical drilling information) from other wells, which may be obtained from a centralized server - thus, the data-based operations comprise data ingestion, data processing, and activity recognition associated with the real time sensor data of the drilling operations/ the received drilling operations data, the historical drilling information, and the contextual drilling information).
As to dependent Claim 5, Chang teaches all the limitations of claim 2. Chang further teaches wherein the analysis-based operations comprise contextualization and generating the drilling advisory recommendation, wherein the drilling advisory recommendations comprise dynamically targeted prescriptions for routine operations of the drilling operations ([0035] drilling conditions (i.e., contextual drilling information) refer to the conditions in the wellbore during the drilling operation; the drilling conditions are comprised of a variety of drilling parameters; [0036] receiving data regarding ongoing drilling operations, specifically data regarding drilling parameters that characterize the drilling operations (i.e., contextualization), at 202; executing a local search engine 203 and a global search engine 204 either in serial or in parallel mode; generating operational recommendations to optimize drilling performance based on a data fusion method, at 206; [0046] at 203, a local search engine that utilizes a statistical model to identify at least one controllable drilling parameter (i.e., targeted prescriptions) having significant correlation to an objective function, or one or more objective functions; [0047] the objective function may be a single variable of ROP, MSE, Depth of Cut (DOC), bit friction factor mu, and/or mathematical combinations thereof; the objective function may also be a function of ROP, MSE, DOC, mu, weight on bit, drill string parameters, bit rotation rate, torque applied to the drillstring, torque applied to the bit, vibration measurements, hydraulic horsepower (e.g., mud flow rate, viscosity, pressure, etc.) etc. (i.e., routine operations of the drilling operations), and mathematical combinations thereof - thus, the analysis operations to generate recommendations comprising targeted prescriptions/ drilling parameters for drilling operations).
As to dependent Claim 6, Chang teaches all the limitations of claim 1. Chang further teaches wherein the two more drilling advisory models comprise a Rate of Penetration (ROP) optimization model and a connections model ([0014] optimizing one or more controllable drilling operational parameters, which are controllable variables that are associated with drilling the wellbore, so as to improve a system performance property, such as rate of penetration (i.e. optimizing ROP); [0036] receiving data regarding ongoing drilling operations, specifically data regarding drilling parameters that characterize the drilling operations; executing a local search engine 203 and a global search engine 204 either in serial or in parallel mode; generating operational recommendations to optimize drilling performance; [0046] at 203, a local search engine that utilizes a statistical model to identify at least one controllable drilling parameter having significant correlation to an objective function, or one or more objective functions, incorporating two or more drilling performance measurements, such as ROP, MSE, vibration measurements, etc., and mathematical combinations thereof; two or more statistical models (i.e., two or more drilling advisory models comprising a Rate of Penetration (ROP) optimization model and a connections model) may be used in cooperation, synchronously, iteratively, or in other arrangements to identify the significantly correlated and controllable drilling parameters; [0052] the operational recommendations may be subject to boundary limits, such as maximum rate of rotation, minimum acceptable mud flow rate, top-drive torque limits, maximum duration of a specified level of vibrations (i.e., duration of routine operations), etc., that represent either physical equipment limits or limits derived by consideration of other operational aspects of the drilling process), wherein the ROP optimization model support optimizing rate of penetration as a measure of drilling speed ([0067] local search methods attempted to correlate a single control variable to a single measure of drilling performance, such as, the rate of penetration, and to increase ROP by iteratively and sequentially adjusting the identified single control variable; the local search methods of the present systems and methods are believed to improve upon that paradigm by correlating control variables to two or more drilling performance measurements - thus, the models comprise ROP optimization model; [0103] temporally evolving data while drilling consist of measured quantities taken at a certain frequency, such as once every second; [0110] a rate of penetration (ROP) for each response point can be based on the change in block position over a duration of time (i.e., measure of drilling speed)); and where connection model provides prescriptions associated with durations of routine operations of the drilling operations ([0052] the operational recommendations may be subject to boundary limits, such as maximum rate of rotation, minimum acceptable mud flow rate, top-drive torque limits, maximum duration of a specified level of vibrations (i.e., duration of routine operations), etc., that represent either physical equipment limits or limits derived by consideration of other operational aspects of the drilling process).
Claim 15 is a medium claim corresponding to the system claim 1 above and therefore, rejected for the same reasons. Chang further teaches one or more computer-storage media having computer-executable instructions embodied thereon that, when executed by a computing system having a processor and memory, cause the processor to perform method ([0058] the systems comprise a computer-based system for use in association with drilling operations; the processor is adapted to execute instructions and include one or more processors; computer-based systems comprises a processor, a storage medium, and at least one instruction set; the processor is adapted to execute instructions and include one or more processors; the storage medium (i.e., computer-storage media) is adapted to communicate with the processor and to store data and other information, including the at least one instruction set).
Claims 18 and 19 are method claims corresponding to the system claims 1 and 2 above and therefore, rejected for the same reasons.
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 7-8 and 20 are rejected under 35 U.S.C. 103 as being unpatentable over Chang et al. (US 2014/0277752 A1 hereinafter Chang).
As to dependent Claim 7, Chang teaches all the limitations of claim 1. Chang further teaches wherein the drilling advisory recommendation is associated with a plurality of drilling advisory recommendation interfaces, the drilling advisory recommendation interfaces comprising a drilling screen, a connection screen, a tripping screen, a casing screen, and a cleaning screen ([0035] drilling parameters may include rotary speed (RPM), WOB, characteristics of the drill bit and drillstring, mud weight, mud flow rate, lithology of the formation, pore pressure of the formation, torque, pressure, temperature, ROP, MSE, vibration measurements, etc.; [0042] controllable drilling parameters are recommended to be incremented a prescribed amount; by what amounts the WOB and rotary speed (RPM) are to be either increased or decreased; also prescribe the order in which the controllable drilling parameters should be changed; [0047] the objective function may be a function of ROP, MSE, depth of cut (DOC), mu, weight on bit, drill string parameters, bit rotation rate, torque applied to the drillstring, torque applied to the bit, vibration measurements, hydraulic horsepower (e.g., mud flow rate, viscosity, pressure, etc.) etc., and mathematical combinations thereof; [0052] the operational recommendations may be subject to boundary limits, such as maximum rate of rotation, minimum acceptable mud flow rate, top-drive torque limits, maximum duration of a specified level of vibrations (i.e., connection information), etc., that represent either physical equipment limits or limits derived by consideration of other operational aspects of the drilling process; [0059] the generated operational recommendations may be exported to a display 312 for consideration by a user; the decision tree activates an application mode and displays the drilling parameters corresponding to the response point with the optimum interval-averaged objective function value; [0060] the results presented to a user for consideration via one or more visual displays; [0073] the operating parameter space is provided by consideration of the maximum available WOB, the rig rotary speed limitations (i.e., maximum/ minimum target speeds), minimum RPM for hole cleaning, as well as any other operational factors to be considered by the drilling organization; [0096] displays the drilling parameters corresponding to the response point with the optimum interval-averaged objective function value; [0098] a stick-slip branch of a decision tree may be activated such as due to a TSE greater than 1.1, and recommendations of WOB of 10,000 pounds and a lower RPM of 110 may be displayed - thus, the various recommendations are displayed to the user (i.e., drilling advisory recommendation interfaces) and the recommendations are associated with ROP, weight on bit, RPM, duration of vibrations, maximum/ minimum target speeds, DOC, etc. (i.e., recommendations associated with a plurality of recommendation interfaces comprising a drilling screen, a connection screen, a tripping screen, a casing screen, and a cleaning screen)).
As to dependent Claim 8, Chang teaches all the limitations of claim 1. Chang further teaches wherein the drilling screen comprises drilling advisory interface recommendation data associated with rotations per minute, weight-on-bit, flow, and rate of penetration ([0035] drilling parameters may include rotary speed (RPM), WOB, characteristics of the drill bit and drillstring, mud weight, mud flow rate, lithology of the formation, pore pressure of the formation, torque, pressure, temperature, ROP, MSE, vibration measurements, etc.; [0096] displays the drilling parameters (i.e., drilling screen) corresponding to the response point with the optimum interval-averaged objective function value).
Claim 20 is a method claim corresponding to the system claim 7 above and therefore, rejected for the same reasons.
Claims 4, 6, 13, and 17 are rejected under 35 U.S.C. 103 as being unpatentable over Chang in view of Jain et al. (US 2019/0345809 A1 hereinafter Jain).
As to dependent Claim 4, Chang teaches all the limitations of claim 2. Chang further teaches wherein the model-based operations comprise continuously updating the two or more drilling advisory model ([0036] receiving data regarding ongoing drilling operations (i.e., continuous real-time data), specifically data regarding drilling parameters that characterize the drilling operations; executing a local search engine 203 and a global search engine 204 either in serial or in parallel mode; generating operational recommendations to optimize drilling performance based on a data fusion method, at 206; [0046] at 203, a local search engine that utilizes a statistical model to identify at least one controllable drilling parameter having significant correlation to an objective function, or one or more objective functions, incorporating two or more drilling performance measurements, such as ROP, MSE, vibration measurements, etc., and mathematical combinations thereof; two or more statistical models may be used in cooperation, synchronously, iteratively, or in other arrangements to identify the significantly correlated and controllable drilling parameters - thus, the model operations comprise continuously updating the model with the received data of the ongoing drilling operations), wherein the two or more drilling advisory models include any of the following: a machine learning model, a statistical model, or a physical model ([0046] at 203, a local search engine that utilizes a statistical model to identify at least one controllable drilling parameter having significant correlation to an objective function, or one or more objective functions, incorporating two or more drilling performance measurements, such as ROP, MSE, vibration measurements, etc., and mathematical combinations thereof; two or more statistical models (i.e., two or more drilling advisory models include statistical model) may be used in cooperation, synchronously, iteratively, or in other arrangements to identify the significantly correlated and controllable drilling parameters; the statistical model may be utilized in substantially real-time utilizing the received data).
However, Chang fails to expressly teach wherein the model is employed to reduce Invisible Lost Time (ILT) in drilling operations.
In the same filed of endeavor, Jain teaches wherein the model is employed to reduce Invisible Lost Time (ILT) in drilling operations ([0092] the predictive system 129 of the present disclosure may reduce invisible lost time and non-productive time, which may lead to cost savings and more efficient drilling operations; [0027] the prediction system combines strengths of physics-based models with strengths of machine-learning models to form a hybrid physics and machine-learning model).
It would have been obvious to a person having ordinary skill in the art before the effective filing date of the claimed invention to have incorporated wherein the model is employed to reduce Invisible Lost Time (ILT) in drilling operations, as taught by Jain into Chang. Doing so would be desirable because it would lead to cost savings and more efficient drilling operations (Jain [0092]).
As to dependent Claim 6, Chang teaches all the limitations of claim 1. Chang further teaches wherein the two or more drilling advisory models include any of the following: a machine learning model, a statistical model, or a physical model ([0046] at 203, a local search engine that utilizes a statistical model to identify at least one controllable drilling parameter having significant correlation to an objective function, or one or more objective functions, incorporating two or more drilling performance measurements, such as ROP, MSE, vibration measurements, etc., and mathematical combinations thereof; two or more statistical models (i.e., two or more drilling advisory models) may be used in cooperation, synchronously, iteratively, or in other arrangements to identify the significantly correlated and controllable drilling parameters; the statistical model may be utilized in substantially real-time utilizing the received data - thus, the models include a statistical model).
However, Chang fails to expressly teach wherein a material processing engine is configured to train two or more drilling advisory models that are associated with drilling operations features, wherein the two or more drilling advisory models that are employed to reduce Invisible Lost Time (ILT) in drilling operations.
In the same filed of endeavor, Jain teaches wherein a material processing engine is configured to train two or more drilling advisory models that are associated with drilling operations features ([0027] the prediction system combines strengths of physics-based models with strengths of machine-learning models to form a hybrid physics and machine-learning model; [0057] the prediction system 129 (i.e., material processing engine) train the hybrid model 201 and generate one or more ROP and wear predictive models for given earth-boring tools and planned drilling operations; [0073] the physics models 203 and the machine-learning models 205 within the hybrid model 201), wherein the two or more drilling advisory models that are employed to reduce Invisible Lost Time (ILT) in drilling operations ([0027] the prediction system combines strengths of physics-based models with strengths of machine-learning models to form a hybrid physics and machine-learning model; [0092] the predictive system 129 of the present disclosure may reduce invisible lost time and non-productive time, which may lead to cost savings and more efficient drilling operations).
It would have been obvious to a person having ordinary skill in the art before the effective filing date of the claimed invention to have incorporated wherein a material processing engine is configured to train two or more drilling advisory models that are associate