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 .
Priority
Application 18/697,789, filed on 04/02/2024, is a 371 of PCT/US2022/047807 filed on 10/26/2022, which claims benefit of 63/271,890 filed on 10/26/2021.
Current Status
This office action is a first office action, non-final rejection based on the merits wherein claims 1-20 are pending and have been considered below.
Claim Rejections - 35 USC § 112
The following is a quotation of 35 U.S.C. 112(b):
(b) CONCLUSION.—The specification shall conclude with one or more claims particularly pointing out and distinctly claiming the subject matter which the inventor or a joint inventor regards as the invention.
The following is a quotation of 35 U.S.C. 112 (pre-AIA ), second paragraph:
The specification shall conclude with one or more claims particularly pointing out and distinctly claiming the subject matter which the applicant regards as his invention.
Claims 6-7 and 14 rejected under 35 U.S.C. 112(b) or 35 U.S.C. 112 (pre-AIA ), second paragraph, as being indefinite for failing to particularly point out and distinctly claim the subject matter which the inventor or a joint inventor (or for applications subject to pre-AIA 35 U.S.C. 112, the applicant), regards as the invention.
Regarding claim 6: Applicant claims, “the plurality of physics models” (line 2). There is insufficient antecedent basis for this limitation in the claim.
Claim 6 will be examined based on the merits as best understood.
Regarding claim 7 and 14: Applicant claims, “the plurality of hybrid physics models” (claim 7 line 1, claim 14 line 1). There is insufficient antecedent basis for this limitation in the claim.
Additionally, regarding claim 7 and 14: Claim 7 depends from claim 6 and claim 14 depends from claim 13 where, both claim 6 and 13 claim “the one or more hybrid physics models” (line 1-2). Examiner is uncertain if “the plurality of hybrid physics models” is in reference to “the one or more hybrid physics models” as “plurality” has several meanings such as more than half of the whole, a number greater than one, a large number, or numerous (Dictionary.com) but is always greater than one. Or if “the one or more hybrid physics models” is in reference to “the plurality of physics models.”
Claims 7 and 14 will be examined based on the merits as best understood.
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. The claims recite an abstract idea as discussed below. This abstract idea is not integrated into a practical application for the reasons discussed below. The claims do not include additional elements that are sufficient to amount to significantly more than the judicial exception for the reasons discussed below.
Step 1 of the 2019 Guidance requires the examiner to determine if the claims are to one of the statutory categories of invention. Applied to the present application, the claims belong to one of the statutory classes of a process or product as a computer implemented method or a computer system/product.
Step 2A of the 2019 Guidance is divided into two Prongs. Prong 1 requires the examiner to determine if the claims recite an abstract idea, and further requires that the abstract idea
belong to one of three enumerated groupings: mathematical concepts, mental processes, and certain methods of organizing human activity.
Claim 1 is copied below, with the limitations belonging to an abstract idea being underlined.
“A method, comprising:
generating one or more hybrid physics models each configured to predict a value for a drilling condition based on training data;
training a machine learning model to predict a drilling condition severity based on the training data and the value of the drilling condition predicted by the one or more hybrid physics models;
receiving sensor data representing present drilling data;
predicting the drilling condition, based at least in part on the sensor data, using the hybrid physics model; and
predicting the drilling condition severity, based at least in part on the drilling condition that was predicted and the sensor data, using machine learning model that was trained.”
Claim 10 is copied below, with the limitations belonging to an abstract idea being underlined.
A computing system, comprising:
one or more processors; and
a memory system comprising one or more non-transitory computer-readable media storing instructions that, when executed by at least one of the one or more processors, cause the computing system to perform operations, the operations comprising:
generating one or more hybrid physics models each configured to predict a value for a drilling condition based on training data;
training a machine learning model to predict a drilling condition severity based on the training data and the value of the drilling condition predicted by the one or more hybrid physics models;
receiving sensor data representing present drilling data;
predicting the drilling condition, based at least in part on the sensor data, using the hybrid physics model; and
predicting the drilling condition severity, based at least in part on the drilling condition that was predicted and the sensor data, using machine learning model that was trained.
Claim 17 is copied below, with the limitations belonging to an abstract idea being underlined.
A non-transitory computer-readable medium storing instructions that, when executed by at least one processor of a computing system, cause the computing system to perform operations, the operations comprising:
generating one or more hybrid physics models each configured to predict a value for a drilling condition based on training data;
training a machine learning model to predict a drilling condition severity based on the training data and the value of the drilling condition predicted by the one or more hybrid physics models;
receiving sensor data representing present drilling data;
predicting the drilling condition, based at least in part on the sensor data, using the hybrid physics model; and
predicting the drilling condition severity, based at least in part on the drilling condition that was predicted and the sensor data, using machine learning model that was trained.
The limitations underlined can be considered to describe a series of mathematical concepts where “generating,” “training,” and “predicting” may include a series of calculations leading to one or more numerical results or answers, obtained by a sequence of mathematical operations on numbers. The lack of a specific equation in the claim merely points out that the claim would monopolize all possible appropriate equations/two-group significance tests for accomplishing this purpose in all possible systems. These steps recited by the claim therefore amount to a series of mathematical steps, making these limitations amount to an abstract idea.
Regarding the underlined limitation “generating one or more hybrid physics models each configured to predict a value for a drilling condition based on training data,” it is an abstract idea as it is a set of programming routines and patterns for generating a hybrid physics models configured to predict. It is an algorithm or program which is a mathematical routine.
Regarding the underlined limitation “training a machine learning model to predict a drilling condition severity based on the training data and the value of the drilling condition predicted by the one or more hybrid physics models,” using the broadest reasonable interpretation, training a machine learning algorithm requires specific mathematical calculations and a set of programming routines and patterns for training. It is an algorithm or program which is a mathematical routine.
Regarding the underlined limitation “predicting the drilling condition, based at least in part on the sensor data, using the hybrid physics model,” it is an abstract idea as it is a set of programming routines and patterns for predicting drilling conditions. It is an algorithm or program which is a mathematical routine.
Regarding the underlined limitation “predicting the drilling condition severity, based at least in part on the drilling condition that was predicted and the sensor data, using machine learning model that was trained,” it is an abstract idea as it is a set of programming routines and patterns for predicting the severity of a drilling condition using a trained machine learning model. It is an algorithm or program which is a mathematical routine.
In summary, the highlighted steps in the claims above therefore recite an abstract idea at Prong 1 of the 101 analysis.
The additional elements in the claim have been left in normal font. This judicial exception is not integrated into a practical application because the additional elements within the claims only amount to instructions to implement the judicial exception using a computer [MPEP 2106.05(f)].
Claims 10 and 17 recite memory for storage and one or more processors. The published application in ¶ 0098 - ¶ 0100 supports a general purpose computer which including processors and memory. Each of these elements is only recited as a tool for performing steps of the abstract idea, such as the use of the storage mediums to store data and data processing devices to apply the algorithm. These additional elements therefore only amount to mere instructions to perform the abstract idea using a computer and are not sufficient to amount to significantly more than the abstract idea (MPEP 2016.05(f) see for additional guidance on the “mere instructions to apply an exception”).
The additional concept of “receiving” equates to routine data gathering and extra solution data activity (See MPEP 2106.05(g)).
The claims do not integrate the abstract idea into a practical application. Various considerations are used to determine whether the additional elements are sufficient to integrate the abstract idea into a practical application. The claim does not recite a particular machine applying or being used by the abstract idea. The claim does not effect a real-world transformation or reduction of any particular article to a different state or thing. (Manipulating data from one form to another or obtaining a mathematical answer using input data does not qualify as a transformation in the sense of Prong 2.)
The claim does not contain additional elements which describe the functioning of a computer, or which describe a particular technology or technical field, being improved by the use of the abstract idea. (This is understood in the sense of the claimed invention from Diamond v Diehr, in which the claim as a whole recited a complete rubber-curing process including a rubber-molding press, a timer, a temperature sensor adjacent the mold cavity, and the steps of closing and opening the press, in which the recited use of a mathematical calculation served to improve that particular technology by providing a better estimate of the time when curing was complete. Here, the claim does not recite carrying out any comparable particular technological process.) In all of these respects, the claim fails to recite additional elements which might possibly integrate the claim into a particular practical application. Instead, based on the above considerations, the claim would tend to monopolize the abstract idea itself, rather than integrate the abstract idea into a practical application.
Step 2b of the 2019 Guidance requires the examiner to determine whether the additional elements cause the claim to amount to significantly more than the abstract idea itself. The considerations for this particular claim are essentially the same as the considerations for Prong 2 of Step 2a, and the same analysis leads to the conclusion that the claim does not amount to significantly more than the abstract idea.
Therefore, claims 1, 10, and 17 are rejected under 35 U.S.C. 101 as directed to an abstract idea without significantly more.
Dependent claims 2-9, 11-16, and 18-20 are similarly ineligible. The dependent claims merely add limitations which further detail or limit the abstract idea with limitations such as:
“generating the one or more hybrid physics models and training the machine learning model occur simultaneously” (claim 2 and 11),
“generating the one or more hybrid physics models comprises training the plurality of physics models that include a neural network” (claim 6 and 13),
“wherein the drilling condition comprises a stick-slip condition, and wherein predicting comprises predicting the drilling condition severity for a next stand of drill pipes to be added to a drill string” (claim 8, 15, and 19),
“predicting the drilling condition severity for a plurality of drilling settings for the next stand; and selecting a drilling setting from the plurality of drilling settings based at least in part on the predicted drilling condition severity” (claim 9, 16, and 20).
The limitations of claim 3, “further comprising visualizing the drilling condition severity at a plurality of different drilling settings, wherein a drilling setting is selected based at least in part on the visualizing” are only recited as a tool for performing steps of the abstract idea, such as the use of a display monitor. These additional elements therefore only amount to mere instructions to perform the abstract idea using a computer and are not sufficient to amount to significantly more than the abstract idea (MPEP 2016.05(f) see for additional guidance on the “mere instructions to apply an exception”).
Claims 4, 7, 12, 14, and 18 recite different types of models and are not sufficient to amount to significantly more than the abstract idea.
Claim 5 describes the types of data received and is not sufficient to amount to significantly more than the abstract idea.
Considering all the limitations individually and in combination, the claimed additional elements do not show any inventive concept to applying algorithms such as improving the performance of a computer or any technology, and do not meaningfully limit the performance of the application.
Claim Rejections - 35 USC § 103
In the event the determination of the status of the application as subject to AIA 35 U.S.C. 102 and 103 (or as subject to pre-AIA 35 U.S.C. 102 and 103) is incorrect, any correction of the statutory basis (i.e., changing from AIA to pre-AIA ) for the rejection will not be considered a new ground of rejection if the prior art relied upon, and the rationale supporting the rejection, would be the same under either status.
The following is a quotation of 35 U.S.C. 103 which forms the basis for all obviousness rejections set forth in this Office action:
A patent for a claimed invention may not be obtained, notwithstanding that the claimed invention is not identically disclosed as set forth in section 102, if the differences between the claimed invention and the prior art are such that the claimed invention as a whole would have been obvious before the effective filing date of the claimed invention to a person having ordinary skill in the art to which the claimed invention pertains. Patentability shall not be negated by the manner in which the invention was made.
Claims 1-20 are rejected under 35 U.S.C. 103 as being unpatentable over Aniket et al., hereinafter Aniket, U.S. Pub. No. 2017/0292362 A1 in view of Jain et al., hereinafter Jain, U.S. Pub. No. 2019/0345809 A1.
Regarding Independent claim 1Aniket teaches:
A method, comprising:
generating one or more hybrid physics models each configured to predict a value for a drilling condition based on training data (Aniket teaches “there are disclosed herein methods and systems for predicting casing wear (drilling condition) using a physics-driven model and a data-driven model” (¶ 0005). Moreover, Aniket teaches different data-driven models such as those using different data analysis techniques, models that combine both adaptive and non-adaptive elements, models with one or two layers of single-direction logic, models with complicated multi-input, multi-layer, and multi-directional feedback loops (¶ 0021) disclosing “generating one or more hybrid physics models” as the hybrid physics models include a physic-driven model and a data-driven model therefore changing the data-driven model discloses a different physics hybrid model.)
training a machine learning model to predict a drilling condition severity based on the training data and the value of the drilling condition predicted by the one or more hybrid physics models (Aniket, ¶ 0023-0025: Aniket teaches “the data-driven model is trained based on casing wear estimates by a physics-driven model and actual casing wear measurements. Once trained, the data-driven model is able to predict casing wear (drilling condition) based on subsequent casing wear estimates from a physics-driven model”(¶ 0023).)
predicting the drilling condition, based at least in part on the sensor data, using the hybrid physics model (Aniket, ¶ 0023-0025: Aniket teaches “Along with estimated casing wear from a physics-driven model, other input parameters that may be used for training a data-driven model or predicting casing wear (predicting the drilling condition) using a data-driven model include” among others, “casing and drill string parameters (flexibility, resistance to wear, diameter, thickness)” (¶ 0023) disclosing “the sensor data.”)
predicting the drilling condition severity, based at least in part on the drilling condition that was predicted and the sensor data, using machine learning model that was trained” (Aniket, ¶ 0023-0025: Aniket teaches “the predicted casing wear can be compared to a predetermined threshold indicative of casing failure likelihood” (¶ 0025) where the comparison discloses “predicting the drilling condition severity.” The “predicted casing wear” is determined by a data-driven model that is trained using estimates from the physics model and casing wear measurements (sensor data) (see above and ¶ 0023) disclosing “drilling condition that was predicted and the sensor data, using machine learning model that was trained.”)
While Aniket teaches “logging-while-drilling (LWD) tools” (¶ 0044), Aniket does not explicitly teach “receiving sensor data representing present drilling data.”
Jain teaches “Fig. 5 shows one or more embodiments of a simplified sequence-flow that the prediction system 129 utilizes to validate and retrain the hybrid model 201 based on real-time data and provide real-time predictive ROP and wear models” (¶ 0088) disclosing “receiving sensor data representing present drilling data.”
It would have been obvious for one of ordinary skill in the art before the effective filing date of the claimed invention to have modified the methods and system for predicting drilling conditions using physics-driven and data-driven models as taught by Aniket by including real-time data as taught by Jain because real-time data allows operators insight to be able to identify mechanical wear and vibration issues in order to schedule maintenance before costly work stoppages in order to provide a system with reduced “lost time and non-productive time, which may lead to cost savings and more efficient drilling operations” (Jain, ¶ 0092).
Regarding claim 2 Aniket teaches:
“generating the one or more hybrid physics models and training the machine learning model occur simultaneously” (Aniket, ¶ 0005, ¶ 0022-0023, ¶ 0044: Aniket teaches “if more measured casing wear logs become available, the training of the data-driven model can be updated accordingly” (¶ 0044) “updating” the “machine learning model” discloses “generating the one or more hybrid physics models” because the “data-driven model” is the “machine learning model” (¶ 0022-0023) and the “hybrid physics model” is “a physic-driven model and a data-driven model” (¶ 0005) therefore updating the data-driven model updates (generates) a “hybrid physics model.”)
Regarding claim 3 Aniket teaches:
“visualizing the drilling condition severity at a plurality of different drilling settings, wherein a drilling setting is selected based at least in part on the visualizing” (Aniket, fig. 8, ¶ 0030:Aniket teaches “the computer system employing the physics-driven model and data-driven model may provide a user interface for viewing, selecting, and adjusting physics-driven model options, data-driven model options, training options, warning options, and/or prediction validation options. The computer 38 or another computer may also enable a drilling operator to adjust drilling operations based on the predicted casing wear output from a data-driven model (or related data such as a warning).” (¶ 0030).)
Regarding claim 4 Aniket does not teach:
“the one or more hybrid physics models comprises a plurality of physics models, each including a state transition model and a state observation model”
Jain teaches:
“the one or more hybrid physics models comprises a plurality of physics models, each including a state transition model and a state observation model” (Jain teaches the “hybrid model includes one or more physics models, which include drill bit mechanics simulation models (‘mechanics models’)” (¶ 0028) disclosing “a state transition model” and the “machining-learning models of the hybrid models also determine (e.g., capture) influence of unaccounted influencing factors in complementary black-box models” where “influencing factors” include measured parameters (¶ 0028) thereby disclosing “a state observation model.”)
It would have been obvious for one of ordinary skill in the art before the effective filing date of the claimed invention to have modified the methods and system for predicting drilling conditions using physics-driven and data-driven models as taught by Aniket by including gamma ray data as taught by Jain because gamma ray data measure natural radioactivity in rocks and is used to identify underground layers so drillers can map rock types, match depths across multiple different wells, and locate product zones in order to provide a predictive system with reduced “lost time and non-productive time, which may lead to cost savings and more efficient drilling operations” (Jain, ¶ 0092).
Regarding claim 5 Aniket teaches:
“the one or more hybrid physics models are configured to receive a speed parameter (RPM) and a weight-on-bit parameter (WOB) and estimate hidden state variables comprising Collar RPM (CRPM), Torque (TORQ), Measured Depth (DEPTH)” (Aniket teaches “other input parameters that may be used for training a data-driven model or predicting casing wear using a data-driven model include (among others) drilling parameters (weight-on-bit, rotation rate, torque) (¶ 0023). Additionally, Aniket teaches “input parameters may correspond to drilling parameters, borehole trajectory parameters, downhole condition parameters, casing attributes, drill string attributes, and/or other parameters employed by physics-driven models” which include “weight-on-bit, rotation rate, rate of penetration, and/or drilling fluid parameters” (¶ 0046) where “drill string attributes” such as “rotation rate” discloses “Collar RPM (CRPM).” Moreover, “In fig. 6 wear volume (in cubic inches) is plotted as a function of measured depth” (¶ 0047).
Aniket teaches rock mechanical properties (see claim 2) however, Aniket does not explicitly teach gamma ray (GAMMA) data.
Jain teaches “rock mechanical properties based on formation logs such as gamma ray data” (¶ 0042).
It would have been obvious for one of ordinary skill in the art before the effective filing date of the claimed invention to have modified the methods and system for predicting drilling conditions using physics-driven and data-driven models as taught by Aniket by including gamma ray data as taught by Jain because gamma ray data measure natural radioactivity in rocks and is used to identify underground layers so drillers can map rock types, match depths across multiple different wells, and locate product zones in order to provide a predictive system with reduced “lost time and non-productive time, which may lead to cost savings and more efficient drilling operations” (Jain, ¶ 0092).
Regarding claim 6 Aniket does not teach:
“training the plurality of physics models that include a neural network.”
Jain teaches:
“training the plurality of physics models that include a neural network.” (Jain teaches “The machine-learning models 205 of the hybrid model 201 may inform (e.g., teach) the physics models 203 of hybrid model 201 about reality (i.e., based on real measured input data). Likewise, because portions of the input data originate from physics models, the physics models 203 of the hybrid model 201 inform (e.g., teach) the machine-learning models 205 about physics” (¶ 0085) disclosing the physics models include a neural network and are trainable.)
It would have been obvious for one of ordinary skill in the art before the effective filing date of the claimed invention to have modified the methods and system for predicting drilling conditions using physics-driven and data-driven models as taught by Aniket by including physics models that include a neural network as taught by Jain to cut down the amount of data needed to solve complex math equations saving time and money in order to provide a predictive system with reduced “lost time and non-productive time, which may lead to cost savings and more efficient drilling operations” (Jain, ¶ 0092) and to keep the predictions true to science.
Regarding claim 7 Aniket teaches:
“a first model comprising: a state transition model comprising ordinary differential equations; and an observation model comprising algebraic equations; a second model comprising: a state transition model comprising ordinary differential equations; and an observation model comprising an extreme gradient boosting model; a third model comprising: a state transition model comprising a fully connected neural network; and an observation model comprising a fully connected neural network; a fourth model comprising: a state transition model comprising a long short-term memory network; and an observation model comprising a fully connected neural network; and a fifth model comprising: a state transition model comprising a Markov recurrent neural network; and an observation model comprising a fully connected neural network” (Aniket, ¶ 0020- ¶ 0021: Aniket teaches examples of physics driven models and data driven models comprising “ordinary differential equations” including “a non-linear casing wear model,” “impact wear model,” and “wellbore energy model” (¶ 0020) in addition, “data driven models may combine both adaptive (flexible, updating layer) and non-adaptive (fixed, static core) elements (which include hybrid physics-informed neural networks?) and “data-driven models can vary in complexity from those with only one or two layers of single direction logic (comprising algebraic equations) to models employing complicated multi-input, multi-layer, and multi-directional feedback loops” where “weights’ can be applied to model parameters, model outputs, or model feedback loops” (¶ 0021) where multi-layers and multi-inputs disclose a “fully connected neural network,” multi-layers, multi-inputs, and multi-directional feedback loops disclose “long short-term memory,” and multi-layers, multi-inputs and multi-directional feedback loops with weights disclose a “Markov recurrent neural network”
Jain teaches: using a “gradient boosted tree, multilayer perceptron, (fully connected neural network) one vs rest (algebraic equations single layer, single input) (¶ 0074) where an “extreme gradient boosting model” is a “gradient boosted tree.”)
It would have been obvious for one of ordinary skill in the art before the effective filing date of the claimed invention to have modified the methods and system for predicting drilling conditions using physics-driven and data-driven models as taught by Aniket by including gradient boosted trees as taught by Jain because gradient boosted trees have a high predictive accuracy through the use of trees in addition to generating data on the importance of certain features that drive the prediction thereby reducing “lost time and non-productive time, which may lead to cost savings and more efficient drilling operations” (Jain, ¶ 0092).
Regarding claim 8 Aniket does not teach:
“wherein the drilling condition comprises a stick-slip condition, and wherein predicting comprises predicting the drilling condition severity for a next stand of drill pipes to be added to a drill string”
Jain teaches:
“wherein the drilling condition comprises a stick-slip condition, and wherein predicting comprises predicting the drilling condition severity for a next stand of drill pipes to be added to a drill string” (Jain teaches “the hybrid model 201 may use the estimation of downhole vibrations module 214 to estimate downhole vibrations” (¶ 0067) where “via the estimation of downhole vibrations module 314, the hybrid model 201 may estimate stick/slip” (¶ 0048) and “the hybrid model 201 pre-screens the prepared input data” where “pre-screening the prepared input data may include determining a quality of making drill pipe connections (e.g., duration, damage to threads), a quality of restarting drilling operations after a connection, etc” (¶ 0068) where estimating “downhole vibrations” discloses “the drilling condition comprises a stick-slip condition” and “a quality of restarting drilling operations after a connection” discloses “predicting the drilling condition severity for a next stand of drill pipes to be added to a drill string.”)
It would have been obvious for one of ordinary skill in the art before the effective filing date of the claimed invention to have modified the methods and system for predicting drilling conditions using physics-driven and data-driven models as taught by Aniket by including physics models that including determining a vibrations and the quality of restarting a drilling operation after a new pipe is connected as taught by Jain because understanding downhole vibrations and the quality of a restart prevents equipment damage and thereby cuts costs by reducing “lost time and non-productive time, which may lead to cost savings and more efficient drilling operations” (Jain, ¶ 0092).
Regarding claim 9 Aniket teaches:
selecting a drilling setting from the plurality of drilling settings based at least in part on the predicted drilling condition severity” (Aniket, fig. 8, ¶ 0030:Aniket teaches “the computer system employing the physics-driven model and data-driven model may provide a user interface for viewing, selecting, and adjusting physics-driven model options, data-driven model options, training options, warning options, and/or prediction validation options. The computer 38 or another computer may also enable a drilling operator to adjust drilling operations based on the predicted casing wear output from a data-driven model (or related data such as a warning).” (¶ 0030).)
Aniket does not teach: “predicting the drilling condition severity for a plurality of drilling settings for the next stand.”
Jain teaches “pre-screening the prepared input data may include determining a quality of making drill pipe connections (e.g., duration, damage to threads), a quality of restarting drilling operations after a connection, etc” (¶ 0068) disclosing “predicting the drilling condition severity for a plurality of drilling settings for the next stand”
It would have been obvious for one of ordinary skill in the art before the effective filing date of the claimed invention to have modified the methods and system for predicting drilling conditions using physics-driven and data-driven models as taught by Aniket by including drilling conditions with respect to pipe connections as taught by Jain because understanding pipe connections and the quality of a restart after a new pipe has been connected prevents equipment damage and thereby cuts costs by reducing “lost time and non-productive time, which may lead to cost savings and more efficient drilling operations” (Jain, ¶ 0092).
Regarding Independent claim 10 Aniket teaches:
“one or more processors; and a memory system comprising one or more non-transitory computer-readable media storing instructions that, when executed by at least one of the one or more processors, cause the computing system to perform operations” (Aniket teaches a computer system with “one or more processors” (¶ 0052- ¶ 0054) and “a memory system comprising one or more non-transitory computer-readable media” (¶ 0049).)
“the operations comprising: generating one or more hybrid physics models each configured to predict a value for a drilling condition based on training data” (Aniket, ¶ 0005, ¶ 0021: Aniket teaches “there are disclosed herein methods and systems for predicting casing wear (drilling condition) using a physics-driven model and a data-driven model” (¶ 0005). Moreover, Aniket teaches different data-driven models such as those using different data analysis techniques, models that combine both adaptive and non-adaptive elements, models with one or two layers of single-direction logic, models with complicated multi-input, multi-layer, and multi-directional feedback loops (¶ 0021) disclosing “generating one or more hybrid physics models” as the hybrid physics models include a physic-driven model and a data-driven model therefore changing the data-driven model discloses a different physics hybrid model.)
“training a machine learning model to predict a drilling condition severity based on the training data and the value of the drilling condition predicted by the one or more hybrid physics models” (Aniket, ¶ 0023-0025: Aniket teaches “the data-driven model is trained based on casing wear estimates by a physics-driven model and actual casing wear measurements. Once trained, the data-driven model is able to predict casing wear (drilling condition) based on subsequent casing wear estimates from a physics-driven model”(¶ 0023).)
“predicting the drilling condition, based at least in part on the sensor data, using the hybrid physics model” (Aniket, ¶ 0023-0025: Aniket teaches “Along with estimated casing wear from a physics-driven model, other input parameters that may be used for training a data-driven model or predicting casing wear (predicting the drilling condition) using a data-driven model include” among others, “casing and drill string parameters (flexibility, resistance to wear, diameter, thickness)” (¶ 0023) disclosing “the sensor data.”)
“predicting the drilling condition severity, based at least in part on the drilling condition that was predicted and the sensor data, using machine learning model that was trained” (Aniket, ¶ 0023-0025: Aniket teaches “the predicted casing wear can be compared to a predetermined threshold indicative of casing failure likelihood” (¶ 0025) where the comparison discloses “predicting the drilling condition severity.” The “predicted casing wear” is determined by a data-driven model that is trained using estimates from the physics model and casing wear measurements (sensor data) (see above and ¶ 0023) disclosing “drilling condition that was predicted and the sensor data, using machine learning model that was trained.”)
While Aniket teaches “logging-while-drilling (LWD) tools” (¶ 0044), Aniket does not explicitly teach “receiving sensor data representing present drilling data.”
Jain teaches “Fig. 5 shows one or more embodiments of a simplified sequence-flow that the prediction system 129 utilizes to validate and retrain the hybrid model 201 based on real-time data and provide real-time predictive ROP and wear models” (¶ 0088) disclosing “receiving sensor data representing present drilling data.”
It would have been obvious for one of ordinary skill in the art before the effective filing date of the claimed invention to have modified the methods and system for predicting drilling conditions using physics-driven and data-driven models as taught by Aniket by including real-time data as taught by Jain because real-time data allows operators insight to be able to identify mechanical wear and vibration issues in order to schedule maintenance before costly work stoppages in order to provide a system with reduced “lost time and non-productive time, which may lead to cost savings and more efficient drilling operations” (Jain, ¶ 0092).
Regarding claim 11: Claim 11 cites analogous limitations to claim 2 above and is therefore rejected on the same premise.
Regarding claim 12: Claim 12 cites analogous limitations to claim 4 above and is therefore rejected on the same premise.
Regarding claim 13: Claim 13 cites analogous limitations to claim 6 above and is therefore rejected on the same premise.
Regarding claim 14: Claim 14 cites analogous limitations to claim 7 above and is therefore rejected on the same premise.
Regarding claim 15: Claim 15 cites analogous limitations to claim 8 above and is therefore rejected on the same premise.
Regarding claim 16: Claim 16 cites analogous limitations to claim 9 above and is therefore rejected on the same premise.
Regarding Independent claim 17 Aniket teaches:
“A non-transitory computer-readable medium storing instructions that, when executed by at least one processor of a computing system, cause the computing system to perform operations” (Aniket teaches a computer system with “one or more processors” (¶ 0052- ¶ 0054) and “A non-transitory computer-readable medium storing instructions” (¶ 0049).)
“the operations comprising: generating one or more hybrid physics models each configured to predict a value for a drilling condition based on training data” (Aniket teaches “there are disclosed herein methods and systems for predicting casing wear (drilling condition) using a physics-driven model and a data-driven model” (¶ 0005). Moreover, Aniket teaches different data-driven models such as those using different data analysis techniques, models that combine both adaptive and non-adaptive elements, models with one or two layers of single-direction logic, models with complicated multi-input, multi-layer, and multi-directional feedback loops (¶ 0021) disclosing “generating one or more hybrid physics models” as the hybrid physics models include a physic-driven model and a data-driven model therefore changing the data-driven model discloses a different physics hybrid model.)
training a machine learning model to predict a drilling condition severity based on the training data and the value of the drilling condition predicted by the one or more hybrid physics models (Aniket, ¶ 0023-0025: Aniket teaches “the data-driven model is trained based on casing wear estimates by a physics-driven model and actual casing wear measurements. Once trained, the data-driven model is able to predict casing wear (drilling condition) based on subsequent casing wear estimates from a physics-driven model”(¶ 0023).)
predicting the drilling condition, based at least in part on the sensor data, using the hybrid physics model (Aniket, ¶ 0023-0025: Aniket teaches “Along with estimated casing wear from a physics-driven model, other input parameters that may be used for training a data-driven model or predicting casing wear (predicting the drilling condition) using a data-driven model include” among others, “casing and drill string parameters (flexibility, resistance to wear, diameter, thickness)” (¶ 0023) disclosing “the sensor data.”)
predicting the drilling condition severity, based at least in part on the drilling condition that was predicted and the sensor data, using machine learning model that was trained” (Aniket, ¶ 0023-0025: Aniket teaches “the predicted casing wear can be compared to a predetermined threshold indicative of casing failure likelihood” (¶ 0025) where the comparison discloses “predicting the drilling condition severity.” The “predicted casing wear” is determined by a data-driven model that is trained using estimates from the physics model and casing wear measurements (sensor data) (see above and ¶ 0023) disclosing “drilling condition that was predicted and the sensor data, using machine learning model that was trained.”)
While Aniket teaches “logging-while-drilling (LWD) tools” (¶ 0044), Aniket does not explicitly teach “receiving sensor data representing present drilling data.”
Jain teaches “Fig. 5 shows one or more embodiments of a simplified sequence-flow that the prediction system 129 utilizes to validate and retrain the hybrid model 201 based on real-time data and provide real-time predictive ROP and wear models” (¶ 0088) disclosing “receiving sensor data representing present drilling data.”
It would have been obvious for one of ordinary skill in the art before the effective filing date of the claimed invention to have modified the methods and system for predicting drilling conditions using physics-driven and data-driven models as taught by Aniket by including real-time data as taught by Jain because real-time data allows operators insight to be able to identify mechanical wear and vibration issues in order to schedule maintenance before costly work stoppages in order to provide a system with reduced “lost time and non-productive time, which may lead to cost savings and more efficient drilling operations” (Jain, ¶ 0092).
Regarding claim 18: Claim 18 cites analogous limitations to claim 4 above and is therefore rejected on the same premise.
Regarding claim 19: Claim 19 cites analogous limitations to claim 8 above and is therefore rejected on the same premise.
Regarding claim 20: Claim 20 cites analogous limitations to claim 9 above and is therefore rejected on the same premise.
Conclusion
The prior art made of record and not relied upon is considered pertinent to applicant's disclosure. Gooneratne et al., U.S. Pub. No. 2020/0190959 A1, teaches using a hybrid model to identify hidden patterns in data and make predictions to prevent problems associated with drilling (¶ 0087).
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/DENISE R KARAVIAS/Examiner, Art Unit 2857
/ARLEEN M VAZQUEZ/Supervisory Patent Examiner, Art Unit 2857
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