DETAILED ACTION
This office action is in response to communication filed on December 23, 2025.
Response to Amendment
Amendments filed on December 23, 2025 have been entered.
The specification has been amended.
Claims 1-4, 7, 10-14 have been amended.
Claims 8 and 15 have been canceled.
Claims 16-22 have been added.
Claims 1-7, 9-14 and 16-22 have been examined.
Response to Arguments
Applicant’s arguments, see Remarks (p. 9), filed on 12/23/2025, with respect to the objections to the specification have been fully considered. In view of the amendments to the specification addressing the informalities raised in the previous office action and the arguments presented, the objections to the specification have been withdrawn.
Applicant’s arguments, see Remarks (p. 9-10), filed on 12/23/2025, with respect to the objections to the claims have been fully considered. In view of the amendments to the claims addressing the informalities raised in the previous office action and the arguments presented, the objections to the claims have been withdrawn. However, upon further consideration, new objections to the claims are presented below due to additional informalities introduced by the amendments.
Applicant’s arguments, see Remarks (p. 10-13), filed on 12/23/2025, with respect to the rejection of claims 1-7 and 9-14 under 35 U.S.C. 101 have been fully considered but are not persuasive.
Applicant argues (p. 11) that the claims are directed to a technical solution of problems arising in the field of hazard event detection and remediation … The claimed methods and systems provide a solution to these problems, namely by providing data collection, cleaning, and model training methods to produce artificial intelligence models capable of predicting hazard events, modifying drilling operations to avoid hazard events, and planning for optimized wellbore locations.
This argument is not persuasive.
The examiner submits that, when considering the claimed invention as a whole, applicant seeks patent protection for a series of steps that, under the broadest reasonable interpretation and in light of the specification, refer to generic data collection (i.e., “receiving real-time sensor data from one or more sensors in communication with active drilling equipment within a wellbore”, see specification at [0013]) and data manipulation (i.e., “generating a prediction of an impending hazard event during active drilling of the wellbore”, see specification at [0014]-[0019] and [0020]-[0022]), while appending mere instructions to apply the judicial exception on a computer (i.e., “one or more artificial intelligence models”); transformations at a high level of generality such that substantially all practical applications of the judicial exception are covered (i.e., “modifying drilling operations of the active drilling equipment, via the one or more artificial intelligence models, in response to the prediction generated by the one or more artificial intelligence models”) and generally linking the use of the judicial exception to a particular technological environment or field of use (e.g., wellbore data analysis for hazard prediction purposes), which:
as indicated in the October 2019 Patent Eligibility Guidance Update: “A claim that recites a mathematical calculation will be considered as falling within the “mathematical concepts” grouping. A mathematical calculation is a mathematical operation (such as multiplication) or an act of calculating using mathematical methods to determine a variable or number, e.g., performing an arithmetic operation such as exponentiation. There is no particular word or set of words that indicates a claim recites a mathematical calculation. That is, a claim does not have to recite the word “calculating” in order to be considered a mathematical calculation. For example, a step of “determining” a variable or number using mathematical methods or “performing” a mathematical operation may also be considered mathematical calculations when the broadest reasonable interpretation of the claim in light of the specification encompasses a mathematical calculation” (p. 4, section “iii. “Mathematical Calculations””, par. 1; see also July 2024 Subject Matter Eligibility Examples, Example 47, claim 2);
as described in the 2024 Guidance Update on Patent Subject Matter Eligibility, Including on Artificial Intelligence: “Even if the judicial exception is narrow (e.g., a particular mathematical formula or detailed mental process), the Court has held that a claim may not preempt that judicial exception” (see “III. Update on Certain Areas of the USPTO’s Patent Subject Matter Eligibility Guidance Applicable to AI Inventions”, section “A. Evaluation of Whether a Claim Is Directed to a Judicial Exception (Step 2A)”); and
as explained in the MPEP “A transformation applied to a generically recited article or to any and all articles would likely not provide significantly more than the judicial exception” (see MPEP 2106.05(c)).
Applicant argues (p. 12) that The claims embody the solution described in the Specification, for example, by reciting at least: (1) receiving real-time sensor data for an active drilling operation; (2) predicting, via one or more artificial intelligence models, an impending hazard event based upon the real-time sensor data; and (3) modifying drilling operations, via the one or more artificial intelligence models, to avoid the impending hazard event. As such, the alleged judicial exception amounts to more than simple instructions to perform Mental Processes or solve Mathematical Concepts in isolation, but rather integrate the alleged judicial exception into an eligible design process to predict impending hazard events from real-time data and automatically take corrective actions to avoid the impending hazard events. These features reflect an improvement in the relevant technical field by providing a method and system of at least predicting impending hazard events and automatically altering drilling operations or taking remedial actions … Applicant asserts that this feature is an improvement to a technology and integrates any alleged abstract idea into a practical application.
This argument is not persuasive.
The examiner submits that, as indicated above, the claimed invention refers to gathering data from generic sensors, manipulating these data using mathematical concepts to obtain a result (i.e., impending hazard event) and covering any solution to the result, while generally reciting a field of use and mere computer implementation, which as explained in the October 2019 Update: Subject Matter Eligibility: “… in Parker v. Flook, the Court found that the claim recited a mathematical formula. This determination was not altered by the fact that the math was being used to solve an engineering problem (i.e., updating an alarm limit during catalytic conversion processes)” (p. 3).
Additionally, the examiner submits that as indicated in the MPEP: “In addition, a specific way of achieving a result is not a stand-alone consideration in Step 2A Prong Two. However, the specificity of the claim limitations is relevant to the evaluation of several considerations including the use of a particular machine, particular transformation and whether the limitations are mere instructions to apply an exception. See MPEP §§ 2106.05(b), 2106.05(c), and 2106.05(f). For example, in Parker v. Flook, 437 U.S. 584, 198 USPQ 193 (1978), the Supreme Court noted that the “patent application does not purport to explain how to select the appropriate margin of safety, the weighting factor, or any of the other variables” in the claimed mathematical formula, “[n]or does it purport to contain any disclosure relating to the chemical processes at work, the monitoring of process variables, or the means of setting off an alarm or adjusting an alarm system.” 437 U.S. at 586, 198 USPQ at 195. The Court found this failure to explain any specifics of how to use the claimed formula informative when deciding that the additional elements in the claim were insignificant post-solution activity and thus not meaningful enough to render the claim eligible. 437 U.S. at 589-90, 198 USPQ at 197” (see MPEP 2106.04(d)).
Applicant’s arguments, see Remarks (p. 13-15), filed on 12/23/2025, with respect to the rejection of claims 1-7 and 9-14 under 35 U.S.C. 102(a)(1) and 35 U.S.C. 103 have been fully considered but are moot in view of new grounds of rejection.
Applicant argues (p. 13) that independent claim 1 requires “modifying drilling operations of the active drilling equipment, via the one or more artificial intelligence models.” Independent claim 10 includes a similar limitation in “a hazard prediction application configured to ... modify drilling operations of communicatively-coupled drilling equipment to avoid the hazard event.” Bahlany fails to teach, show, or suggest these limitations … The mitigation plans, however, fail to be modifications to active drilling equipment performed “via the one or more artificial intelligence models” as required by the claimed invention. Rather, Bahlany discloses only that “field personnel can view ... the risk predictions from well planning ... and can prepare and communicate in advance to the entire rig crew the risk mitigation plans for the specific depths.” Bahlany pg. 7, para. 6. However, Bahlany is silent as to the automatic control of drilling equipment operations via the equated artificial intelligence models, as required by the claimed invention.
These arguments are not persuasive.
First, in response to applicant’s argument that the references fail to show certain features of the invention, it is noted that the features upon which applicant relies (i.e., automatic control of drilling equipment operations) are not recited in the rejected claims. Although the claims are interpreted in light of the specification, limitations from the specification are not read into the claims. See In re Van Geuns, 988 F.2d 1181, 26 USPQ2d 1057 (Fed. Cir. 1993).
Furthermore, the examiner submits that Bahlany discloses an artificial intelligence tool that predicts wellbore stability problems during well planning and well execution, and prevents these problems by suggesting mitigation actions to take drilling decisions to avoid the predicted wellbore stability problems (see Abstract; see also Figure 1 - “Active Rig” and p. 3, section “Modeling Strategy” regarding the Well Execution Tool being used while drilling). Therefore, based on the teachings of Bahlany, the examiner submits that the argued features are obvious over the prior art of record.
Additionally, the examiner submits that “Prior art is not limited just to the references being applied, but includes the understanding of one of ordinary skill in the art. The prior art reference (or references when combined) need not teach or suggest all the claim limitations, however, Office personnel must explain why the difference(s) between the prior art and the claimed invention would have been obvious to one of ordinary skill in the art. The “mere existence of differences between the prior art and an invention does not establish the invention’s nonobviousness.” Dann v. Johnston, 425 U.S. 219, 230, 189 USPQ 257, 261 (1976). The gap between the prior art and the claimed invention may not be “so great as to render the [claim] nonobvious to one reasonably skilled in the art.” Id. In determining obviousness, neither the particular motivation to make the claimed invention nor the problem the inventor is solving controls. The proper analysis is whether the claimed invention would have been obvious to one of ordinary skill in the art after consideration of all the facts. See 35 U.S.C. 103 or pre-AIA 35 U.S.C. 103(a). Factors other than the disclosures of the cited prior art may provide a basis for concluding that it would have been obvious to one of ordinary skill in the art to bridge the gap” (MPEP 2141, section III).
Claim Objections
Claim 3 is objected to because of the following informalities:
Claim language “mining, sorting, and filtering the training data to correlate the data to a previous hazard event within the one or more existing wellbores” should read “mining, sorting, and filtering the training data to correlate the training data to a previous hazard event within the one or more existing wellbores” in order to provide appropriate antecedence basis.
Appropriate correction is required.
Claim 5 is objected to because of the following informalities:
Claim language should read “The method of claim 3, wherein mining the training data comprises using natural language processing algorithms for extracting contextual information” in order to provide appropriate antecedence basis.
Appropriate correction is required.
Claim 9 is objected to because of the following informalities:
Claim language should read “The method of claim 1, further comprising generating a warning alert to an operator or a drilling crew about [[the]]a probability of .
Appropriate correction is required.
Claim 16 is objected to because of the following informalities:
Claim language “collecting training data including drilling reports describing a hazard event, static wellbore data detailing geographical and geological characteristics, and real-time sensor data related to the hazard event” should read “collecting training data including drilling reports describing a previous hazard event, static wellbore data detailing geographical and geological characteristics, and real-time sensor data related to the previous hazard event” in order to provide appropriate antecedence basis.
Claim language “mining, sorting, and filtering the training data to correlate the data to the previous hazard event within one or more existing wellbores” should read “mining, sorting, and filtering the training data to correlate the training data to the previous hazard event within one or more existing wellbores” in order to provide appropriate antecedence basis.
Claim language “outputting a heat-map of ideal and dangerous drilling locations within the area using the one or more artificial intelligence models” should read “outputting a heat-map of ideal drilling locations and dangerous drilling locations within the area using the one or more artificial intelligence models” in order to provide appropriate antecedence basis.
Appropriate correction is required.
Claim 19 is objected to because of the following informalities:
Claim language “receiving real-time sensor data in the one or more artificial intelligence models from one or more sensors in communication with active drilling equipment” should read “receiving additional real-time sensor data in the one or more artificial intelligence models from one or more sensors in communication with active drilling equipment” in order to clarify the recited subject matter (e.g., “real-time sensor data” is already recited in claim 16).
Appropriate correction is required.
Claim 20 is objected to because of the following informalities:
Claim language should read “The method of claim 19, further comprising: determining, via the one or more artificial intelligence models, a probability of a future hazard event during active drilling at the one of the ideal drilling locations using the additional real-time sensor data” in order to provide appropriate antecedence basis.
Appropriate correction is required.
Claim 21 is objected to because of the following informalities:
Claim language should end with a period.
Appropriate correction is required.
Claim 22 is objected to because of the following informalities:
Claim language should read “The method of claim 21, further comprising: determining if the future hazard event occurred or was averted; and providing the additional real-time sensor data and a determination of the future hazard event as feedback data to an artificial intelligence model training application to perform further training using the feedback data to improve the one or more artificial intelligence models” in order to provide appropriate antecedence basis.
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 1-7, 9-14 and 16-22 are rejected under 35 U.S.C. 101 because the claimed invention is directed to a judicial exception without significantly more.
Regarding claim 1, the examiner submits that under Step 1 of the 2024 Guidance Update on Patent Subject Matter Eligibility, Including on Artificial Intelligence (see also 2019 Revised Patent Subject Matter Eligibility Guidance) for evaluating claims for eligibility under 35 U.S.C. 101, the claim is to a process, which is one of the statutory categories of invention.
Continuing with the analysis, under Step 2A - Prong One of the test (see italic text for abstract idea):
the limitation “generating a prediction, via the one or more artificial intelligence models, of an impending hazard event during active drilling of the wellbore” is a process that, under its broadest reasonable interpretation in light of the specification, covers performance of the limitation using mathematical concepts to manipulate data and obtain a result (i.e., a prediction; see specification at [0012], [0019]-[0021]). Except for the recitation of the particular technological environment or field of use, and the computer implementation (i.e., one or more artificial intelligence models), the limitation in the context of the claim mainly refers to applying mathematical concepts to manipulate data to obtain a result.
Therefore, the claim recites a judicial exception under Step 2A - Prong One of the test.
Furthermore, under Step 2A - Prong Two of the test, this judicial exception is not integrated into a practical application. In particular, the additional elements recited in the claim:
“receiving, in one or more artificial intelligence models, real-time sensor data from one or more sensors in communication with active drilling equipment within a wellbore” adds extra-solution activities (e.g., mere data gathering, source/type of data to be manipulated; see MPEP 2106.05(g)), computer implementation (i.e., one or more artificial intelligence models; see MPEP 2106.05(f)) and generally links the use of the judicial exception to a particular technological environment or field of use (see MPEP 2106.05(h)); and
“modifying drilling operations of the active drilling equipment, via the one or more artificial intelligence models, in response to the prediction generated by the one or more artificial intelligence models” appends a transformation at a high level of generality such that substantially all practical applications of the judicial exception are covered (see MPEP 2106.05(c)) while generally linking the use of the judicial exception to a particular technological environment or field of use (see MPEP 2106.05(h)).
Accordingly, these additional elements, when considered individually and in combination, do not integrate the judicial exception into a practical application because they do not impose any meaningful limits on practicing the abstract idea when considering the claim as a whole. The claim is directed to a judicial exception under Step 2A of the test.
Additionally, under Step 2B of the test, the claim does not include additional elements that, when considered individually and in combination, are sufficient to amount to significantly more than the judicial exception because the additional elements:
generally link the use of the judicial exception to a particular technological environment or field of use (e.g., wellbore data analysis for hazard prediction purposes), which as indicated in the MPEP: “As explained by the Supreme Court, a claim directed to a judicial exception cannot be made eligible “simply by having the applicant acquiesce to limiting the reach of the patent for the formula to a particular technological use.” Diamond v. Diehr, 450 U.S. 175, 192 n.14, 209 USPQ 1, 10 n. 14 (1981). Thus, limitations that amount to merely indicating a field of use or technological environment in which to apply a judicial exception do not amount to significantly more than the exception itself, and cannot integrate a judicial exception into a practical application” (see MPEP 2106.05(h));
recite extra-solution activities (i.e., mere data gathering by selecting a particular data source/type to be manipulated) specified at a high level of generality, which as indicated in the MPEP: “Another consideration when determining whether a claim integrates the judicial exception into a practical application in Step 2A Prong Two or recites significantly more in Step 2B is whether the additional elements add more than insignificant extra-solution activity to the judicial exception. The term “extra-solution activity” can be understood as activities incidental to the primary process or product that are merely a nominal or tangential addition to the claim. Extra-solution activity includes both pre-solution and post-solution activity. An example of pre-solution activity is a step of gathering data for use in a claimed process” (see MPEP 2106.05(g));
append a transformation at a high level of generality such that substantially all practical applications of the judicial exception are covered (i.e., modifying drilling operations of the active drilling equipment, via the one or more artificial intelligence models, in response to the prediction generated by the one or more artificial intelligence models), which as indicated in the MPEP: “A transformation applied to a generically recited article or to any and all articles would likely not provide significantly more than the judicial exception” (see MPEP 2106.05(c)).
The claim, when considered as a whole, does not provide significantly more under Step 2B of the test.
Based on the analysis, the claim is not patent eligible.
Similarly, independent claims 10 and 16 are directed to a judicial exception (abstract idea) without significantly more as explained above with regards to claim 1.
With regards to the dependent claims they are also directed to the non-statutory subject matter because:
they just extend the abstract idea of the independent claims by additional limitations (Claims 3, 5-7, 11, 20 and 22), that under the broadest reasonable interpretation in light of the specification, cover performance of the limitations using mental processes and/or mathematical concepts, and
the additional elements recited in the dependent claims, when considered individually and in combination, refer to extra-solution activities (e.g., mere data gathering using a data type or source), generic computer components and/or field of use (Claims 2-4, 9, 12-14, 17-19 and 21-22), which as indicated in the Office’s guidance does not integrate the judicial exception into a practical application (Step 2A – Prong Two) and/or does not provide significantly more (Step 2B).
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.
This application currently names joint inventors. In considering patentability of the claims the examiner presumes that the subject matter of the various claims was commonly owned as of the effective filing date of the claimed invention(s) absent any evidence to the contrary. Applicant is advised of the obligation under 37 CFR 1.56 to point out the inventor and effective filing dates of each claim that was not commonly owned as of the effective filing date of the later invention in order for the examiner to consider the applicability of 35 U.S.C. 102(b)(2)(C) for any potential 35 U.S.C. 102(a)(2) prior art against the later invention.
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 1-2 and 9-14 are rejected under 35 U.S.C. 102(a)(1) as being unpatentable over Bahlany (Bahlany, Salah, Maharbi, Mohammed, Zakwani, Saud, Busaidi, Faisal, and Ferrante Benvenuti. “STEP Change in Preventing Stuck Pipe and Tight Hole Events Using Machine Learning.” Paper presented at the Abu Dhabi International Petroleum Exhibition & Conference, Abu Dhabi, UAE, November 2021. doi: https://doi.org/10.2118/207823-MS), hereinafter ‘Bahlany’.
Regarding claim 1.
Bahlany discloses:
A method (Abstract: an artificial intelligence tool STEP (Stuck pipe and Tight spot Events Prediction) is used to evaluate different data in order to predict and prevent wellbore stability problems such as stuck pipe and tight spots) comprising:
receiving, in one or more artificial intelligence models, real-time sensor data from one or more sensors in communication with active drilling equipment within a wellbore (p. 3-7, sections “Machine Learning for Reduction of NPTs” – “Model Performances”: predictions of tight spots and stuck pipes occurring during drilling operations were performed using a Well Planning Tool (WPT) and a Well Execution Tool (WET), with these tools using data of wells including lithology, trajectories and mud programs as well as sensor data from active rig (see Fig. 1) as inputs for machine learning models (see also Tables 1-3)); and
generating a prediction, via the one or more artificial intelligence models, of an impending hazard event during active drilling of the wellbore (p. 6-7, sections “Model Performance” and “User Interface features”: the tools were trained with the corresponding data in order to obtain risk predictions of tight spots and stuck pipes (see also p. 5, section “The Well Execution Tool” regarding the execution tool providing accurate indications on the risk of experiencing tight sports of stuck pipes)).
Bahlany does not explicitly disclose:
“modifying drilling operations of the active drilling equipment, via the one or more artificial intelligence models, in response to the prediction generated by the one or more artificial intelligence models”.
However, Bahlany teaches:
“The tool informs well engineers and rig crews about possible risks both during the well planning and well execution phase, suggesting possible mitigation actions to avoid getting stuck” (Abstract: mitigation actions are suggested by the tool for avoiding wellbore stability problems (see also p. 3, par. 3-4 and p. 7, section “User Interface features”, par. 2 regarding providing mitigation actions and useful information to take drilling decisions; see also Figure 1 - “Active Rig” and p. 3, section “Modeling Strategy” regarding the Well Execution Tool being used while drilling)).
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to modify Bahlany to modify drilling operations of the active drilling equipment, via the one or more artificial intelligence models, in response to the prediction generated by the one or more artificial intelligence models, in order to apply real-time decisions to mitigate the predicted wellbore stability problems impacting the current drilling operations and improve overall efficiency.
Regarding claim 2.
Bahlany discloses all the features of claim 1 as described above.
Bahlany further discloses:
the one or more artificial intelligence model further receives data comprising a drilling report containing hazard event information, static data about the wellbore, or any combination thereof (Tables 1-3: data includes event data such as daily drilling reports (DDOR) including information regarding Non-Productive Time (NPT) (see also p. 2, par. 1-2; p. 4, section “The Well Planning Tool”), contextual data about the wellbore and sensor data).
Regarding claim 9.
Bahlany discloses all the features of claim 1 as described above.
Bahlany further discloses:
generating a warning alert to an operator or a drilling crew about the probability of the hazard event or the impending hazard event (Abstract; p. 7, section “User Interface features”: risk predictions alarms are given to field personnel for preparing in advance mitigation plans to take drilling decisions (see also p. 4, par. 1)).
Regarding claim 10.
Bahlany discloses:
A system (Fig. 1; Abstract: an artificial intelligence tool STEP (Stuck pipe and Tight spot Events Prediction) is used to evaluate different data in order to predict and prevent wellbore stability problems such as stuck pipe and tight spots) comprising:
a data processing application comprising a data mining module and a data filter module for receiving, extracting, and filtering a training dataset (p. 3-7, sections “Machine Learning for Reduction of NPTs” – “Model Performances”: predictions of tight spots and stuck pipes occurring during drilling operations were performed using a Well Planning Tool (WPT) and a Well Execution Tool (WET), with these tools using data of wells as well as sensor data from the rig as inputs for training machine learning models (see Fig. 1; see also Tables 1-3; see further p. 3, par. 5)), the data being cleaned (filtered) by the system to reduce noise);
an artificial intelligence model training application comprising one or more machine learning training modules operable to construct one or more artificial intelligence models using the training dataset (p. 3-7, sections “Machine Learning for Reduction of NPTs” – “Model Performances”: predictions of tight spots and stuck pipes occurring during drilling operations were performed using a Well Planning Tool (WPT) and a Well Execution Tool (WET), with these tools using data of wells as well as sensor data from the rig as inputs for training machine learning models (see Fig. 1 and p. 3, par. 5)); and
a hazard prediction application configured to predict a hazard event within a wellbore utilizing the one or more artificial intelligence models (p. 6-7, sections “Model Performance” and “User Interface features”: the tools were trained with the corresponding data in order to obtain risk predictions of tight spots and stuck pipes).
Bahlany does not explicitly disclose:
“the hazard prediction application including a real-time warning module operable to modify drilling operations of communicatively-coupled drilling equipment to avoid the hazard event”.
However, Bahlany teaches:
“The tool informs well engineers and rig crews about possible risks both during the well planning and well execution phase, suggesting possible mitigation actions to avoid getting stuck” (Abstract: mitigation actions are suggested by the tool for avoiding wellbore stability problems (see also p. 3, par. 3-4 and p. 7, section “User Interface features”, par. 2 regarding providing mitigation actions and useful information to take drilling decisions; see also Figure 1 - “Active Rig” and p. 3, section “Modeling Strategy” regarding “Well Execution Tool” being used while drilling)).
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to modify Bahlany to incorporate the hazard prediction application including a real-time warning module operable to modify drilling operations of communicatively-coupled drilling equipment to avoid the hazard event, in order to apply real-time decisions to mitigate the predicted wellbore stability problems impacting the current drilling operations and improve overall efficiency.
Regarding claim 11.
Bahlany discloses all the features of claim 10 as described above.
Bahlany does not explicitly disclose:
the hazard prediction application further comprises a predictive warning module configured to determine a probability of a future hazard event in the wellbore, a planning module configured to predict an optimal well location in a geographical area, or a combination thereof.
However, Bahlany teaches:
“The tool informs well engineers and rig crews about possible risks both during the well planning and well execution phase, suggesting possible mitigation actions to avoid getting stuck. Since the alarms are given ahead of the bit, several hours before the possible occurrence of the event, the well engineers and rig crews have ample time to react to the alarms and prevent its occurrence” (Abstract: the tools are used for risk predictions (analogous to probability of a future hazard event) during well planning and well execution phase, and for suggesting mitigation actions to avoid getting stuck (analogous to predict an optimal well location in a geographical area) (see also p. 3-6, sections “Machine Learning for Reduction of NPTs” – “The Well Planning Tool”)).
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to modify Bahlany to incorporate the hazard prediction application further comprising a predictive warning module configured to determine a probability of a future hazard event in the wellbore, a planning module configured to predict an optimal well location in a geographical area, or a combination thereof, in order to apply real-time decisions to mitigate the predicted wellbore stability problems impacting the well planning and improve overall efficiency.
Regarding claim 12.
Bahlany discloses all the features of claim 10 as described above.
Bahlany further discloses:
one or more sensors within the wellbore and in communication with active drilling equipment, the one or more sensors configured to provide real-time sensor data to the system (Fig. 1; p. 3-4, section “Modeling Strategy”; p. 6-7 section “The Well Execution Tool”: sensors provide real-time operational data for the model (see Tables 2-3; see also Figure 1 - “Active Rig” and p. 3, section “Modeling Strategy” regarding “Well Execution Tool” being used while drilling)).
Regarding claim 13.
Bahlany discloses all the features of claim 10 as described above.
Bahlany further discloses:
a database storing historical data comprising hazard event reports, static wellbore data, sensor data, or any combination thereof from existing wellbores (p. 7, section “User Interface Features”: database stores all information for later extraction and analysis (see also p. 3, section “Modelling Strategy” regarding historical data, which examiner interprets to be stored with all other data in a database for easy access)).
Regarding claim 14.
Bahlany discloses all the features of claim 10 as described above.
Bahlany further discloses:
the historical data stored on the database is input to the artificial intelligence model training application within the training dataset for training or updating the one or more artificial intelligence models (p. 4, section “The Well Planning Tool”: historical data is used for training the models (see also p. 6, section ‘Data’)).
Claims 3 and 5-7 are rejected under 35 U.S.C. 103 as being unpatentable over Bahlany, in view of Magana-Mora (Magana-Mora, Arturo, AlJubran, Mohammad, Ramasamy, Jothibasu, AlBassam, Mohammed, Gooneratne, Chinthaka, Gonzalez, Miguel, Thiel, Tim, and Max Deffenbaugh. “Machine-Learning for the Prediction of Lost Circulation Events - Time Series Analysis and Model Evaluation.” Paper presented at the SPE Middle East Oil & Gas Show and Conference, event canceled, November 2021. doi: https://doi.org/10.2118/204706-MS), hereinafter ‘Magana’.
Regarding claim 3.
Bahlany discloses all the features of claim 1 as described above.
Bahlany further discloses:
collecting training data about one or more existing wellbores (section “Machine Learning for reduction of NPTs”, p. 3-7: the tools use historical data of offset wells including lithology, trajectories and mud programs as inputs for machine learning models (see Tables 1-3));
mining, sorting, and filtering the training data to correlate the data to a previous hazard event within the one or more existing wellbores (p. 2, par. 2; p. 4, section “The Well Planning Tool”; p. 6, section ‘Data’: data was cleaned (filtered), mined (using a Natural Language Processing (NLP) algorithm) and integrated (which implies sorted));
flagging the previous hazard event (p. 4, section “The Well Planning Tool”: NPTs were labeled (flagged) 0-1);
generating a training dataset comprising the training data sorted by depth, time, and event (p. 5-6, section “The Well Execution Tool”: a training dataset is generated by merging past NPT events on trajectories based on depth); and
training the one or more artificial intelligence models on the training dataset and the previous hazard event (p. 5-6, section “The Well Execution Tool”: training set is fed to the learning algorithm).
Bahlany does not explicitly disclose:
quantifying a severity of the previous hazard event.
Magana teaches:
“Lost circulation is classified broadly into three major categories based on the number of barrels (bbl)of mud lost.
Seepage lost circulation < 40 bbl/hr
Partial lost circulation > 40 bbl/hr < 100 bbl/hr
Severe/Total lost circulation > 100 bbl/hr
Different types of LCMs, such as particulates, fibrous materials, flaky materials, sized bridged materials, etc., are used for mitigating lost circulation (Ramasamy and Amanullah, 2017; Ramasamy et al., 2018; Ramasamy et al., 2019). In the case of drilling seepage loss zones, the drilling fluid is added with the above-mentioned materials in a specific ratio based on the porosity and permeability of the formation and the drilling operation is not ceased. For partial and severe/total loss scenarios, the drilling operation is ceased and the LCM slurry is prepared with the above-mentioned materials and pumped downhole. The LCM slurry might not work in the case of severe/total losses” (p. 3-4: Lost of circulation events can be classified (e.g., based on severity) during use of machine learning techniques for predicting drilling nonproductive time (NPT) events (see Abstract; see also p. 2, section ‘Introduction’; p. 5, section ‘Dataset’)).
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to modify Bahlany in view of Magana to quantify a severity of the previous hazard event, in order to accurately predict the risk of the event and take appropriate actions for safety of the personnel and overall operational costs.
Regarding claim 5.
Bahlany in view of Magana discloses all the features of claim 3 as described above.
Bahlany further discloses:
mining the data comprises using natural language processing algorithms for extracting contextual information (p. 2, par. 2; p. 4, section “The Well Planning Tool”: data is mined using a Natural Language Processing (NLP) algorithm to identify hidden invisible lost time events that were not classified as NPTs but impacted drilling efficiency).
Regarding claim 6.
Bahlany in view of Magana discloses all the features of claim 3 as described above.
Bahlany further discloses:
the one or more artificial intelligence models are trained using one or more machine learning techniques selected from the group consisting of logistic regression, naïve Beyes, k-nearest neighbor, decision tree, Ada boost, deep neural network, random forest, and any combination thereof (p. 6-7, section “Model performances”: Gradient Boosted Trees (decision tree) and Random Forest classifiers were used as the machine learning models).
Regarding claim 7.
Bahlany in view of Magana discloses all the features of claim 3 as described above.
Bahlany does not explicitly disclose:
filtering the training data comprises identifying a change in the training data within a predefined range and excluding any training data correlated to expected drilling events.
However, Bahlany teaches:
“Data cleansing: operational data is affected by sensor noise and human-registered data is affected by the typical compilation errors or heterogeneity that hamper automatic data processing, so we have implemented cleaning routines to reduce noise and standardize formats” (p. 6, section ‘Data’: data was filter to remove sensor noise and compilation errors in human reports (analogous to excluding data correlated to expected drilling events); examiner notes that removing expected drilling events such as scheduled maintenance increases the accuracy of the model while reducing the computational cost and the number of false alarms).
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to modify Bahlany in view of Magana to filter the training data by identifying a change in the training data within a predefined range and excluding any training data correlated to expected drilling events, in order to improve the model accuracy while removing false alarms that unnecessarily alert the crew to interfere with the drilling operations.
Claim 4 is rejected under 35 U.S.C. 103 as being unpatentable over Bahlany, in view of Magana, and in further view of Vijapur (US 20230274126 A1), hereinafter ‘Vijapur’.
Regarding claim 4.
Bahlany in view of Magana discloses all the features of claim 3 as described above.
Bahlany does not explicitly disclose:
training the one or more artificial intelligence models on feedback comprising the prediction generated by the one or more artificial intelligence models and an outcome of the modified drilling operations.
Vijapur teaches:
“In a step 190 of the process flow, feedback is provided to the CNN model to tune the CNN model for better accuracy. For example, the feedback may include the actual volume of alerts generated, and how that compares with the predicted volume of alerts … In this manner, the feedback loop may improve the accuracy of the CNN model, so that each subsequent prediction is more on target. In other words, the various model parameters of the CNN model (or other models used to make predictions) may be updated with each iteration of the feedback loop, so that the CNN model with the updated model parameters may be able to make better predictions with each iteration of the feedback loop” ([0032]: machine learning is employed for predicting alerts (see [0001]), with the actual results and how they compare with the predicted results (analogous to prediction generated by the one or more artificial intelligence models and an outcome of the modified drilling operations) being feedback to the machine learning model for tunning and better accuracy).
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to modify Bahlany in view of Magana, and in further view of Vijapur, to train the one or more artificial intelligence models on feedback comprising the prediction generated by the one or more artificial intelligence models and an outcome of the modified drilling operations, in order to tune the model for better accuracy with each iteration, as discussed by Vijapur ([0032]).
Claims 16-21 are rejected under 35 U.S.C. 103 as being unpatentable over Bahlany, in view of Magana, and in further view of Wrobel (US 20240185119 A1, previously cited), hereinafter ‘Wrobel’.
Regarding claim 16.
Bahlany discloses:
A method (Abstract: an artificial intelligence tool STEP (Stuck pipe and Tight spot Events Prediction) is used to evaluate different data in order to predict and prevent wellbore stability problems such as stuck pipe and tight spots) comprising:
collecting training data including drilling reports describing a hazard event, static wellbore data detailing geographical and geological characteristics, and real-time sensor data related to the hazard event (p. 3-7, sections “Machine Learning for Reduction of NPTs” – “Model Performances”: predictions of tight spots and stuck pipes occurring during drilling operations were performed using a Well Planning Tool (WPT) and a Well Execution Tool (WET), with these tools using data such as daily drilling reports (DDOR) including information regarding Non-Productive Time (NPT) (see also p. 2, par. 1-2; p. 4, section “The Well Planning Tool”), lithology, trajectories and mud programs as well as sensor data from active rig as inputs for machine learning models (see Fig. 1; see also Tables 1-3));
mining, sorting, and filtering the training data to correlate the data to the previous hazard event within one or more existing wellbores (p. 2, par. 2; p. 4, section “The Well Planning Tool”; p. 6, section ‘Data’: data was cleaned (filtered), mined (using a Natural Language Processing (NLP) algorithm) and integrated (which implies sorted));
generating a training dataset comprising the training data sorted by depth, time, and the severity of the previous hazard event (p. 5-6, section “The Well Execution Tool”: a training dataset is generated by merging past NPT events on trajectories based on depth); and
constructing one or more artificial intelligence models using the training dataset (p. 5-6, section “The Well Execution Tool”: training set is fed to the learning algorithm).
Bahlany does not explicitly disclose:
quantifying a severity of the previous hazard event;
receiving geographical and geological data for an area around a subterranean formation in the one or more artificial intelligence models; and
outputting a heat-map of ideal and dangerous drilling locations within the area using the one or more artificial intelligence models.
Regarding “quantifying a severity of the previous hazard event”, Magana teaches:
“Lost circulation is classified broadly into three major categories based on the number of barrels (bbl)of mud lost.
Seepage lost circulation < 40 bbl/hr
Partial lost circulation > 40 bbl/hr < 100 bbl/hr
Severe/Total lost circulation > 100 bbl/hr
Different types of LCMs, such as particulates, fibrous materials, flaky materials, sized bridged materials, etc., are used for mitigating lost circulation (Ramasamy and Amanullah, 2017; Ramasamy et al., 2018; Ramasamy et al., 2019). In the case of drilling seepage loss zones, the drilling fluid is added with the above-mentioned materials in a specific ratio based on the porosity and permeability of the formation and the drilling operation is not ceased. For partial and severe/total loss scenarios, the drilling operation is ceased and the LCM slurry is prepared with the above-mentioned materials and pumped downhole. The LCM slurry might not work in the case of severe/total losses” (p. 3-4: Lost of circulation events can be classified (e.g., based on severity) during use of machine learning techniques for predicting drilling nonproductive time (NPT) events (see Abstract; see also p. 2, section ‘Introduction’; p. 5, section ‘Dataset’)).
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to modify Bahlany in view of Magana to quantify a severity of the previous hazard event, in order to accurately predict the risk of the event and take appropriate actions for safety of the personnel and overall operational costs.
Regarding “receiving geographical and geological data for an area around a subterranean formation in the one or more artificial intelligence models; and outputting a heat-map of ideal and dangerous drilling locations within the area using the one or more artificial intelligence models”, Wrobel teaches:
“Certain aspects and examples of the present disclosure relate to machine-learning techniques involving historical geological data for a wellbore operation. The wellbore operation can include a wellbore exploration operation, a wellbore drilling operation, a wellbore production operation, or any other suitable wellbore operation or combination thereof … In some examples, the historical geological data can include data about an area of interest, which may include the earth or any subset thereof such as a continent, a country, an island, etc., or any geographical area that can be defined by a user. The data can include lithology, porosity, permeability, geological events, mineral or other natural resource deposits, etc. The historical geological data can be incorporated into the machine-learning techniques via pre-processing techniques, training techniques, and the like to cause the machine-learning techniques to provide predictions for natural resource locations, topographical changes, and other geological phenomena based on the historical geological data” ([0010]: machine learning models are used with geological data from a geographical area in order to predict natural resource locations (see also [0018])); and
“At block 310, the computing device 140 provides a user interface that includes a visualization of the one or more predictions. The user interface may include a background that can include a geographic representation of the geological area of interest 100. Additionally, the user interface can include a set of visual indicators arranged on the background. Each visual indicator of the set of visual indicators can be positioned on a particular location of the background that corresponds to a particular real-world location at the surface or in the sub-surface of the geological area of interest 100. Additionally, each visual indicator can indicate, for example via a heat map, colors or shading, sizing, or the like, a likelihood of the geological phenomenon of interest existing at the corresponding particular real-world location. The computing device 140 can generate the set of visual indicators using the predictions generated by the trained machine-learning model” ([0052]: predictions are output including geographic representations of geological area of interest as a heat map (see Fig. 7, [0068])).
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to modify Bahlany in view of Magana, and in further view of Wrobel, to receive geographical and geological data for an area around a subterranean formation in the one or more artificial intelligence models; and output a heat-map of ideal and dangerous drilling locations within the area using the one or more artificial intelligence models, in order to provide a robust analysis for potential drilling locations and facilitate decision-making process regarding drilling planning.
Regarding claim 17.
Bahlany in view of Magana and Wrobel discloses the features of claim 16 as described above.
Bahlany does not explicitly disclose:
outputting one or more trajectories for drilling in the ideal drilling locations optimized to avoid possible hazard events using the one or more artificial intelligence models.
However, Bahlany teaches:
“The tool informs well engineers and rig crews about possible risks both during the well planning and well execution phase, suggesting possible mitigation actions to avoid getting stuck” (Abstract: mitigation actions are suggested by the tool for avoiding wellbore stability problems during well planning (analogous to output one or more trajectories for drilling in the ideal drilling locations optimized to avoid possible hazard events; see also p. 3, par. 3-4 and p. 7, section “User Interface features”, par. 2 regarding providing mitigation actions and useful information to take drilling decisions)).
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to modify Bahlany in view of Magana and Wrobel to output one or more trajectories for drilling in the ideal drilling locations optimized to avoid possible hazard events using the one or more artificial intelligence models, in order to provide additional details for accurate decision-making process regarding drilling planning.
Regarding claim 18.
Bahlany in view of Magana and Wrobel discloses the features of claim 16 as described above.
Bahlany does not explicitly disclose:
outputting one or more recommended drilling parameters for drilling operations in the ideal drilling locations using the one or more artificial intelligence models.
However, Bahlany teaches:
“The tool informs well engineers and rig crews about possible risks both during the well planning and well execution phase, suggesting possible mitigation actions to avoid getting stuck” (Abstract: mitigation actions are suggested by the tool for avoiding wellbore stability problems during well planning (analogous to output one or more recommended drilling parameters for drilling operations in the ideal drilling locations; see also p. 3, par. 3-4 and p. 7, section “User Interface features”, par. 2 regarding providing mitigation actions and useful information to take drilling decisions)).
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to modify Bahlany in view of Magana and Wrobel to output one or more recommended drilling parameters for drilling operations in the ideal drilling locations using the one or more artificial intelligence models, in order to provide additional details for accurate decision-making process regarding drilling planning.
Regarding claim 19.
Bahlany in view of Magana and Wrobel discloses the features of claim 16 as described above.
Bahlany further discloses:
receiving real-time sensor data in the one or more artificial intelligence models from one or more sensors in communication with active drilling equipment (p. 3-7, sections “Machine Learning for Reduction of NPTs” – “Model Performances”: predictions of tight spots and stuck pipes occurring during drilling operations were performed using a Well Planning Tool (WPT) and a Well Execution Tool (WET), with these tools using data of wells including lithology, trajectories and mud programs as well as sensor data from active rig as inputs for machine learning models (see Fig. 1; see also Tables 1-3)).
Bahlany does not explicitly disclose:
initiating a drilling operation in one of the ideal drilling locations of the heat-map.
Wrobel further teaches:
“The computing device 140 can, in response to the trained machine-learning model generating the predictions, generate and output a command to control the operation. In some examples, the command can cause a wellbore, a mine, or other natural-resource-extraction system to be formed in at least a portion of the geological area of interest 100” ([0057]: in response to the machine-learning model predictions, a wellbore can be formed (analogous to initiating a drilling operation)).
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to modify Bahlany in view of Magana and Wrobel to initiate a drilling operation in one of the ideal drilling locations of the heat-map, in order to apply real-time decisions regarding wellbore planning based on the analysis.
Regarding claim 20.
Bahlany in view of Magana and Wrobel discloses the features of claim 19 as described above.
Bahlany does not explicitly disclose:
determining, via the one or more artificial intelligence models, a probability of a future hazard event during active drilling at the one of the ideal drilling locations using the real-time sensor data.
However, Bahlany teaches:
“The tool informs well engineers and rig crews about possible risks both during the well planning and well execution phase, suggesting possible mitigation actions to avoid getting stuck. Since the alarms are given ahead of the bit, several hours before the possible occurrence of the event, the well engineers and rig crews have ample time to react to the alarms and prevent its occurrence” (Abstract: risk predictions (analogous to probability of a future hazard event) during well planning and well execution phase are given to field personnel for preparing in advance mitigation plans to take drilling decisions (see also p. 3, par. 2 and 4; and p. 7, section “User Interface features”; see further p. 4, par. 1 regarding using sensor data)).
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to modify Bahlany in view of Magana and Wrobel to determine, via the one or more artificial intelligence models, a probability of a future hazard event during active drilling at the one of the ideal drilling locations using the real-time sensor data, in order to provide possible risk scenarios during drilling operations for appropriate decision-making.
Regarding claim 21.
Bahlany in view of Magana and Wrobel discloses the features of claim 20 as described above.
Bahlany does not explicitly disclose:
modifying drilling operations of the active drilling equipment, via the one or more artificial intelligence models, in response to the probability of the future hazard event generated by the one or more artificial intelligence models.
However, Bahlany teaches:
“The tool informs well engineers and rig crews about possible risks both during the well planning and well execution phase, suggesting possible mitigation actions to avoid getting stuck” (Abstract: mitigation actions are suggested by the tool for avoiding wellbore stability problems (see also p. 3, par. 3-4 and p. 7, section “User Interface features”, par. 2 regarding providing mitigation actions and useful information to take drilling decisions; see also Figure 1 - “Active Rig” and p. 3, section “Modeling Strategy” regarding the Well Execution Tool being used while drilling)).
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to modify Bahlany in view of Magana and Wrobel to modify drilling operations of the active drilling equipment, via the one or more artificial intelligence models, in response to the probability of the future hazard event generated by the one or more artificial intelligence models, in order to apply real-time decisions to mitigate the predicted wellbore stability problems impacting drilling operations and improve overall efficiency.
Claim 22 is rejected under 35 U.S.C. 103 as being unpatentable over Bahlany in view of Magana and Wrobel, and in further view of Albert (US 20210199110 A1), hereinafter ‘Albert’.
Regarding claim 22.
Bahlany in view of Magana and Wrobel discloses the features of claim 20 as described above.
Bahlany does not explicitly disclose:
determining if the future hazard event occurred or was averted;
providing the real-time sensor data and a determination of the future hazard event as feedback data to an artificial intelligence model training application to perform further training using the feedback data to improve the one or more artificial intelligence models.
Albert teaches:
“In some embodiments, the method further includes receiving and processing the set of operational data through the machine learning model in real time, and generating an alert indicating a predicted failure. In some embodiments, the method further includes obtaining actual health and failure conditions of the hydraulic pump fluid end, and updating the trained machine learning model by correlating the set of operational data with the actual health and failure conditions” ([0006]: a machine learning model is trained to predict failures of hydraulic fracturing equipment (see Abstract), with actual health and failure conditions (analogous to determining if the future hazard event occurred or was averted) being correlated with operational data (analogous to real-time sensor data) for updating the trained machine learning model (analogous to provide feedback data to an artificial intelligence model training application to perform further training using the feedback data to improve the one or more artificial intelligence models) (see also [0034]; see further [0031] regarding collecting additional training data for refining the model)).
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to modify Bahlany in view of Magana and Wrobel, and in further view of Albert, to determine if the future hazard event occurred or was averted; and to provide the real-time sensor data and a determination of the future hazard event as feedback data to an artificial intelligence model training application to perform further training using the feedback data to improve the one or more artificial intelligence models, in order to update the model with actual results and corresponding data for improving accuracy.
Conclusion
The prior art made of record and not relied upon is considered pertinent to applicant's disclosure.
Srivastava, Saket & Teodoriu, Catalin & Shah, Rushit. (2021). Natural Language Processing Based Information Extraction from Drilling Reports to Classify Drilling Dysfunction Severity, GRC Transactions, Vol. 45.
Reference discloses using natural language processing to extract information from drilling reports.
Tang; Yuxin et al., US 20200332627 A1, Detecting Events in Well Reports
Reference discloses using drilling reports to identify drilling process and train a model.
Applicant’s amendment necessitated the new ground(s) of rejection presented in this Office action. Accordingly, THIS ACTION IS MADE FINAL. See MPEP § 706.07(a). Applicant is reminded of the extension of time policy as set forth in 37 CFR 1.136(a).
A shortened statutory period for reply to this final action is set to expire THREE MONTHS from the mailing date of this action. In the event a first reply is filed within TWO MONTHS of the mailing date of this final action and the advisory action is not mailed until after the end of the THREE-MONTH shortened statutory period, then the shortened statutory period will expire on the date the advisory action is mailed, and any nonprovisional extension fee (37 CFR 1.17(a)) pursuant to 37 CFR 1.136(a) will be calculated from the mailing date of the advisory action. In no event, however, will the statutory period for reply expire later than SIX MONTHS from the mailing date of this final action.
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/LINA CORDERO/Primary Examiner, Art Unit 2857