DETAILED ACTION
This office action is in response to application filed on March 21, 2024.
Notice of Pre-AIA or AIA Status
The present application, filed on or after March 16, 2013, is being examined under the first inventor to file provisions of the AIA .
Information Disclosure Statement
The information disclosure statement (IDS) submitted on 07/26/2024 is in compliance with the provisions of 37 CFR 1.97. Accordingly, the information disclosure statement is being considered by the examiner.
Response to Amendment
Preliminary amendments filed on 05/28/2025 have been entered.
The specification has been amended.
Claims 1-20 have been examined.
Drawings
The drawings are objected to because:
Fig. 9: label “Mouldic Porosoty %” should read “Mouldic Porosity %”.
Fig. 9: label “Micro Porosoty %” should read “Micro Porosity %”.
Fig. 18, item ‘494’: label “D70O” should read “DTCO” as described in the specification (at [0080]).
Fig. 18, item ‘496’: label “D75M” should read “DTSM” as described in the specification (at [0080]).
Fig. 19, item ‘506’: label “UCSH” should read “UCS” as described in the specification (at [0082]).
Corrected drawing sheets in compliance with 37 CFR 1.121(d) are required in reply to the Office action to avoid abandonment of the application. Any amended replacement drawing sheet should include all of the figures appearing on the immediate prior version of the sheet, even if only one figure is being amended. The figure or figure number of an amended drawing should not be labeled as “amended.” If a drawing figure is to be canceled, the appropriate figure must be removed from the replacement sheet, and where necessary, the remaining figures must be renumbered and appropriate changes made to the brief description of the several views of the drawings for consistency. Additional replacement sheets may be necessary to show the renumbering of the remaining figures. Each drawing sheet submitted after the filing date of an application must be labeled in the top margin as either “Replacement Sheet” or “New Sheet” pursuant to 37 CFR 1.121(d). If the changes are not accepted by the examiner, the applicant will be notified and informed of any required corrective action in the next Office action. The objection to the drawings will not be held in abeyance.
The drawings are objected to as failing to comply with 37 CFR 1.84(p)(5) because they do not include the following reference sign(s) mentioned in the description:
Fig. 12, items ‘382’ and ‘384’, as described in the specification (at [0073]).
Fig. 18, items ‘470’, ‘472’ and ‘474’, as described in the specification (at [0080]).
Fig. 19, item ‘512’, as described in the specification (at [0082]).
Corrected drawing sheets in compliance with 37 CFR 1.121(d) are required in reply to the Office action to avoid abandonment of the application. Any amended replacement drawing sheet should include all of the figures appearing on the immediate prior version of the sheet, even if only one figure is being amended. Each drawing sheet submitted after the filing date of an application must be labeled in the top margin as either “Replacement Sheet” or “New Sheet” pursuant to 37 CFR 1.121(d). If the changes are not accepted by the examiner, the applicant will be notified and informed of any required corrective action in the next Office action. The objection to the drawings will not be held in abeyance.
Specification
The disclosure is objected to because of the following informalities:
[0070]: Language “FIG. 9 is a plot 330 of reservoir porosity type as determined by thin section analysis. Intergranular porosity 332 forms 0.347% porosity. Moldic porosity 334 form 0.800% porosity ...” should read “FIG. 9 is a plot [[330]]320 of reservoir porosity type as determined by thin section analysis. Intergranular porosity 332 forms 0.347% porosity. Moldic porosity 334 forms 0.800% porosity …” in accordance with the details of Fig. 9 and to correct minor informalities.
[0075]: Language “FIG. 14 is a composite plot 400 ... and conventional core analysis 420 (porosity 422 and permeability424) data for Well Al …” should read “FIG. 14 is a composite plot 400 ... and conventional core analysis 420 (porosity 422 and permeability 424) data for Well Al …” in order to correct minor informalities (e.g., add space).
[0076]: Language “… As demonstrated in FIGS. 9 and 10 the softness is caused by clay which also gives the rock its higher porosity (as microporosity) …” should read “… As demonstrated in FIGS. 9 and 10, the softness is caused by clay which also gives the rock its higher porosity (as microporosity) …” in order to correct minor informalities (e.g., add comma).
Appropriate correction is required.
Claim Objections
Claim 4 is objected to because of the following informalities:
Claim language should read: “The method of claim 1, wherein forming the rock type clusters comprises applying principal component analysis to the well log data and the unconfined compressive strength log to reduce inputs to the unsupervised machine learning model” in order to provide appropriate antecedence basis.
Appropriate correction is required.
Claim 10 is objected to because of the following informalities:
Claim language should read: “The method of claim 1, wherein obtaining the logging-while-drilling data comprises obtaining time-domain logging-while-drilling data, and wherein the method further comprises converting the time-domain logging-while-drilling data to depth domain logging-while-drilling data for input to the supervised machine learning model” in order to provide appropriate antecedence basis.
Appropriate correction is required.
Claim 13 is objected to because of the following informalities:
Claim language should read: “The system of claim 11, wherein forming the rock type clusters comprises applying principal component analysis to the well log data and the unconfined compressive strength log to reduce inputs to the unsupervised machine learning model” in order to provide appropriate antecedence basis.
Appropriate correction is required.
Claim 16 is objected to because of the following informalities:
Claim language should read: “The system of claim 11, wherein obtaining the logging-while-drilling data comprises obtaining time-domain logging-while-drilling data; and wherein the operations further comprise converting the time-domain logging-while-drilling data to depth domain logging-while-drilling data for input to the supervised machine learning model, wherein the well log data and the logging-while-drilling data comprise one or more of a rate of penetration log, a gamma ray log, a weight on bit log, and a mechanical specific energy log” in order to provide appropriate antecedence basis.
Appropriate correction is required.
Claim 18 is objected to because of the following informalities:
Claim language should read: “The one or more non-transitory machine-readable storage devices of claim 17, wherein the operations further comprise: in response to determining the rock types, controlling a rate of penetration of the drilling equipment, steering the drilling equipment, or updating a three-dimensional static and dynamic reservoir model based on the determined rock types” in order to provide appropriate antecedence basis.
Appropriate correction is required.
Claim 19 is objected to because of the following informalities:
Claim language should read: “The one or more non-transitory machine-readable storage devices of claim 17, wherein generating the unconfined compressive strength log comprises: generating additional log data using a machine learning model that takes as input the well log data and outputs the additional log data, wherein the well log data comprises rate of penetration data and drilling parameters; and validating the unconfined compressive strength log data with one or more of micro-rebound hammer uniaxial compressive strength data, thin section point count data, and x-ray diffraction mineralogical data” in order to provide appropriate antecedence basis.
Appropriate correction is required.
Claim 20 is objected to because of the following informalities:
Claim language should read: “The one or more non-transitory machine-readable storage devices of claim 17, wherein obtaining the logging-while-drilling data comprises obtaining time-domain logging-while-drilling data, and wherein the operations further comprise converting the time-domain logging-while-drilling data to depth domain logging-while-drilling data for input to the supervised machine learning model, wherein the well log data and the logging-while-drilling data comprise one or more of a rate of penetration log, a gamma ray log, a weight on bit log, and a mechanical specific energy log” 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 1-2, 4-11 and 13-20 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, based on the well log data and the core sample data, an unconfined compressive strength log for the subsurface formation” is a process that, under its broadest reasonable interpretation in light of the specification, covers performance of the limitation using mental processes and/or mathematical concepts to manipulate data and obtain additional information (i.e., an unconfined compressive strength log, see specification at [0044], [0055]-[0059], [0072]). Except for the recitation of the extra-solution activities (e.g., source/type of data being evaluated) and/or the particular technological environment or field of use, the limitation in the context of the claim mainly refers to performing a mental evaluation and/or applying mathematical concepts to transform data.
the limitation “using an unsupervised machine learning model to form rock type clusters based on the unconfined compressive strength log and the well log data” is a process that, under its broadest reasonable interpretation in light of the specification, covers performance of the limitation using mental processes and/or mathematical concepts to manipulate data for classification purposes (i.e., rock type clusters, see specification at [0045], [0081]-[0082]). Except for the recitation of the extra-solution activities (e.g., source/type of data being evaluated), the particular technological environment or field of use, and the generic computer implementation (i.e., an unsupervised machine learning model), the limitation in the context of the claim mainly refers to performing a mental evaluation and/or applying mathematical concepts to classify information.
the limitation “forming a training dataset including the well log data, the training dataset labeled based on the rock type clusters” is a process that, under its broadest reasonable interpretation in light of the specification, covers performance of the limitation using mental processes to classify data and obtain additional information (i.e., a training dataset, see specification at [0047]). Except for the recitation of the extra-solution activities (e.g., source/type of data being evaluated) and/or the particular technological environment or field of use, the limitation in the context of the claim mainly refers to performing a mental evaluation to transform data.
the limitation “determining rock types in the subsurface formation using the supervised machine learning model and the logging-while-drilling data” is a process that, under its broadest reasonable interpretation in light of the specification, covers performance of the limitation using mental processes and/or mathematical concepts to manipulate data and obtain a result (i.e., rock types, see specification at [0048], [0050]). Except for the recitation of the extra-solution activities (e.g., source/type of data being evaluated), the particular technological environment or field of use, and the generic computer implementation (i.e., the supervised machine learning model), the limitation in the context of the claim mainly refers to performing a mental evaluation and/or applying mathematical concepts to manipulate data and obtain additional information.
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 when considering the claim as a whole. In particular, the additional elements recited in the claim (see non-italic text for additional elements):
“obtaining well log data and core sample data of a subsurface formation” adds extra-solution activities (e.g., mere data gathering, source/type of data to be manipulated) (see MPEP 2106.05(g)) and/or generally links the use of the judicial exception to a particular technological environment or field of use (see MPEP 2106.05(h));
“using an unsupervised machine learning model to form rock type clusters based on the unconfined compressive strength log and the well log data” adds extra-solution activities (e.g., mere data gathering, source/type of data to be manipulated) (see MPEP 2106.05(g)), generally links the use of the judicial exception to a particular technological environment or field of use (see MPEP 2106.05(h)), and/or adds the words “apply it” (or an equivalent) with the judicial exception, or mere instructions to implement an abstract idea on a computer, or merely uses a computer as a tool to perform an abstract idea (i.e., an unsupervised machine learning model) (see MPEP 2106.05(f));
“training a supervised machine learning model using the training dataset” adds the words “apply it” (or an equivalent) with the judicial exception, or mere instructions to implement an abstract idea on a computer, or merely uses a computer as a tool to perform an abstract idea (i.e., training a supervised machine learning model) (see MPEP 2106.05(f));
“while drilling a well in the subsurface formation, obtaining logging-while-drilling data from drilling equipment used to drill the well” adds extra-solution activities (e.g., machine/transformations used for mere data gathering, source/type of data to be manipulated) (see MPEP 2106.05(b), MPEP 2106.05(c) and MPEP 2106.05(g)) and/or generally links the use of the judicial exception to a particular technological environment or field of use (see MPEP 2106.05(h)); and
“determining rock types in the subsurface formation using the supervised machine learning model and the logging-while-drilling data” adds extra-solution activities (e.g., source/type of data to be manipulated) (see MPEP 2106.05(g)), generally links the use of the judicial exception to a particular technological environment or field of use (see MPEP 2106.05(h)), and/or adds the words “apply it” (or an equivalent) with the judicial exception, or mere instructions to implement an abstract idea on a computer, or merely uses a computer as a tool to perform an abstract idea (i.e., the unsupervised machine learning model) (see MPEP 2106.05(f)).
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, when considered as a whole, 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 (i.e., determining rock types in the subsurface formation), 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), 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)); “Use of a machine that contributes only nominally or insignificantly to the execution of the claimed method (e.g., in a data gathering step or in a field-of-use limitation) would not provide significantly more” (see MPEP 2106.05(b), section III), and “A transformation that contributes only nominally or insignificantly to the execution of the claimed method (e.g., in a data gathering step or in a field-of-use limitation) would not provide significantly more (or integrate a judicial exception into a practical application)” (see MPEP 2106.05(c)); and
append computer implementation (e.g., using/training unsupervised/supervised machine learning models, see specification at [0045], [0048], [0057], [0080]-[0081]), which as indicated in the MPEP: “It is important to note that a general purpose computer that applies a judicial exception, such as an abstract idea, by use of conventional computer functions does not qualify as a particular machine … Merely adding a generic computer, generic computer components, or a programmed computer to perform generic computer functions does not automatically overcome an eligibility rejection” (see MPEP 2106.05(b)); “Courts have held computer‐implemented processes not to be significantly more than an abstract idea (and thus ineligible) where the claim as a whole amounts to nothing more than generic computer functions merely used to implement an abstract idea …” (see MPEP 2106.05(d)); and “ Use of a computer or other machinery in its ordinary capacity for economic or other tasks (e.g., to receive, store, or transmit data) or simply adding a general purpose computer or computer components after the fact to an abstract idea (e.g., a fundamental economic practice or mathematical equation) does not integrate a judicial exception into a practical application or provide significantly more” (see MPEP 2106.05(f)).
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 11 and 17 are directed to a judicial exception (Step 2A – Prong One) without integrating the judicial exception into a practical application (Step 2A – Prong Two) and/or without providing significantly more (Step 2B) when considering the claimed invention as a whole 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 4, 8-10, 13-16 and 18-20), 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/implementation, generic transformations and/or field of use (Claims 2, 5-8, 10, 14, 16 and 19-20), 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) when considering the claimed invention as a whole (the examiner notes that although claim 18 recites particular features that may incorporate the judicial exception into a practical application (i.e., in response to determining the rock types, controlling a rate of penetration of the drilling equipment, steering the drilling equipment), these features are recited as alternatives (i.e., “or”) with another feature that under the analysis is considered part of the judicial exception (i.e., “updating a three-dimensional static and dynamic reservoir model based on the determined rock types”)).
Examiner’s Note
Claims 3 and 12 were evaluated for patent eligibility under 35 U.S.C. 101 using the SUBJECT MATTER ELIGIBILITY TEST FOR PRODUCTS AND PROCESSES described in the 2024 Guidance Update on Patent Subject Matter Eligibility, Including on Artificial Intelligence (see also 2019 Revised Patent Subject Matter Eligibility Guidance) to determine patent eligibility under 35 U.S.C. 101.
Regarding claim 3, the examiner submits that under Step 1 of the test 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, the examiner submits that claim 3 recites the judicial exception described with respect to claim 1.
Furthermore, under Step 2A - Prong Two of the test, the claim recites the additional elements recited in claim 1 and:
In claim 2: “in response to determining the rock types, controlling the drilling equipment based on the determined rock types”, which appends a transformation at a high level of generality, and 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)), and
In claim 3: “controlling the drilling equipment comprises controlling a rate of penetration of the drilling equipment or steering the drilling equipment”, which, when considering the claim as a whole, integrates the judicial exception into a practical application by effecting a transformation or reduction of a particular article to a different state or thing (e.g., controlling a rate of penetration of the drilling equipment or steering the drilling equipment), and as explained in the MPEP: “A transformation that can be specifically identified, or that applies to only particular articles, is more likely to provide significantly more (or integrates a judicial exception into a practical application)” (see MPEP 2106.05(c)).
Therefore, these additional elements, when considered individually and in combination, integrate the judicial exception into a practical application. The claim, when considered as a whole, is eligible at Prong Two of the Revised Step 2A (see 2019 Revised Patent Subject Matter Eligibility Guidance – Revised Step 2A, see also MPEP 2106.04(d)).
Similarly, claim 12 is directed to patent eligible subject matter as explained above with regards to claim 3.
Subject Matter Not Rejected Over Prior Art
Claims 1-2, 4-11 and 13-20 are distinguished over the prior art of record for the following reasons:
Regarding claim 1.
Andrew (Andrew, EbunOluwa, Xu, Chicheng, Odi, Uchenna, Sheludko, Stanislav, Silver, Andrew, and Yaser Zayer. “Machine Learning-Assisted Petrophysical Rock Type Classification and Permeability Estimation with Flow Zone Indicators.” Paper presented at the International Petroleum Technology Conference, Dhahran, Saudi Arabia, February 2024. doi: https://doi.org/10.2523/IPTC-23537-EA) discloses/teaches:
A method (Abstract: predicting petrophysical rock types (PRT) from well log using unsupervised and supervised machine learning algorithms is presented (see also Fig. 2)) comprising:
obtaining well log data and core sample data of a subsurface formation (Abstract; p. 2, section “Geological Setting of the Study Area”: well log and core data set are acquired for analysis (see also p. 3, section “Methodology”));
using an unsupervised machine learning model to form rock type clusters based on the well log data (p. 4, section “Unsupervised Machine Learning Methodology”: K-means clustering unsupervised machine learning method is used to distinguish petrophysical rock types in the well log);
forming a training dataset including the well log data, the training dataset labeled based on the rock type clusters (p. 4, section “Hydraulic Fluid Units Analysis Methodology”: core data and well log data were used to identify a rock type in order to determine parameters (e.g., well log depth and porosity) for training a Random Forest machine learning technique);
training a supervised machine learning model using the training dataset (p. 4-6, section “Supervised Machine Learning Methodology”: the combined log-core dataset is split into training and test data for training the random forest classifier for petrophysical rock type classification).
Regarding “while drilling a well in the subsurface formation, obtaining logging-while-drilling data from drilling equipment used to drill the well; and determining rock types in the subsurface formation using the supervised machine learning model and the logging-while-drilling data”, Andrew (Andrew, EbunOluwa, Xu, Chicheng, Odi, Uchenna, Sheludko, Stanislav, Silver, Andrew, and Yaser Zayer. “Machine Learning-Assisted Petrophysical Rock Type Classification and Permeability Estimation with Flow Zone Indicators.” Paper presented at the International Petroleum Technology Conference, Dhahran, Saudi Arabia, February 2024. doi: https://doi.org/10.2523/IPTC-23537-EA) further discloses:
“The results indicate that the integrated approach serve to characterize the well. The K-means analysis reveals the diagenetic makeup of the reservoir, hinting at zones with non-reservoir and reservoir rocks. The HFU analysis supports the inherent permeability trend within the well, taking advantage of similar porosity-permeability correlations within lateral heterogeneities. The prediction of lithofacies makes it feasible to generalize the Random Forest classifier for use as a rock type classifier, and permeability regression predictor, for all uncored intervals drilled in the reservoir across the studied field with adequate certainty” (p. 13, section “Conclusions”: Random Forest classifier can be implemented for rock type classification for all intervals drilled in the field).
Amendt (Amendt et al., “Mechanical Characterization in Unconventional Reservoirs: A Facies-Based Methodology,” Petrophysics, October 2013, 54(5):457-464, 8 pages, IDS reference) discloses:
“A new core-testing protocol has been created to characterize rock mechanical parameters based on lithologic composition and rock texture. The goal is to characterize the main rock types in the basin using the geologic model as the integration point. Four individual core plugs are cut at the same depth reference and rock facies. Each plug is brought to failure in a single-stage triaxial test, the four independent tests are used to construct a Mohr-Coulomb failure envelop … For each rock type, representative distributions of Young’s modulus, Poisson’s ratio, unconfined compressive strength, cohesion and angle of internal friction are generated” (Abstract: main rock type characterization is achieved using core data, with unconfined compressive strength being generated for each rock type).
The closest prior art of record, taken individually or in combination, fail to teach or suggest (see italic text):
“generating, based on the well log data and the core sample data, an unconfined compressive strength log for the subsurface formation;
using an unsupervised machine learning model to form rock type clusters based on the unconfined compressive strength log and the well log data”
in combination with all other limitations within the claim, as claimed and defined by the applicant (the examiner submits that the prior art of record does not disclose the use of unconfined compressive strength with unsupervised and supervised machine learning for rock type determination).
Regarding claim 11.
Andrew (Andrew, EbunOluwa, Xu, Chicheng, Odi, Uchenna, Sheludko, Stanislav, Silver, Andrew, and Yaser Zayer. “Machine Learning-Assisted Petrophysical Rock Type Classification and Permeability Estimation with Flow Zone Indicators.” Paper presented at the International Petroleum Technology Conference, Dhahran, Saudi Arabia, February 2024. doi: https://doi.org/10.2523/IPTC-23537-EA) discloses/teaches:
A system (Abstract: predicting petrophysical rock types (PRT) from well log using unsupervised and supervised machine learning algorithms is presented (see also Fig. 2); examiner interprets method to be performed on a computer system) comprising:
at least one processor and a memory storing instructions that, when executed by the at least one processor, cause the at least one processor to perform operations (examiner interprets method to be performed on a computer system having processor and memory capabilities) comprising:
obtaining well log data and core sample data of a subsurface formation (Abstract; p. 2, section “Geological Setting of the Study Area”: well log and core data set are acquired for analysis (see also p. 3, section “Methodology”));
using an unsupervised machine learning model to form rock type clusters based on the well log data (p. 4, section “Unsupervised Machine Learning Methodology”: K-means clustering unsupervised machine learning method is used to distinguish petrophysical rock types in the well log);
forming a training dataset including the well log data, the training dataset labeled based on the rock type clusters (p. 4, section “Hydraulic Fluid Units Analysis Methodology”: core data and well log data were used to identify a rock type in order to determine parameters (e.g., well log depth and porosity) for training a Random Forest machine learning technique);
training a supervised machine learning model using the training dataset (p. 4-6, section “Supervised Machine Learning Methodology”: the combined log-core dataset is split into training and test data for training the random forest classifier for petrophysical rock type classification).
Regarding “while drilling a well in the subsurface formation, obtaining logging-while-drilling data from drilling equipment used to drill the well; and determining rock types in the subsurface formation using the supervised machine learning model and the logging-while-drilling data”, Andrew (Andrew, EbunOluwa, Xu, Chicheng, Odi, Uchenna, Sheludko, Stanislav, Silver, Andrew, and Yaser Zayer. “Machine Learning-Assisted Petrophysical Rock Type Classification and Permeability Estimation with Flow Zone Indicators.” Paper presented at the International Petroleum Technology Conference, Dhahran, Saudi Arabia, February 2024. doi: https://doi.org/10.2523/IPTC-23537-EA) further discloses:
“The results indicate that the integrated approach serve to characterize the well. The K-means analysis reveals the diagenetic makeup of the reservoir, hinting at zones with non-reservoir and reservoir rocks. The HFU analysis supports the inherent permeability trend within the well, taking advantage of similar porosity-permeability correlations within lateral heterogeneities. The prediction of lithofacies makes it feasible to generalize the Random Forest classifier for use as a rock type classifier, and permeability regression predictor, for all uncored intervals drilled in the reservoir across the studied field with adequate certainty” (p. 13, section “Conclusions”: Random Forest classifier can be implemented for rock type classification for all intervals drilled in the field).
Amendt (Amendt et al., “Mechanical Characterization in Unconventional Reservoirs: A Facies-Based Methodology,” Petrophysics, October 2013, 54(5):457-464, 8 pages, IDS reference) discloses:
“A new core-testing protocol has been created to characterize rock mechanical parameters based on lithologic composition and rock texture. The goal is to characterize the main rock types in the basin using the geologic model as the integration point. Four individual core plugs are cut at the same depth reference and rock facies. Each plug is brought to failure in a single-stage triaxial test, the four independent tests are used to construct a Mohr-Coulomb failure envelop … For each rock type, representative distributions of Young’s modulus, Poisson’s ratio, unconfined compressive strength, cohesion and angle of internal friction are generated” (Abstract: main rock type characterization is achieved using core data, with unconfined compressive strength being generated for each rock type).
The closest prior art of record, taken individually or in combination, fail to teach or suggest (see italic text):
“generating, based on the well log data and the core sample data, an unconfined compressive strength log for the subsurface formation;
using an unsupervised machine learning model to form rock type clusters based on the unconfined compressive strength log and the well log data”
in combination with all other limitations within the claim, as claimed and defined by the applicant (the examiner submits that the prior art of record does not disclose the use of unconfined compressive strength with unsupervised and supervised machine learning for rock type determination).
Regarding claim 17.
Andrew (Andrew, EbunOluwa, Xu, Chicheng, Odi, Uchenna, Sheludko, Stanislav, Silver, Andrew, and Yaser Zayer. “Machine Learning-Assisted Petrophysical Rock Type Classification and Permeability Estimation with Flow Zone Indicators.” Paper presented at the International Petroleum Technology Conference, Dhahran, Saudi Arabia, February 2024. doi: https://doi.org/10.2523/IPTC-23537-EA) discloses/teaches:
One or more non-transitory machine-readable storage devices storing instructions, the instructions being executable by one or more processors, to cause performance of operations (Abstract: predicting petrophysical rock types (PRT) from well log using unsupervised and supervised machine learning algorithms is presented (see also Fig. 2); examiner interprets method to be performed on a computer system having processor and memory capabilities) comprising:
obtaining well log data and core sample data of a subsurface formation (Abstract; p. 2, section “Geological Setting of the Study Area”: well log and core data set are acquired for analysis (see also p. 3, section “Methodology”));
using an unsupervised machine learning model to form rock type clusters based on the well log data (p. 4, section “Unsupervised Machine Learning Methodology”: K-means clustering unsupervised machine learning method is used to distinguish petrophysical rock types in the well log);
forming a training dataset including the well log data, the training dataset labeled based on the rock type clusters (p. 4, section “Hydraulic Fluid Units Analysis Methodology”: core data and well log data were used to identify a rock type in order to determine parameters (e.g., well log depth and porosity) for training a Random Forest machine learning technique);
training a supervised machine learning model using the training dataset (p. 4-6, section “Supervised Machine Learning Methodology”: the combined log-core dataset is split into training and test data for training the random forest classifier for petrophysical rock type classification).
Regarding “while drilling a well in the subsurface formation, obtaining logging-while-drilling data from drilling equipment used to drill the well; and determining rock types in the subsurface formation using the supervised machine learning model and the logging-while-drilling data”, Andrew (Andrew, EbunOluwa, Xu, Chicheng, Odi, Uchenna, Sheludko, Stanislav, Silver, Andrew, and Yaser Zayer. “Machine Learning-Assisted Petrophysical Rock Type Classification and Permeability Estimation with Flow Zone Indicators.” Paper presented at the International Petroleum Technology Conference, Dhahran, Saudi Arabia, February 2024. doi: https://doi.org/10.2523/IPTC-23537-EA) further discloses:
“The results indicate that the integrated approach serve to characterize the well. The K-means analysis reveals the diagenetic makeup of the reservoir, hinting at zones with non-reservoir and reservoir rocks. The HFU analysis supports the inherent permeability trend within the well, taking advantage of similar porosity-permeability correlations within lateral heterogeneities. The prediction of lithofacies makes it feasible to generalize the Random Forest classifier for use as a rock type classifier, and permeability regression predictor, for all uncored intervals drilled in the reservoir across the studied field with adequate certainty” (p. 13, section “Conclusions”: Random Forest classifier can be implemented for rock type classification for all intervals drilled in the field).
Amendt (Amendt et al., “Mechanical Characterization in Unconventional Reservoirs: A Facies-Based Methodology,” Petrophysics, October 2013, 54(5):457-464, 8 pages, IDS reference) discloses:
“A new core-testing protocol has been created to characterize rock mechanical parameters based on lithologic composition and rock texture. The goal is to characterize the main rock types in the basin using the geologic model as the integration point. Four individual core plugs are cut at the same depth reference and rock facies. Each plug is brought to failure in a single-stage triaxial test, the four independent tests are used to construct a Mohr-Coulomb failure envelop … For each rock type, representative distributions of Young’s modulus, Poisson’s ratio, unconfined compressive strength, cohesion and angle of internal friction are generated” (Abstract: main rock type characterization is achieved using core data, with unconfined compressive strength being generated for each rock type).
The closest prior art of record, taken individually or in combination, fail to teach or suggest (see italic text):
“generating, based on the well log data and the core sample data, an unconfined compressive strength log for the subsurface formation;
using an unsupervised machine learning model to form rock type clusters based on the unconfined compressive strength log and the well log data”
in combination with all other limitations within the claim, as claimed and defined by the applicant (the examiner submits that the prior art of record does not disclose the use of unconfined compressive strength with unsupervised and supervised machine learning for rock type determination).
Regarding claims 2, 4-10, 13-16 and 18-20.
They are also distinguished over the prior art of record due to their dependency.
Allowable Subject Matter
Claims 3 and 12 are objected to as being dependent upon a rejected base claim, but would be allowable if rewritten in independent form including all of the limitations of the base claim and any intervening claims (see Claim Rejections - 35 USC § 101 section).
Conclusion
The prior art made of record and not relied upon is considered pertinent to applicant's disclosure.
Zhang; Jiazuo et al., US 20220004919 A1, PROBABILITY DISTRIBUTION ASSESSMENT FOR CLASSIFYING SUBTERRANEAN FORMATIONS USING MACHINE LEARNING
Reference discloses selecting machine learning models for lithology classification based on probability distributions.
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/LINA CORDERO/Primary Examiner, Art Unit 2857
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