DETAILED ACTION
The present application, filed on or after March 16, 2013, is being examined under the first inventor to file provisions of the AIA . The amendment filed 02/17/2026 has been received and considered. Claims 1-20 are presented for examination.
Response to Arguments
Regarding the Requirement for Information - 37 C.F.R. § 1.105, Applicant did not provide a response. As pointed out by in Examiner's rejection: '[a] complete reply to this Office action must include a complete reply to this requirement'.
Regarding the claim objections, the amendment corrected all deficiencies, and those objections are withdrawn.
Regarding the rejections under 101, Applicant's arguments have been considered, but they are not persuasive. Applicant argues, (see page 7, 2nd to next to last paragraph):
‘… Each independent claim has been amended to recite, in part, "based on the PRT labeling, implementing core labelling guided log space clustering for validation of core labeled classes and identification of unlabeled new classes" and "... based on the trained models, determining a reservoir reserve and reservoir production for the reservoir." Such operations cannot be performed in the human mind or by a human using a pen and paper, and consequently are not mental processes or an abstract idea.
Moreover, each independent claim has been amended to recite "based on the PRT labeling, classifying the first set of wells into tier 1 wells having well logs data and core samples data, tier 2 wells having well logs data only and tier 3 data having core samples data only" and "wherein the training comprises training tier 1 wells with well logs data and core samples data, training tier 2 wells with well logs data only and training tier 3 wells with core samples data only." Thus, each amended claim now recites more than just an idea of a solution or outcome, and instead recites sufficient details of how the solution to the problem of classifying wells, training models using well data, and using the trained models to determine a reservoir reserve and reservoir production are implemented…’
The MPEP reads (underline emphasis added):
‘2106.04… II… A… 2. Prong Two asks does the claim recite additional elements that integrate the judicial exception into a practical application?… If the additional elements in the claim integrate the recited exception into a practical application of the exception, then the claim is not directed to the judicial exception (Step 2A: NO) and thus is eligible at Pathway B… For a claim reciting a judicial exception to be eligible, the additional elements (if any) in the claim must "transform the nature of the claim" into a patent-eligible application of the judicial exception, Alice… either at Prong Two or in Step 2B’
‘2106.05(f) Mere Instructions To Apply An Exception [R-10.2019]… In addition to the abstract idea, the claims also recited the additional element of…’.
‘2106.07(a)… II… After identifying the judicial exception in the rejection, identify any additional elements (features/limitations/steps) recited in the claim beyond the judicial exception and explain why they do not integrate the judicial exception into a practical application and do not add significantly more to the exception’
About "additional elements", BASCOM1, (BASCOM hereinafter) reads:
“the ‘elements of each claim both individually and ‘as an ordered combination’ to determine whether the additional elements [beyond those that recite the abstract idea”.
Examiner's response: Applicant’s argument is not persuasive, because Applicant’s arguments conflate judicial exception(s) or abstract idea(s) (Step 2A, Prong One) with additional elements (Step 2A, Prong Two or Step 2B). Throughout the prosecution of this application, in accordance with the guidance set forth in MPEP (supra) and in several decisions, BASCOM (supra) for example, the Examiner does not conflate judicial exception(s) or abstract idea(s) (Step 2A, Prong One) with additional elements (Step 2A, Prong Two or Step 2B).
Applicant argues that the additional elements "based on the PRT labeling, implementing core labelling guided log space clustering for validation of core labeled classes and identification of unlabeled new classes" and "at least based on the one or more petro-rock type (PRT) labeling, training one or more models for the reservoir using one or more machine learning algorithms, wherein the training comprises training tier 1 wells with well logs data and core samples data, training tier 2 wells with well logs data only and training tier 3 wells with core samples data only" are not judicial exception(s) or abstract idea(s), but the additional elements were addressed in Examiner's rejection Step 2A, Prong Two and/or Step 2B. Applicant's arguments do not address these limitations as additional elements, as pointed out by the Examiner. Therefore, the rejections are maintained.
Regarding the arguments with respect to the rejection under 103, Applicant’s arguments with respect to the independent claims have been fully considered, but they are not persuasive. Applicant argues that the prior art disclosures in the previous rejection fail to teach the newly added limitations. These features of Applicants' claims and arguments were newly added. The previous Office Action could not have pointed out disclosures of a limitation that was not claimed before. Claims are rejected over Xu taken in view of Kushwaha instead of Xu taken in view of Alghazal, and Kushwaha is newly cited. While the claimed subject matter is rejected under 112 as noted below, Examiner has applied prior art based on a good faith interpretation of the claimed language and delimited information provided in the specification.
Requirement for Information under 37 CFR 1.105
Applicant and the assignee of this application are required under 37 CFR 1.105 to provide the following information that the Examiner has determined is reasonably necessary to the examination of this application.
This is in response to the information disclosure statement (IDS) filed 09/20/2022. In response to this requirement, please provide answers to each of the following interrogatories eliciting factual information:
Please indicate which of the cited references of the IDS are materially relevant to the claim limitations “… petro-rock type (PRT) labeling… training one or more models for the reservoir using one or more machine learning algorithms… applying the one or more models to the second pool of input data to determine a characteristic of the reservoir…”. The Examiner has considered a random sampling of references of the IDS. The sampling of these references failed to produce any materially relevant subject matter with respect to the claimed limitations. Note, references not considered by the Examiner are crossed out on the form 1449.
Some references sampled are provided as background information for architecture – "Digital Image Analysis in Microscopy for Objects and Architectural Conservation" (No. 391); adversarial nets – “Generative Adversarial Nets" (No. 396); and time domain – "Matching Pursuits With Time-Frequency Dictionaries" (No. 460). However, these references fail to teach the subject matter of the claims, and bear no material relevance to the claimed subject matter. Specifically, the prior art sampled fails to teach the limitations “… petro-rock type (PRT) labeling… training one or more models for the reservoir using one or more machine learning algorithms… applying the one or more models to the second pool of input data to determine a characteristic of the reservoir…”.
Another reference sampled, the NPL reference with 847 pages titled “Principles of Optics, Electromagnetic Theory of Propagation, Interference and Diffraction of Light” (No. 336) is a book about interference and diffraction of light. The examiner could not find a relationship to any of the claimed subject matter. Therefore, this book may not be prior art to the claimed subject matter. No interference or diffraction of light are described in the Specification either.
Another reference sampled, the NPL reference titled “A deep learning framework for morphologic detail beyond the diffraction limit in infrared spectroscopic imaging” (No. 378) is about diffraction in infrared spectroscopic imaging. The examiner could not find a relationship to any of the claimed subject matter. Therefore, this reference may not be prior art to the claimed subject matter. No diffraction or infrared imaging are described in the Specification either.
For these reasons, the request for information is considered necessary. Applicant is asked to specify which of the cited references are materially relevant to the limitations “… petro-rock type (PRT) labeling… training one or more models for the reservoir using one or more machine learning algorithms… applying the one or more models to the second pool of input data to determine a characteristic of the reservoir…”.
In responding to those requirements that require copies of documents, where the document is a bound text or a single article over 50 pages, the requirement may be met by providing copies of those pages that provide the particular subject matter indicated in the requirement, or where such subject matter is not indicated, the subject matter found in applicant’s disclosure.
The applicant is reminded that the reply to this requirement must be made with candor and good faith under 37 CFR 1.56. Where the applicant does not have or cannot readily obtain an item of required information, a statement that the item is unknown or cannot be readily obtained may be accepted as a complete reply to the requirement for that item.
A complete reply to this Office action must include a complete reply to this requirement. The time period for reply to this requirement coincides with the time period for reply to the enclosed Office action.
Claim Objections
Claims refer to the terms “the PRT labeling” and “the one or more petro-rock type (PRT) labeling”, it would be better to uniquify to avoid any possible antecedent issues.
Appropriate correction or clarification is required.
Claim Rejections - 35 USC § 112
The following is a quotation of 35 U.S.C. 112(b):
(b) CONCLUSION.—The specification shall conclude with one or more claims particularly pointing out and distinctly claiming the subject matter which the inventor or a joint inventor regards as the invention.
The following is a quotation of 35 U.S.C. 112 (pre-AIA ), second paragraph:
The specification shall conclude with one or more claims particularly pointing out and distinctly claiming the subject matter which the applicant regards as his invention.
Claims 1-20 are rejected under 35 U.S.C. 112(b) or 35 U.S.C. 112 (pre-AIA ), second paragraph, as being indefinite for failing to particularly point out and distinctly claim the subject matter which applicant regards as the invention.
As to claim 1, line(s) 14-15, the cited feature "training tier 1 wells with well logs data and core samples data, training tier 2 wells with well logs data only and training tier 3 wells with core samples data only" makes the claim indefinite because it fails to point out the precise meaning of the cited feature. It is unclear if wells can be trained to do anything. Examiner notes that the specification is mute about how wells are trained.
As to claim(s) 10 and 19, the same deficiency applies.
Claim 1 recites the limitation "the trained models" in line(s) 16. There is insufficient antecedent basis for this limitation in the claim. Antecedent calls for “the trained one or more models” and not “the trained models".
As to claim(s) 10 and 19, the same deficiency applies.
Claim 2 recites the limitation "the second pool of input data" in line(s) 7. There is insufficient antecedent basis for this limitation in the claim. The anteceding limitation was amended out.
As to claim(s) 11 and 20, the same deficiency applies.
Claim 2 recites the limitation "the characteristic of the reservoir" in line(s) 7-8. There is insufficient antecedent basis for this limitation in the claim. The anteceding limitation was amended out.
As to claim(s) 8, 11, 17, and 20, the same deficiency applies.
Dependent claims inherit the defect of the claim from which they depend.
Claim Rejections - 35 USC § 101
35 U.S.C. 101 reads as follows:
Whoever invents or discovers any new and useful process, machine, manufacture, or composition of matter, or any new and useful improvement thereof, may obtain a patent therefor, subject to the conditions and requirements of this title.
Claims 1-20 are rejected under 35 U.S.C. 101 because the claimed invention is directed to an abstract idea without significantly more.
Independent claim 1, Step 1: a method (process = 2019 PEG Step 1 = yes).
Independent claim 1, Step 2A, Prong One: claim recites:
performing one or more petro-rock type (PRT) labeling at least based on the first pool of input data…
based on the PRT labeling, classifying the first set of wells into tier 1 wells having well logs data and core samples data, tier 2 wells having well logs data only and tier 3 data having core samples data only…
based on the trained models, determining a reservoir reserve and a reservoir production for the reservoir.
Independent claim 1 is substantially drawn to mental concepts; observation, evaluation, judgment, opinion. Information and data also fall within the realm of abstract ideas because information and/or data are intangible. See Electric Power Group2 (Electric Power hereinafter); “Information… is an intangible”.
As to the limitations "performing one or more petro-rock type (PRT) labeling at least based on the first pool of input data" and "based on the PRT labeling, classifying the first set of wells into tier 1 wells having well logs data and core samples data, tier 2 wells having well logs data only and tier 3 data having core samples data only", these activities can be characterized as entailing a user labeling/deciding/classifying (judgments, opinions) based on information that can be performed in the human mind or by a human using a pen and paper (mental processes). Examiner notes that there is no elaboration of any special meanings for the "classifying" in the claims and Specification. See Specification paragraphs [0037], [0038].
As to the limitations "based on the trained models, determining a reservoir reserve and a reservoir production for the reservoir", determinations are mental in nature. These limitations, as drafted and under a broadest reasonable interpretation, can be characterized as entailing a user analyzing deciding/determining (judgments, opinions), that can be performed in the human mind or by a human using a pen and paper. Examiner notes that there is no elaboration of any special meanings for these amended limitations in the claims and Specification. See specification paragraphs:
'[0042]… implementation may apply the one or more models to the second pool of input data to determine a characteristic of the reservoir (516). Examples of characteristic of the reservoir can include a reservoir reserve, or a reservoir production'.
If a claim limitation, under its broadest reasonable interpretation, covers mental processes, then it falls within the "(c) Mental processes" grouping of abstract ideas (2019 PEG Step 2A, Prong One; Abstract Idea Grouping? = Yes, (c) Mental processes).
Independent claim 1, Step 2A, Prong Two: The claim recites the additional element computer-implemented.
As to the limitations "accessing a first pool of input data encoding a plurality of petrophysical properties of core samples extracted from a first set of wells of a reservoir and encoding well logs obtained from the first set of wells", they appear to be analogous to “mere data gathering”. Data gathering, including when limited to particular content does not change its character as information, is also within the realm of abstract ideas. Data gathering has not been held by the courts to be enough to qualify as “significantly more”. See Electric Power.
As to the limitations "at least based on the one or more petro-rock type (PRT) labeling, training one or more models for the reservoir using one or more machine learning algorithms, wherein the training comprises training tier 1 wells with well logs data and core samples data, training tier 2 wells with well logs data only and training tier 3 wells with core samples data only", these limitations represent no more than just “apply it” limitations, because they recite only the idea of a solution or outcome, i.e. these claim limitations fail to recite details of how a solution to a problem is accomplished.
As to the limitations "based on the PRT labeling, implementing core labelling guided log space clustering for validation of core labeled classes and identification of unlabeled new classes", these limitations represent no more than just “apply it” limitations, because they invoke computers or other machinery merely as a tool to perform an existing process.
This judicial exception is not integrated into a practical application (2019 PEG Step 2A, Prong Two: Additional elements that integrate the Judicial exception/Abstract idea into a practical application? = NO).
Independent claim 1, Step 2B: As discussed with respect to Step 2A, the claim recites the additional element computer-implemented. It is recited at a high level of generality and is recited as performing generic computer functions routinely used in computer applications. Generic computer components recited as performing generic computer functions that are well-understood, routine and conventional activities amount to no more than implementing the abstract idea with a computerized system. See MPEP 2106.05 Particular Machine (b). The use of a computer to implement the abstract idea of a mathematical or mental algorithm has not been held by the courts to be enough to qualify as “significantly more”. The implementation on a computing system is described in the Specification (underline emphasis added):
"[0043]… computer 602 is intended to encompass any computing device such as a server, desktop computer, laptop/notebook computer, wireless data port, smart phone, personal data assistant (PDA), tablet computing device, one or more processors within these devices, another computing device, or a combination of computing devices, including physical or virtual instances of the computing device, or a combination of physical or virtual instances of the computing device".
As discussed with respect to Step 2A, the claim recites data gathering, these limitations are recited at a high level of generality; and therefore, remain insignificant extra-solution activity even upon reconsideration.
As discussed with respect to Step 2A, Prong two, limitations reciting only the idea of a solution or outcome are just “apply it” limitations, because these claim limitations fail to recite details of how a solution to a problem is accomplished. The training models claimed limitations are so broad that little is known about how the training is performed. There is no elaboration of any special meanings for these limitations in the claims and Specification. See MPEP 2106.05(f)(1). As to the limitations "training tier 1 wells with well logs data and core samples data, training tier 2 wells with well logs data only and training tier 3 wells with core samples data only", the limitations are so broad that little is known about how they are performed. Examiner notes that the specification is mute about how wells are trained (see 112 Rejection above).
As discussed with respect to Step 2A, Prong two, limitations invoking computers or other machinery merely as a tool to perform an existing process are just “apply it” limitations – simply adding a general purpose computer or computer components after the fact to an abstract idea (e.g., a mathematical equation). See MPEP 2106.05(f)(2). These limitations amount to implementation of mental concepts, labelling (judgments, opinions), See for example in the Specification:
"[0033]… implementations may pursue core labelling guided log space clustering for validation of core labeled classes and identification of unlabeled new classes. The implementations may also augment PRT labeling (e.g., label for Tier 1) to cover, for example, uncored or bypassed classes (non-reservoirs), hybrid classes (mixed layers or heterogeneous), and fluid effect (e.g., an oil/water ratio)".
Thus, taken alone the individual additional elements do not amount to significantly more than the above-identified judicial exception (the abstract idea). Looking at the additional elements as an ordered combination adds nothing that is not already present when looking at the additional elements taken individually. There is no indication that their combination improves the functioning of a computer itself or improves any other technology (underline emphasis added). Therefore, the claim does not amount to significantly more than the abstract idea itself (2019 PEG Step 2B: NO).
Claims 10 and 19 recite substantially the same elements as claim 1 and are rejected for the same reasons above. Further, the additional elements of claims 10 and 19 are rejected below:
Independent claims 10 and 19, Step 2A Prong two and 2B: As to the further additional elements computer processors and a computer-readable medium, they are interpreted as drawn to a generic computer. (See Independent claim 1, Step 2B above). The use of a computer to implement the abstract idea of a mathematical or mental algorithm has not been held by the courts to integrate a judicial exception into a practical application or provide significantly more" “significantly more”.
Dependent claims Step 2A, Prong One: Dependent claims are substantially drawn to mental processes as their independent claims. (See Independent claim 1, Step 2A, Prong One above). As to the limitations "8/17… wherein the characteristic of the reservoir includes: a reservoir reserve, or a reservoir production", see 112 Rejection above. If a claim limitation, under its broadest reasonable interpretation, covers mental processes, then it falls within the "(c) Mental processes" grouping of abstract ideas (2019 PEG Step 2A, Prong One; Abstract Idea Grouping? = Yes, (c) Mental processes).
Dependent claims Step 2A Prong two:
Dependent claims further recite "2/11/20… wherein the first pool of input data include more than one type of core data, wherein the more than one type of core data encode the plurality of petrophysical properties of core samples extracted from the reservoir", "3/5/12/14/… wherein the petrophysical properties comprise: a porosity, a permeability, a pore geometry, a capillary pressure, and a saturation height function", and "4/13/… wherein the first pool of input data include one or more measurement logs wherein the one or more measurement logs encode petrophysical properties of rocks in boreholes drilled at the reservoir"; it appears to be analogous to “mere data gathering”. These claim limitations further the data gathering of their independent claims. (See Independent claim 1, Step 2A Prong two above).
As to the limitations "2/11/20… wherein the one or more models identify at least one correlation among the plurality of petrophysical properties, and wherein, when applying the one or more models to the second pool of input data, the characteristic of the reservoir is determined, at least in part, based on the at least one correlation", "7/16… wherein the one or more models are configured to perform at least one of: a regression, a classification, a clustering, or a segmentation", and "8/17… wherein the characteristic of the reservoir includes: a reservoir reserve, or a reservoir production"; these limitations represent no more than just “apply it” limitations, because they invoke computers or other machinery merely as a tool to perform an existing process.
As to the limitations "6/15… wherein the one or more machine learning algorithms comprise: a support vector machine (SVM), a self-organizing map, a random forest, an artificial neural network, and a convolutional neural network (CNN)" and "9/18… validating the one or more models at least based on a testing pool of input data, wherein the testing pool of input data is different from the first pool of input data", these limitations represent no more than just “apply it” limitations, because they recite only the idea of a solution or outcome, i.e. these claim limitations fail to recite details of how a solution to a problem is accomplished.
This judicial exception is not integrated into a practical application of the exception (2019 PEG Step 2A, Prong Two: Additional elements that integrate the Judicial exception/Abstract idea into a practical application? = NO).
Dependent claims Step 2B:
As discussed with respect to Step 2A, claims recite data gathering at a high level of generality; and therefore, these limitations remain insignificant extra-solution activity even upon reconsideration. See MPEP § 2106.05(g).
As discussed with respect to Step 2A, Prong two, the limitations identified as just “apply it” merely invoke computers as a tool to perform an existing process. (See Independent claim 1, Step 2B above).
As discussed with respect to Step 2A, Prong two, limitations reciting only the idea of a solution or outcome are just “apply it” limitations, because these claim limitations fail to recite details of how a solution to a problem is accomplished. See MPEP 2106.05(f)(1). The validating the models claimed limitations are so broad that little is known about how the validating is performed. There is no elaboration of any special meanings for these limitations in the claims and Specification. (See Independent claim 1, Step 2B above).
Therefore, the claims do not amount to significantly more than the abstract idea itself (2019 PEG Step 2B: NO).
Claim Rejections - 35 USC § 103
While the claimed subject matter is rejected under 112 as noted above, Examiner has applied prior art based on a good faith interpretation of the claimed language and delimited information provided in the specification.
The following is a quotation of 35 U.S.C. 103 which forms the basis for all obviousness rejections set forth in this Office action:
A patent for a claimed invention may not be obtained, notwithstanding that the claimed invention is not identically disclosed as set forth in section 102, if the differences between the claimed invention and the prior art are such that the claimed invention as a whole would have been obvious before the effective filing date of the claimed invention to a person having ordinary skill in the art to which the claimed invention pertains. Patentability shall not be negated by the manner in which the invention was made.
The factual inquiries set forth in Graham v. John Deere Co., 383 U.S. 1, 148 USPQ 459 (1966), that are applied for establishing a background for determining obviousness under 35 U.S.C. 103(a) are summarized as follows:
1. Determining the scope and contents of the prior art.
2. Ascertaining the differences between the prior art and the claims at issue.
3. Resolving the level of ordinary skill in the pertinent art.
4. Considering objective evidence present in the application indicating obviousness or nonobviousness.
Examiner would like to point out that any reference to specific figures, columns and lines should not be considered limiting in any way, the entire reference is considered to provide disclosure relating to the claimed invention.
Claims 1-20 are rejected under 35 U.S.C. 103(a) as being unpatentable over Xu et al., (Xu hereinafter), "Rock classification in carbonate reservoirs based on static and dynamic petrophysical properties estimated from conventional well logs", taken in view of Amit Kushwaha, (Kushwaha hereinafter), U.S. Patent 11668853 (see PTO-892 Notice of Reference Cited dated 11/10/2025).
As to claim 1, Xu discloses a computer-implemented method comprising: accessing a first pool of input data encoding a plurality of petrophysical properties of core samples extracted from a first set of wells of a reservoir (see "use Leverett’s method to classify hydraulic rock types based on core measurements" in page 2, 3rd paragraph)… performing one or more petro-rock type (PRT) labeling at least based on the first pool of input data… at least based on the one or more petro-rock type (PRT) labeling, (see "labeling" as "classified" and "machine learning" in "neural network", "KGS… conducted a comprehensive core study and classified rocks into 11 lithofacies based on depositional sequences (siliciclastic or carbonate), rock texture, and principal pore size. A standard single-hidden-layer neural network was then applied to predict lithofacies based on wireline well logs in 1,600 node wells" in page 4, 1st paragraph)… and based on the trained models, determining a reservoir reserve and a reservoir production for the reservoir (see "We apply the proposed method to data acquired in the Hugoton gas field (Kansas), which comprises mixed clastic-carbonate gas-bearing rock sequences, to classify petrophysical rock types" in page 2, 3rd paragraph).
Xu does not disclose, but Kushwaha discloses encoding well logs obtained from the first set of wells (see "During training, the encoder network 320 may characterize input space 310 in terms of a low-dimensional encoded space" in col. 11, lines 51-53)… based on the PRT labeling, implementing core labelling guided log space clustering for validation of core labeled classes and identification of unlabeled new classes (see "machine learning networks may be selected based on prediction performance (e.g., Precision, Recall, F1-score, etc.) on a validation dataset and/or on a test dataset" in col. 13, lines 64-67); based on the PRT labeling, classifying the first set of wells into tier 1 wells having well logs data and core samples data, tier 2 wells having well logs data only and tier 3 data having core samples data only (see "With the benefit of the trained decoder network 340, the optimization may be able to search a low-dimensional, geology-conforming space for models which are consistent with quantifiable data (e.g., geophysical, seismic, electromagnetic, gravimetric, well-logs, core samples, etc.)" in col. 12, lines 2-17), training tier 2 wells with well logs data only and training tier 3 wells with core samples data only (see "training dataset may exhibit plausible geologic behavior relevant to the subsurface region of interest, including petrophysical parameters (e.g., porosity, permeability, density, resistivity, elastic wave velocities, etc.) and corresponding rock types. The training dataset may comprise actual field-recorded data, or interpretations thereof, in geologic model form, and/or models resulting from computer simulations of earth processes. The training dataset may comprise multiple petrophysical parameters. For example, the training dataset may include a tabular listing of petrophysical parameters and potentially corresponding rock types. As another example, the training dataset may include a listing of petrophysical parameters and probability-weighted listings of pluralities of potentially corresponding rock types. As another example, the training dataset may include charts, graphs, and/or other data structures relating petrophysical parameters to potentially corresponding rock types. As yet another example, the training dataset may include representations of subsurface regions (e.g., models and/or images) with identified rock types (e.g., labels). In some embodiments, a combination of any two or more of these types of datasets may be included in the training dataset" in col. 7, lines 8-30)… training (see "utilizing a machine learning system to infer rock type from petrophysical parameters. For example, a deep neural network (DNN) may be trained to infer rock type from petrophysical parameters" in col. 6, lines 54-58)… wherein the training comprises training tier 1 wells with well logs data and core samples data (see "With the benefit of the trained decoder network 340, the optimization may be able to search a low-dimensional, geology-conforming space for models which are consistent with quantifiable data (e.g., geophysical, seismic, electromagnetic, gravimetric, well-logs, core samples, etc.)" in col. 12, lines 2-17), training tier 2 wells with well logs data only and training tier 3 wells with core samples data only (see "training dataset may exhibit plausible geologic behavior relevant to the subsurface region of interest, including petrophysical parameters (e.g., porosity, permeability, density, resistivity, elastic wave velocities, etc.) and corresponding rock types. The training dataset may comprise actual field-recorded data, or interpretations thereof, in geologic model form, and/or models resulting from computer simulations of earth processes. The training dataset may comprise multiple petrophysical parameters. For example, the training dataset may include a tabular listing of petrophysical parameters and potentially corresponding rock types. As another example, the training dataset may include a listing of petrophysical parameters and probability-weighted listings of pluralities of potentially corresponding rock types. As another example, the training dataset may include charts, graphs, and/or other data structures relating petrophysical parameters to potentially corresponding rock types. As yet another example, the training dataset may include representations of subsurface regions (e.g., models and/or images) with identified rock types (e.g., labels). In some embodiments, a combination of any two or more of these types of datasets may be included in the training dataset" in col. 7, lines 8-30)…
Xu and Kushwaha are analogous art because they are related to petrophysical reservoir characterization.
Therefore, it would have been obvious to one of ordinary skill in this art before the effective filing date of the claimed invention to use Kushwaha with Xu, because Kushwaha provides "enhanced systems and methods for estimating rock properties", and as a result, Kushwaha reports the following advantages "better estimation of rock properties that may directly enable improved results from geophysical modeling and/or interpretation (e.g., identification of geologic features, faults, horizons, salt domes, etc.). For example, rock type probability models may exhibit sharper boundaries than seismic data models, thereby facilitating more precise interpretation… producing sharp, geologically-consistent predictions for object extraction; incorporating geological priors and/or interpreters' expectations (e.g., image priors) into training for learning seismic patterns (especially training of a machine learning system); mitigating uncertainty in rock type probability models with the use of additional data, such as geologic priors (geological information that was available before the solution was formed and which was incorporated into the solution), well logs, and/or joint inversion of different geophysical data sets; utilizing machine learning technology to automatically infer rock types from petrophysical parameters in the context of a sequence labeling problem; and enhanced automation of procedures for generating subsurface models. Such automation may accelerate the generation of subsurface models, reduce subjective bias or error, and reduce the geoscience workforce's exposure to ergonomic health risks (e.g., exposure to repetitive tasks and injuries therefrom)" (see col. 6, lines 21-50).
As to claim 2, Xu discloses wherein the first pool of input data include more than one type of core data, wherein the more than one type of core data encode the plurality of petrophysical properties of core samples extracted from the reservoir (see "After log-based rock typing, we implement rock-type based porosity-permeability correlations to estimate permeability based on porosity estimates from well logs. In addition, the vertical distribution of water saturation can be calculated using rock-type-based saturation-eight relations derived from core mercury injection capillary pressure (MICP) data. Estimates of permeability and water saturation are then verified using core measurements" in page 3, last paragraph), wherein the one or more models identify at least one correlation among the plurality of petrophysical properties (see "After log-based rock typing, we implement rock-type based porosity-permeability correlations to estimate permeability based on porosity estimates from well logs. In addition, the vertical distribution of water saturation can be calculated using rock-type-based saturation-height relations derived from core mercury injection capillary pressure (MICP) data. Estimates of permeability and water saturation are then verified using core measurements" in page 3, last paragraph), and wherein, when applying the one or more models to the second pool of input data, the characteristic of the reservoir is determined, at least in part, based on the at least one correlation (see "classifies rock types via cluster analysis on all relevant petrophysical attributes including estimated petrophysical properties using well logs, saturation-height relation (only applicable above the hydrocarbon-water contact), and invasion-induced log signatures such as separation between resistivity logs with different radial lengths of investigation" in page 3, next to last paragraph).
As to claim 3, Xu discloses wherein the petrophysical properties comprise: a porosity, a permeability (see "characterization of invasion-induced log signatures can be integrated into rock classification based on conventional well logs. Permeability is then estimated via porosity-permeability correlations specific for each rock type" in page 2, 3rd paragraph), a pore geometry, a capillary pressure (see "Core-Based Hydraulic Rock Typing. Hydraulic rock typing considers both storage and flow capacity of reservoir rocks and should be based on both pore size distribution and connectivity… we use hydraulic rock types to rank dynamic rock-fluid properties including saturation-dependent capillary pressure" in page 3, 2nd paragraph), and a saturation height function (see "we obtain… saturation-height relations" in page 2, 3rd paragraph).
As to claim 4, Xu discloses wherein the first pool of input data include one or more measurement logs wherein the one or more measurement logs encode petrophysical properties of rocks in boreholes drilled at the reservoir (see "Well logs input to the inversion include gamma ray (GR), apparent electrical resistivity, density, neutron porosity, and PEF… Based on GR-spectroscopy data in addition to PEF and density logs in other wells penetrating this formation" in page 3, 1st paragraph).
As to claim 5, Xu discloses wherein the petrophysical properties comprise: a porosity, a permeability (see "characterization of invasion-induced log signatures can be integrated into rock classification based on conventional well logs. Permeability is then estimated via porosity-permeability correlations specific for each rock type" in page 2, 3rd paragraph), a pore geometry, a capillary pressure (see "Core-Based Hydraulic Rock Typing. Hydraulic rock typing considers both storage and flow capacity of reservoir rocks and should be based on both pore size distribution and connectivity… we use hydraulic rock types to rank dynamic rock-fluid properties including saturation-dependent capillary pressure" in page 3, 2nd paragraph), and a saturation height function (see "we obtain… saturation-height relations" in page 2, 3rd paragraph).
As to claim 6, Xu discloses wherein the one or more machine learning algorithms comprise: (see "KGS… conducted a comprehensive core study and classified rocks into 11 lithofacies based on depositional sequences (siliciclastic or carbonate), rock texture, and principal pore size. A standard single-hidden-layer neural network was then applied to predict lithofacies" in page 4, 1st paragraph).
As to claim 7, Xu discloses wherein the one or more models are configured to perform at least one of: (see "petrophysical rock classification in carbonate reservoirs that honors multiple well logs and emphasizes the signature of mud-filtrate invasion… classifies rocks based on both static and dynamic petrophysical properties" in page 1, 1st paragraph)
As to claim 8, Xu discloses wherein the characteristic of the reservoir includes: a reservoir reserve, or a reservoir production (see "We apply the proposed method to data acquired in the Hugoton gas field (Kansas), which comprises mixed clastic-carbonate gas-bearing rock sequences, to classify petrophysical rock types" in page 2, 3rd paragraph).
As to claim 9, Xu discloses validating the one or more models at least based on a testing pool of input data, wherein the testing pool of input data is different from the first pool of input (see "validating" as “verified", "After log-based rock typing, we implement rock-type based porosity-permeability correlations to estimate permeability based on porosity estimates from well logs. In addition, the vertical distribution of water saturation can be calculated using rock-type-based saturation-height relations derived from core mercury injection capillary pressure (MICP) data. Estimates of permeability and water saturation are then verified using core measurements" in page 3, last paragraph).
As to claims 10-20, these claims recite a computer system and a computer-readable medium for performing the method of claims 1-9. Xu discloses "we implement a depth-by-depth joint inversion of well logs instead of bed-by-bed interpretation in order to reduce CPU time" (see page 2, next to last paragraph)) for performing a method that teaches claims 1-9. Therefore, claims 10-20 are rejected for the same reasons given above.
Conclusion
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.
The prior art made of record and not relied upon is considered pertinent to applicant's disclosure.
Nadege Bize-Forest, U.S. Patent 11814931, discloses "validation… may be the k-fold cross-validation. For example, the model may be trained on k (−X) wells and validated on X wells. The procedure is repeated k times to give the chance for each well to be part of the training data set and be part of the testing data at least once" (see col. 7, lines 18-23).
Examiner would like to point out that any reference to specific figures, columns and lines should not be considered limiting in any way, the entire reference is considered to provide disclosure relating to the claimed invention.
Any inquiry concerning this communication or earlier communications from the examiner should be directed to JUAN CARLOS OCHOA whose telephone number is (571)272-2625. The examiner can normally be reached Mondays, Tuesdays, Thursdays, and Fridays 9:30AM - 8:00 PM.
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/JUAN C OCHOA/Primary Examiner, Art Unit 2186
1 BASCOM Global Internet Services, Inc. v. AT&T Mobility LLC, U.S. Court of Appeals for the Federal Circuit, No. 2015-1763 (June 27, 2016)
2 Electric Power Group, LLC v. Alstom S.A., 119 USPQ2d 1739 Fed. Cir. 2016
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