DETAILED ACTION
Information Disclosure Statement
1. Applicant has listed more than 300 references in the IDS since they are deemed relevant to the claimed invention according to the Applicant, with many NPL documents, each with lengthy pages. The Examiner requests the Applicant to assist in identifying the most relevant references so that references are accurately vetted for relevance and patentability, lest any error in the Examiner’s examination of the IDS. Applicant is reminded of the section 2004, paragraph 13 of the MPEP (reproduced below for convenience), which provides guidance on too many IDS.
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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.
3. Claims 1-20 are rejected under 35 U.S.C. 101 because the claimed invention is directed to a judicial exception (i.e., a law of nature, a natural phenomenon, or an abstract idea) without being integrated into a practical application and do not include additional elements that amount to significantly more than the judicial exception.
Utilizing the two step process adopted by the Supreme Court (Alice Corp vs CLS Bank Int'l, US Supreme Court, 110 USPQ2d 1976 (2014) and the recent 101 guideline, Federal Register Vol. 84, No., Jan 2019)), determination of the subject matter eligibility under the 35 USC 101 is as follows: Specifically, the Step 1 requires claim belongs to one of the four statutory categories (process, machine, manufacture, or composition of matter). If Step 1 is satisfied, then in the first part of Step 2A (Prong one), identification of any judicial recognized exceptions in the claim is made. If any limitation in the claim is identified as judicial recognized exception, then proceeding to the second part of Step 2A (Prong two), determination is made whether the identified judicial exception is being integrated into practical application. If the identified judicial exception is not integrated into a practical application, then in Step 2B, the claim is further evaluated to see if the additional elements, individually and in combination, provide “inventive concept” that would amount to significantly more than the judicial exception. If the element and combination of elements do not amount to significantly more than the judicial recognized exception itself, then the claim is ineligible under the 35 USC 101.
Looking at the claims, the claims satisfy the first part of the test 1A, namely the claims are directed to two of the four statutory classes, apparatus and method. In Step 2A Prong one, we next identify any judicial exceptions in the claims. In Claim 1 (as a representative example), we recognize that the limitations “obtaining reservoir parameters representing properties of the subsurface reservoir, forming a discrete fracture network by processing the obtained reservoir parameters to identify the presence and extent of natural fractures at locations in the subsurface hydrocarbon reservoir, determining, using the discrete fracture network, a fracture density index (FDI), wherein determining, using the discrete fracture network, a fracture density index (FDI) comprises generating a raster map from the discrete fracture network, the raster map representing a fracture density per area, receiving, first formation testing data produced by one or more formation tests, the formation tests comprising a leak-off test (LOT), a formation integrity test (FIT), and an extended leak-off test (XLOT),
receiving, second formation testing data produced by a diagnostic fracture injection test (DFIT), and determining, three-dimensional (3D) minimum horizontal stress horizontal stress in the naturally fractured hydrocarbon reservoir, receiving as input, the reservoir parameters, the fracture density index, the first formation testing data, and the second first formation testing data” are abstract idea, as they recite mental process, under the BRI. Similar rejections are made for other independent and dependent claims. Furthermore, for the dependent claim 8, the limitation “using extreme gradient boosting” in “wherein the machine learning model is trained using extreme gradient boosting,” is an abstract idea as it recites mathematical concept. With the identification of abstract ideas, we proceed to Step 2A, Prong two, where with additional elements and taken as a whole, we evaluate whether the identified abstract idea is being integrated into a practical application.
In Step 2A, Prong two, the claims additionally recite using a machine learning model,” but said limitation is merely a recitation of generic machine learning. The claims also recite “the data processing system,” but said limitation is merely a recitation of general-purpose computer for implementing the abstract idea. The claims do not improve the functioning of any machines, and do not improve other technology. The claims also do not improve the functioning of the machine learning itself; rather, it uses generic machine learning to determine the minimum stress. At most, the claims are an improvement in the abstract idea of determining a three-dimensional minimum horizontal stress. However, improved or new abstract ideas are nevertheless abstract ideas, and not eligible under the 101. In short, the claims do not recite sufficient evidence to show that they are more than a drafting effort to monopolize the abstract idea. As such, the abstract idea is not integrated into a practical application. Consequently, with the identified abstract idea not being integrated into a practical application, we proceed to Step 2B and evaluate whether the additional elements provide “inventive concept” that would amount to significantly more than the abstract idea.
In Step 2B, the claims additionally recite the claims additionally recite using a machine learning model,” but said limitation is merely recitation of generic machine learning that is well-understood, routine and conventional. The claims additionally recite “the data processing system,” but said limitation is merely a recitation of general-purpose computer for implementing the abstract idea, that is well-understood, routine and conventional. As such, the claims do not provide additional elements that would amount to significantly y more than the abstract idea.
In Summary, the claims recite abstract idea without being integrated into a practical application, and do not provide additional elements that would amount to significantly more than the abstract idea. As such, taken as a whole, the claims are ineligible under the 35 USC 101.
Claim Rejections - 35 USC § 103
The following is a quotation of 35 U.S.C. 103 which forms the basis for all obviousness rejections set forth in this Office action:
A patent for a claimed invention may not be obtained, notwithstanding that the claimed invention is not identically disclosed as set forth in section 102, if the differences between the claimed invention and the prior art are such that the claimed invention as a whole would have been obvious before the effective filing date of the claimed invention to a person having ordinary skill in the art to which the claimed invention pertains. Patentability shall not be negated by the manner in which the invention was made.
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.
Claims 1-3, 9-11 and 15-18 are rejected under 35 U.S.C. 103 as being unpatentable over Noufal, US-PGPUB 2022/0291418 (hereinafter Noufal) (cited by the Applicant) in view of Lin et al., “An investigation of machine learning techniques to estimate minimum horizontal stress magnitude from borehole breakout,” Int. J or Mining Science and Technology (June, 2022) (hereinafter Lin) and Teran, US-PGPUB 2017/0269244 (hereinafter Teran)
Regarding Claims 1, 9 and 15. Noufal discloses determining three-dimensional minimum horizontal stress in a naturally fractured hydrocarbon reservoir (Paragraph [0168], 3D, minimum horizontal stress; Fig. 1), comprising:
obtaining reservoir parameters representing properties of the subsurface reservoir for
processing in a data processing system (Paragraph [0002], [0039], properties);
forming a discrete fracture network by processing the obtained reservoir parameters in
the data processing system to identify the presence and extent of natural fractures at locations in the subsurface hydrocarbon reservoir (Paragraph [0114], Discrete Fracture Network; Paragraphs [0126]-[0135]; Paragraph [0112], natural fractures),
determining, by the data processing system and using the discrete fracture network, a
fracture density index (FDI) (Paragraphs [0137]-[0139]), wherein determining, using the discrete fracture network, a fracture density index (FDI) comprises generating a raster map from the discrete fracture network, the raster map representing a fracture density per area (Paragraphs [0101]-0106], [0145], characterize fracture density using images),
receiving, at the data processing system, first formation testing data produced by one or
more formation tests, the formation tests comprising a leak-off test (LOT), a formation integrity test (FIT), and an extended leak-off test (XLOT) (Paragraph [0084], LOT/ELOT);
and determining, by the data processing system, three-dimensional (3D) minimum horizontal stress horizontal stress in the naturally fractured hydrocarbon reservoir using a machine learning model receiving, as input, the reservoir parameters, the fracture density index, the first formation testing data, and the second first formation testing data (Paragraph [0168], 3D, minimum horizontal stress; Fig. 1)
Noufal does not disclose receiving, at the data processing system, second formation testing data produced by a diagnostic fracture injection test (DFIT), and determining, by the data processing system, three-dimensional (3D) minimum horizontal stress horizontal stress in the naturally fractured hydrocarbon reservoir using a machine learning model receiving, as input, the reservoir parameters, the fracture density index, the first formation testing data, and the second first formation testing data.
Teran discloses well tests such as diagnostic fracture injection test that are routinely used to estimate the minimum horizontal stress (Paragraph [0009])
Lin discloses using machine learning techniques to estimate the minimum horizontal stress (Title; Abstract; Methodology section, Results section, and Conclusions, pages 1022-1028)
At the time of the invention filed, it would have been obvious to a person of ordinary skill in the art to use the teaching of Teran and Lin and receive, at the data processing system, second formation testing data produced by a diagnostic fracture injection test (DFIT), and determine, by the data processing system, three-dimensional (3D) minimum horizontal stress horizontal stress in the naturally fractured hydrocarbon reservoir using a machine learning model receiving, as input, the reservoir parameters, the fracture density index, the first formation testing data, and the second first formation testing data, with increased accuracy.
Regarding Claims 2, 10 and 16. Noufal discloses the reservoir parameters comprise seismic attributes from seismic surveys of the subsurface geological structure (Paragraph [0053], seismic velocities; [0064], [0066], [0076])
Regarding Claims 3, 11 and 17. Noufal discloses the properties comprise geomechanical properties comprising Young’s modulus, Poisson’s ratio, unconfined compressive strength, of any combination thereof (Paragraph [0123], [0131], [0167], Young’s modulus and ratio)
Regarding Claims 4, 12 and 18. Noufal discloses the properties comprise geomechanical properties comprising bulk density, vertical stress, pore pressure, or any combination thereof (Paragraph [0174], [0199], pore pressure)
Regarding Claims 5-6, 13-14 and 19-20. Noufal discloses determining a sweet spot for hydraulic fracturing stimulation based on the 3D minimum horizontal stress ,and performing a hydraulic fracturing stimulation operation based on the determined sweet spot (Paragraphs [0226]-[0230], hydrocarbon accumulations based on the simulation results; Figs. 1-2)
Regarding Claim 7. Teran discloses performing the diagnostic fracture injection test (DFIT) (Paragraph [0009])
Claim Rejections - 35 USC § 103
The following is a quotation of 35 U.S.C. 103 which forms the basis for all obviousness rejections set forth in this Office action:
A patent for a claimed invention may not be obtained, notwithstanding that the claimed invention is not identically disclosed as set forth in section 102, if the differences between the claimed invention and the prior art are such that the claimed invention as a whole would have been obvious before the effective filing date of the claimed invention to a person having ordinary skill in the art to which the claimed invention pertains. Patentability shall not be negated by the manner in which the invention was made.
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.
Claim 8 is rejected under 35 U.S.C. 103 as being unpatentable over Noufal, US-PGPUB 2022/0291418 in view of Lin, “An investigation of machine learning techniques to estimate minimum horizontal stress magnitude from borehole breakout,” Int. J or Mining Science and Technology (June, 2022) and Teran, US-PGPUB 2017/0269244 as applied to Claim 1 above, and further in view of Weijers, US-PGPUB 2022/0373711 (hereinafter Weijers)
Regarding Claim 8. The modified Lin does not explicitly disclose the machine learning model is trained using extreme gradient boosting.
Weijers discloses using extreme gradient boosting, one of the well-known machine learning techniques, in statistical modeling of hydraulic fracturing operations (Paragraph [0007])
Lin discloses testing various machine learning techniques to accurately determine the minimum horizontal stress. Although Lin does not explicitly disclose the extreme gradient boosting technique, it would have been obvious to use the well-known machine learning technique like extreme gradient boosting to obtain accurate minimum horizontal stress.
Conclusion
The prior art made of record and not relied upon is considered pertinent to applicant's disclosure.
Camargo et al., US-PGPUB 2019/0080122
Any inquiry concerning this communication or earlier communications from the examiner should be directed to HYUN D PARK whose telephone number is (571)270-7922. The examiner can normally be reached 11-4.
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/HYUN D PARK/Primary Examiner, Art Unit 2857
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