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
2. Previous rejection is withdrawn in view of the Applicant’s amendment filed on 07/17/2026.
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-1) (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), Teran, US-PGPUB 2017/0269244 (hereinafter Teran), Noufal et al., US-PGPUB 2024/0418905 (hereinafter Noufal-2) and Edwards, US-PGPUB 2012/0043077 (hereinafter Edwards)
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), 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)
Noufal does not disclose receiving, at the data processing system, second formation testing data produced by a diagnostic fracture injection test (DFIT), wherein the second formation testing data comprises a pressure falloff record for a pressure falloff period following the cessation of pumping into the formation, determining, by the data processing system, a fracture closure pressure (FCP) from the pressure falloff record of the second formation testing data, 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, and fracture closure pressure (FCP) determined from the second 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)
Edwards discloses a pressure falloff record for a pressure falloff period following the cessation of pumping into the formation, and determining a fracture closure pressure (FCP) from the pressure falloff record of the second formation testing data, where the pressure at fracture closure is known to be a measure of the minimum horizontal stress (Paragraph [0031])
Noufal-2 discloses using numerous type of fracture data, including fracture closure pressure as inputs to machine learning to determine minimum horizonal stress (Paragraph [0075]-[0077])
At the time of the invention filed, it would have been obvious to a person of ordinary skill in the art to use the teachings of Teran, Edwards, Noufal-2 and Lin and receive, at the data processing system, second formation testing data produced by a diagnostic fracture injection test (DFIT), wherein the second formation testing data comprises a pressure falloff record for a pressure falloff period following the cessation of pumping into the formation, determining, by the data processing system, a fracture closure pressure (FCP) from the pressure falloff record of the second formation testing data, 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, and fracture closure pressure (FCP) determined from the second 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 Claim 7. Teran discloses performing the diagnostic fracture injection test (DFIT) (Paragraph [0009])
6. 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), Teran, US-PGPUB 2017/0269244, Noufal-2, US-PGPUB 2024/0418905 and Edwards, US-PGPUB 2012/0043077 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.
Response to Arguments
Applicant’s arguments with respect to claims have been considered but are moot in view of new grounds of rejection.
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.
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
