DETAILED ACTION
Claim Rejections - 35 USC § 101
1. Previous rejection is withdrawn in view of the Applicant’s amendment filed on 10/01/2025.
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 1-20 are rejected under 35 U.S.C. 103 as being unpatentable over Temizel et al., “Geology-Driven EUR forecasting in unconventional fields,” SPE 204583, 2021 (hereinafter Temizel) (cited by the Applicant) in views of Tapscott et al., US-PGPUB 2011/0264430 (hereinafter Tapscott) and Roth et al., US-PGPUB 2019/0361146 (hereinafter Roth)
The applied reference has a common assignee and one inventor with the instant application. Based upon the earlier effectively filed date of the reference, it constitutes prior art under 35 U.S.C. 102(a)(2). This rejection under 35 U.S.C. 102(a)(2) might be overcome by: (1) a showing under 37 CFR 1.130(a) that the subject matter disclosed in the reference was obtained directly or indirectly from the inventor or a joint inventor of this application and is thus not prior art in accordance with 35 U.S.C. 102(b)(2)(A); (2) a showing under 37 CFR 1.130(b) of a prior public disclosure under 35 U.S.C. 102(b)(2)(B) if the same invention is not being claimed; or (3) a statement pursuant to 35 U.S.C. 102(b)(2)(C) establishing that, not later than the effective filing date of the claimed invention, the subject matter disclosed in the reference and the claimed invention were either owned by the same person or subject to an obligation of assignment to the same person or subject to a joint research agreement.
Regarding Claims 1, 8 and 15. Temizel discloses collecting well data, including comprising basin data, completion data, and production data, for each well in an unconventional field (Introduction, page 2, bottom paragraphs, starting with “Development of a quantitative…” to page 3; Fig. 1; page 11, Basin wells),
generating spatial features for each well in different regions in the unconventional field, generating, using the well data and the spatial features, a combined well features dataset mapping the well data to the spatial features for each well in the different regions of the unconventional field (pages 3-4, Geology-Driven EUR section, discussing zones; Fig. 4; Model section, pages 13-21, and the Figs. 16, 19, 23, 25), generating, by splitting the combined well features dataset, a training dataset, and a testing dataset (Fig. 4, training and test dataset),
training a machine learning model using cross-validation and tuning on the training dataset to predict estimated ultimate recovery (EUR) (Abstract; Section 2, Geology-driven, zones are trained with neural network; Fig. 11; page 12, top paragraph, “training”; pages 24-25, “Machine Learning based Data Modeling” section”);
determining, based on the spatial features a machine learning EUR model, an impact of each attribute of completion data and production data of the well data relative to the basin data for each of the geographical locations, (page 25, “Discussions” section),
Temziel does not explicitly disclose the basin data comprising a three-dimensional characterization of a subsurface volume dived by geographical locations, and adjusting, using the impact of each attribute of completion data and production data, drilling of each well, relative to the geographical locations, by changing settings of a drilling equipment.
Roth discloses and adjusting, using the impact of each attribute of completion data and production data, drilling of each well, relative to the geographical locations, by changing settings of a drilling equipment (Paragraphs [0004], [0016]; Paragraph [0152], Fig. 15)
Tapscott discloses the basin data comprising a three-dimensional characterization of a subsurface volume divided by geographical locations (Paragraph [0038]; Abstract)
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 Roth and Tapscott and have the basin data comprising a three-dimensional characterization of a subsurface volume divided by geographical locations, and adjusting, using the impact of each attribute of completion data and production data, drilling of each well, relative to the geographical locations, by changing settings of a drilling equipment, with optimal efficiency for maximum production.
Regarding Claims 2, 9 and 16. Temizel discloses cleaning and normalizing the well data to remove well data for wells with missing data and to normalize the well data per requirements for machine learning (page 8, data normalization, outlier detection and feature selection)
Regarding Claims 3, 10 and 17. Temizel discloses the spatial features include (x,y) coordinates indicating a horizontal position and a z-coordinate indicating depth (Model section, Figs. 16, 19, 23, 25)
Regarding Claims 4, 11 and 18. Temizel discloses the splitting is performed using machine learning modeling and includes applying stratifications based on region having representative well for each regions (Fig. 4)
Regarding Claims 5, 12 and 19. Temizel discloses using the impact of each attribute of the well data in designing wells and stimulating wells (page 3, Geology driven EUR, designs, simulation)
Regarding Claims 6, 13 and 20. Temizel discloses determining, using the impact of each attribute of the well data, horizontal and spatial well placement (page 3, Geology driven EUR, designs, simulation)
Regarding Claims 7 and 14. Temizel discloses the spatial features are generated for a time period of 30, 60, or 90 days (Fig 10) Note: If the Applicant disagrees, then it would have been obvious to have said days, as it depends on the user’s design criteria, absent criticality.
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.
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/HYUN D PARK/Primary Examiner, Art Unit 2857