Notice of Pre-AIA or AIA Status
1. The present application, filed on or after March 16, 2013, is being examined under the first inventor to file provisions of the AIA .
Detailed Action
2. This Non-Final Office Action is responsive to Applicants’ arguments received 5/11/26 and with respect to the pending claims as amended on 11/14/25. Claims 1-20 remain pending, of which claims 1, 8, and 15 are independent.
Claim Rejections - 35 USC § 102
3. In the event the determination of the status of the application as subject to AIA 35 U.S.C. 102 and 103 (or as subject to pre-AIA 35 U.S.C. 102 and 103) is incorrect, any correction of the statutory basis (i.e., changing from AIA to pre-AIA ) for the rejection will not be considered a new ground of rejection if the prior art relied upon, and the rationale supporting the rejection, would be the same under either status.
4. The following is a quotation of the appropriate paragraphs of 35 U.S.C. 102 that form the basis for the rejections under this section made in this Office Action:
A person shall be entitled to a patent unless –
(a)(1) the claimed invention was patented, described in a printed publication, or in public use, on sale, or otherwise available to the public before the effective filing date of the claimed invention.
5. Claims 1-6, 8-13, and 15-19 are rejected under 35 U.S.C. 102(a)(1) as being anticipated by Non-Patent Literature “Deep learning for prediction of complex geology ahead of drilling” (“Fossum”).
Regarding claim 1, FOSSUM teaches A method of identifying a drilling target (Fossum’s Abstract, page 1: “During a geosteering operation the well path is intentionally adjusted in response to the new data acquired while drilling. To achieve consistent high-quality decisions, especially when drilling in complex environments, decision support systems can help cope with high volumes of data and interpretation complexities. They can assimilate the real-time measurements into a probabilistic earth model and use the updated model for decision recommendation.”; and Introduction section beginning on Fossum’s page 1: “To place a well in its optimal position, operators apply geosteering. Here, the well trajectory is adjusted while drilling in response to real-time measurement of the geology surrounding the drill bit.”, where the adjusted drilling determinable in view of the taught modeling constitutes a geosteered drill path for a well that constitutes “a drilling target” as identified/determined, as recited, based on the model and real-time measurement information), comprising:
obtaining a training set of base subsurface models and generating, using a first artificial intelligence neural network, a plurality of subsurface model realizations based on the training set of base subsurface models and simulating, for each subsurface model realization among the plurality of subsurface model realizations, a synthetic seismic dataset (Fossum’s page 3, 1st column, 1st full paragraph: “To construct a reference earth model we generate realizations of a fluvial geological environment using a commercial software. These realizations are then sub-sampled to form a training dataset for the offline training of a Generative Adversarial Network (GAN). The GAN is then used, online, to generate plausible geological realizations from a low-dimensional Gaussian input vector.”, where the sub-sampling of initial realizations of a geological environment obtained using a commercial software and used “to form a training dataset for ... training of a ... GAN” as mentioned are equivalent to the recited “training set”, and the GAN as taught is equivalent to the recited first AINN, and where the generated “plausible geological realizations” generated by the GAN as mentioned are equivalent to the simulated “synthetic seismic dataset” (see also Fossum’s section 2 for elaboration));
training a second artificial intelligence neural network, using the plurality of subsurface model realizations and the synthetic seismic dataset for each subsurface model realization, to predict an inferred subsurface model from a seismic dataset (staying with Fossum’s page 3, 1st column, 1st full paragraph, there is a mention that “For modeling the extra-deep EM measurements we use a forward deep neural network (FDNN) trained on a dataset generated using a commercial simulator.” (see also Fossum’s section 3 for elaboration));
obtaining an observed seismic dataset for a subterranean region of interest and predicting, using the trained second artificial intelligence neural network, a predicted inferred subsurface model from the observed seismic dataset and identifying the drilling target based on the predicted inferred subsurface model (Fossum’s section 4, beginning on page 5: “In the DSS for geosteering [3], one uses data assimilation to condition the earth model to measurements made while drilling. The fundamental idea is that if a poorly known earth model can be made consistent with measurements it will provide more accurate forecasts, and, hence, provide a better basis for decisions.”, where the DSS (Decision Support System) as described in the aforementioned section is understood to be informed in real-time based on measurements as they are processed by the FDNN, as is made clear in section 3 found on page 5, where the real-time geosteering advantage that is the purpose of this framework is to intelligently explore and drill what is essentially “a subterranean region of interest” based on predictions made by the FDNN and adjustments made by the DSS).
Regarding claim 2, Fossum teaches The method of claim 1, further comprising:
determining a wellbore path to intersect the drilling target; and drilling a wellbore guided by the wellbore path (Fossum’s DSS as discussed above in relation to claim 1, uses real-time measurements and the trained FDNN’s outputs to make geosteering adjustments to effectively drill/place a well).
Regarding claim 3, Fossum teaches The method of claim 1, wherein each base subsurface model among the training set of base surface models comprises:
a base background model; and a plurality of base canonical geological structures (Fossum’s section 2, starting on page 3, and specifically the 3rd-5th paragraphs, discussing the construction of its earth model from a synthetic structural framework (i.e., a base background model) and a facies model (i.e., capable of representing geological structures), and where the facies model itself is characterized as having a “background/shale” aspect, and based on this the Examiner reasons that there is some version of background modeling operative, whether through the taught synthetic structural framework or the background/shale aspect of the facies model, in addition to other facies information).
Regarding claim 4, Fossum teaches The method of claim 3, wherein a canonical geological structure within one of the plurality of base canonical geological structures comprises at least one of an archaic sand dune, a wadi, or a karst (based on the Examiner’s previous interpretation of a wadi, established in the prior Office Actions, the Examiner believes Fossum’s discussion of channels (page 4’s 2nd paragraph) reads on the aforementioned interpreted term).
Regarding claim 5, Fossum teaches The method of claim 1, wherein the first artificial intelligence neural network comprises a Generative Adversarial Neural Network (as discussed in relation to claim 1, Fossum contemplates the use of a GAN, see e.g., section 2 of the reference).
Regarding claim 6, Fossum teaches The method of claim 1, wherein the second artificial intelligence neural network comprises a Deep Neural Network (as discussed in relation to claim 1, Fossum contemplates the use of a type of DNN, see e.g., section 3 of the reference).
Regarding claim 8, the claim includes the same or similar limitations as claim 1 discussed above, and is therefore rejected under the same rationale. The present claim additionally recites a non-transitory computer readable medium, which the Examiner believe Fossum’s computer-implemented framework would necessarily have to function/operate as described, at least in the form of conventional memory elements for storing instructions/code as is widely known and used in the state of the art.
Regarding claim 9, the claim includes the same or similar limitations as claim 2 discussed above, and is therefore rejected under the same rationale.
Regarding claim 10, the claim includes the same or similar limitations as claim 3 discussed above, and is therefore rejected under the same rationale.
Regarding claim 11, the claim includes the same or similar limitations as claim 4 discussed above, and is therefore rejected under the same rationale.
Regarding claim 12, the claim includes the same or similar limitations as claim 5 discussed above, and is therefore rejected under the same rationale.
Regarding claim 13, the claim includes the same or similar limitations as claim 6 discussed above, and is therefore rejected under the same rationale.
Regarding claim 15, the claim includes the same or similar limitations as claims 1-2 discussed above, and is therefore rejected under the same rationale. The present claim additionally recites a memory configured to store a training set of base subsurface models and a computer processor, which the Examiner believe Fossum’s computer-implemented framework would necessarily have to function/operate as described, at least in the form of conventional memory elements for storing information/data subject to processing/use and a processor/CPU aspect, as is widely known and used in the state of the art.
Regarding claim 16, the claim includes the same or similar limitations as claim 2 discussed above, and is therefore rejected under the same rationale.
Regarding claim 17, the claim includes the same or similar limitations as claim 3 discussed above, and is therefore rejected under the same rationale.
Regarding claim 18, the claim includes the same or similar limitations as claim 4 discussed above, and is therefore rejected under the same rationale.
Regarding claim 19, the claim includes the same or similar limitations as claim 5 discussed above, and is therefore rejected under the same rationale.
Claim Rejections - 35 USC § 103
6. 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.
7. The factual inquiries for establishing a background for determining obviousness under 35 U.S.C. 103 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.
8. Claims 7, 14, and 20 are rejected under 35 U.S.C. 103 as being unpatentable over Fossum in view of U.S. Patent Application No. 2019/0383965 (“Salman”).
Regarding claim 7, Fossum teaches The method of claim 1, wherein simulating the synthetic seismic dataset (generally, page 3’s section 2, 2nd paragraph, discussing the GAN’s generation of geological realizations as part of its training in tandem with the discriminator, and again in the next/3rd paragraph discussing use of other synthetic facies data) but does not teach more specifically where the simulation comprises using a finite-difference solution to an elastic wave equation. Rather, the Examiner relies upon SALMAN to teach what Fossum otherwise lacks, see e.g., Salman’s [0197] discussing wavefield modelling and synthetic data generation, which the Examiner reasons is like Fossum’s disclosure of the same, and specifically inclusive of “finite difference modelling (FDMOD)”, where waves of elastic energy are involved (Salman’s [0002] and [0113]), which are subject to involvement in a computation (e.g., Salman’s [0084] discussing generation of synthetic data as modeled for example via a wave equation).
Fossum and Salman both relate to geosteering and related aspects of well placement and drilling. Hence, they are similarly directed and therefore analogous. It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to incorporate Salman’s synthetic data generation aspect, as cited to here, into Fossum’s framework, with a reasonable expectation of success, for purposes of generating synthetic data that is more tethered to the actual location of interest.
Regarding claim 14, the claim includes the same or similar limitations as claim 7 discussed above, and is therefore rejected under the same rationale.
Regarding claim 20, the claim includes the same or similar limitations as claim 7 discussed above, and is therefore rejected under the same rationale.
Conclusion
9. Any inquiry concerning this communication or earlier communications from the examiner should be directed to SHOURJO DASGUPTA whose telephone number is (571)272-7207. The examiner can normally be reached M-F 8am-5pm CST.
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/SHOURJO DASGUPTA/Primary Examiner, Art Unit 2144
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