DETAILED ACTION
Notice of Pre-AIA or AIA Status
The present application, filed on or after March 16, 2013, is being examined under the first inventor to file provisions of the AIA .
Claims 1-20 and 22 are pending and have been examined.
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 7, 9, 17, and 19 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 the inventor or a joint inventor (or for applications subject to pre-AIA 35 U.S.C. 112, the applicant), regards as the invention.
Claims 7, 9, 17, and 19 each recite “the probability map.” There are insufficient antecedent bases for these limitations in the claims.
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.
Claim 22 is rejected under 35 U.S.C. 101 because the claimed invention is directed to non-statutory subject matter.
Claim 22 does not fall within at least one of the four categories of patent eligible subject matter because the broadest reasonable interpretation of “computer program” encompasses non-statutory software per se.
Claims 1-5, 7-15, 17-20 and 22 are rejected under 35 U.S.C. 101 because the claimed invention is directed to an abstract idea without significantly more.
Claim 1 recites receiving data representing one or more reservoir properties for a subsurface volume; conducting a probabilistic uncertainty analysis by simulating a plurality of different model realizations representing the subsurface volume; identifying a first hot spot of the subsurface volume based at least in part on the uncertainty analysis, the first hot spot representing an area having a high predicted performance, relative to other areas of the subsurface volume, based on the simulating of the different model realizations representing the subsurface volume; identifying a second hot spot of the subsurface volume using a model that is trained to predict well performance based at least in part on the one or more reservoir properties; evaluating the first and second hot spots for well placement based at least in part on the predicted well performance at the first and second hot spots, respectively; and selecting at least one of the first hot spot or the second hot spot for well construction, which is an abstract idea reasonably characterized as a mental process, i.e., the observation and evaluation of information.
The additional elements unencompassed by the abstract idea include a single recitation that a model used to evaluate information is a “machine learning” model that was trained at some point in time outside of the scope of the claim. This additional element fails to integrate the abstract idea into a practical application because the additional elements merely serve as generic computer components on which the abstract idea is implemented. See MPEP 2106.05(f).
The claim does not include limitations sufficient, either alone or in combination, to amount to significantly more than the claimed abstract idea because the aforementioned additional elements merely serve as generic computer components on which the abstract idea is implemented. See MPEP 2106.05(f).
Claims 2-5 and 7-10 describe the receipt and evaluation of input information and thus further describe the abstract idea.
Claim 11 recites receiving data representing one or more reservoir properties for a subsurface volume; conducting a probabilistic uncertainty analysis by simulating a plurality of different model realizations representing the subsurface volume; identifying a first hot spot of the subsurface volume based at least in part on the uncertainty analysis, the first hot spot representing an area having a high predicted performance, relative to other areas of the subsurface volume, based on the simulating of the different model realizations representing the subsurface volume; identifying a second hot spot of the subsurface volume using a model that is trained to predict well performance based at least in part on the one or more reservoir properties; evaluating the first and second hot spots for well placement based at least in part on the predicted well performance at the first and second hot spots, respectively; and selecting at least one of the first hot spot or the second hot spot for well construction, which is an abstract idea reasonably characterized as a mental process, i.e., the observation and evaluation of information.
The additional elements unencompassed by the abstract idea include one or more processors, a memory system comprising one or more non-transitory, computer-readable media storing instructions, and a “machine learning” model that was trained at some point in time outside of the scope of the claim. These additional elements fail to integrate the abstract idea into a practical application because the additional elements merely serve as generic computer components on which the abstract idea is implemented. See MPEP 2106.05(f).
The claim does not include limitations sufficient, either alone or in combination, to amount to significantly more than the claimed abstract idea because the aforementioned additional elements merely serve as generic computer components on which the abstract idea is implemented. See MPEP 2106.05(f).
Claims 12-15 and 17-20 describe the receipt and evaluation of input information and thus further describe the abstract idea.
Claim 22 is not directed to a statutory category of invention as discussed above. Assuming it was, the claim recites receiving data representing one or more reservoir properties for a subsurface volume; conducting a probabilistic uncertainty analysis by simulating a plurality of different model realizations representing the subsurface volume; identifying a first hot spot of the subsurface volume based at least in part on the uncertainty analysis, the first hot spot representing an area having a high predicted performance, relative to other areas of the subsurface volume, based on the simulating of the different model realizations representing the subsurface volume; identifying a second hot spot of the subsurface volume using a model that is trained to predict well performance based at least in part on the one or more reservoir properties; evaluating the first and second hot spots for well placement based at least in part on the predicted well performance at the first and second hot spots, respectively; and selecting at least one of the first hot spot or the second hot spot for well construction, which is an abstract idea reasonably characterized as a mental process, i.e., the observation and evaluation of information.
The additional elements unencompassed by the abstract idea include a computer program and a “machine learning” model that was trained at some point in time outside of the scope of the claim. These additional elements fail to integrate the abstract idea into a practical application because the additional elements merely serve as generic computer components on which the abstract idea is implemented. See MPEP 2106.05(f).
The claim does not include limitations sufficient, either alone or in combination, to amount to significantly more than the claimed abstract idea because the aforementioned additional elements merely serve as generic computer components on which the abstract idea is implemented. See MPEP 2106.05(f).
Claim Rejections - 35 USC § 102
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.
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.
Claims 1, 4-6, 9-11, 14-16, 19, 20, and 22 are rejected under 35 U.S.C. 102(a)(1) as being anticipated by Prochnow et al. (WO 2020/172268 A1).
As per claim 1, Prochnow disclose a method, comprising:
receiving data representing one or more reservoir properties for a subsurface volume (FIGS. 12, item 1202, p. [0111]; input data includes subsurface data and well data; input data includes subsurface data and well data);
conducting a probabilistic uncertainty analysis based on the simulating of a plurality of different model realizations representing the subsurface volume (FIG. 12; items 1206, 1208; p. [0082]; production parameter values may be filtered based on statistical significance and/or co-linearity using, for example, a Pearson correlation matrix; calculating statistical significance and co-linearity of the production parameter values is conducting a probabilistic uncertainty analysis; the Pearson correlation matrix is calculated based on plurality of reservoir property maps (model realizations representing the subsurface volume 1206));
identifying a first hot spot of the subsurface volume based at least in part on the uncertainty analysis, the first hot spot representing an area having a high predicted performance, relative to other areas of the subsurface volume, based on the simulating of the different model realizations representing the subsurface volume (FIG. 12, items 1208, 1214; identified “high production reservoir potential” locations are hotspots; output at step 1214 is based on uncertainty analysis of step 1208);
identifying a second hot spot of the subsurface volume using a machine learning model that is trained to predict well performance based at least in part on the one or more reservoir properties (FIG. 1B; items 150, 160; p. [0011, 0112, 0128]; a parameter model may be implemented by statistical analysis at 150, which may comprise a machine learning model; “applying the parameter model to the production parameter values may generate representations 160. Individual pseudo wells, or an estimated reservoir productivity, may be in the representation of a function of position in the subsurface volume”; evaluation at 162 identifies type curve generation and decline analysis for selected pseudo wells of representations 160; implicit that some of the pseudo wells are evaluated to have better performance, and therefore that second hotspots of representations 160 are identified based on evaluation 162; generating the representations 160 and evaluation 162 is based on the machine learning model 150 as shown in FIG. 1B);
evaluating the first and second hot spots for well placement based at least in part on the predicted well performance at the first and second hot spots, respectively; and selecting at least one of the first hot spot or the second hot spot for well construction (FIG. 12B, p. [0134–0135]; first hotspot in 1214 evaluated at 1218; 1218 parameterized by reservoir productivity parameter (evaluation of 1218 is based on predicted well performance at first hot spot in 1214); optimal well design 1220 at first hot spot used for well construction; FIG. 1B, p. [0112]; pseudo wells at second hotspot in 160 are evaluated at 162 for type curve generation and decline analysis; implicit that a well at the second hotspot is selected for construction when the evaluation 162 is favorable).
As per claims 4 and 14, Prochnow discloses determining a production performance of a well located at the first hot spot and a production performance of a well located at the second hot spot using a machine learning model that is trained to predict well performance from measured reservoir properties ([0085-90]. Using machine learning, initial productivity algorithm is conditioned to more accurately predict a reservoir productivity value given multiple well designs in the subsurface volume of interest and the reservoir characteristics of the subsurface volume of interest as input and using the actual productivity in the reservoirs as a guide to improve predictions.).
As per claims 5 and 15, Prochnow discloses receiving historical well performance data and reservoir properties (p. [0073]; quantify the expected recovery across subsurface volume of interest given historical correspondences between production and reservoir and completion practices); and training the machine learning model based on the historical well performance data and the reservoir properties (FIG. 2A; p. [0011, 0090, 0128]; the statistical analysis at 150 may comprise a machine learning model (i.e., random forest); FIG. 2A; random forest takes as input training data sets, implicitly including the historical correspondences), such that the machine learning model is configured to predict the performance of both existing and new wells without simulating a physics-based model of the subsurface volume (FIG. 1B, item 150; p. [0011, 0128, 0160]; the machine learning model 150 (which does not simulate a physics-based model of the subsurface volume) is used to generate representations of reservoir productivity as a function of position for pseudo wells (performance prediction for new wells); implicit that existing well reservoir productivity can also be predicted using the machine learning model 150).
As per claims 6 and 16, Prochnow discloses wherein training the machine learning model comprises: mapping historical well performance in a vector space based on one or more parameters selected from the group consisting of production and injection history, well location, log data, core data, and reservoir properties; and identifying one or more clusters in the vector space; and determining a relationship between the one or more parameters and the historical well performance based on the identified one or more clusters.
As per claim 9 and 19, Prochnow discloses visualizing a digital model of the subsurface volume including a visual representing of the probability map, the well at the selected location, or both (Fig. 12 item 1214.).
As per claims 10 and 20, Prochnow discloses selecting one or more model realizations for conducting the uncertainty analysis; wherein selecting the one or more model realizations comprises: receiving model inputs and model outputs; identifying clusters in the model outputs; and selecting one or more representative model realizations from the individual clusters.
As per claims 11 and 22, Prochnow discloses a system and a computer program for implementing the method of claim 1, and the claims are rejected for substantially the same reasons as claim 1.
Claim Rejections - 35 USC § 103
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.
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 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.
Claims 2, 3, 7, 8, 12, 13, 17, and 18 are rejected under 35 U.S.C. 103 as being unpatentable over Prochnow et al. (WO 2020/172268 A1) in view of Kumar et al. (A Machine Learning Application for Field Planning, Offshore Technology Conference, Published 26 April 2019 Available from: https://onepetro.org/OTCONF/proceedings-abstract/19OTC/2-19OTC/D021S026R005/180712).
As per claims 2 and 12, Prochnow discloses generating probability maps of the subsurface domain including representations of the first hot spot and the second hot spot. Prochnow does not necessarily explicitly disclose that the first and second hotspots are included on the same probability map, but discloses a map with varying degrees of reservoir potential associated with multiple different reservoir properties (Fig. 12 item 1214.).
It would have been obvious to one of ordinary skill before the effective filing date of the claimed invention to display first and second hotspots are included on the same probability map as the combination of prior art elements according to known methods to yield predictable results (i.e., displaying two separately displayed pieces of information on the same display).
Furthermore, all of the claimed elements were known in the prior art of Prochnow and Kumar and one skilled in the art could have combined the elements as claimed by known methods with no change in their respective functions, and the combination would have yielded predictable results to one of ordinary skill in the art before the effective filing date of the claimed invention.
As per claims 3 and 13, Prochnow renders obvious the limitations of claims 2 and 12 as discussed above. Prochnow further discloses receiving geomechanical data for the subterranean domain ([0079], [0111]); and determining a completion quality for at least a portion of the subsurface volume based at least in part on the geomechanical data ([0079]), wherein generating the probability map comprises generating the probability map based at least in part on the completion quality (Fig. 12 item 1214.).
As per claims 7 and 17, Prochnow discloses wherein the probability map includes a plurality of first hot spots and a plurality of second hot spots (e.g. [Fig. 12 item 1214), but does not explicitly disclose that a single probability map includes the first and second hotspots, some of the first hotspots being the same. However, it would be obvious to one or ordinary skill in the art before the effective filing date of the claimed invention when seeking to combine the hotspot predictions of Prochnow, to include both the first and second pieces of location-identifying information of Prochnow in a single display as choosing from a finite number of identified, predictable solutions, with a reasonable expectation of success.
Furthermore, all of the claimed elements were known in the prior art of Prochnow and Kumar and one skilled in the art could have combined the elements as claimed by known methods with no change in their respective functions, and the combination would have yielded predictable results to one of ordinary skill in the art before the effective filing date of the claimed invention.
As per claims 8 and 18, Although Prochnow discloses heatmaps describing production reservoir potential which is an implicit ranking of each area’s production reservoir potential, the reference does not explicitly disclose ranking the first hot spots, the second hot spots, or both based on the forecasted performance. However, Kumar discloses generating a prioritized list of proposed well locations based on an evaluation of proposed well locations (Kumar p. 5.). Therefore, when seeking to provide information on proposed well locations in a time-efficient manner, it would be obvious to evaluate the performance of the hotspots of Prochnow to provide a ranking of the hotspots based on their forecasted performance as the application of a known information organization technique to known information organization ready for improvement to yield predictable results.
Furthermore, all of the claimed elements were known in the prior art of Prochnow and Kumar and one skilled in the art could have combined the elements as claimed by known methods with no change in their respective functions, and the combination would have yielded predictable results to one of ordinary skill in the art before the effective filing date of the claimed invention.
Conclusion
The prior art made of record and not relied upon is considered pertinent to
Applicant's disclosure. Please see PTO-892.
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JEFF ZIMMERMAN
Supervisory Patent Examiner
Art Unit 3628
/JEFF ZIMMERMAN/Supervisory Patent Examiner, Art Unit 3628
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