DETAILED ACTION
Claims 1-20 are presented for examination.
This Office Action is in response to submission of documents on March 11, 2026.
Provisional nonstatutory double patenting rejection of claims 1-20 is withdrawn.
New rejection of claims 1-20 under 35 U.S.C. 112(a) for failing to meet the written description requirement.
New rejection of claims 1-20 under 35 U.S.C. 112(b) for being indefinite (Previous grounds withdrawn).
Rejection of claims 1-20 under 35 U.S.C. 101 for being directed to unpatentable subject matter is maintained.
Rejection of claims 1, 3-4, 6, 9, 11-12, 14, and 17 under 35 U.S.C. 103 for being obvious over Tawil in view of Dusterhoft, Burch and Anderson is withdrawn.
Rejection of claims 2, 5, 10, 13, 18, and 19 under 35 U.S.C. 103 for being obvious over Tawil in view of Dusterhoft, Burch, Fornel, and Anderson is withdrawn.
Rejection of claims 7-8, 15-16, and 20 under 35 U.S.C. 103 for being obvious over Tawil in view Dusterhoft, Burch, Chandra, and Anderson is withdrawn.
New rejection of claims 1-6, 9-14, and 17-18 under 35 U.S.C. 103 as being obvious over Dupont in view of Tawil and Shelley.
New rejection of claims 7 and 15 under 35 U.S.C. 103 as being obvious over Dupont in view of Tawil, Shelley, and further in view of Chandra.
New rejection of claims 8, 16, and 20 under 35 U.S.C. 103 as being obvious over Dupont in view of Tawil, Shelley, Chandra, and further in view of Burch.
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 .
Response to Arguments
Regarding the provisional double patenting rejection of the claims, the rejection is withdrawn. However, in light of currently pending rejections of the claims, particularly under 35 U.S.C. 112(a) and 35 U.S.C. 112(b), amendments to one or more of the limitations of the pending claims may result in reassertion of the provisional rejection.
Regarding the rejection of claims 1-20 under 35 U.S.C. 112(b), Examiner is persuaded by the amendments to claims 2, 10, and 18. Accordingly, the rejection is withdrawn.
However, in light of new amendments to the claims, a new rejection of claims 1-20 under 35 U.S.C. 112(b) is asserted herein.
Regarding rejection of the claims under 345 U.S.C. 101, Examiner is not persuaded by the amendments and arguments for the following reasons:
While the invention, as disclosed in the Specification, may have a practical application and/or improve the technology of well development, the claims, as currently presented, do not adequately convey the practical application and/or improvement. First, the limitation of “adjusting one or more of the completion design parameters for the new production well to produce optimal hydrocarbon production” may be interpreted as an improvement, but the element is not clear as to what is being optimized, maximized, and/or minimized in order to improve the operation of the new production well. Thus, if the limitation were clearer as to how the adjustments ultimately improve the operation of the completed well, as disclosed in [0056]-[0057], the limitation may be considered an additional element that recites an improvement.
Further, with regards to “providing real time control…” and “operating the new production well…,” neither limitation, even with the new amendments, integrates the judicial exceptions into a practical application. As further elaborated upon with regards to the rejection of the claims under 35 U.S.C. 112(a) and 35 U.S.C. 112(b), the addition of “using the second ML model” does not automatically incorporate the ML model with the element. For example, “providing real time control of downhole production…using the adjusted one or more of the completion design parameters based on the second ML model” does not provide a clear indication of how the model is utilized in performing the step. The second ML model appears to provide the completion design parameters as output, but it is unclear how those parameters are necessary to “provide real time control.” For example, does “providing real time control” mean providing a means (e.g., an interface) to allow control of the devices or does it mean automating control of the devices (as disclosed in [0060] of the Specification)?
Generally, the claims, as currently amended, are unclear and, in some instances, not supported by the Specification. While Examiner appreciates that a practical application and/or an improvement in well development may be present, the claims do not clearly convey it to a person having ordinary skill in the art.
Accordingly, the rejection of claims 1-20 under 35 U.S.C. 101 is maintained.
Regarding rejection of claims 1-20 under 35 U.S.C. 103:
In the Response, Applicant indicates that the references are not combinable because “incorporating machine learning into production of an existing well would [not] necessarily lead to incorporating machine learning into production of a new well. Tawil, Dusterhoft, and Burch are explicit in that in their principle of operation they tune a second ML model from a first ML model to predict parameters of a second well model for an existing well. As such, modifying Tawil, Dusterhoft, and Burch to tune a second ML model from a first ML model to predict parameters of a second well model for an existing well, as the Office Action attempts with Anderson, would clearly change the principle of operation of Tawil, Dusterhoft, and Burch.” Response at pg. 15. Examiner agrees. Accordingly, the rejection is withdrawn.
However, Tawil does disclose that machine learning can be applied to a model of a new well during drilling and/or planning: “WPO module 312 is configured to optimize and automate an example well planning process…An application of the WPO module 312 can invoke or call a predictive model of the ML engine 250 to perform the probabilistic modeling. The WPO module 312 is operable to process and exploit the information derived from the probabilistic model to determine uncertainties relating to one or more properties of the reservoir. The WPO module 312 can use the uncertainties and information derived from the probabilistic model to determine planning parameters such as preferred geographic locations and resources of a well plan, including geo-steering parameters for improved directional control of drilling trajectories at the locations.” Tawil at [0093]-[0094]. Thus, at the least, Tawil contemplates the use of machine learning to adjust one or more models of a new well based on a model generated from one or more existing wells.
While the previous rejection of the claims is withdrawn, a new rejection under 35 U.S.C. 103 is asserted herein with the combination of Dupont, Tawil, and Shelley. To Applicant’s point of the previous combination not being operable, Shelley explicitly discloses “Referring now to FIG. 2,…the map 110 shows the location of a plurality of completed wells 112 (each marked with an “x”, some numeric labels have been omitted for clarity) and the location of uncompleted wells 114 (each marked with an “o”).…More specifically, in some instances a predictive model identified as the “best” predictive model for the field is utilized to estimate well production for a plurality of different completion parameters in order to identify a combination of completion parameters that optimizes well production.” Shelley at col. 3, lines 19-34. Thus, the combination of the cited references clearly would be operable.
Claim Rejections - 35 USC § 112
The following is a quotation of the first paragraph of 35 U.S.C. 112(a):
(a) IN GENERAL.—The specification shall contain a written description of the invention, and of the manner and process of making and using it, in such full, clear, concise, and exact terms as to enable any person skilled in the art to which it pertains, or with which it is most nearly connected, to make and use the same, and shall set forth the best mode contemplated by the inventor or joint inventor of carrying out the invention.
The following is a quotation of the first paragraph of pre-AIA 35 U.S.C. 112:
The specification shall contain a written description of the invention, and of the manner and process of making and using it, in such full, clear, concise, and exact terms as to enable any person skilled in the art to which it pertains, or with which it is most nearly connected, to make and use the same, and shall set forth the best mode contemplated by the inventor of carrying out his invention.
Claims 1-20 are rejected under 35 U.S.C. 112(a) or 35 U.S.C. 112 (pre-AIA ), first paragraph, as failing to comply with the written description requirement. The claim(s) contains subject matter which was not described in the specification in such a way as to reasonably convey to one skilled in the relevant art that the inventor or a joint inventor, or for applications subject to pre-AIA 35 U.S.C. 112, the inventor(s), at the time the application was filed, had possession of the claimed invention.
Independent claims 1, 8, and 17 recite the following limitations that are not described in the Specification:
estimating completion costs for the new production well, based on the completion design parameters using the second ML model…
adjusting one or more of the completion design parameters for the new production well…using the second ML model…
completing the new production well based on the adjusted completion design parameters using the second ML model;
In each of the above instances, the second machine learning model (model 440 of FIG. 4) is recited as being utilized for a purpose other than to generate predicted model parameters 445. The Specification lacks disclosure relating to any use of the second machine learning model other than to predict model parameters for the second well model and to generate completion design parameters.
providing real time control of downhole production and injection control devices and real time control of surface production and injection control devices for the new production well based on an operating performance of the new production well using the adjusted one or more of the completion design parameters based on the second ML model compared to an expected performance of the new production well;
As understood in the context of the Specification, “providing real time control” is analogous to step 808 of process 800. However, the Specification does not include disclosure regarding an “operating performance of the new production well” nor “an expected performance of the new production well.” Further, the providing of a visualization (i.e., “real time controls”) is not disclosed as being based on the second ML model.
operating the new production well based on the real time control completed with the adjusted one or more of the completion design parameters based on the second ML model.
The Specification does not disclose a step of “operating the new production well….” The disclosure ends at a step of providing a visual display to a user (e.g., block 808 of process 800).
Claims 2-7, 9-16, and 18-20 are rejected under 35 U.S.C. 112(a) for depending from a rejected claim.
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 1-20 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 1, 8, and 17 are rejected under 35 U.S.C. 112(b) or 35 U.S.C. 112 (pre-AIA ), second paragraph, as being incomplete for omitting essential elements, such omission amounting to a gap between the elements. See MPEP § 2172.01. The claims recite “parameters predicted by the second ML model,” but do not include a step of using the second ML model to predict parameters. Instead, the claims recite “training a second ML model to predict parameters,” which only recites the prediction as an intended use of the trained model. Thus, the claims lack generation of the predicted parameters because such a step is outside the scope of the claim.
Independent claims 1, 8, and 17 recite the following indefinite limitations:
estimating completion costs for the new production well, based on the completion design parameters using the second ML model and the forecasted production over the period of time: It is unclear how the second ML model can be utilized to estimate completion costs.
adjusting one or more of the completion design parameters for the new production well…using the second ML model…
The second ML model is disclosed and claimed as generating the “completion design parameters” as part of the “parameters predicted by the second ML model.” It is unclear how the same ML model can adjust the completion design parameters that it generated. Further, it is unclear how the second ML model, in conjunction with forecasted production, can be utilized to adjust the completion design parameters.
completing the new production well based on the adjusted completion design parameters using the second ML model: It is unclear how a second ML model is utilized to complete a well, which would not reasonably be a step that can be undertaken by a computer. For the purposes of Examination, “completing the new production well” is interpreted as meaning “finish drilling a well.”
providing real time control of downhole production and injection control devices and real time control of surface production and injection control devices for the new production well based on an operating performance of the new production well using the adjusted one or more of the completion design parameters based on the second ML model compared to an expected performance of the new production well: As indicated with regards to the rejection under 35 U.S.C. 112(a), the limitation includes elements that are absent from the Specification. Further, a reasonable interpretation is not possible because the elements are indefinite and cannot be defined with certainty as to the meaning. For example, “operating performance of the new production well” can mean any number of metrics, including maximal hydrocarbon production, cost effectiveness, and/or similarity to estimated operation, all of which are reasonable in light of the specification. “Expected performance” has similar issues. Further, as previously indicated, it is unclear how providing controls for devices is facilitated by the completion design parameters, which are not related to providing a visualization, as the limitation is interpreted to mean.
operating the new production well based on the real time control completed with the adjusted one or more of the completion design parameters based on the second ML model: As previously indicated, the limitation is absent from the Specification. Further, a single reasonable interpretation of the element is not possible because “operating the new well” can include automated operation and/or a human performing the operation.
Claims 2-7, 9-16, and 18-20 are rejected under 35 U.S.C. 112(b) for depending from a rejected claim.
For each of the limitations included in these rejections, proper correction or clarification is required. For any limitations that Applicant asserts is present and definite in the Specification, Examiner requests citations to the Specification to ensure proper review of the arguments.
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.
Claims 17-20 are rejected under 35 U.S.C. 101 because the claimed invention is directed to non-statutory subject matter. The claims do not fall within at least one of the four categories of patent eligible subject matter because they are directed to a signal per se. The claims recite “a storage medium,” which can encompass both transitory and non-transitory media. Because transitory media includes a signal, the claimed invention fails at Step 1 of the 35 U.S.C. analysis and therefore is not directed to patentable subject matter. Applicant can amend the claims to recite “a non-transitory storage medium” to overcome the rejection based on being a signal per se.
Claims 1-20 are rejected under 35 U.S.C. 101 because the claimed invention is directed to judicial exceptions without significantly more. The claims recite mathematical calculations and mental processes. This judicial exception is not integrated into a practical application because the additional elements that are recited in the claims are extra-solution activities that do not integrate the judicial exceptions into a practical application. The claims do not include additional elements that are sufficient to amount to significantly more than the judicial exception because courts have found that the step of data gathering is not significantly more than a judicial exception.
Claim 1
Step 1: The claim is directed to a process, falling under one of the four statutory categories of invention.
Step 2A, Prong 1: The claim 1 limitations include (bolded for abstract idea identification):
Claim 1
Mapping Under Step 2A Prong 1
A computer-implemented method of parameter matching for well planning and production forecasting, the method comprising:
acquiring, by a computing device from a data store, wellsite data for one or more existing production wells in a hydrocarbon producing field;
transforming, by the computing device, the wellsite data into one or more model data sets for predictive modeling;
training a first machine learning (ML) model to predict well logs for the one or more existing production wells, based on the one or more model data sets;
predicting the well logs using the trained first ML model;
generating a first well model to estimate production for the one or more existing production wells, based on the well logs predicted using the trained first ML model;
tuning parameters of the first well model, based on a comparison between the estimated production and an actual production of the one or more existing production wells;
training a second ML model to predict parameters of a second well model for a new production well to be drilled at a new well site in the hydrocarbon producing field, based on the tuned parameters of the first well model;
generating the second well model to forecast production using the parameters predicted by the second ML model;
forecasting production of the new production well over a period of time, based on the parameters predicted by the second ML model for the second well model, the parameters predicted by the second ML model for the second well model including completion design parameters for the new production well;
estimating completion costs for the new production well, based on the completion design parameters using the second ML model and the forecasted production over the period of time;
adjusting one or more of the completion design parameters for the new production well to produce optimal hydrocarbon production, based on the estimated completion costs using the second ML model and the forecasted production;
completing the new production well based on the adjusted completion design parameters using the second ML model;
providing real time control of downhole production and injection control devices and real time control of surface production and injection control devices for the new production well based on an operating performance of the new production well using the adjusted one or more of the completion design parameters based on the second ML model compared to an expected performance of the new production well; and
operating the new production well based on the real time control completed with the adjusted one or more of the completion design parameters based on the second ML model.
Abstract Idea: Mathematical Calculations
Transforming data from one type to another includes performing one or more mathematical operations to generate new data from existing data. See MPEP § 2106.04(a)(2), Subsection I.
Abstract Idea: Mathematical Calculations Training a machine learning model includes performing one or more mathematical operations to provide training data to the machine learning model, which itself is a mathematical concept because the model is comprised of one or more mathematical functions. See MPEP § 2106.04(a)(2), Subsection I.
Abstract Idea: Mental Process
Predicting requires observation, evaluation, opinion, and judgment, which are mental processes that can be performed by a human. See MPEP § 2106.04(a)(2), Subsection III. For example, based on the output from a ML model, a trained technician can make predictions of a well log. This would constitute “predicting…using the trained first ML model.”
Abstract Idea: Mathematical Calculations
A model is a mathematical construct that is comprised of multiple mathematical functions to simulate a real world phenomena. See MPEP § 2106.04(a)(2), Subsection I.
Abstract Idea: Mental Process
Tuning numerical parameters includes the process of evaluation and judgment, such as evaluating the model to determine (i.e., judge) if the output of the model meets a standard and to change the values until the model performs as intended. See e.g., MPEP 2106.04(a)(2), Subsection III.
Abstract Idea: Mathematical Calculations Training a machine learning model includes performing one or more mathematical operations to provide training data to the machine learning model, which itself is a mathematical concept because the model is comprised of one or more mathematical functions. See MPEP § 2106.04(a)(2), Subsection I.
Abstract Idea: Mathematical Calculations Generating a model includes applying one or more parameters to mathematical operations to output data indicative of real world phenomena. See MPEP § 2106.04(a)(2), Subsection I.
Abstract Idea: Mental Process
Forecasting involves the mental processes of observation, evaluation, and judgment. A human, through observation, can observe and evaluate the output of the model and, through judgment, project future well production. See e.g., MPEP 2106.04(a)(2), Subsection III.
Abstract Idea: Mental Process
Estimating involves the mental processes of observation, evaluation, and judgment. A human that has experience in the art, through observation, can review forecasts generated by the second ML model. Based on the forecasts and, the experiences human can weigh the forecasted output of the well against the costs to create the well according to the completion design. See e.g., MPEP 2106.04(a)(2), Subsection III.
Abstract Idea: Mental Process
Adjusting parameters is a process that can be performed by a human. For example, a user can change one or more variables, review the results (i.e., observation), and determine whether the completion costs and forecasted production changed. See e.g., MPEP 2106.04(a)(2), Subsection III.
Step 2A, Prong 2: The claim 1 limitations recite (bolded for additional element identification):
Claim 1
Mapping Under Step 2A Prong 2
A computer-implemented method of parameter matching for well planning and production forecasting, the method comprising:
acquiring, by a computing device from a data store, wellsite data for one or more existing production wells in a hydrocarbon producing field;
transforming, by the computing device, the wellsite data into one or more model data sets for predictive modeling;
training a first machine learning (ML) model to predict well logs for the one or more existing production wells, based on the one or more model data sets;
predicting the well logs using the trained first ML model;
generating a first well model to estimate production for the one or more existing production wells, based on the well logs predicted using the trained first ML model;
tuning parameters of the first well model, based on a comparison between the estimated production and an actual production of the one or more existing production wells;
training a second ML model to predict parameters of a second well model for a new production well to be drilled at a new well site in the hydrocarbon producing field, based on the tuned parameters of the first well model;
generating the second well model to forecast production using the parameters predicted by the second ML model;
forecasting production of the new production well over a period of time, based on the parameters predicted by the second ML model for the second well model, the parameters predicted by the second ML model for the second well model including completion design parameters for the new production well;
estimating completion costs for the new production well, based on the completion design parameters using the second ML model and the forecasted production over the period of time;
adjusting one or more of the completion design parameters for the new production well to produce optimal hydrocarbon production, based on the estimated completion costs using the second ML model and the forecasted production;
completing the new production well based on the adjusted completion design parameters using the second ML model;
providing real time control of downhole production and injection control devices and real time control of surface production and injection control devices for the new production well based on an operating performance of the new production well using the adjusted one or more of the completion design parameters based on the second ML model compared to an expected performance of the new production well; and
operating the new production well based on the real time control completed with the adjusted one or more of the completion design parameters based on the second ML model.
Using a generic computer to perform an abstract idea is mere instructions to apply an exception. See MPEP 2106.05(f).
Acquiring data is mere data gathering, which is an extra-solution activity. All uses of the judicial exceptions (e.g., training a ML model, generating a model) require such data gathering and output. See MPEP 2106.05(g)(3).
Intending to cause a result based on data that is determined using a judicial exception (mental process and/or mathematical concepts) is an idea of a solution that is not recited with specificity such that it integrates the judicial exception into a practical application and/or improves a technology. See MPEP 2106.05(f)(1).
The limitation recites a result of performance of the step, but does not recite, with specificity, how the step is accomplished based on the recited parameters and/or using a ML model. Thus, the limitation is an idea of a solution that is not recited with specificity such that it integrates the judicial exception into a practical application and/or improves a technology. See MPEP 2106.05(f)(1).
Providing control data (i.e., transmitting data) or providing a visualization is an extra-solution activity that does not integrate the judicial exception into a practical application. The limitation does not recite, with specificity, how the data is provided and therefore does not improve the functioning of a computer. See MPEP 2106.05(d)(II).
Intending to cause a result based on data that is determined using a judicial exception (mental process and/or mathematical concepts) is an idea of a solution that is not recited with specificity such that it integrates the judicial exception into a practical application and/or improves a technology. See MPEP 2106.05(f)(1).
Step 2B: Regarding Step 2B, the inquiry is whether any of the additional elements (i.e., the elements that are not the judicial exception) amount to significantly more than the recited judicial exception.
Claim 1 recites “a computer-implemented method,” which is not claimed as being performed by a specified computer that has any unique characteristics. Using a generic computer to perform an abstract idea is mere instructions to apply an exception and courts have found that claiming a method on a generic computer is merely applying the judicial exception (i.e., reciting a judicial exception and further reciting ‘apply it.’) . See MPEP 2106.05(f).
Further, claim 1 recites the additional element of “acquiring…data,” which is an extra-solution activity that courts have found to be insignificantly more than the judicial exception. See Mayo, 566 U.S. at 79, 101 USPQ2d at 1968; OIP Techs., Inc. v. Amazon.com, Inc., 788 F.3d 1359, 1363, 115 USPQ2d 1090, 1092-93 (Fed. Cir. 2015) (presenting offers and gathering statistics amounted to mere data gathering); In re Grams, 888 F.2d 835, 839-40; 12 USPQ2d 1824, 1827-28 (Fed. Cir. 1989); CyberSource v. Retail Decisions, Inc., 654 F.3d 1366, 1375, 99 USPQ2d 1690, 1694 (Fed. Cir. 2011); Ultramercial, 772 F.3d at 715, 112 USPQ2d at 1754.
Transmitting data is an extra-solution activity that courts have found does not amount to significantly more than the recited judicial exception. See Intellectual Ventures I v. Symantec, 838 F.3d at 1321, 120 USPQ2d at 1362 (utilizing an intermediary computer to forward information); TLI Communications LLC v. AV Auto. LLC, 823 F.3d 607, 610, 118 USPQ2d 1744, 1745 (Fed. Cir. 2016) (using a telephone for image transmission); OIP Techs., Inc., v. Amazon.com, Inc., 788 F.3d 1359, 1363, 115 USPQ2d 1090, 1093 (Fed. Cir. 2015) (sending messages over a network); buySAFE, Inc. v. Google, Inc., 765 F.3d 1350, 1355, 112 USPQ2d 1093, 1096 (Fed. Cir. 2014) (computer receives and sends information over a network).
Regarding ideas of a solution as being insignificantly more, see Electric Power Group, LLC v. Alstom, S.A., 830 F.3d 1350, 1356, 119 USPQ2d 1739, 1743-44 (Fed. Cir. 2016); Intellectual Ventures I v. Symantec, 838 F.3d 1307, 1327, 120 USPQ2d 1353, 1366 (Fed. Cir. 2016); Internet Patents Corp. v. Active Network, Inc., 790 F.3d 1343, 1348, 115 USPQ2d 1414, 1417 (Fed. Cir. 2015).
Accordingly, claim 1 is rejected under 35 U.S.C. 101 for being directed to unpatentable subject matter.
Claim 2
Claim 2 recites:
wherein tuning parameters of the first well model comprises: comparing the estimated production of the one or more existing production wells with the actual production of the one or more existing production wells;
This step is directed to the mental process of observation and evaluation. A human, with the aid of a generic computer, can review the values acquired from the wells with the predicted values from the model to evaluate the similarity of the corresponding values. See MPEP 2106.04(a)(2), Subsection III.
determining whether there is a match between the estimated production and the actual production of the one or more existing wells, based on the comparison and an error tolerance; and
This step is directed to the mental process of evaluation and judgment. A human, with the aid of a generic computer, can evaluate the differences between the values acquired from the wells with the predicted values from the model to evaluate whether the model produced acceptable results. See MPEP 2106.04(a)(2), Subsection III.
when it is determined that there is no acceptable match between the estimated production and the actual production, adjusting one or more parameters of the first well model to reduce a difference between the estimated production and the actual production of the one or more existing production wells.
This step is directed to the mental process of evaluation and judgment. A human, with the aid of a generic computer, can evaluate the differences between the values acquired from the wells with the predicted values from the model to evaluate whether the model produced acceptable results and, if not, can use expertise and experience to adjust parameters to improve the functioning of the model. See MPEP 2106.04(a)(2), Subsection III.
Accordingly, claim 2 is rejected under 35 U.S.C. 101 for being directed to unpatentable subject matter.
Claim 3
Claim 3 recites wherein the wellsite data acquired for the one or more existing production wells includes static and dynamic data. The claim merely specifies a type of data that is acquired, which is an additional element that is an insignificant extra-solution activity, and the claim does not add additional elements that may integrate the judicial exception into a practical application. See e.g., Tawil at [0141]: “For example, data values of the seismic data that indicate or describe properties of underground formations in the subterranean region are provided as inputs to data models of the ML engine 250.” See also Tawil at [0110]: “The input data may comprise real-time well borehole data, including well header data, well drilling status information, well logs, datasets for various well and core samples, and information relating to well tests.” Accordingly, claim 3 is rejected under 35 U.S.C. 101 for being directed to unpatentable subject matter.
Claim 4
Claim 4 recites wherein the wellsite data includes production data, well completion data, and geologic data associated with the one or more existing production wells. The claim merely specifies a type of data that is acquired, which is an additional element that amounts to an insignificant extra-solution activity that does not integrate the judicial exception into a practical application. See, e.g., Tawil at [0110]: “The input data may comprise real-time well borehole data, including well header data, well drilling status information, well logs, datasets for various well and core samples, and information relating to well tests.” Accordingly, claim 4 is rejected under 35 U.S.C. 101 for being directed to unpatentable subject matter.
Claim 5
Claim 5 recites wherein the parameters of the first well model include a porosity, a permeability, and a fluid saturation of an underlying reservoir formation associated with the one or more existing production wells. The claim merely specifies types of parameters that are included in the model, which is a mathematical calculation. Thus, the claim does not add additional elements that may integrate the judicial exception into a practical application. Accordingly, claim 5 is rejected under 35 U.S.C. 101 for being directed to unpatentable subject matter.
Claim 6
Claim 6 recites wherein each of the first and second ML models is a neural network. The first and second ML models are both mathematical calculations, as described with regards to claim 1. This claim merely specifies a type of ML model, each of which is a judicial exception. The claim does not include additional elements that would integrate the judicial exceptions into a practical application. Accordingly, claim 6 is rejected under 35 U.S.C. 101 for being directed to unpatentable subject matter.
Claim 7
Claim 7 recites wherein each of the first and second well models is a near wellbore model. The first and second models are both mathematical constructs comprised of multiple functions, as described with regards to claim 1. This claim merely specifies types ML models, each of which is a judicial exception. The claim does not include additional elements that would integrate the judicial exceptions into a practical application. Accordingly, claim 7 is rejected under 35 U.S.C. 101 for being directed to unpatentable subject matter.
Claim 8
Claim 8 recites wherein the adjusting of the one or more completion design parameters for the new production well is performed as part of an automated workflow for monitoring production operations of the one or more existing production wells to acquire production data and automatically history matching the one or more completion design parameters for the new production well based on the acquired production data along with the estimated completion costs and the forecasted production for the new production well.
In one aspect, the claim recites the abstract idea of a mental process of “adjusting…completion design parameters” that is also present in claim 1. Further, the claim recites that the process is performed as part of an “automated workflow” and then performing other actions “automatically.” However, a mental process that is performed by a generic computer (i.e., “automated”) is still an application of the judicial exception, which courts have found amounts to no more than reciting “apply it.” “Claims can recite a mental process even if they are claimed as being performed on a computer. The Supreme Court recognized this in Benson, determining that a mathematical algorithm for converting binary coded decimal to pure binary within a computer’s shift register was an abstract idea. The Court concluded that the algorithm could be performed purely mentally even though the claimed procedures “can be carried out in existing computers long in use, no new machinery being necessary.” 409 U.S at 67, 175 USPQ at 675. See also Mortgage Grader, 811 F.3d at 1324, 117 USPQ2d at 1699 (concluding that concept of “anonymous loan shopping” recited in a computer system claim is an abstract idea because it could be “performed by humans without a computer”). MPEP 2106.05, Subsection III(C).
In another aspect, if not claiming the abstract idea of a mental process, the limitation is directed to the additional element of an idea of a solution (MPEP 2106.05(f)(1)), or alternatively, the limitations merely links the judicial exception to a particular technological environment (MPEP 2106.05(h)). In either case, the claim does not describe, with specificity, how the “adjusting” is performed nor how adjusting the parameter solves any particular problem in the field of oil well drilling. In both cases, courts have found that ideas of solutions and linking a judicial exception to a particular field are additional elements that are not significantly more than the recited judicial exception. See Electric Power Group, LLC v. Alstom, S.A., 830 F.3d 1350, 1356, 119 USPQ2d 1739, 1743-44 (Fed. Cir. 2016); Intellectual Ventures I v. Symantec, 838 F.3d 1307, 1327, 120 USPQ2d 1353, 1366 (Fed. Cir. 2016); Internet Patents Corp. v. Active Network, Inc., 790 F.3d 1343, 1348, 115 USPQ2d 1414, 1417 (Fed. Cir. 2015).
Accordingly, claim 8 is rejected under 35 U.S.C. 101 for being directed to unpatentable subject matter.
Claim 9
Claim 9 recites: A system comprising: a processor; and a memory coupled to the processor having instructions stored therein, which when executed by the processor, cause the processor to perform a plurality of functions, including functions to: perform a method substantially the same as the method recited in claim 1.
Reciting generic computer components to perform a judicial exception is merely using a computer in its normal capacity, which does not integrate the abstract idea into a practical application. Further, courts have found that reciting a computer is not significantly more than the judicial exception. See MPEP 2106.05(f); Affinity Labs v. DirecTV, 838 F.3d 1253, 1262, 120 USPQ2d 1201, 1207 (Fed. Cir. 2016) (cellular telephone); TLI Communications LLC v. AV Auto, LLC, 823 F.3d 607, 613, 118 USPQ2d 1744, 1748 (Fed. Cir. 2016) (computer server and telephone unit). Similarly, “claiming the improved speed or efficiency inherent with applying the abstract idea on a computer” does not integrate a judicial exception into a practical application or provide an inventive concept. Intellectual Ventures I LLC v. Capital One Bank (USA), 792 F.3d 1363, 1367, 115 USPQ2d 1636, 1639 (Fed. Cir. 2015).
According, claim 8 is rejected under 35 U.S.C. 101 for being directed to unpatentable subject matter.
Claims 10-16
Claims 10-16 recite substantially the same limitations as claims 2-8. Accordingly, for at least the same reasons as asserted regarding claims 2-8, claims 9-14 are rejected under 35 U.S.C. 101 for being directed to unpatentable subject matter.
Claim 17
Claim 17 recites a computer-readable storage medium having instructions stored therein, which when executed by a computer cause the computer to perform a plurality of functions, including functions to: perform a method substantially the same as the method recited in claim 1. As previously asserted, the claim is rejected at Step 1 of the 101 analysis for being a signal per se. However, even if amended to recite a statutory category (e.g., “non-transitory medium”), claim 17 is still directed to unpatentable subject matter and therefore unpatentable subject matter for at least the same reasons as claim 1.
Claim 18-20
Claims 18-20 recite substantially the same limitations as claims 2, 3, and 5-8. Accordingly, for at least the same reasons as asserted regarding claims 2, 3, and 5-8 are rejected under 35 U.S.C. 101 for being directed to unpatentable subject matter.
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.
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 1-6, 9-14, and 17-18 are rejected under 35 U.S.C. 103 as being obvious over Dupont, et al., (U.S. Pat. Pub. No. 2018/0335538, hereinafter “Dupont”), in view of Tawil, et al. (U.S. Patent Pub. No. 2021/0317726, hereinafter “Tawil”), and Shelley, et al., (U.S. Pat. No. 9,262,713, hereinafter “Shelley”).
Claim 1
Dupont discloses:
A computer-implemented method of parameter matching for completion design, the method comprising:
acquiring, by a computing device, wellsite data for one or more existing production wells in a hydrocarbon producing field;1
For example, the reception block 304 can include receiving production data along with data for a plurality of factors for at least a portion of the plurality of wells. In such an example, the production data can be utilized for purposes of training a model per the training block 316 and optionally for validating a model and/or testing a model. Dupont at [0053].
transforming, by the computing device, the wellsite data into one or more model data sets for predictive modeling; 2
See FIG. 3, block 308: “Process Data (e.g., Clean, Aggregate).”
generating a first well model to estimate production for the one or more existing production wells, based on the well logs
In such an example, the prediction block 324 can include receiving information as to an existing well and/or as to a new well (e.g., a proposed well, a partially drilled well, etc.) and based at least in part on at least a portion of the received information predicting production for the existing well and/or the new well. In such an example, the received information may include production data or may not include production data. As an example, received information for a subject well for which a production prediction is desired may include information as to multiple factors associated with the subject well. In such an example, the information may include time dependent information such as, for example, production data and/or one or more other types of time dependent information. Dupont at [0052].
tuning parameters of the first well model, based on a comparison between the estimated production and an actual production of the one or more existing production wells;
Training of a model can include making production predictions, comparing the production predictions to actual productions and revising the model to “tune” the model such that the production predictions better match the actual productions. Dupont at [0053].
training a second ML model to predict parameters of a second well model for a new production well to be drilled at a new well site in the hydrocarbon producing field, based on the tuned parameters of the first well model;
As an example, a problem may be formulated for making forecasts in a field that includes hundreds of three year old wells. In such an example, a new well can be drilled in the field and commence production. Given the first three months of production of the new well, a forecast is desired for how much fluid the new well will produce after three years. As the field includes hundreds of three year old wells, production and other data are available for a large percentage of those hundreds of three year old wells. In such an example, a method can include training a single machine learning (ML) regression model (e.g. a random forest) using at least a portion of the available data, which may, for example, be subjected to data cleansing. Dupont at [0063].
generating the second well model to forecast production using the parameters predicted by the second ML model;
Such a method can include outputting a trained regression model (e.g., a trained random forest model) that can be utilized to make predictions as to production of the “young” three month old well, as a recently drilled “new” well. Dupont at [0063].
forecasting production of the new production well over a period of time, based on the parameters predicted by the second ML model for the second well model,3
As explained with respect to the block 324 of the method 300 of FIG. 3, a trained model that is output (e.g., generated) may be utilized given input data about one or more wells in a field. For example, a few months of production data may be acquired for one or more wells where such production is for the first few months of production life of such one or more wells. As an example, a trained model can be utilized in an inference mode for predicting future production of one or more wells (e.g., new and/or young wells). As an example, multiple instances of the method 300 may be implemented in series and/or parallel for prediction of production versus time for a plurality of wells. Dupont at [0072].
providing real time control of downhole production and injection control devices and real time control of surface production and injection control devices for the new production well based on an operating performance of the new production well using the adjusted one or more of the completion design parameters based on the second ML model compared to an expected performance of the new production well;4 and
As an example, the system 370 may be configured to output one or more control signals, for example, to control equipment (e.g., exploration equipment, production equipment, etc.). As an example, the system 370 may be part of and/or operatively coupled to equipment illustrated in FIG. 1. Dupont at [0057].
operating the new production well based on the real time control completed with the adjusted one or more of the completion design parameters based on the second ML model.
FIG. 4 shows an example of a method 400 that includes…a generation block 424 for generating a forecast based at least in part on one or more predictions of the trained model, an output block 428 for outputting a forecast, and a control block 432 for controlling one or more field processes based at least in part on the forecast. Dupont at [0073].
Like Dupont, Tawil discloses:
acquiring, by a computing device from a data store, wellsite data for one or more existing production wells in a hydrocarbon producing field;
The data processing capabilities of the earth model allow for computing more accurate estimations of hydrocarbon reserves in underground formations. Tawil at [0017].
WDS module 320 is a computing platform that enables earth model 300 to gain access to valuable well data and well data sources. The WDS module 320 can access the well data in real-time from various drillings rigs via satellite communication. Tawil at [0108].
“WDS” is “well data sources,” see also Fig. 3, element 320.
transforming, by the computing device, the wellsite data into one or more model data sets for predictive modeling;
The exploration data module 318 executes one or more of its workflow processes using data mining and business intelligence methods that leverage AI and ML technologies. For example, a data analysis and optimization program of the exploration data module 318 can invoke a predictive model of the ML engine 250 to perform statistical and stochastic analysis or modeling of the received hydrocarbon field and reservoir data. In some implementations, the statistical and stochastic approaches are employed to quantify, or precisely quantify, abnormal data conditions and data heterogeneity that can exists among the data values and variables of the received hydrocarbon data... The exploration data module 318 is configured to ensure consistent clean data is available to be utilized by the modules of the earth model 300. Tawil at [0107].
“Exploration data module” 318 performs data analysis and optimization, which is analogous to a “transformation” of the “wellsite data.”
In some implementations, the GDS module 322 is designed to bridge the gap between various geophysical data formats used by different vendors. In particular, the GDS module 322 is configured to receive multiple sets of geophysical data. At least two sets of the geophysical data can have distinct vendor-specific geophysical data formats. The GDS module 322 is operable to apply one or more data standards across the sets of geophysical data. For example, the GDS module 322 can use the one or more data standards to convert the respective vendor-specific formats to a standardized geophysical data format. The GDS module 322 uses the standardized geophysical data format to streamline and simplify the geophysical data and to make the data and related information more accessible by modules of the earth model 300. Tawil at [0113].
The “GDS module” transforms data by standardizing formats from various sources.
However, Dupont does not appear to disclose:
training a first machine learning (ML) model to predict well logs for the one or more existing production wells, based on the one or more model data sets;
predicting the well logs using the trained first ML model;
Tawil, which is analogous art to the claimed invention, discloses:
training a first machine learning (ML) model to predict well logs for the one or more existing production wells, based on the one or more model data sets;
The data processed by the AI system 200 to generate the predictive models may be annotated training data…. Tawil at [0048].
the SWG module 310 is operable to use certain pattern recognition outputs of the predictive model as a main technique for developing the processing methods of its automation workflow. In some examples, the processing methods can include the generation and forward modeling of pseudo well logs and structural updates. Tawil at [0092].
The “training data” is analogous to “acquired wellsite data,” which is utilized to generate the “model data sets” at the “transforming” step.
“Pseudo well logs” is analogous to “predicted well logs.”
predicting the well logs using the trained first ML model;
the SWG module 310 is operable to use certain pattern recognition outputs of the predictive model as a main technique for developing the processing methods of its automation workflow. In some examples, the processing methods can include the generation and forward modeling of pseudo well logs and structural updates. Tawil at [0092].
Tawil is analogous art to the claimed invention because both are directed to utilizing machine learning models to generate well logs that mirror actual well logs based on other wellsite data. It would have been obvious to a person having ordinary skill in the art, before the effective filing date of the claimed invention, to combine the machine learning technique to generate well logs that is disclosed in Tawil with the process of forecasting production from wellsite data, as described in Dupont, to result in a system that acquires wellsite data and operates a well according to completion parameters that optimize resource production. Motivation to combine includes allowing for production to be predicted in instances where well logs are not available, such as new wells that have yet to be completed, thus providing an indication of the predicted production before spending the resources to complete a new well.
Dupont and Tawil do not appear to disclose:
the parameters predicted by the second ML model for the second well model including completion design parameters for the new production well;
estimating completion costs for the new production well, based on the completion design parameters using the second ML model and the forecasted production over the period of time;
adjusting one or more of the completion design parameters for the new production well to produce optimal hydrocarbon production, based on the estimated completion costs using the second ML model and the forecasted production;
completing the new production well based on the adjusted completion design parameters using the second ML model;
Shelley, which is analogous art to the claimed invention, discloses:
the parameters predicted by the second ML model for the second well model including completion design parameters for the new production well;
In this regard, the predictive models of the present disclosure can be utilized to evaluate and optimize new wells 238 (e.g., well placement, depth, profile, and/or completion), evaluate and optimize under-performing existing wells 240 (e.g., determine whether improved completion design parameters can improve well production in a profitable manner), evaluate and optimize prospective well locations 242 (e.g., desired well placement, depth, profile, and/or completion), evaluate completion design parameters 244, including hydraulic fracturing designs, for completed wells (e.g., evaluate utilized parameters to resulting production for wells having similar profiles)…. Shelley at col. 15, lines 39-50.
estimating completion costs for the new production well, based on the completion design parameters using the second ML model and the forecasted production over the period of time;
An example of a real-world implementation of optimization 234 and economic analysis 236 in accordance with the present disclosure will now be described with reference to FIGS. 17 and 18…The estimated cost of this proposed stimulation was around $6,600,000. Shelley at col. 15, line 55-col. 16, line 3.
adjusting one or more of the completion design parameters for the new production well to produce optimal hydrocarbon production, based on the estimated completion costs using the second ML model and the forecasted production;
As a result of the neural network predictive model, it was estimated that comparable production and recovery could be achieved by reducing treatment volume. Further, the predictive model indicated that the number of hydraulic fracturing stages could be reduced from 40 to 30 without significant loss of production. FIG. 17 shows a graph 280 that plots the estimated production and recovery predictions made by the predictive model using the reduced treatment volumes for various numbers of hydraulic fracturing stages. Shelley at col. 16, lines 8-17.
completing the new production well based on the adjusted completion design parameters using the second ML model;
Referring now to FIG. 21, shown therein is a graph 320 illustrating actual oil recovery from a well relative to a predicted oil recovery based on a predictive model according to an embodiment of the present disclosure. In this regard, a predictive model in accordance with the present disclosure was used to predict production for 10 wells that were part of a project to evaluate the effectiveness of various completion and hydraulic fracturing methods. Graph 320 compares the data-driven model predicted production relative to the actual best calendar month oil production for these wells, which are located in McKenzie County, North Dakota (Truax area) and Divide/Williams Counties, North Dakota (Wildrose area). Shelley at col. 17, lines 30-41.
Shelley is analogous art to the claimed invention because both are directed to optimizing production in light of other considerations, such as cost to complete a well. It would have been obvious to a person having ordinary skill in the art, before the effective filing date of the claimed invention, to combine the process of acquiring wellsite data and predicting production of a new well, as disclosed in Dupont and Tawil, with the optimization technique of Shelley to result in a system that adjusts one or more completion factors in order to optimize both resource recovery and associated costs. Motivation to do so includes maximizing profits from a well by determining the most cost efficient manner in which to complete and operate a well, thus improving profitability of the well.
Claim 2
Dupont discloses:
wherein tuning parameters of the first well model comprises: comparing the estimated production of the one or more existing production wells with the actual production of the one or more existing production wells;
Training of a model can include making production predictions, comparing the production predictions to actual productions…Dupont at [0053].
determining whether there is an acceptable match between the estimated production and the actual production of the one or more existing wells, based on the comparison and an error tolerance; and
…and revising the model to “tune” the model such that the production predictions better match the actual productions. Dupont at [0053].
when it is determined that there is no acceptable match between the estimated production and the actual production, adjusting one or more parameters of the first well model to reduce a difference between the estimated production and the actual production of the one or more existing production wells.
…and revising the model to “tune” the model such that the production predictions better match the actual productions. Dupont at [0053].
Claim 3
Tawil discloses:
wherein the wellsite data acquired for the one or more existing production wells includes
This specification also describes systems and techniques for performing probabilistic modeling of reservoir properties in a subterranean region using well logs and relevant seismic data. Tawil at [0038].
static (data) and
For example, data values of the seismic data that indicate or describe properties of underground formations in the subterranean region are provided as inputs to data models of the ML engine 250. Tawil at [0141].
Seismic data can be “static” data that is a property of formations at a well.
dynamic data.
The input data may comprise real-time well borehole data, including well header data, well drilling status information, well logs, datasets for various well and core samples, and information relating to well tests. Tawil at [0110].
“Input data” (analogous to “wellsite data”) can include “real-time” (i.e., “dynamic”) data.
It would have been obvious to a person having ordinary skill in the art, before the effective filing date of the claimed invention, to utilize the wellsite data described in Tawil to predict production as disclosed in Dupont because, in both instances, various types of wellsite data can be utilized to train machine learning models to make predictions. Motivation to combine includes improved variability of the system to predict production based on available wellsite data, which may vary in form depending on the application and/or well reservoir.
Claim 4
Tawil discloses:
wherein the wellsite data includes production data, well completion data, and
The input data may comprise real-time well borehole data, including well header data, well drilling status information, well logs, datasets for various well and core samples, and information relating to well tests. Tawil at [0110].
“Well header data” is analogous to “well completion data” because both are well-specific information.
“Well and core samples” and “well borehole data” are analogous to “production data” (e.g., “wellsite production data may include, for example, production system measurements from various downhole devices or surface sensors/meters, as described above.” Spec. at [0032], “Examples of such downhole tools include, but are not limited to, a LWD tool, a MWD tool, and any of various sensors for measuring various downhole conditions and formation properties.” Spec. at [0029]).
geologic data associated with the one or more existing production wells.
The method includes obtaining, by the integrated multi-dimensional geological model, seismic data describing an underground formation in the subterranean region of a geological area… Tawil at [0009].
It would have been obvious to a person having ordinary skill in the art, before the effective filing date of the claimed invention, to utilize the wellsite data described in Tawil to predict production as disclosed in Dupont because, in both instances, various types of wellsite data can be utilized to train machine learning models to make predictions. Motivation to combine includes improved variability of the system to predict production based on available wellsite data, which may vary in form depending on the application and/or well reservoir.
Claim 5
Dupont discloses:
wherein the parameters of the first well model include a porosity, a permeability, and a fluid saturation of an underlying reservoir formation associated with the one or more existing production wells.
Type-curve analysis may be applied, for example, for quantifying well and reservoir parameters such as permeability, skin, fracture half-length, dual-porosity parameters, and others, by comparing pressure change and its derivative of acquired data (e.g., data for existing wells) to reservoir model curve families, called “type curves”. Dupont at [0042].
With respect to a shale formation that includes hydrocarbons (e.g., a hydrocarbon reservoir), its hydrocarbon producing potential may depend on various factors such as, for example, thickness and extent, organic content, thermal maturity, depth and pressure, fluid saturations, permeability, etc. Dupont at [0032].
Claim 6
Dupont discloses:
wherein…the…second ML models is a neural network.
As to examples of regression models that may be utilized, consider one or more of the following: linear regression, ridge regression, Huber regression, Random Forest Regression, Gradient Boosted Random Forest Regression, Neural Network regression, lasso regression, and locally weighted linear regression. Dupont at [0070].
Dupont does not appear to disclose wherein…the first…ML models is a neural network. Tawil discloses:
wherein…the first…ML models is a neural network.
For example, the techniques can be implemented using predictive and autonomous software controls that are derived initially from trained neural networks of a machine-learning (ML) engine. More specifically, the machine-learning engine can generate multiple predictive models in response to processing various types of information and datasets of seismic data using one or more neural networks. Tawil at [0006].
Claim 9
Dupont discloses:
A system comprising: a processor; and a memory coupled to the processor having instructions stored therein, which when executed by the processor, cause the processor to perform a plurality of functions, including functions to:
As an example, a system can include a processor; memory operatively coupled to the processor; and instructions stored in the memory and executable by the processor to instruct the system… Dupont at [0133].
perform a method substantially the same as the method recited in claim 1.
Accordingly, for at least the same reasons and based on the same prior art as claim 1, claim 9 is rejected under 35 U.S.C. 103 as being obvious over Dupont in view of Tawil and Shelley.
Claims 10-14
Claims 10-14 recite limitations that are substantially the same as the method disclosed in claim 2-6. Accordingly, for at least the same reasons and based on the same prior art as claims 2-6, claims 10- 14 are rejected under 35 U.S.C. 103 as being obvious over Dupont in view of Tawil and Shelley.
Claim 17
Dupont discloses:
A computer-readable storage medium having instructions stored therein, which when executed by a computer cause the computer to perform a plurality of functions, including functions to:
As an example, one or more computer-readable storage media can include computer-executable instructions to instruct a computing system… Dupont at [0134].
perform a method substantially the same as the method recited in claim 1.
Accordingly, for at least the same reasons and based on the same prior art as claim 1, claim 15 is rejected under 35 U.S.C. 103 as being obvious over Dupont in view of Tawil and Shelley.
Claim 18
Claim 18 recites substantially the same limitations as claim 2. Accordingly, for at least the same reasons and based on the same prior art, claim 18 is rejected under 35 U.S.C. 103 as being obvious over Dupont in view of Tawil and Shelley.
Claim 19
Claim 19 recites substantially the same limitations as recited in claims 3 and 5. Accordingly, for at least the same reasons and based on the same prior art, claim 19 is rejected under 35 U.S.C. 103 as being obvious over Dupont in view of Tawil and Shelley.
Claims 7 and 15 are rejected under 35 U.S.C. 103 as being obvious over Dupont in view of Tawil, Shelley, and further in view of Chandra, et al. (“Improving Reservoir Characterization and Simulation With Near-Wellbore Modeling”), hereinafter “Chandra.”
Claim 7
Dupont, Tawil, and Shelley do not appear to disclose:
wherein each of the first and second well models is a near wellbore model.
Chandra, which is analogous art, discloses:
wherein each of the first and second well models is a near wellbore model.
In this paper, we illustrate a novel workflow involving high-resolution near-wellbore modeling (NWM), which allows us to accurately include seismic, wireline data, image logs, and well core logs from highly heterogeneous reservoirs in field-scale reservoir simulations. We demonstrate that an NWM-enhanced geoengineering workflow has the potential to improve reservoir characterization by applying it to a realistic clastic reservoir with high variance at small scale…. Our results show that the use of NWM tools for reservoir modeling yields more precise flow calculations… Chandra at Summary.
“Flow calculations” is analogous to “production,” which is what is estimated by both the “first well model” and the “second well model.”
Chandra is analogous art to the claimed invention because both are directed to performing well simulations based on wellsite data. It would have been obvious to a person of ordinary skill in the art, before the effective filing date of the application, to combine the near-wellbore model of Chandra with the models of Tawil and Dusterhoft to result in a well-planning process that is directed to near-wellbore data. Motivation to combine includes improved characterization of a reservoir based on the limited data from wells within in the field that includes the reservoir. Thus, a model of other new wells and/or potential wells in the same field is more accurate.
Claims 8, 16, and 20 are rejected under 35 U.S.C. 103 as being obvious over Dupont in view of Tawil, Shelley, Chandra, and further in view of Burch, et al. (U.S. Patent No. 10,963,815), hereinafter “Burch”).
Claim 8
Dupont, Tawil, Shelley, and Chandra do not appear to disclose:
wherein the adjusting of the one or more completion design parameters for the new production well is performed as part of an automated workflow for monitoring production operations of the one or more existing production wells to acquire production data and
automatically history matching the one or more completion design parameters for the new production well based on the acquired production data along with the estimated completion costs and the forecasted production for the new production well.
Burch, which is analogous art to the claimed invention, discloses:
wherein the adjusting of the one or more completion design parameters for the new production well is performed as part of an automated workflow for monitoring production operations of the one or more existing production wells to acquire production data and
As shown in FIG. 2, field data 202 is input to a model learner 204 of the well performance predictor 201. The field data 202 may include geologic data, e.g., a geologic model, relating to the hydrocarbon field in which the well is to be drilled, historical production data relating to nearby wells, or physical models of the hydrocarbon field, for example. Burch at Col. 12, lines 7-13.
As illustrated in FIG. 2, the flow of data and operations within well performance predicter 201 is performed without human intervention, thus being an “automated workflow.”
automatically history matching the one or more completion design parameters for the new production well based on the acquired production data along with the estimated completion costs and the forecasted production for the new production well.
a potential well parameter combination 208 is generated by a well-parameter combination generator 210. In various embodiments, the well-parameter combination generator 210 generates the set of all possible well counts, locations, and completion strategies, available to the operator. In any practical implementation, there are an infinite number of possible well parameter combinations, so the well parameter combinations may not be explicitly enumerated. Instead, a finite number of well parameter combinations may be systematically and adaptively generated and compared by the well-parameter combination generator 210. Burch at Col. 12, lines 27-37.
As shown in FIG. 2, field data 202 is input to a model learner 204 of the well performance predictor 201. The field data 202 may include geologic data, e.g., a geologic model, relating to the hydrocarbon field in which the well is to be drilled, historical production data relating to nearby wells, or physical models of the hydrocarbon field, for example. Burch at Col. 12, lines 7-13.
The well costs calculator 214 then determines costs 216 for the given well parameter combination 208. The costs 216 may include the actual implementation costs, e.g., the initial capital costs and ongoing operating costs, for the well. Burch at Col. 12, lines 53-57.
As best illustrated in FIG. 2, the “completion design parameters” (208), “acquired production data” (202), “estimated completion costs” (216), and “forecasted production” (212) are matched at one or more locations in the well performance predictor 201.
Burch is analogous art to the claimed invention because both are directed to determining whether to drill a particular well based on a predicted cost analysis. It would have been obvious to a person of ordinary skill in the art, before the effective date of the claimed invention, to combine the references to result in a system that can determine whether a completion scheme and forecasted production are will have an acceptable monetary advantage. Motivation to combine includes reducing human error from the decision process and potentially avoiding drilling wells unnecessarily that do not result in an adequate return on investment.
Claim 16
Claim 16 recites limitations that are substantially the same as the limitations recited in claim 8. Accordingly, for at least the same reasons and based on the same prior art as claim 8, claim 16 is rejected under 35 U.S.C. 103 as being obvious over Dupont in view of Tawil, Shelley, Chandra, and Burch.
Claim 20
Claim 20 recites limitations that are substantially the same as the limitations recited in claims 6-8. Accordingly, for at least the same reasons and based on the same prior art as claims 6-8, claim 20 is rejected under 35 U.S.C. 103 as being obvious over Dupont in view of Tawil, Shelley, Chandra, and Burch.
Conclusion
The prior art made of record and not relied upon is considered pertinent to applicant's disclosure:
Anifowose, et al., (U.S. Patent Pub. No. 2021/0349001)
Teaches a machine learning model that predicts well logs based on wellsite data.
Teaches a neural network.
Duan, et al., (U.S. Patent Pub. No. 2023/0203925)
Teaches a machine learning model that predicts porosity for a new production well.
Teaches a neural network.
Nabors, et al., “Basin-Specific Machine Learning Models for Efficient Completions Optimization.”
Discloses generating a reservoir-wide model that can be utilized at different locations on the reservoir to predict production of an unbuilt well at a given location.
Wicker, et al., “Improving Well Designs and Completion Strategies Utilizing Multivariate Analysis.”
Discloses using machine learning techniques to predict well production of a well given early production data and wellsite information.
Chan, et al., “The Art of Balancing the Cost and Value for Field Development.”
Discloses optimizing hydrocarbon production while additionally minimizing cost estimations for well completion.
Communication
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JOSEPH MORRIS
Examiner
Art Unit 2188
/JOSEPH P MORRIS/Examiner, Art Unit 2188
/RYAN F PITARO/Supervisory Patent Examiner, Art Unit 2188
1 Also disclosed by Tawil, see below.
2 Also disclosed by Tawil, see below.
3 This limitation does not require utilizing the second well model to forecast production. As recited, the production can be forecasted based only the output of the second ML model (i.e., the parameters of the second well model and completion design parameters). Examiner suggests amending the limitation to recite “forecasting production of the new production well over a period of time using the second well model.”
4 As previously asserted with regards to the rejection of the claim under 35 U.S.C. 112(a) and 112(b), the limitation is indefinite because it is unclear what is meant by “providing…control of downhole production and injection control devices…using the adjusted one or more of the completion design parameters based on the second ML model.” Accordingly, for purposes of examination, the limitation is interpreted to mean “controlling, in real time, one or more aspects of well injection and production in accordance with a completion plan.”
Prosecution Insights