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 .
Response to Arguments
Applicant’s arguments with respect to claim(s) 1-14 and 17-23 have been considered but are moot in view of the new grounds of rejection necessitated by the applicant’s amendments to the claims.
Explanation has been given below as to why the applicant’s amendments do not overcome the 35 U.S.C. 101 rejection.
Examiner’s Note - Drawings
As previously discussed, the drawing amendments of 07/30/25 are accepted.
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 1-14 and 17-23 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 claim 1 discloses:
during a first stage prior to data collection, iteratively performing the following steps until an uncertainty reduction for one or more quantities of interest (QoIs) reaches a target uncertainty criteria:
assessing a plurality of potential data collection components …
at each iteration, quantiatively analyzing …
selecting, based on the quantitative analysis …
during a second stage for data collection, subsequent to the first stage:
implementing testing with the selected set of data collection components …
generating a set of subsurface models using the collected data; and
executing a decision based on the set of subsurface models
The examiner could not find support for this limitation in the applicant’s disclosure.
The examiner could not find any instances of “first stage” or “second stage” within the applicant’s original specification. The closest support the examiner could find was applicant’s figures 1-2. It would appear that since figure 1, reference 116 discloses, “8. Perform the data collection …”, the “first stage prior to data collection” would be referring to references 102-114. However, there does not appear to be a “second stage” after reference 116 that teaches the claimed second stage steps. If anything, these steps appear to be part of figure 1, reference 108 (see further details of reference 108 in figure 2), which appears before reference 116.
The examiner requests that the applicant show where the amended limitations are supported by the disclosure.
Independent claim 22 discloses:
appending the selected data collection component for a present iteration to an end of an order of the data collection components for testing
The examiner could not find support for this limitation in the applicant’s disclosure.
The only mention of “append” that the examiner could find in the applicant’s specification was in paragraph 0006, which states, “the appended drawings illustrate …” which is something entirely different.
The examiner requests that the applicant show where the amended limitation is supported by the disclosure.
All other claims depend on independent claims 1 and 22. They are also rejected, as a result of their dependency.
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 1-14 and 17-23 are rejected under 35 U.S.C. 101 because the claimed invention is directed to a judicial exception (i.e., a law of nature, a natural phenomenon, or an abstract idea) without significantly more.
With respect to step 1 of the patent subject matter eligibility analysis, the claims are directed to a process, machine, manufacture, or composition of matter. Independent claim 1 is directed to a computer-implemented method, which is a process. All other claims depend on independent claim 1. As such, claims 1-20 are directed to a statutory category.
With respect to step 2A, prong one, the claims recite an abstract idea, law of nature, or natural phenomenon. Specifically, the following limitations recite mathematical concepts and/or mental processes.
Claim 1
the one or more QoIs comprising one or more parameters that define a subsurface model or one or more outputs of the subsurface model (This limitation recites abstract mathematical concepts. The QoI parameters represent mathematical variables within a mathematical model, which reflects mathematical relationships.)
quantitatively analyzing each of the plurality of potential data collection components for an uncertainty reduction on the one or more QoIs (This limitation recites abstract mathematical concepts. As seen in the applicant’s specification, the quantitative analysis is defined by mathematical calculations and relationships. Also, a general quantitative analysis is an observation, evaluation, judgment, and/or opinion that can be performed in the human mind.)
an uncertainty space is adjusted by updating the subsurface model and QoIs based on an assumption that each selected data collection component has been performed and the selected data collection components are removed from the plurality of potential data collection components (As stated above, the QoI parameters represent mathematical variables within a mathematical model, which reflects mathematical relationships. Updating these values based on the presence or absence of data further reflects mathematical relationships. As such, the limitation recites abstract mathematical concepts.)
generating a set of subsurface models using the collected data (The generation of models reflects mathematical relationships. The limitation recites abstract mathematical concepts.)
executing a decision based on the set of subsurface models (This limitation recites an abstract mental process that can be performed in the human mind. Paragraph 0004 of the Applicant’s original specification states, “In the oil and gas industry, business decisions are typically made based on an understanding of the subsurface …” Business decisions are decisions that can be made in the minds of human beings. The claim does not specify what “executing a decision” is, which may just be mentally deciding that something will happen and then mentally preparing the steps to make that thing happen.)
Claim 22
determining the uncertainty reduction for each of the plurality of potential data collection components not previously selected for one or more previous iterations (A general determination of a value, such as an uncertainty reduction, is an observation, evaluation, judgment, and/or opinion that can be performed in the human mind. A more sophisticated uncertainty reduction, via mathematical data processing, may not be able to be performed in the human mind. But in that situation, the limitation would recite abstract mathematical concepts.)
selecting a data collection component that maximizes the uncertainty reduction and that was not previously selected for the one or more previous iterations (Selecting a value or component, based on general criteria, is a concept that can be performed in the human mind.)
All dependent claims depend on independent claims 1 and 22 and also recite their abstract limitations by virtue of their dependence. The claims, as a whole, are directed to mathematical data processing, and the dependent claims merely serve to further refine and detail the nature of the mathematical data processing.
For example, dependent claim 2 discloses characterizing the one or more QoIs, which represents mathematical relationships.
Dependent claim 3 discloses iterations, which represents mathematical concepts.
Dependent claim 4 discloses meeting or exceeding a value, which represents mathematical relationships.
Dependent claim 5 discloses basing analysis on parameters such as uncertainty and cost, which represents mathematical relationships.
Dependent claim 6 discloses different types of models and mathematical determinations within those models.
Dependent claim 7 discloses percentage reduction, which represents a mathematical calculation.
Dependent claim 8 discloses narrowing a range of values, which represents mathematical relationships.
Dependent claim 11 discloses target values, which represents mathematical relationships.
Dependent claim 12 discloses quantifying the reduction of a value in reference to a target values, which represents mathematical relationships.
Dependent claim 13 discloses weighting values, which represents mathematical calculations.
Dependent claim 19 discloses mathematical variables and the mathematical relationships pertaining to reducing uncertainty for those variables.
Dependent claim 23 discloses comparing results to a report, which is an observation, evaluation, judgment, and/or opinion that can be performed in the human mind.
With respect to step 2A, prong two, the claims do not recite additional elements that integrate the judicial exception into a practical application. The following limitations are considered “additional elements” and explanation will be given as to why these “additional elements” do not integrate the judicial exception into a practical application.
Claim 1
A method for performing hydrocarbon management that employs selecting and implementing a plurality of data collection components in order to effectively collect data regarding at least a part of a subsurface (This limitation is not indicative of integration into a practical application because it merely uses a computer as a tool to perform an abstract idea (see MPEP 2106.05(f)). The claimed limitations beneath the preamble are directed to data processing using a computer. Furthermore, the disclosure of “performing hydrocarbon management” merely serves to generally link the use of the judicial exception to a particular technological environment or field of use. (see MPEP 2106.05(h)). The claims do not specify the nature of then hydrocarbon management performance, such as whether the management is a data processing operation or a structural transformation.)
during a first stage prior to data collection, iteratively performing the following steps until an uncertainty reduction for one or more quantities of interest (QoIs) reaches a target uncertainty criteria (This limitation is not considered indicative of integration into a practical application because it is directed to general computer processing that merely uses instructions to implement an abstract idea on a computer, or merely uses a computer as a tool to perform an abstract idea (see MPEP 2106.05(f)). Regardless of where in the process flow the steps are being performed, the limitation, as a whole, is still directed to the collection and processing of data, without positive recitation of applying the judicial exception with, or by use of, a particular machine (see MPEP 2106.05(b)) or effecting a transformation or reduction of a particular article to a different state or thing (see MPEP 2106.05(c)).)
accessing a plurality of potential data collection components, wherein at each iteration, a component of the data collection program is selected and added to a final data collection program based on its resulting uncertainty reduction, each of the potential data collection components having one or both of an associated well and an associated test or seismic survey parameter designs for collecting data, the data used to characterize one or more (QoIs) regarding the subsurface (This limitation is not indicative of integration into a practical application because accessing data for data processing merely adds insignificant extra-solution activity to the judicial exception (see MPEP 2106.05(g)). Furthermore, the disclosure here of “an associated well” and “seismic survey parameter designs” merely serve to generally link the use of the judicial exception to a particular technological environment or field of use (see MPEP 2106.05(h)). There is a distinction between positively reciting and utilizing “structure” versus processing data about or involving structure. The disclosure of iterative data processing is not considered indicative of integration into a practical application because gathering data and computerized data analysis is still extra-solution activity and merely using a computer as a tool to perform the abstract idea still is not indicative of integration into a practical application, regardless of the iteration. Furthermore, generalized iterative processing is well-understood, routine, and conventional, with one example of how iterative processing is known or obvious being shown in the rejection below. Also, please note the recent case of Recentive Analytics v. Fox Corp (see attached case), which stated (on page 12, paragraph 1), “The requirements that the machine learning model be ‘iteratively trained’ … do not represent a technological improvement … Iterative training using selected training material and dynamic adjustments based on real-time changes are incident to the very nature of machine learning … the model is trained, and then the algorithm is actually updated and improved over time based on the input.’ …”)
at each iteration, analyzing iteratively based on a target uncertainty criteria (This limitation is not indicative of integration into a practical application for the reasons discussed with respect to iteration above. Merely adding generalized iterative data processing is not indicative of integration into a practical application.)
selecting, based on the quantitative analysis and from a remaining set of the plurality of potential data collection components, a set of data collection components for testing (This limitation is not indicative of integration into a practical application because the selecting appears to merely use a computer as a tool to perform an abstract idea. The disclosure of iterative selecting is not indicative of integration into a practical application for the reasons discussed with respect to iteration above.)
wherein in each iteration (This is not indicative of integration into a practical application for the reasons discussed with respect to iteration above.)
during a second stage for data collection, subsequent to the first stage (This limitation is not indicative of integration into a practical application, for similar reasons as discussed above.)
implementing testing with the selected set of data collection components in order to collect the data to characterize at least a part of the subsurface (This limitation is not indicative of integration into a practical application because the implementing appears to merely use a computer as a tool to perform an abstract idea.)
Claim 22
A method (This limitation merely serves to generally link the use of the judicial exception to a particular technological environment or field of use.)
receiving a plurality of potential data collection components, the plurality of the potential data collection components being associated with one or more wells (Receiving data to be processed merely serves to add insignificant extra-solution activity to the judicial exception.)
prior to data collection, iteratively performing the following steps until a target criteria is met for an uncertainty reduction for one or more quantities of interest (QoIs) (see comments, with respect to claim 1 above)
appending the selected data collection component for a present iteration to an end of an order of the data collection components for testing (This is a data processing operation that merely uses a computer as a tool to perform an abstract idea.)
generate a report comprising the data collection components in the order for display on a display device (Generating a report is akin to “outputting” data, which merely adds insignificant extra-solution activity to the judicial exception. It does not positively recite a structural transformation, such as pausing/activating drilling or removing a drill string from a well.)
All dependent claims depend on independent claims 1 and 22 and also recite similar limitations that are not indicative of integration into a practical application by virtue of their dependence. In addition, the dependent claims also recite their own limitations that are not indicative of integration into a practical application. The claims, as a whole, are directed to data processing, and the dependent claims merely serve to further highlight the data processing, rather than using the output of the data processing in a positive step of being applied with, or by use of, a particular machine (see MPEP 2106.05(b)) or effecting a transformation or reduction of a particular article to a different state or thing (see MPEP 2106.05(c)). It is noted that the output of the data processing stays on the computer, rather than being positively used for a transformation or practical application that exists outside of the computer.
For example, dependent claim 9 mentions hydrocarbon resource management, but this limitation merely serves to generally link the use of the judicial exception to a particular technological environment or field of use.
Dependent claim 10 discloses data about the wells, which merely serve to implement an abstract idea on a computer.
Dependent claim 14 discloses computer processing using selection, which merely uses a computer as a tool to perform an abstract idea.
Dependent claim 17 discloses data about the wells, which merely serve to implement an abstract idea on a computer.
Dependent claim 18 discloses data about the seismic surveys, which merely serve to implement an abstract idea on a computer.
Dependent claim 20 discloses data about the wells and seismic surveys, which merely serve to implement an abstract idea on a computer.
Dependent claim 21 discloses extracting hydrocarbons from the subsurface using the generated set of subsurface models. However, no detail is given as to how hydrocarbons are extracted. This limitation merely serves to generally link the use of the judicial exception to a particular technological environment or field of use (see MPEP 2106.05(h)). Also, the claimed extracting could be merely part of the overall process, such as a business leader making a decision in his or her mind to extract hydrocarbons, without any affirmative action of hydrocarbons actually being physically extracted.)
Dependent claim 23 discloses receiving testing results, which adds insignificant extra-solution activity to the judicial exception. Claim 23 also discloses modifying the data collection components, the order of the data collection components, or combinations thereof, which is computer processing that merely uses a computer as a tool to perform an abstract idea.
With respect to step 2B, the claims do not recite additional elements that amount to significantly more than the judicial exception. The claimed invention does not add significantly more because, as discussed above in step 2A, prong two, the claims do nothing more than merely use a computer as a tool to perform an abstract idea; add insignificant extra-solution activity to the judicial exception; and/or generally link the use of the judicial exception to a particular technological environment or field of use. The claims are directed to receiving and processing data. This is well-understood, routine, and conventional. Simply appending well-understood, routine, and conventional activities previously known to the industry, and specified at a high level of generality, to the judicial exception is not indicative of an inventive concept (aka “significantly more”) (see MPEP 2106.05(d) and Berkheimer Memo).
Claim Rejections - 35 USC § 103
The following is a quotation of 35 U.S.C. 103 which forms the basis for all obviousness rejections set forth in this Office action:
A patent for a claimed invention may not be obtained, notwithstanding that the claimed invention is not identically disclosed as set forth in section 102, if the differences between the claimed invention and the prior art are such that the claimed invention as a whole would have been obvious before the effective filing date of the claimed invention to a person having ordinary skill in the art to which the claimed invention pertains. Patentability shall not be negated by the manner in which the invention was made.
Claim(s) 1-14 and 17-23 is/are rejected under 35 U.S.C. 103 as being unpatentable over Li NPL (Li, Lewis – “A Bayesian Approach to Causal and Evidential Analysis for Uncertainty Quantification Throughout the Reservoir Forecasting Process”; A Dissertation Submitted to the Department of Energy Resources Engineering and the Committee on Graduate Studies of Stanford University in Partial Fulfillment of the Requirements for the Degree of Doctor of Philosophy; September 2017) in view of Pradhan et al NPL (Pradhan, Anshuman and Mukerji, Tapan – “Seismic Bayesian evidential learning: estimation and uncertainty quantification of sub-resolution reservoir properties”; Computations Geosciences, (2020) 24:1121-1140).
With respect to claim 1, Li NPL discloses:
A method for performing hydrocarbon management (page 4, first paragraph, under Section 1.2 states, “We follow a Bayesian formulation, in which prior information regarding the field under study is used in conjunction with collected seismic data to generate multiple realizations of the subsurface velocity model that match the observed data.”; page 35, first paragraph, under Section 2.4 states, “Today reservoir simulation is to inform a wide variety of decisions faced in reservoir engineering, ranging from whether to develop a field, where to drill new wells, to if enhanced oil recovery projects should be implemented. The reservoir simulator solves a set of partial differential equations that describe how the flow behavior of the components of a reservoir (hydrocarbons) in different phases (oil, water, gas). This is used to forecast the prediction variable h, for instance spatial distribution of fluid pressure, saturations, recovery factors, future injection/production rates from both existing and new wells, etc.”; see also chapters 4-5 that discuss the concepts of Bayesian Evidential Analysis (BEA) (such as on page 79) and Sequential Importance Resampling (SIR) (such as on page 113).)
assessing a plurality of potential data collection components, each of the potential data collection components having one or both of an associated well and an associated test or seismic survey parameter designs for collecting data, the data used to characterize one or more QoIs regarding the subsurface, the one or more QoIs comprising one or more parameters that define a subsurface model or one or more outputs of the subsurface model (pages 36-37, sections on “Numerical Solutions” and “Parameter Uncertainty” state, “The input to the reservoir simulator is the reservoir model m, a numerical representation of the parameters of the reservoir under study. These parameters may be spatially varying *such as porosity) or could be global … solving the numerical PDEs requires the specification of a number of reservoir parameters. Consider, for a basic incompressible black oil simulator, the reservoir model must delineate: 1. The overall geological geometry of the reservoir; 2. The permeability, porosity, and depth of each grid cell; 3. Initial reservoir pressure; 4. Relative permeability, capillary pressure curves, PVT data, etc that describe fluid flow characteristics – The specification of these parameters is a multidisciplinary and complicated task. Geological studies, seismic imaging and petrophysical analysis of logs and cores can provide insight into the geometry of the reservoir as well as permeability and porosity at select locations …”)
quantitatively analyzing each of the plurality of potential data collection components for an uncertainty reduction on the one or more QoIs (abstract states, “In the oil and gas industry, decisions with often large financial implications and risks depend on quantities subject to substantial uncertainty.”; page 73 under section 3.6 states, “The ensemble of the locations of that structure of interest provide a quantitative assessment of the uncertainty of the structure. This can then be used to estimate and quantify uncertainty properties such as reservoir volume, etc.”; Page 76 under section 3.6.2.2 states, “This yields a set of interpretations on the rest of the images, which now provides a quantitative assessment of where that interpreted horizon could be located.” Quantifying uncertainty is disclosed throughout the disclosure of Li and serves as a form of quantitative analysis.)
selecting, based on the quantitative analysis and from a remaining set of the plurality of potential data collection components, a set of data collection components for testing (see section 4.7.2, as discloses on pages 106-108; The last paragraph of page 106 states, “In this section, we develop a hypothesis test to determine the significance of the reduction in uncertainty …”); and
implementing testing with the selected set of data collection components in order to collect the data to characterize at least a part of the subsurface (Page 109, section 4.8 states, “In this chapter, we introduced an alternative paradigm to the conventional causal analysis termed Bayesian Evidential Analysis …” Please note that the implementation of Bayesian Analysis is discussed throughout the disclosure of Li NPL. Furthermore, Bayesian approaches also form the foundation of the applicant’s implementation for determining reduction of uncertainty (see paragraph 0037 of the applicant’s own specification).)
With respect to claim 1, Li NPL differs from the claimed invention in that it does not explicitly disclose:
during a first stage prior to data collection, iteratively performing the following steps until an uncertainty reduction for one or more quantities of interest (QoIs) reaches a target uncertainty criteria
assessing a plurality of potential data collection components, wherein at each iteration, a component of the data collection program is selected and added to a final data collection program based on its resulting uncertainty reduction
at each iteration, analyzing based on the target uncertainty criteria
selecting, wherein at each iteration, an uncertainty space is adjusted by updating the subsurface model and QoIs based on an assumption that each selected data collection component has been performed and the selected data collection components are removed from the plurality of potential data collection components
during a second stage for data collection, subsequent to the first stage
generating a set of subsurface models using the collected data
executing a decision based on the set of subsurface models
With respect to claim 1, the following limitations are obvious, in view of the total teachings of Li NPL:
during a first stage prior to data collection, iteratively performing the following steps until an uncertainty reduction for one or more quantities of interest (QoIs) reaches a target uncertainty criteria (obvious in view of total teachings of Li NPL. Page 3, lines 4-8 of Li NPL discloses, “It follows that UQ should be performed at all stages of the reservoir forecasting process … To quantify uncertainty, we would require multiple realizations of possible subsurface models at each stage of the forecasting process.” In view of this disclosure, it would be obvious to one of ordinary skill in the art to perform uncertainty data processing operations, at all stages, which includes the claimed first and second stages.)
during a second stage for data collection, subsequent to the first stage
With respect to claim 1, it would have been obvious to one having ordinary skill in the art before the effective filing date of the invention to incorporate the teachings of Li NPL. The motivation for the skilled artisan in doing so is to gain the benefit of reducing propagation errors and uncertainties.
With respect to claim 1, Pradhan et al NPL discloses:
assessing a plurality of potential data collection components, wherein at each iteration, a component of the data collection program is selected and added to a final data collection program based on its resulting uncertainty reduction (Please note paragraphs 0036-0037 of the applicant’s original specification, which states, “The plurality of data collection components are then analyzed (such as in an iterative manner) … Various methodologies may be used in order to determine reduction of uncertainty. Merely by way of example, Bayesian Evidential Learning (BEL) … may be used as efficient approaches to determine the reduction of uncertainty.” Please also note paragraph 0059 of the applicant’s own specification, which states, “At 220, the methodology quantifies the uncertainty reduction of QoI given the program different data collection components … Once the data is collected, one may apply one or more data drive workflows, which may provide efficient ways of estimating the posterior uncertainty … the methodology may apply a Bayesian inversion method (e.g., Bayesian Evidential Learning or BEL) to quantify the posterior uncertainty of QoI.” The abstract of Pradhan et al NPL states, “We present a framework that enables estimation of low-dimensional sub-resolution reservoir properties directly from seismic data … Our workflow is based on the Bayesian evidential learning approach and exploits learning the direct relation between seismic data and reservoir properties to efficiently estimate reservoir properties … Uncertainty quantification is performed with approximate Bayesian computation … we demonstrate the efficacy of our approach by estimating posterior uncertainty …” The claimed limitation is obvious in view of applying Pradhan’s seismic Bayesian evidential learning (BEL) teachings to the teachings of Li NPL. Based on the applicant’s specification, it would appear that the claimed limitations merely describe an exemplary application of BEL to seismic survey data, in order to estimate posterior uncertainty, which is what Li NPL in view of Pradhan et al NPL does. Please also note Li NPL page 121, first paragraph of section 5.5.1, which states, “SIR retains the Bayesian formulation of evidential learning, as additional information is collected … the posterior is iteratively updated.” (emphasis mine).)
at each iteration, analyzing based on the target uncertainty criteria (This limitation is obvious in view of applying BEL, which is what Pradhan et al NPL teaches. Pradhan et al NPL further discloses the principle of “target” throughout its disclosure. For example, page 1121, column 2, last paragraph of Pradhan et al NPL states, “it would be preferable for the estimation strategy to entail the following: (1) quantify the seismic signatures of the target properties …”; page 1122, column 1, first paragraph of Pradhan et al NPL states, “The general recipe for Bayesian evidential learning (BEL) entails learning the statistical relationship between the target variables and the data (Fig. 1) with the help of a training set.”)
selecting, wherein at each iteration, an uncertainty space is adjusted by updating the subsurface model and QoIs based on an assumption that each selected data collection component has been performed and the selected data collection components are removed from the plurality of potential data collection components (It should be noted that the applicant’s original specification does not mention the word “removed.” The support for this limitation appears to be applicant’s figures 3A-3C, which appear to be exemplary applications of a technique, such as BEL, when applied to the specific context of seismic data. The examiner contends that the limitation would be obvious to ordinary skill in the art, when applying the principle of BEL to seismic data, which is taught in Pradhan et al NPL. The claimed limitation is considered an obvious variation of a limited subset of applications using BEL. One of the KSR rationales for obviousness is “Design Incentives or Market Forces Prompting Variations.” Here, the prior art teaches a base method that is similar or analogous to the claims. Design incentives or market forces would have prompted change to the base device. Known variations or principles would meet the difference between the claimed invention and the prior art and the implementation would have been predictable. Another KSR rationale for obviousness is “Applying Known Technique to Known Art Ready for Improvement.” Here, even if the specifics of the claimed limitation could be construed as an “improvement” and not just an “obvious variation,” the prior art teaches a known technique that is applicable to the base method (i.e. BEL). Those in the art would have recognized applying the known technique would have yielded an improvement and was predictable.)
generating a set of subsurface models using the collected data (obvious in view of combination; Pradhan et al NPL discloses generating both prior (page 1123, column 2, paragraph 1) and posterior (page 1131, column 2, last paragraph) samples. Li NPL page 44, paragraph 1 states, “We will next present the details of the methodology; namely sampling the prior, and computing the likelihood and generating samples from the posterior.”)
executing a decision based on the set of subsurface models (obvious in view of combination; Li NPL abstract states, “In the oil and gas industry, decisions with often large financial implications and risks depend on quantities subject to substantial uncertainty.” Section 7.1.3 is directed to “Decision Scenarios”. Making and executing a decision is obvious to modified Li NPL.)
With respect to claim 1, it would have been obvious to one having ordinary skill in the art before the effective filing date of the invention to incorporate the teachings of Pradhan et al NPL into the invention of Li NPL. The motivation for the skilled artisan in doing so is to gain the benefit of efficient quantification of posterior uncertainty.
With respect to claim 2, Li NPL, as modified, discloses:
wherein quantitatively analyzing comprises interdependently analyzing the plurality of potential data collection components at least by the data for one potential data collection component assumed to characterize the one or more QoIs when analyzing one or more other potential data collection components (Under broadest reasonable interpretation (BRI), the examiner interprets this limitation to mean that the quantitative analysis consists of considering multiple variables and parameters together, rather than in a vacuum. This is reflected by the various equations disclosed throughout the disclosure of Li et al. Also, from paragraph 0034 of the applicant’s specification, it would appear that this limitation relates to sequential and iterative consideration of data, which Li et al also discloses throughout its disclosure, such as on pages v, 15, 24-25, 32, 76, 94, 121, and 230 (for teachings of “iterative”) and pages vii, xi-xii, xviii, xx-xxi, xxvi, 3, 10-11, 20, 68, 113-114, and 121 (for teachings of “sequential”). Also, please note BEL teachings of Pradhan et al NPL, as discussed above.)
With respect to claim 3, Li NPL, as modified, discloses:
wherein a first data collection component is selected in a first iteration based on a greatest uncertainty reduction on the one or more QoIs (obvious in view of combination; please note the last paragraph of Li NPL page 49, which states, “The product of BCS is this set of models from the posterior that match dobs. However, the ultimate goal is to quantify uncertainty on the seismic image … we can identify the regions of greatest uncertainty and the possible locations of structures of interest.”)
wherein, in one or more subsequent iterations, data collected from the first data collection component is assumed to be acquired in order to evaluate one or more remaining data collection components for the greatest uncertainty reduction on the one or more QoIs (obvious in view of combination; Please note section Li NPL 7.6.6 Sequential Updating on pages 199-202. Pages 200-201 state, “Using the resampled proposal models from the first iteration of SIR (Figure 7.44), the entire process is repeated. The updated prediction is shown in Figure 7.45 along with the resulting resampled proposal models to be used in subsequent iterations of SIR … This indicates that the additional data that is being collected is informative of the prediction variable. Therefore, the additional information gathered allows us to reduce uncertainty on the prediction variable.” Li NPL uses slightly different language that what is claimed, and it discloses its principles through multiple embodiments and examples. However, the principles taught in Li NPL coincide with the principles reflected in the claimed limitation, and the claimed limitation would be obvious to one of ordinary skill in the art, in light of the principles taught by Li NPL. Li NPL not only teaches identifying regions of greatest uncertainty. It also teaches that iterative processing helps to reduce uncertainty. Furthermore, Li NPL also teaches quantifying uncertainty, as discussed above. Also, please note BEL teachings of Pradhan et al NPL, as discussed above. The combination of these principles would render the claimed limitation obvious.)
With respect to claim 4, Li NPL, as modified, discloses:
wherein the iterations are performed until one or both of: at least one metric meets or exceeds a predetermined uncertainty percentage value or a predetermined range of values; or an uncertainty percentage value for all of the one or more QoIs or a range of values for all of the one or more QoIs change less than a predetermined amount from a previous iteration to a current iteration (obvious in view of combination; As discussed above, Li NPL discloses both quantifying uncertainty, as well iterating using more data, which will further reduce uncertainty. Assigning a specific threshold (such as a percentage value or amount of change) to quantify when the stop iteration would be mathematically obvious to one of ordinary skill in the art. Also, please note BEL teachings of Pradhan et al NPL, as discussed above.)
With respect to claim 5, Li NPL, as modified, discloses:
wherein quantitatively analyzing is based on both the uncertainty reduction and cost associated with performing a respective data collection component (obvious in view of combination; As discussed above, quantitative analysis and quantifying uncertainty is disclosed throughout the disclosure of Li, as is uncertainty reduction. Li also considers “cost” throughout its disclosure (pages 3, 34, 39, 67, 75, 140, 143, 151, 171, etc …; For example, page 177, paragraph 2 states, “The same BEA analysis would be re-run and a second prediction of the quality map is produced and used to select a second location. This methodology could be repeated until the cost of drilling additional wells becomes uneconomical …” Also, please note BEL teachings of Pradhan et al NPL, as discussed above.)
With respect to claim 6, Li NPL, as modified, discloses:
The method of claim 1 (as applied to claim 1 above)
wherein the one or more QoIs comprise both of: one or more geological parameters or fluid parameters defining the subsurface model; or static or dynamic behaviors of the subsurface as the one or more outputs of the subsurface model (Li NPL page 36, see section on “Parameter Uncertainty”)
wherein quantitatively analyzing each of the plurality of potential data collection components for effect on the one or more QoIs comprises: determining, for each of the at least some of the plurality of potential data collection components, a corresponding uncertainty reduction for the one or more QoIs by performing a respective potential data collection component (Li NPL page 37, last paragraph under section 2.4.2 states, “Since the historical performance of the reservoir should be informative of the reservoir parameters, it can be used to reduce uncertainty.”; Li NPL page 201, paragraph 1 states, “the additional information gathered allows us to reduce uncertainty on the prediction variable.”)
and wherein selecting the set of data collection components comprises: selecting the respective potential data collection components for the plurality of potential data collection components based on the corresponding uncertainty reduction for the one or more geological parameters or fluid parameters and the static or dynamic behaviors of the subsurface (suggested by Li NPL page 106, section 4.7.2, paragraph 1, which states, “In evidential analysis, any reduction in uncertainty between the prior and posterior pdfs should be achieved due to the data variable being informative of the prediction variable. While the cause of this informativeness is physical (i.e. geology, physical and chemical processes), we have modeled it using a statistical model, estimated from samples of and obtained from the sampling the prior.”)
With respect to claim 7, Li NPL, as modified, discloses:
wherein the uncertainty reduction for a respective QoI comprises a percentage uncertainty reduction for the respective QoI (obvious in view of combination; As discussed above, Li NPL discloses both quantifying uncertainty, as well iterating using more data, which will further reduce uncertainty. Assigning a specific threshold (such as a percentage value) to quantify how much uncertainty has been reduced would be mathematically obvious to one of ordinary skill in the art. Also, please note BEL teachings of Pradhan et al NPL, as discussed above.)
With respect to claim 8, Li NPL, as modified, discloses:
wherein the uncertainty reduction for a respective QoI comprises a narrowing of a range of values associated with a respective QoI (obvious in view of combination; As discussed above, Li NPL discloses both quantifying uncertainty, as well iterating using more data, which will further reduce uncertainty. Narrowing a range of values is mathematically obvious to iterating to reduce uncertainty. Also, please note BEL teachings of Pradhan et al NPL, as discussed above.)
With respect to claim 9, Li NPL, as modified, discloses:
wherein the first stage and the second stage are conducted during an appraisal phase of hydrocarbon resource management (Please note the last paragraph of Li NPL page 2, which states, “As production data from appraisal wells become available …”; See also the first paragraph of Li NPL page 44, section 3.2.1, which states, “Most important decisions made during the exploration and appraisal phase of an oil field are made using a seismic image …” The claimed limitation would be obvious to one of ordinary skill in the art, in view of the fact that Li NPL recognizes the prevalence of appraisals in the technology. Using pertinent data for appraisal purposes would be obvious to one of ordinary skill in the art, in order to help make better decisions. Also, as discussed above, Li NPL performs its uncertainty processing operations at all stages.)
With respect to claim 10, Li NPL, as modified, discloses:
wherein the plurality of potential data collection components comprises one or both of: a plurality of locations to drill appraisal wells into the subsurface and a plurality of tests to perform in the appraisal wells; or a plurality of seismic survey parameter designs; and wherein the set of data collection components selected are indicative of one or both of the locations at which to drill the appraisal wells and the tests to perform at the appraisal wells (obvious in view of combination; Li NPL page 49 states, “The product of BCA is this set of models from the posterior … we can identify the regions of greatest uncertainty and the possible locations of structures of interest.”; Li NPL page 73, last paragraph of section 3.6, states, “we can identify the location of predefined structures of interest … The ensemble of the locations of that structure of interest provide a quantitative assessment of the uncertainty of the structure. This can then be used to estimate and quantify uncertainty properties …” Also, please note BEL teachings of Pradhan et al NPL, as discussed above.)
With respect to claim 11, Li NPL, as modified, discloses:
wherein a first QoI and a second QoI are subject to uncertainty reduction (obvious in view of combination; Please note the vast uncertainty quantification/reduction teachings of Li NPL; As discussed above, Li discloses multiple parameters of interest, as well as uncertainty quantification and reduction. Pradhan et al NPL also discloses uncertainty reduction. For example, page 1126, column 1, paragraph 1 of Pradhan et al NPL states, “Summary statistics highly informative of H will exhibit significant reduction of the prior uncertainty in the corresponding posterior distributions.”);
wherein the first QoI has a first target uncertainty reduction and the second QoI has a second target uncertainty reduction, the first target uncertainty reduction being different from the second target uncertainty reduction (obvious in view of combination; see second to last paragraph on Li NPL page 49, which states, “The product of BCA is this set of models from the posterior that match dobs. However, the ultimate goal is to quantify uncertainty on the seismic image … we can identify the regions of greatest uncertainty and the possible locations of structures of interest.”; One of ordinary skill in the art recognizes that different QoI will have different model outputs, as well as possibly different seismic images, which will result in different uncertainty quantifications.); and
wherein the set of data collection components are selected so that the uncertainty reduction for the first QoI at least meets the first target uncertainty reduction and for the second QoI at least meets the second target uncertainty reduction (obvious in view of combination; As discussed above, Li NPL not only teaches quantifying and identifying uncertainty, it also teaches iteration to further reduce uncertainty. Setting targets for uncertainty reduction and then selecting data collection components in accordance with those targets are obvious in view of the broad uncertainty teachings of modified Li NPL.)
With respect to claim 12, Li NPL, as modified, discloses:
wherein the set of data collection components are selected by: first selecting the data collection components that most reduce the uncertainty for the first QoI to at least the first target uncertainty reduction (obvious in view of identifying the regions of greatest uncertainty, as taught on Li NPL page 49); and
thereafter selecting the data collection components that most reduce a remaining uncertainty for the second QoI to at least the second target uncertainty reduction (obvious in view of identifying the regions of greatest uncertainty, as taught on Li NPL page 49 and further reducing uncertainty with additional data, as taught on Li NPL page 201)
With respect to claim 13, Li NPL, as modified, discloses:
wherein the set of data collection components are selected by: weighting the uncertainty reduction for both of the first QoI and the second QoI in order to determine which of the plurality of potential data collection components to select to reduce the uncertainty reduction for the first QoI toward the first target uncertainty reduction and the uncertainty reduction for the second QoI toward the second target uncertainty reduction (obvious in view of broader weighting principles taught throughout the disclosure of Li NPL; Li teaches the principles of weighting throughout its disclosure, and it would be obvious to one of ordinary skill in the art to apply well-established weighting principles to the uncertainty reduction principles that are also taught by Li. For an example of Li’s teachings on weights, please see section 7.6.3, which discloses the computation of weights.)
With respect to claim 14, Li NPL, as modified, discloses:
wherein selecting the set of data collection components for testing comprises: selecting a development phase set of data collection components for use during a development phase of hydrocarbon resource management or a production phase set of data collection components for use during a production phase of hydrocarbon resource management (Li NPL page 25, first paragraph under section 2.3, teaches field development stage; Li NPL page 87, second to last paragraph discloses “PVT analysis involves laboratory experiments on the reservoir fluid to determine its behavior under the various conditions during production.”)
With respect to claim 17, Li NPL, as modified, discloses:
wherein the plurality of potential data collection components comprises a plurality of locations to drill appraisal wells into the subsurface and a plurality of tests to perform in the appraisal wells (As discussed above, Li NPL discloses all of the elements in this limitation, though it doesn’t necessarily disclose them together. Li NPL discloses a plurality of locations, appraisal wells, and various testing. One of ordinary skill in the art recognizes that it is obvious to combine these elements, as they allow for complete analysis of known data in a known context.)
With respect to claim 18, Li NPL, as modified, discloses:
wherein the plurality of potential data collection components comprises a plurality of seismic survey parameter designs (Li NPL page 8, last paragraph states, “In reservoir static modeling, the prior information could be a training image … or measured well logs …”; page 18, first paragraph under section 2.2.1 states, “The formulation of this prior probability may come from a variety of sources, for instance geological studies, laboratory experiments or spatially sparse well logs.”)
With respect to claim 19, Li NPL, as modified, discloses:
a first QoI comprising a subsurface parameter of the subsurface model (Li NPL page vi, first paragraph states, “The resulting posterior velocity models are migrated to yield a set of posterior seismic images.”; see also parameters on Li NPL page 36)
a second QoI comprising an output that is based on the subsurface parameter and at least one input to the subsurface model (Li NPL page 45, first paragraph states, “Since the seismic image is used for structural interpretation and serves as the input for subsequent reservoir modeling procedures …”; Since Li NPL teaches multiple seismic images, a first image can be construed to represent a first QoI, and a second image can be construed to represent an input that results in a second QoI output)
and wherein the set of data collection components for testing reduces uncertainty for both the first QoI and the second QoI to respective target uncertainty reductions (obvious for reasons discussed above; Li NPL discloses quantifying uncertainty for a seismic image, and it also discloses reducing uncertainty, such as through adding data. Multiple seismic images would suggest reducing uncertainty for multiple QoI. Pradhan et al NPL also discloses reducing uncertainty.)
With respect to claim 20, Li NPL, as modified, discloses:
wherein the plurality of potential data collection components comprises both of: a plurality of locations to drill appraisal wells into the subsurface and a plurality of tests to perform in the appraisal wells; or a plurality of seismic survey parameter designs; and wherein the set of data collection components selected are indicative of both of the locations at which to drill the appraisal wells and the tests to perform at the appraisal wells (obvious in view of combination; Li NPL page 49 states, “The product of BCA is this set of models from the posterior … we can identify the regions of greatest uncertainty and the possible locations of structures of interest.”; Li NPL page 73, last paragraph of section 3.6, states, “we can identify the location of predefined structures of interest … The ensemble of the locations of that structure of interest provide a quantitative assessment of the uncertainty of the structure. This can then be used to estimate and quantify uncertainty properties …”)
With respect to claim 21, Li NPL, as modified, discloses:
comprising extracting hydrocarbons from the subsurface using the generated set of subsurface models (Li NPL page 35, first paragraph of section 2.4 states, “The reservoir simulator solves a set of partial differential equations that describe how the flow behavior of the components of a reservoir (hydrocarbons) in different phases (oil, water, gas). This is used to forecast the prediction variable h, for instance spatial distribution of fluid pressure, saturations, recovery factors, future injection/production rates from both existing and new wells, etc.” Extracting hydrocarbons based on reservoir simulator data would be obvious to one of ordinary skill in the art.)
Claim 22 represents a much broader version of the above claims and is rejected for similar reasons.
Some of its more unique limitations include:
appending the selected data collection component for a present iteration to an end of an order of the data collection components for testing (page 11, lines 2-3 of Li NPL states, “We also discuss how this methodology can be applied sequentially …” The principles are sequential data processing are taught throughout the disclosure of Li NPL, and a general appending of a data processing operation/component to an end of an order is obvious in view of sequential processing.)
generate a report comprising the data collection components in the order for display on a display device (pages 72-73 disclose methods of displaying and visualizing data, such as through uncertainty maps and image registration. Such display and visualization of data is broadly construed to serve as a report.)
Claim 23 discloses, receiving results from the testing; comparing the results to the report; and modifying the data collection components, the order of the data collection components, or combinations thereof. Receiving data results and comparing results to some sort of output/report/display are obvious mental processing operations to one of ordinary skill in the art. The claims do not specify how the claimed modifying is performed, but both Li NPL and Pradhan et al NPL disclose general data modifications. For example, page 151, paragraph 1 of Li NPL states, “how much should we modify each injector’s rate?” Page 1133, column 1, paragraph 1 of Pradhan et al NPL states, “the output layer is modified …”)
Conclusion
The prior art made of record and not relied upon is considered pertinent to applicant's disclosure.
Zhou et al (US PgPub 20170017883) discloses ensemble based decision making.
Chugunov et al (US PgPub 20150060053) discloses a method for adaptive optimizing of heterogenous proppant placement under uncertainty.
Applicant's amendment necessitated the new ground(s) of rejection presented in this Office action. Accordingly, THIS ACTION IS MADE FINAL. See MPEP § 706.07(a). Applicant is reminded of the extension of time policy as set forth in 37 CFR 1.136(a).
A shortened statutory period for reply to this final action is set to expire THREE MONTHS from the mailing date of this action. In the event a first reply is filed within TWO MONTHS of the mailing date of this final action and the advisory action is not mailed until after the end of the THREE-MONTH shortened statutory period, then the shortened statutory period will expire on the date the advisory action is mailed, and any nonprovisional extension fee (37 CFR 1.17(a)) pursuant to 37 CFR 1.136(a) will be calculated from the mailing date of the advisory action. In no event, however, will the statutory period for reply expire later than SIX MONTHS from the mailing date of this final action.
Any inquiry concerning this communication or earlier communications from the examiner should be directed to LEONARD S LIANG whose telephone number is (571)272-2148. The examiner can normally be reached M-F 10:00 AM - 7 PM.
Examiner interviews are available via telephone, in-person, and video conferencing using a USPTO supplied web-based collaboration tool. To schedule an interview, applicant is encouraged to use the USPTO Automated Interview Request (AIR) at http://www.uspto.gov/interviewpractice.
If attempts to reach the examiner by telephone are unsuccessful, the examiner’s supervisor, ARLEEN M VAZQUEZ can be reached on (571)272-2619. The fax phone number for the organization where this application or proceeding is assigned is 571-273-8300.
Information regarding the status of published or unpublished applications may be obtained from Patent Center. Unpublished application information in Patent Center is available to registered users. To file and manage patent submissions in Patent Center, visit: https://patentcenter.uspto.gov. Visit https://www.uspto.gov/patents/apply/patent-center for more information about Patent Center and https://www.uspto.gov/patents/docx for information about filing in DOCX format. For additional questions, contact the Electronic Business Center (EBC) at 866-217-9197 (toll-free). If you would like assistance from a USPTO Customer Service Representative, call 800-786-9199 (IN USA OR CANADA) or 571-272-1000.
/LEONARD S LIANG/Examiner, Art Unit 2857 06/14/26
/ARLEEN M VAZQUEZ/Supervisory Patent Examiner, Art Unit 2857
Prosecution Insights