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 .
Information Disclosure Statement
The information disclosure statement (IDS) was submitted on 02/14/2024. The submission is in compliance with the provisions of 37 CFR 1.97. Accordingly, the information disclosure statement is being considered by the examiner.
Drawings
The drawings are objected to as follows:
The drawings are objected to as failing to comply with 37 CFR 1.84(p)(5) because they do not include the following reference sign(s) mentioned in the description: paragraph [0028] recites “reflected seismic waveform 52”. No element labeled “52” is found in any drawing.
FIG. 6 and FIG. 8 are objected to as failing to comply with 37 CFR 1.84(p)(5) because they include the following reference character(s) not mentioned in the description:
In FIG. 6, the leftmost box denoted as “WELL LOG DATA”, the upper left graphical schematic, labeled with “PHI-VpSHALE” contains an element 127. Element 127 does not appear in the specification.
In FIG. 8, element “11” points to a dashed line trace appearing on the diagram. Element “11” is not found in the specification.
FIG. 8 is objected to as failing to comply with 37 CFR 1.84(p)(4) because reference characters “164" and "11" appear to be pointing to the same dashed line trace on the diagram, which is understood according to the legend in the upper right hand corner to designate “Well Data Vsh”. Examiner notes as above, the element number “11” does not appear in the specification.
Corrected drawing sheets in compliance with 37 CFR 1.121(d) are required in reply to the Office action to avoid abandonment of the application. Any amended replacement drawing sheet should include all of the figures appearing on the immediate prior version of the sheet, even if only one figure is being amended. Each drawing sheet submitted after the filing date of an application must be labeled in the top margin as either “Replacement Sheet” or “New Sheet” pursuant to 37 CFR 1.121(d). If the changes are not accepted by the examiner, the applicant will be notified and informed of any required corrective action in the next Office action. The objection to the drawings will not be held in abeyance.
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 1-21 are rejected under 35 U.S.C. 101 because the claimed invention is
directed to an abstract idea without significantly more. These claims fall into statutory
categories as set forth in 35 U.S.C. 101 (See MPEP § 2106.03).
Claim 1 is held to be patent ineligible, as explained below.
Claim 1 recites the following abstract ideas, emphasized in bold below:
“A method, comprising: receiving initial well log data;
generating augmented well log data comprising the initial well log data and modeled well log data based on the initial well log data;
modifying the augmented well log data to generate a training dataset;
training a probabilistic classifier utilizing the training dataset;
calculating a probability volume for each lithofluid class of a set of predetermined lithofluid classes utilizing the probabilistic classifier;
outputting the probability volume for each lithofluid class of the set of predetermined lithofluid classes as a respective probability of an occurrence of a type of lithofluid class in a reservoir;
calculating a posterior probability based on the probability volume for a first lithofluid class of the set of predetermined lithofluid classes; and
outputting the posterior probability as a probability of a property of the reservoir.
STEP 1: Claim 1, and similarly claims 9 and 17, recites an eligible statutory category. (MPEP 2106.03), namely,
a process or method, based on limitation of input data values.
STEP2A-Prong 1: Claim 1 recites the judicial exception. (MPEP2106.04) Claim 1 describes, as discussed above, using broadest reasonable interpretation, processes
that fall within definition of Abstract Idea in the Mathematical Concept grouping.(MPEP
2106.04(a)(2), subsection I).
Lines (2), (3), (4), (5), (6), (7), and (8) recite mathematical processes involved in the method, (emphasized in bold) as carried out by the computer system. This interpretation of is supported by referring to specification in at least
[0006]: "generating the augmented well log data comprises fitting at least one
attribute of a selected rock physics model to at least a portion of the initial well log data”, where one of ordinary skill would understand the process of “fitting” to mean a mathematical comparative process.
[0006]: “modifying the augmented well log data comprises expanding more or
more of a porosity range, saturations, fluid type, mineralogy, or volume of shale range based upon the augmented well log data to generate the training dataset”, where one of ordinary skill would understand the process of “expanding” as recited, to have plain meaning of including additional data values.
[0006]: “training a probabilistic classifier utilizing the training dataset”, where one
of ordinary skill would understand the term “training”, in the context of a machine-learning based computational method to be a mathematical process, carried out by either a supervised or unsupervised algorithm. Further support for the interpretation of “training” as a mathematical process is found in FIG. 5, where training is done in context of Bayesian Classification, Probability Distribution and weighting of values.
The term “probabilistic classifier” would be understood as a mathematical
process using probability density functions. This interpretation is supported in specification in at least FIG. 5 and [0020]: “probabilistic (e.g., Bayesian) approach to classification of probabilistic elastic data volumes”.
The term “calculating” would be understood as an explicit reference to a
mathematical process, which in the context of a machine-learning based method, may be carried out via use of various algorithms. This interpretation is supported in specification in at least [0041]: “computing system 60 may be employed to analyze the acquired seismic data…seismic data processing algorithms may be used to remove noise from the acquired seismic data” and FIG. 5, with [0057]-[0058].
The term “outputting” is interpreted in its plain meaning to be a last step in a
mathematical processing, where a qualitative or quantitative result of a calculation is produced. This interpretation is supported by the claim language, where output is recited as a probability volume which would be understood by one of ordinary skill to mean a mathematical function result; and supported in specification in at least [0006]: “calculating a probability volume for each lithofluid class of a set of predetermined lithofluid classes utilizing the probabilistic classifier, outputting the probability volume… outputting the posterior probability”.
STEP2A - Prong 2: Claim 1 does not integrate the recited judicial exception into
a practical application. In this step, we evaluate whether the claim recites additional elements that integrate the exception into a practical application of that exception.
This judicial exception is not integrated into a practical application because there is no improvement to another technology or technical field; improvements to the functioning of the computer itself; a particular machine; effecting a transformation or reduction of a particular article to a different state or thing. Examiner notes that since the claimed methods and system are not tied to a particular machine or apparatus, they do not represent an improvement to another technology or technical field. Similarly there are no other meaningful limitations linking the use to a particular technological environment. Finally, there is nothing in the claims that indicates an improvement to the functioning of the computer itself or transform a particular article to a new state.
Finally, under Step 2B, we consider whether the additional elements are sufficient to amount to significantly more than the abstract idea. Step 2B: Claim 1 does not amount to significantly more than the recited judicial
exception. Receiving well log data is considered necessary data gathering that does not integrate the abstract idea into a practical application. As recited in MPEP section 2106.05(g), necessary data gathering (i.e. receiving data) is considered extra solution activity in light of 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).
Independent Claims 9 and 17 are likewise held to be patent ineligible using the same rationale and reasoning as applied to Claim 1 above.
Dependent Claims 2-8, 10-16, and 18-21 further limit the abstract ideas without integrating the abstract concept into a practical application or including additional limitations that can be considered significantly more than the abstract idea.
Claim Rejections - 35 USC § 102
The following is a quotation of the appropriate paragraphs of 35 U.S.C. 102 that form the basis for the rejections under this section made in this Office action:
A person shall be entitled to a patent unless –
(a)(1) the claimed invention was patented, described in a printed publication, or in public use, on sale, or otherwise available to the public before the effective filing date of the claimed invention.
Claims 1-2, 8-11, and 17 are rejected under 35 U.S.C. 102a(1) as being anticipated GRANA-2012 (Grana, et. al., "Seismic driven probabilistic classification of reservoir facies for static reservoir modelling: a case history in the Barents Sea", Geophysical Prospecting, Blackwell Science, Vol. 61, No. 3, October 18, 2012).
With respect to Claims 1, 9, and 17, GRANA-2012 teaches:
A method, comprising: receiving initial well log data; (GRANA-2012 teaches method using well log data, Pg.2,Col.2 : “workflow is presented here in three sections: 1) facies definition, which includes a preliminary sensitivity analysis of well log data,”; and generally, Pg.4,§ “Log-facies definition”)
generating augmented well log data comprising the initial well log data and modeled well log data based on the initial well log data; (GRANA-2012 teaches extending well log data using simulations, Pg.10, Col1: “well locations the shale facies contains only a few samples, we extended the training data set by using Monte Carlo simulations and applying the rock-physics model”; Examiner interprets “augmented” to be analogous to reference “extended” to mean generally supplementing sparse data with simulation or model.; Examiner notes GRANA-2012 points to prior included below as relied upon and considered pertinent.)
modifying the augmented well log data to generate a training dataset; (GRANA-2012 teaches modification of well log data that has been previously augmented, as above, and Pg.5, Col.2: “rock physics template is superimposed to the well log data in Fig. 4. The parameters that characterize the model are then fixed so that the model can be applied to different petrophysical scenarios, even to those situations that are not sampled by the well log data.” with Pg.6, Fig. 4.)
training a probabilistic classifier utilizing the training dataset; (GRANA-2012 teaches use of classifier, generally, Pg.4,Col.2, §Log-facies definition, “main target of this section is to derive a suitable facies classification for seismic reservoir characterization” and Pg.5,Col.2, “classification is obtained by applying Ward’s minimum variance linkage method”)
calculating a probability volume for each lithofluid class of a set of predetermined
lithofluid classes utilizing the probabilistic classifier; (GRANA-2012 teaches calculation of probability volume, Abstract: “we show the application of a complete workflow for static reservoir modelling where seismic data are integrated to derive probability volumes of facies and reservoir properties to condition reservoir geostatistical simulations.” And Pg.3,Col1: “probabilistic inversion provides not only the most probable model but also the probability volumes of facies, elastic and petrophysical properties.”; and generally, Pg.8,§”Seismic facies classification”; GRANA-2012 includes lithofluid classification as part of analysis of reservoir properties, Pg.2,Col.1: “workflow we adopt belongs to the category of multistep inversion approaches, where a 3D volume of facies and/or volumes of the probability of facies are estimated from partial stack seismic data…methods are generally based on the traditional Bayesian framework and have been applied to problems related to uncertainty evaluation…and lithofluid prediction from seismic data” and Pg10, Col.1, Equation (4) with text: “R = [φ, C, SW] is the vector of rock properties, i.e., porosity, clay content and water saturation and F are the litho-fluid classes…estimate the posterior probability of six litho-fluid classes: shale, silty shale,”; Examiner notes GRANA-2012 references prior art, with references not disclosed by Applicant included below as relied upon and considered pertinent.)
outputting the probability volume for each lithofluid class of the set of predetermined lithofluid classes as a respective probability of an occurrence of a type of lithofluid class in a reservoir; (GRANA-2012 teaches probability volume as result of method, Abstract: “application of a complete workflow for static reservoir modelling where seismic data are integrated to derive probability volumes of facies and reservoir properties”; P16,Col1 §CONCLUSION: “main advantage of the proposed workflow is that it provides reliable probability volumes of petrophysical properties and reservoir facies”, with lithofluid classes included in calculations and in result, as above.)
calculating a posterior probability based on the probability volume for a first lithofluid class of the set of predetermined lithofluid classes; (GRANA-2012 teaches method using posterior probability, as above, Pg10, Col.1, Equation (4) with text: “estimate the posterior probability of six litho-fluid classes: shale, silty shale,”;
outputting the posterior probability as a probability of a property of the reservoir. (GRANA-2012 teaches use of posterior probability for reservoir properties, Pg.2,Col.2: “workflow…three sections:…3) geostatistical simulations of reservoir properties, where the volumes of the seismic facies probabilities are used as secondary information to condition geostatistical simulations. The final result is a set of models of facies and rock property realizations”; with details Pg.13,Col1, §”Geostatistical simulations of reservoir properties”, Fig.s 14-16, specifically Fig. 14 caption: “posterior probability of sand, silty sand, silty shale and shale.”, and Fig.15 caption: “3D view of the posterior probability of seismic facies”)
With respect to Claim 2, GRANA-2012 teaches the limitations of claim 1.
GRANA-2012 further teaches:
wherein the probabilistic classifier comprises a Bayesian classifier. (GRANA-2012 teaches method using Bayes theory: “combines a set of well-known techniques such as cluster analysis, Bayes’ theory”, with further detail Pg.3,§”Field Application” and Pg.13, Col.1, §” Geostatistical simulations of reservoir properties”: “results of Bayesian classification are finally integrated into the static reservoir modelling of facies and petrophysical parameters.”)
With respect to Claim 8, GRANA-2012 teaches the limitations of claim 1.
comprising characterizing the reservoir in a subsurface region of Earth based upon the probability of the property of the reservoir. (GRANA-2012 teaches probability based reservoir characterization, for example, Pg.13,Col.1,§” Geostatistical simulations of reservoir properties”, with Fig.14, depicting probabilities of various geophysical facies at varying depths as related to sections depicted in Fig.13.
With respect to Claim 10, GRANA-2012 teaches the limitations of claim 9.
comprising instructions to cause the processor to output the probability volume for each lithofluid class of the set of predetermined lithofluid classes as a respective probability of an occurrence of a type of lithofluid class in a reservoir.(GRANA-2012 teaches probability volume for lithofluid class to determine reservoir properties, as above, Pg.2,Col.1 and Pg10, Col.1, Equation (4) with text: “F are the litho-fluid classes…estimate the posterior probability of six litho-fluid classes: shale, silty shale,”;
With respect to Claim 11, GRANA-2012 teaches the limitations of claim 9.
comprising instructions to cause the processor to:
calculate a posterior probability based on the probability volume for a first lithofluid class of the set of predetermined lithofluid classes; and output the posterior probability as a probability of a property of a reservoir.(GRANA-2012 teaches posterior probability results based on probability volume, Pg.13, Col.1, ,§” Geostatistical simulations of reservoir properties”, with results depicted in Fig.s14-16)
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.
Claims 3-6, 12-15, 18- 21 are rejected under 35 U.S.C. § 103(a) as being unpatentable over GRANA-2012 in view of JIANG (Jiang, et. al., "Estimation of the porosity and pore aspect ratio of the Haynesville Shale using the Self-Consistent Model and a Grid Search Method", SEG Technical Program Expanded Abstracts, 2012).
With respect to Claims 3 and 12, GRANA-2012 teaches the limitations of claims 1 and 9.
GRANA-2012 further teaches:
generating the augmented well log data (As above, Claims 1 and 9, GRANA-2012 teaches extending well log data using simulations, Pg.10, Col1:; Examiner notes interpretation as above.)
GRANA-2012 does not teach:
generating the augmented well log data comprises fitting at least one attribute of a selected rock physics model to at least a portion of the initial well log data.
JIANG teaches:
generating the augmented well log data comprises fitting at least one attribute of a selected rock physics model to at least a portion of the initial well log data. (JIANG is in same technical field, Pg.1,Col.1, § “Summary”: “work introduces an algorithm to estimate the porosity distribution and an algorithm to estimate the pore aspect ratio distribution of the Haynesville Shale”; JIANG teaches modeling method via systematic comparison as part of grid-search method, Fig.2, with Pg.2,Col.1, “At each point, the objective function (absolute errors between the modeled and observed grid points) is evaluated.”, and Pg.4, Col1, Conclusions: “provides a method that combines rock physics modeling with a grid search method to estimate independently the porosity and pore aspect ratio”)
It would have been obvious to one of ordinary skill in the art before effective filing date of the claimed invention to modify GRANA-2012 to include using the step of fitting at least one attribute of a selected rock physics model to at least a portion of initial well log data to augment well log data, such as that of JIANG because this would improve the accuracy of a resulting model based on well log data. Including the fitting step as explicitly taught by , and broaden use of the method to data sets that are sparse or incomplete. One of ordinary skill would also understand that the combination of the step taught by JIANG with the method/system of GRANA-2012 would accelerate computational speed while also ensuring model accuracy, making a positive impact on method/system efficiency.
With respect to Claims 4 and 13, GRANA-2012 in view of JIANG teaches the limitations of claim 3.
GRANA further teaches:
at least one attribute of the selected rock physics model to the at least a portion of the initial well log data comprises generating a maximum value for the model parameter of the selected rock physics model; (GRANA-2012 teaches using rock-physics model, as above, and determination of maximum, Pg.10, Col.2: “seismic facies section was obtained as the maximum of the posterior probability of seismic facies”, with Pg. 14, Fig.14;
GRANA-2012does not teach:
fitting the at least one attribute of the selected rock physics model to the at least a portion of the initial well log data comprises: generating a minimum value for a model parameter of the selected rock physics model;
sampling across the model parameter to generate a search result.
JIANG further teaches:
fitting the at least one attribute of the selected rock physics model to the at least a portion of the initial well log data comprises: generating a minimum value for a model parameter of the selected rock physics model; (JIANG teaches point by point fitting, Pg.2,Col.1, § “Summary”: “objective function (absolute errors between the modeled and observed grid points) is evaluated…point that provides the minimum value of the objective function corresponds to the best solution of the reservoir properties”)
sampling across the model parameter to generate a search result. (JIANG teaches point by point comparative analysis via searching, Pg.2, Col.1, § “Methodology”: “solutions of the reservoir properties are obtained by systematically searching through each point in the decision space, which is modeled P-impedance (Ip)”)
It would have been obvious to one of ordinary skill in the art before effective filing date of the claimed invention to modify GRANA-2012 as modified by JIANG, as taught above, to include the steps of generating a minimum value for a model parameter of the selected rock physics model; sampling across the model parameter to generate a search result when fitting the at least one attribute of the selected rock physics model to the at least a portion of the initial well log data, such as that further taught by JIANG because providing physical constraints would improve accuracy and consistency in the resulting predictive model. One of ordinary skill would find the combination of the steps taught by JIANG with the method/system of GRANA-2012 to broaden application of a machine-learning based model by improving optimal parameter estimation, ultimately improving model generalization by allowing for calibration to a localized set of data, and allowing the model to handle non-unique solutions.
With respect to Claims 5 and 14, GRANA-2012 in view of JIANG teaches the limitations of claim 4.
GRANA-2012 does not teach:
wherein fitting the at least one attribute of the selected rock physics model to the at least a portion of the initial well log data comprises comparing the search result against the at least a portion of the initial well log data to generate a determination of a best fit between the search result and the at least a portion of the initial well log data.
JIANG further teaches:
wherein fitting the at least one attribute of the selected rock physics model to the at least a portion of the initial well log data comprises comparing the search result against the at least a portion of the initial well log data to generate a determination of a best fit between the search result and the at least a portion of the initial well log data. (As above, JIANG teaches a systematic search for fitting for point by point attribute comparison using Pg.2, Col.1, § “Methodology”)
It would have been obvious to one of ordinary skill in the art before effective filing date of the claimed invention to modify GRANA-2012 as modified by JIANG, as taught above, to include the steps of comparing the search result against the at least a portion of the initial well log data to generate a determination of a best fit between the search result and the at least a portion of the initial well log data, as a step in fitting the at least one attribute of the selected rock physics model to the at least a portion of the initial well log data, such as that further taught by JIANG because it would ensure that a machine-learning based predictive model is grounded in physical reality, ultimately improving model accuracy and reducing overall uncertainty in resulting predicted reservoir properties. One of ordinary skill would be motivated to including the step taught by JIANG, in the context of using Bayesian machine learning approaches as disclosed and taught by GRANA-2012.
With respect to Claims 6 and 15, GRANA-2012 in view of JIANG teaches the limitations of claim 5.
GRANA-2012 further teaches:
using augmented well log data (As above, Claims 1 and 9, GRANA-2012 teaches extending well log data using simulations, Pg.10, Col1:; Examiner notes interpretation as above.)
GRANA-2012 does not teach:
applying the best fit between the search result and the at least a portion of the initial well log data as the augmented well log data
JIANG further teaches:
applying the best fit between the search result and the at least a portion of the initial well log data as the augmented well log data (As above, JIANG teaches systematic point by point fitting method using search to compare result, Pg.2, Col.1, §“Methodology”: “properties are obtained by systematically searching through
each point in the decision… not only obtained the best solution, but also a probability
distribution of multiple solutions…modeled Ip that provided the smallest absolute error corresponded to the best solution of porosity, and the ones that provided larger absolute errors corresponded to porosities with lower probabilities.”
It would have been obvious to one of ordinary skill in the art before effective filing date of the claimed invention to modify GRANA-2012 as modified by JIANG, as taught above, to include the step of applying the best fit between the search result and the at least a portion of the initial well log data as the augmented well log data, such as that further taught by JIANG because implementing this step improves accuracy and generalization of a predictive model. One of ordinary skill would find the combination of the step taught by JIANG with the method/system of GRANA-2012 would result in improved consistency with real data and physical plausibility, and in the context of a machine-learning-based method, would improve quality of inputs by ensuring model is trained from realistic scenarios.
With respect to Claim 18, GRANA-2012 teaches the limitations of claim teaches the limitations of claim 17.
GRANA-2012 further teaches:
generating the augmented well log data
GRANA-2012 does not teach:
performing a search of the calibrated model parameters with respect to the well log data.
JIANG teaches:
performing a search of the calibrated model parameters with respect to the well log data (As above, JIANG teaches systematic point by point search for model comparison, Pg.2, Col.1, §“Methodology”)
It would have been obvious to one of ordinary skill in the art before effective filing date of the claimed invention to modify GRANA-2012 as modified by JIANG, as taught above, to include the step of performing a search of the calibrated model parameters with respect to the well log data such as that further taught by JIANG because it provides a level of assurance that generated data is consistent with measured data. One of ordinary skill would see the advantage of combining this step taught by JIANG with the method/system of GRANA-2012 to result in a predictive model that is consistent in that it would avoid the possibility of generating physically impossible results, adding a degree of accuracy and reliability to the predictive model.
With respect to Claim 19, GRANA-2012 in view of JIANG teaches the limitations of claim 18.
GRANA-2012 further teaches:
and comparing each calibrated model parameter of the calibrated model parameters with at least a portion of the well log data.(GRANA-2012 teaches comparative analysis, Pg.9, Fig.8, “Seismic inversion results at the well 1 location. Inverted profiles of P- and S-impedances and density (red) compared to the actual log
(blue)”)
GRANA-2012 does not teach:
performing the search of the calibrated model parameters
setting a respective minimum value and maximum value for each calibrated model parameter of the calibrated model parameters
JIANG further teaches:
performing the search of the calibrated model parameters (As above, JIANG teaches systematic point by point search using model parameters, Pg.2, Col.1, §“Methodology”)
setting a respective minimum value and maximum value for each calibrated model parameter of the calibrated model parameters (As above, JIANG teaches evaluation of minimum and maximum points to determine reservoir properties Pg.2, Col.1, §“Methodology”: “point that provides the minimum value of the objective function corresponds to the best solution of the reservoir properties” and Pg.3,Col.1:§ “Methodology”: “upper limit (0.2)was selected as about 30% larger than the maximum observed value.”)
It would have been obvious to one of ordinary skill in the art before effective filing date of the claimed invention to modify GRANA-2012 as modified by JIANG, as taught above, to include the steps of performing the search of the calibrated model parameters and setting a respective minimum value and maximum value for each calibrated model parameter of the calibrated model parameters, such as that further taught by JIANG because these steps would fill a gap in understanding between a simulated/theoretical behavior and real-world/measured data from the field, and would avoid the time waste of overfitting. One of ordinary skill would see the advantage of combining the steps taught by JIANG with the method/system of GRANA-2012 as a way to implement physics constraints into the model, which ensures improved physical realism. Additionally, searching calibrated model parameters in the context of a machine-learning process for training would provide the advantage of improving accuracy and reducing uncertainty of a resulting model for identification of key features.
With respect to Claim 20, GRANA-2012 in view of JIANG teaches the limitations of claim 19.
GRANA-2012 further teaches:
generating the modeled well log data based on a comparison of each calibrated model parameter of the calibrated model parameters with the at least a portion of the well log data (GRANA-2012 teaches comparison of model with well log data, Pg.8, Col.2: “results of the elastic inversion at the same well location are shown in Fig. 8, where we compare the inverted attributes with the corresponding properties measured at the well location”, with Pg.9, Fig.8, “Seismic inversion results at the well 1 location. Inverted profiles of P- and S-impedances and density (red) compared to the actual log (blue).”)
GRANA-2012 does not teach:
a best fit between each calibrated model parameter of the calibrated model parameters and the at least a portion of the well log data.
JIANG further teaches:
a best fit between each calibrated model parameter of the calibrated model parameters and the at least a portion of the well log data. (As above, JIANG teaches systematic point by point fit between model and well log data, properties Pg.2, Col.1, §“Methodology”)
It would have been obvious to one of ordinary skill in the art before effective filing date of the claimed invention to modify GRANA-2012 as modified by JIANG, as taught above, to include the steps of using a best fit between each calibrated model parameter of the calibrated model parameters and the at least a portion of the well log data, such as that further taught by JIANG it would maximize model accuracy. One of ordinary skill would find the teaching of JIANG as an obvious way to improve the physical relevance of a predictive model, and ultimately reduce prediction uncertainty when combined with the method/system of GRANA-2012. The iterative steps taught by JIANG in the context of a machine learning system would allow for identification of an optimized parameter that would better represent the actual reservoir property, as opposed to reliance on assumed or uncalibrated values.
With respect to Claim 21, GRANA-2012 in view of MEIJUAN teaches the limitations of claim 20.
GRANA-2012 further teaches:
comprising: modifying the augmented well log data to generate a training dataset; (As above, GRANA-2012 teaches modification of well log data that has been previously augmented, as above, and Pg.5, Col.2, with Pg.6, Fig. 4.)
training a probabilistic classifier utilizing the training dataset; (As above, GRANA-2012 teaches use of classifier, generally, Pg.4,Col.2, § “Log-facies definition” and Pg.5,Col.2, “classification is obtained by applying Ward’s minimum variance linkage method”)
calculating a probability volume for each lithofluid class of a set of predetermined
lithofluid classes utilizing the probabilistic classifier; (As above, GRANA-2012 teaches calculation of probability volume, Abstract, and Pg.3,Col1, also generally, Pg.8, §”Seismic facies classification”; GRANA-2012 includes lithofluid classification as part of analysis of reservoir properties, Pg.2,Col.1; Examiner notes, as above, GRANA-2012 references prior art, with references not disclosed by Applicant included below as relied upon and considered pertinent.)
outputting the probability volume for each lithofluid class. (As above, GRANA-2012 teaches probability volume as output, Abstract and P16,Col1 §CONCLUSION with lithofluid classes included in calculations and in result, as discussed above)
Claims 7 and 16 are rejected under 35 U.S.C. § 103(a) as being unpatentable over GRANA-2012 in view of MUKERJI (Mukerji, et. al., "Statistical rock physics: Combining rock physics, information theory, and geostatistics to reduce uncertainty in seismic reservoir characterization", The Leading Edge, March 1, 2001)
With respect to Claims 7 and 16, GRANA-2012 teaches the limitations of claims 1 and 9.
GRANA-2012 further teaches:modifying the augmented well log data (As above, GRANA-2012 teaches
extending well log data using simulations, Pg.10, Col1)
GRANA-2012 does not teach:
modifying the augmented well log data comprises expanding one or more of a
porosity range, saturations, a fluid type, mineralogy, or a volume of shale range based upon the augmented well log data to generate the training dataset.
MUKERJI teaches:
modifying the augmented well log data comprises expanding one or more of a
porosity range, saturations, a fluid type, mineralogy, or a volume of shale range based upon the augmented well log data to generate the training dataset. (MUKERJI is in same technical field, Pg.1, Col.2, “paper presents snapshots of current and emerging
trends in applied statistical rock physics for reservoir characterization”; MUKERJI teaches modifying well log data by extending, Pg.313, Col.1, §”Reservoir heterogeneity and uncertainty”: “heterogeneities occur at various scales and can include variations in lithology, pore fluids, clay content, porosity, pressure, and temperature” and Pg.314, Col1, “training set often has to be extended or enhanced using physical models to derive pdfs for situations not sampled in the original training data")
It would have been obvious to one of ordinary skill in the art before effective filing
date of the claimed invention to modify GRANA-2012 to include the step expanding one or more of a porosity range, saturations, a fluid type, mineralogy, or a volume of shale range based upon the augmented well log data to generate the training dataset when modifying the augmented well log data, such as that of MUKERJI because this step would improve a modeling process by accounting for natural geological variability. One of ordinary skill would understand this step taught by MUKERJI as an obvious way to improve the method/system disclosed by GRANA-2012 as an additional way to overcome sparse data and improve generalization capacity of a machine-learning-based model development process. One of ordinary skill would see the obvious connection and have a reasonable expectation of a more robust and accurate model for predicting subsurface reservoir properties.
Conclusion
The prior art made of record and not relied upon is considered pertinent to applicant's disclosure.
ALEARDI (Aleardi, et. al., “Application of different classification methods for litho-fluid facies prediction: a case study from the offshore Nile Delta”, J. Geophys. Eng. 14 , 1087–1102, 2017) – teaches multiple methods and approaches for modeling and prediction specific to lithofluids.
AVSETH (Avseth, et. al., Quantitative Seismic Interpretation. Cambridge University Press, 2005) – teaches general approach for seismic inversion techniques for understanding geologic formations.
BOSCH (BOSCH, et al., “Seismic inversion for reservoir properties combining statistical rock physics and geostatistics: A review”, Geophysics, VOL.75, NO.5, 2010) – teaches generally known methods and approaches for using predictive modeling for reservoir property prediction.
BULAND-2003 (Buland, et. al., “Bayesian linearized AVO inversion”, Geophysics 68 (1), 185–198, 2003) – teaches details using Bayesian statistical approach for inversion methods in reservoir property prediction.
BULAND-2008 (Buland. Et. al., “Bayesian lithology and fluid prediction from seismic prestack Data”, Geophysics 73 (3), C13–C21, 2008) – teaches specific use of Bayesian methods as applied to underground fluid structure prediction.
GRANA-2011(Grana et. al, “The link between seismic inversion, rock physics, and geostatistical simulations in seismic reservoir characterization studies. The Leading Edge 30, 54, 2011) – teaches background and motivation for using inversion techniques for modeling and simulation of reservoir statistics.
JIANG (Jiang, et. al., “Estimation of reservoir properties of the Haynesville Shale by using rock-physics modelling and grid searching”, Geophys. J. Int. , 195, 315–329, 2013) – teaches further elaboration regarding model predictive model development using rock physics.
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/TONI D SAUNCY/Examiner, Art Unit 2863
/Catherine T. Rastovski/Supervisory Primary Examiner, Art Unit 2857