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 .
Priority
Current application, US Application No. 17/644,845, is filed on 12/17/2021.
DETAILED ACTION
This office action is responsive to the amendment filed on 07/24/2025. Claims 1-5, 7-13 and 15-20 are currently pending. Claims 6 and 14 are canceled per applicant’s request.
Response to Amendment
Applicant's amendment is entered into further examination and appreciated by the examiner.
Response to Arguments/Remarks
Regarding remarks on the rejections under 35 USC 101, applicant’s arguments have been fully considered but are not persuasive because of following reasons.
Applicant argues (see pg. 13 par. 2 – pg. 17 par. 1) that the amended claims integrate judicial exception to a practical application at step 2A as the claims recite additional elements, i.e. using particular machine, which is meaningful and the claims also show improvement to simulation and well technology.
Examiner respectfully submits that the newly recited additional elements “acquiring, using a gamma ray logging tool, a plurality of gamma ray logs for a plurality of wells in a geological region of interest” and “acquiring, using a density logging tool, a plurality of density logs for the plurality of wells” are recited in a high level of generality without specific details. These types of data collection steps are standard steps in the art and only add insignificant extra solution activities to the judicial exception. Similarly, the gamma ray logging tool and density logging tool are also recited in a high level of generality without any specific details. These logging tools are not particular in the art. The improvement to the technology, i.e. determining, a location of hydrocarbons in the geological region of interest using the formation property volume, is in the limitations belonging to abstract idea. The improvement of the abstract idea cannot be treated as an improvement to the technology.
Applicant also argues (see pg. 17 par. 2 – pg. 20 par 2) that the amended independent claim 1 provides an unconventional techniques for determining the location of hydrocarbons in a geological region of interest using a formation property volume and predicted logs at step 2B.
Examiner respectfully submits that the alleged unconventional techniques are not part of additional elements. Step 2B analysis applies to the additional elements, not to the judicial exception. Therefore, the arguments are unpersuasive and the rejections are maintained. See the new office action below.
Regarding remarks on the rejections under 35 USC 103, applicant’s arguments have been fully considered but are moot in view of new ground of rejection necessitated by the amendment because the arguments do not apply to any of the references being used in the current rejection. The newly added limitations (underlined) are disclosed by newly found references. See the new office action below.
Claim Interpretation – 35 USC 112(f)
The following is a quotation of 35 U.S.C. 112(f):
(f) Element in Claim for a Combination. – An element in a claim for a combination may be expressed as a means or step for performing a specified function without the recital of structure, material, or acts in support thereof, and such claim shall be construed to cover the corresponding structure, material, or acts described in the specification and equivalents thereof.
The claims in this application are given their broadest reasonable interpretation using the plain meaning of the claim language in light of the specification as it would be understood by one of ordinary skill in the art. The broadest reasonable interpretation of a claim element (also commonly referred to as a claim limitation) is limited by the description in the specification when 35 U.S.C. 112(f) is invoked.
As explained in MPEP § 2181, subsection I, claim limitations that meet the following three-prong test will be interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph:
(A) the claim limitation uses the term “means” or “step” or a term used as a substitute for “means” that is a generic placeholder (also called a nonce term or a non-structural term having no specific structural meaning) for performing the claimed function;
(B) the term “means” or “step” or the generic placeholder is modified by functional language, typically, but not always linked by the transition word “for” (e.g., “means for”) or another linking word or phrase, such as “configured to” or “so that”; and
(C) the term “means” or “step” or the generic placeholder is not modified by sufficient structure, material, or acts for performing the claimed function.
Use of the word “means” (or “step”) in a claim with functional language creates a rebuttable presumption that the claim limitation is to be treated in accordance with 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph. The presumption that the claim limitation is interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph, is rebutted when the claim limitation recites sufficient structure, material, or acts to entirely perform the recited function.
Absence of the word “means” (or “step”) in a claim creates a rebuttable presumption that the claim limitation is not to be treated in accordance with 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph. The presumption that the claim limitation is not interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph, is rebutted when the claim limitation recites function without reciting sufficient structure, material or acts to entirely perform the recited function.
Claim limitations in this application that use the word “means” (or “step”) are being interpreted under 35 U.S.C. 112(f) except as otherwise indicated in an Office action. Conversely, claim limitations in this application that do not use the word “means” (or “step”) are not being interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph, except as otherwise indicated in an Office action.
The current application includes limitations in claim 10 that do not use the word “means,” but are nonetheless interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph, because of the following reasons:
Claim 10 includes a limitation/element that use generic placeholders, “reservoir simulator” that are coupled with functional language, “obtaining”, “assigning”, “determining” and ”generating” without reciting sufficient structure to perform the recited function and the generic placeholder is not preceded by a structural modifier.
The physical structure of “simulator” is interpreted as a general computer hardware and/or software (see specification - a reservoir simulator that includes a computer processor [0005], reservoir simulator ‘160’ [0019, Fig. 1], may include hardware/software [0020, 0039], implemented as part of a software platform for the control system [0021]).
If applicant does not intend to have this limitation interpreted under 35 U.S.C. 112(f), applicant may: (1) amend the claim limitation to avoid it being interpreted under 35 U.S.C. 112(f) (e.g., by reciting sufficient structure to perform the claimed function); or (2) present a sufficient showing that the claim limitation recites sufficient structure to perform the claimed function so as to avoid it being interpreted under 35 U.S.C. 112(f).
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 1-5, 7-13 and 15-20 are rejected under 35 U.S.C. 101 because the claimed invention is directed to nonstatutory subject matter. 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.
Specifically, representative claim 1 recites:
“A method, (1.A) comprising:
acquiring, using a gamma ray logging tool, a plurality of gamma ray logs for a plurality of wells in a geological region of interest; (1.B.1)
acquiring, using a density logging tool, a plurality of density logs for the plurality of wells; (1.B.2)
obtaining, by a computer processor, well log data for the plurality of wells, wherein the well log data comprises the plurality of gamma ray logs and the plurality of density logs; (1.B.3)
assigning, by the computer processor and using a grouping algorithm, a plurality of subsets of the well log data to a plurality of groups based on one or more geological attributes, wherein the grouping algorithm is a k-means clustering algorithm, wherein the plurality of groups comprise a first group of the well log data based on a first subset among the plurality of subsets and a second group of the well log data based on a second subset among the plurality of subsets, and wherein the first group of the well log data corresponds to data at a different depth than the second group of the well log data; (1.C)
determining, by the computer processor and using the plurality of groups, a plurality of well zones for different portions of a respective well among the plurality of wells; (1.D.1)
determining, by the computer processor, a plurality of predicted sonic logs for the plurality of wells using a first machine learning model and the well log data, wherein the first machine learning model uses a portion of the plurality of gamma ray logs and a portion of the plurality of density logs as inputs to an input layer of the machine-learning model; (1.D.2)
determining, by the computer processor, interpolated log data using the well log data, the plurality of predicted sonic logs, the plurality of well zones, and an intrawell interpolation process; (1.D.3)
generating, by the computer processor, a formation property volume based on the interpolated log data and the well log data; (1.E)
and determining, by the computer processor, a location of hydrocarbons in the geological region of interest using the formation property volume. (1.F)”
The claim limitations in the abstract idea have been highlighted in bold above; the remaining limitations are “additional elements”.
Under the Step 1 of the eligibility analysis, we determine whether the claims are to a statutory category by considering whether the claimed subject matter falls within the four statutory categories of patentable subject matter identified by 35 U.S.C. 101: Process, machine, manufacture, or composition of matter. The above claim is considered to be in a statutory category (Process).
Under the Step 2A, Prong One, we consider whether the claim recites a judicial exception (abstract idea). In the above claim, the highlighted portion constitutes an abstract idea because, under a broadest reasonable interpretation, it recites limitations that fall into/recite an abstract idea exception. Specifically, under the 2019 Revised Patent Subject Matter Eligibility Guidance, it falls into the grouping of subject matter when recited as such in a claim limitation, that covers mathematical concepts (mathematical relationships, mathematical formulas or equations, mathematical calculations), and mental processes (concepts performed in the human mind including an observation, evaluation, judgement, and/or opinion).
For example, highlighted limitations/steps (1.C) – (1.E) are treated by the Examiner as belonging to Mathematical Concept grouping or a combination of Mathematical Concept and Mental Process groupings as the limitations include Mathematical Calculations/Algorithms, or show Mathematical Relationship combined with optional Mental evaluations/judgements.
The highlighted limitation (1.F) is treated as belonging to Mental Process grouping or a combination of Mental Process and Mathematical Concept groupings as the limitation include mental judgement/evaluation with an optional Mathematical calculations.
Next, under the Step 2A, Prong Two, we consider whether the claim that recites a judicial exception is integrated 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.
The above claims comprise the following additional elements: (Side Note: duplicated elements are not repeated)
In Claim 1: “A method”, “acquiring, using a gamma ray logging tool, a plurality of gamma ray logs for a plurality of wells in a geological region of interest”, “acquiring, using a density logging tool, a plurality of density logs for the plurality of wells” and “obtaining, by a computer processor, well log data for a plurality of wells, wherein the well log data comprises the plurality of gamma ray logs and the plurality of density logs”;
In Claim 10: “A system”, “a logging system coupled to a gamma ray logging tool and a density logging tool; a well system coupled to the logging system and a wellbore; and a reservoir simulator comprising a computer processor, wherein the reservoir simulator is coupled to the logging system and the well system, the reservoir simulator is configured to perform a method”;
In Claim 18: “A non-transitory computer readable medium storing instructions executable by a computer processor, the instructions comprising functionality”;
As per claim 1, the additional element in the preamble “A method” is not qualified as a meaningful limitation because the method even fails to link with a particular operation or a field of use.
The limitations/steps “acquiring, using a gamma ray logging tool, a plurality of gamma ray logs for a plurality of wells in a geological region of interest”, “acquiring, using a density logging tool, a plurality of density logs for the plurality of wells” and “obtaining, by a computer processor, well log data for a plurality of wells, wherein the well log data comprises the plurality of gamma ray logs and the plurality of density logs” represents a standard well log collection activities in the art. They are recited in a high level of generalities without specific details and only adds insignificant extra solution activities. The gamma ray logging tool, the density logging tool and the computer processor are not particular in the art.
As per claim 10, the additional element in the preamble “A system” is not qualified as a meaningful limitation because the method even fails to link with a particular operation or a field of use.
The limitations/elements “a logging system coupled to a gamma ray logging tool and a density logging tool; a well system coupled to the logging system and a wellbore; and a reservoir simulator comprising a computer processor, wherein the reservoir simulator is coupled to the logging system and the well system, the reservoir simulator is configured to perform a method” represent a standard data collection and analysis, using simulation, tools in the art and they are not particular.
As per claim 18, the additional element in the preamble “A non-transitory computer readable medium storing instructions executable by a computer processor, the instructions comprising functionality” is not qualified as a meaningful limitation because the method even fails to link with a particular operation or a field of use excepting simply reciting use of general computer resources/components. The general computer resources/components are particular in the art.
In conclusion, the above additional elements, considered individually and in combination with the other claim elements as a whole do not reflect an improvement to the computer technology or other technology or technical field, and, therefore, do not integrate the judicial exception into a practical application. No particular machine or real-world transformation are claimed. Therefore, the claims are directed to a judicial exception and require further analysis under the Step 2B.
Under Step 2B analysis, the above claims fail to include additional elements that are sufficient to amount to significantly more than the judicial exception as shown in the prior art of record.
The limitations/elements listed as additional elements above are well understood, routine and conventional steps/elements in the art according to the prior art of record. (See Bukar, Gevi, Gkor AI, Akkurt, Al Tammal and Onalo and others in the list of prior art cited below)
Claims 1-5, 7-13 and 15-20, therefore, are not patent eligible.
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 1, 9-10 and 17-18 are rejected under 35 U.S.C. 103 as being unpatentable over Bukar (Bukar, Idris, and et al. "A machine learning approach to shear sonic log prediction." In SPE Nigeria Annual International Conference and Exhibition, p. D023S026R001. SPE, 2019) in view of Gevirtz (US 20160364654 A1), hereinafter ‘Gevi’, Gkortsas (US 20220146705 A1), hereinafter ‘Gkor’ and Al Ismail (US 20210340861 A1), hereinafter 'Al'.
As per claim 1, Bukar discloses
A method, (A machine learning approach [abs], prediction methods [pg. 2 par. 2], methodology [pg. 3 par. 2]) comprising:
acquiring a plurality of gamma ray logs for a plurality of wells in a geological region of interest; (any of those wells [pg. 2 par. 1], field, wells [pg. 3 par. 2], Well logs available in the wells include … gamma ray ‘GR’ [pg. 3 par. 3])
acquiring a plurality of density logs for the plurality of wells; (Well logs available in the wells include …bulk density ‘RHOB’ [pg. 3 par. 3])
However, Bukar is silent on acquiring logs using a gamma ray logging tool and a density logging tool and use of a processor to obtain the acquired logs for further processing.
Gevi discloses use of tools to measure well log data including gamma ray logs and density logs and use of a processor to process the measured well logs (tools to an area of interest, measure parameters of the wellbore [0014], MWD, LWD [0015], types of well logging data, porosity logs, density logs [0016], gamma ray logs [0017], processor [0035-0036, 0038, 0058-0065, Fig. 2]).
Gevi is in the same oil and gas industry field characterizing a petroleum reservoir for the purpose of improving estimation of reserves and making decisions regarding the development of the field (see Gevi – [0002, 004-0006]) like Bukar.
Therefore, it would have been obvious to one of ordinary skill in the art at the time when invention is filed before the effective filing date of the current application to modify the teachings of Bukar in view of Gevi to acquire logs using a gamma ray logging tool and a density logging tool and use of a processor to obtain the acquired logs for further processing to accurately estimate the formation property volume and determine a location of hydrocarbons in the geological interest using the formation property volume (Bukar - an improvement in the accuracy of the prediction in hydrocarbon-bearing intervals [pg. 9 par. 1], prediction in hydrocarbon-bearing rocks [pg. 3 par. 2], rock mechanical properties for rock physics, quantitative seismic interpretation and geomechanics interpretation [pg. 1 introduction]) (Gevi - provide better
volumetric estimates and can better define "sweet spots" [0033]).
However, the combined prior art is silent regarding assigning, by the computer processor and using a grouping algorithm, a plurality of subsets of the well log data to a plurality of groups based on one or more geological attributes, wherein the grouping algorithm is a k-means clustering algorithm, wherein the plurality of groups comprise a first group of the well log data based on a first subset among the plurality of subsets and a second group of the well log data based on a second subset among the plurality of subsets, and wherein the first group of the well log data corresponds to data at a different depth than the second group of the well log data and determining, by the computer processor and using the plurality of groups, a plurality of well zones for different portions of a respective well among the plurality of wells.
Gkor discloses assigning, by the computer processor and using a grouping algorithm, a plurality of subsets of the well log data to a plurality of groups based on one or more geological attributes, wherein the grouping algorithm is a k-means clustering algorithm, wherein the plurality of groups comprise a first group of the well log data based on a first subset among the plurality of subsets and a second group of the well log data based on a second subset among the plurality of subsets, and wherein the first group of the well log data corresponds to data at a different depth than the second group of the well log data (Facies of a formation are classified from data charactering properties of a portion of the formation as a function of depth [abs, 0008], classification workflow, k-means clustering, partitions logs into uniquely recognizable patterns, discriminate analysis component ‘cluster tagging’ that finds the same pattern in a different data set [0005], depth -based method, depth-by-depth basis [0011, 0050, 0055-0056, Fig. 5, 7, 8F], facies classification as a function of depth [0028-0029, Fig. 9F-9G], k-means clustering [0041], processor [0062, 0065-0066, 0072-0074, 0079, claims 16, 18, Fig. 10]) and
determining, by the computer processor and using the plurality of groups, a plurality of well zones for different portions of a respective well among the plurality of wells; (facies classification based on layer-based properties which are determined from well log data obtained from a plurality of different well logging tools [0008, 0081], processor [0062, Fig. 10], nine gas wells, well logs, clustering results for the … well [0060, Figs. 5, 9A-9F], determine the number of facies along a selected portion of the formation from input well logs, better classification, honoring of different vertical resolution of varying input logs, consistent layer boundaries with high-resolution logs, and better accuracy for formations with thin layers [0061]).
Gkor is also in the same oil and gas industry field evaluating formation and
characterizing a petroleum reservoir properties to improve the chances of success of a well like the combined prior art.
Therefore, it would have been obvious to one of ordinary skill in the art at the time when invention is filed before the effective filing date of the current application to modify the teachings of the combined prior art in view of Gkor to assig, by the computer processor and using a grouping algorithm, a plurality of subsets of the well log data to a plurality of groups based on one or more geological attributes, wherein the grouping algorithm is a k-means clustering algorithm, wherein the plurality of groups comprise a first group of the well log data based on a first subset among the plurality of subsets and a second group of the well log data based on a second subset among the plurality of subsets, and wherein the first group of the well log data corresponds to data at a different depth than the second group of the well log data and determine, by the computer processor and using the plurality of groups, a plurality of well zones for different portions of a respective well among the plurality of wells in order to accurately estimate the formation property volume and determine a location of hydrocarbons in the geological interest using the formation property volume.
Bukar further discloses
determining a plurality of predicted sonic logs for the plurality of wells using a first machine learning model and the well log data, wherein the first machine learning model uses a portion of the plurality of gamma ray logs and a portion of the plurality of density logs as inputs to an input layer of the machine-learning model (A machine learning approach to shear sonic log prediction [abs], Well logs available in the wells include … gamma ray (GR), bulk density (RHOB) … sonic log prediction [pg. 3 par. 2]).
However, Bukar is silent regarding determining interpolated log data using the well log data, the plurality of predicted sonic logs, the plurality of well zones, and an intrawell interpolation process.
AI discloses determining interpolated log data using the well log data, the plurality of well zones, and a statistical interpolation process for both intrawell and interwell (various well logs and a statistical interpolation method [abs, 0003-0005], computer processor [0003-0005], 2D … interpolation method, top depth [0028, Fig. 2], interpolation method [0033-0034, claims 1, 4, 9 and 15]).
AI is in the same oil and gas industry field evaluating the formation of the reservoir by analyzing well logs (see AI – [0001, 0003-0005) like the combined prior art.
Therefore, it would have been obvious to one of ordinary skill in the art at the time when invention is filed before the effective filing date of the current application to modify the teachings of the combined prior art in view of AI to determine, by the computer processor, interpolated log data using the well log data, the plurality of predicted sonic logs, the plurality of well zones, and an intrawell interpolation process and generate, by the computer processor, a formation property volume based on the interpolated log data and the well log data in order to accurately estimate the formation property volume without error (see AI – subjectivity and error [0002]).
Gevi further discloses
generating, by the computer processor, a formation property volume based on the well log data. (initial partition of the scaled raw log data [0032], provide better volumetric estimates and can better define "sweet spots" [0033])
Bukar discloses
determining a location of hydrocarbons in the geological region of interest using the formation property volume (hydrocarbon-bearing intervals [abs, pg. 4 par. 2], hydrocarbon-bearing rocks [pg. 2 par. 2], prediction in hydrocarbon-bearing rocks [pg. 3 par. 2], hydrocarbon-bearing zone [pg. 5 par. 4, pg. 6 par. 1, pg. 8 par. 1, Fig. 8, 9], an improvement in the accuracy of the prediction in hydrocarbon-bearing intervals [pg. 9 par. 1], rock mechanical properties for rock physics, quantitative seismic interpretation and geomechanics interpretation [pg. 1 introduction]).
As per claim 10, Bukar discloses
acquire a plurality of gamma ray logs for a plurality of wells in a geological region of interest, and acquire a plurality of density logs for the plurality of wells (any of those wells [pg. 2 par. 1], field, wells [pg. 3 par. 2], Well logs available in the wells include … gamma ray ‘GR’, bulk density ‘RHOB’ [pg. 3 par. 3])
However, Bukar is silent on reciting a system, a logging tool coupled to a gamma ray tool and a density tool, a well system couples to the logging system and a wellbore, and a reservoir simulator comprising a computer processor, wherein the reservoir simulator is coupled to the logging system and the well system, the reservoir simulator is configured to perform a method, and obtaining, using the logging system, well log data for the plurality of wells, wherein the well log data comprises the plurality of gamma ray logs and the plurality of density logs;
Gevirtz discloses
A system, (apparatus [abs], system [0002, 0009-0010]) comprising:
a logging system coupled to a gamma logging tool and a density logging tool; (measurement tools are coupled to or integrated with the drill string, drilling rig, LWD, tools [0015], types of well logging data, porosity logs, density logs [0016], gamma ray logs [0017])
a well system coupled to the logging system and a wellbore; (drilling of wellbore using tools, logging equipment [0014])
and a reservoir simulator comprising a computer processor, wherein the reservoir simulator is coupled to the logging system and the well system, the reservoir simulator is configured to perform a method comprising: (computer program product [0010], program [0045-0046], processors [0035-0038, Fig. 2])
obtaining, using the logging system, well log data for the plurality of wells, wherein the well log data comprises the plurality of gamma ray logs and the plurality of density logs; (tools to an area of interest, measure parameters of the wellbore [0014], MWD, LWD [0015], types of well logging data, porosity logs, density logs [0016], gamma ray logs [0017], processor [0035-0036, 0038, 0058-0065, Fig. 2]).
Gevi is in the same oil and gas industry field characterizing a petroleum reservoir for the purpose of improving estimation of reserves and making decisions regarding the development of the field (see Gevi – [0002, 004-0006]) like Bukar.
Therefore, it would have been obvious to one of ordinary skill in the art at the time when invention is filed before the effective filing date of the current application to modify the teachings of Bukar in view of Gevi to use a system, comprising: a logging system coupled to a gamma ray logging tool and a density logging tool, wherein the gamma ray tool is configured to acquire a plurality of gamma ray logs for a plurality of wells in a geological region of interest, and wherein the density logging tool is configured to acquire a plurality of density logs for the plurality of wells; a well system coupled to the logging system and a wellbore; and a reservoir simulator comprising a computer processor, wherein the reservoir simulator is coupled to the logging system and the well system, the reservoir simulator is configured to perform a method comprising: obtaining, using the logging system, well log data for the plurality of wells, wherein the well log data comprises the plurality of gamma ray logs and the plurality of density logs to accurately estimate the formation property volume and determine a location of hydrocarbons in the geological interest using the formation property volume.
However, the combined prior art is silent regarding assigning, by the computer processor and using a grouping algorithm, a plurality of subsets of the well log data to a plurality of groups based on one or more geological attributes, wherein the grouping algorithm is a k-means clustering algorithm, wherein the plurality of groups comprise a first group of the well log data based on a first subset among the plurality of subsets and a second group of the well log data based on a second subset among the plurality of subsets, and wherein the first group of the well log data corresponds to data at a different depth than the second group of the well log data and determining, by the computer processor and using the plurality of groups, a plurality of well zones for different portions of a respective well among the plurality of wells.
Gkor discloses assigning, by the computer processor and using a grouping algorithm, a plurality of subsets of the well log data to a plurality of groups based on one or more geological attributes, wherein the grouping algorithm is a k-means clustering algorithm, wherein the plurality of groups comprise a first group of the well log data based on a first subset among the plurality of subsets and a second group of the well log data based on a second subset among the plurality of subsets, and wherein the first group of the well log data corresponds to data at a different depth than the second group of the well log data (Facies of a formation are classified from data charactering properties of a portion of the formation as a function of depth [abs, 0008], classification workflow, k-means clustering, partitions logs into uniquely recognizable patterns, discriminate analysis component ‘cluster tagging’ that finds the same pattern in a different data set [0005], depth -based method, depth-by-depth basis [0011, 0050, 0055-0056, Fig. 5, 7, 8F], facies classification as a function of depth [0028-0029, Fig. 9F-9G], k-means clustering [0041], processor [0062, 0065-0066, 0072-0074, 0079, claims 16, 18, Fig. 10]) and
determining, by the computer processor and using the plurality of groups, a plurality of well zones for different portions of a respective well among the plurality of wells; (facies classification based on layer-based properties which are determined from well log data obtained from a plurality of different well logging tools [0008, 0081], processor [0062, Fig. 10], nine gas wells, well logs, clustering results for the … well [0060, Figs. 5, 9A-9F], determine the number of facies along a selected portion of the formation from input well logs, better classification, honoring of different vertical resolution of varying input logs, consistent layer boundaries with high-resolution logs, and better accuracy for formations with thin layers [0061]).
Gkor is also in the same oil and gas industry field evaluating formation and
characterizing a petroleum reservoir properties to improve the chances of success of a well like the combined prior art.
Therefore, it would have been obvious to one of ordinary skill in the art at the time when invention is filed before the effective filing date of the current application to modify the teachings of the combined prior art in view of Gkor to assig, by the computer processor and using a grouping algorithm, a plurality of subsets of the well log data to a plurality of groups based on one or more geological attributes, wherein the grouping algorithm is a k-means clustering algorithm, wherein the plurality of groups comprise a first group of the well log data based on a first subset among the plurality of subsets and a second group of the well log data based on a second subset among the plurality of subsets, and wherein the first group of the well log data corresponds to data at a different depth than the second group of the well log data and determine, by the computer processor and using the plurality of groups, a plurality of well zones for different portions of a respective well among the plurality of wells in order to accurately estimate the formation property volume and determine a location of hydrocarbons in the geological interest using the formation property volume.
Bukar further discloses
determining a plurality of predicted sonic logs for the plurality of wells using a first machine learning model and the well log data, wherein the first machine learning model uses a portion of the plurality of gamma ray logs and a portion of the plurality of density logs as inputs to an input layer of the machine-learning model (A machine learning approach to shear sonic log prediction [abs], Well logs available in the wells include … gamma ray (GR), bulk density (RHOB) … sonic log prediction [pg. 3 par. 2]).
However, Bukar is silent regarding determining interpolated log data using the well log data, the plurality of predicted sonic logs, the plurality of well zones, and an intrawell interpolation process.
AI discloses determining interpolated log data using the well log data, the plurality of well zones, and a statistical interpolation process for both intrawell and interwell (various well logs and a statistical interpolation method [abs, 0003-0005], computer processor [0003-0005], 2D … interpolation method, top depth [0028, Fig. 2], interpolation method [0033-0034, claims 1, 4, 9 and 15]).
AI is in the same oil and gas industry field evaluating the formation of the reservoir by analyzing well logs (see AI – [0001, 0003-0005) like the combined prior art.
Therefore, it would have been obvious to one of ordinary skill in the art at the time when invention is filed before the effective filing date of the current application to modify the teachings of the combined prior art in view of AI to determine, by the computer processor, interpolated log data using the well log data, the plurality of predicted sonic logs, the plurality of well zones, and an intrawell interpolation process and generate, by the computer processor, a formation property volume based on the interpolated log data and the well log data in order to accurately estimate the formation property volume without error.
Gevi further discloses
generating, by the computer processor, a formation property volume based on the well log data. (initial partition of the scaled raw log data [0032], provide better volumetric estimates and can better define "sweet spots" [0033])
Bukar discloses
determining a location of hydrocarbons in the geological region of interest using the formation property volume (hydrocarbon-bearing intervals [abs, pg. 4 par. 2], hydrocarbon-bearing rocks [pg. 2 par. 2], prediction in hydrocarbon-bearing rocks [pg. 3 par. 2], hydrocarbon-bearing zone [pg. 5 par. 4, pg. 6 par. 1, pg. 8 par. 1, Fig. 8, 9], an improvement in the accuracy of the prediction in hydrocarbon-bearing intervals [pg. 9 par. 1], rock mechanical properties for rock physics, quantitative seismic interpretation and geomechanics interpretation [pg. 1 introduction]).
As per claim 18, Bukar discloses
a method, (A machine learning approach [abs], prediction methods [pg. 2 par. 2], methodology [pg. 3 par. 2]) comprising:
acquiring a plurality of gamma ray logs for a plurality of wells in a geological region of interest; (any of those wells [pg. 2 par. 1], field, wells [pg. 3 par. 2], Well logs available in the wells include … gamma ray ‘GR’ [pg. 3 par. 3])
acquiring a plurality of density logs for the plurality of wells; (Well logs available in the wells include …bulk density ‘RHOB’ [pg. 3 par. 3])
However, Bukar is silent on using a non-transitory computer readable medium storing instructions executable by a computer processor, the instructions configured to perform a method, and acquiring logs using a gamma ray logging tool and a density logging tool.
Gevi discloses use of a non-transitory computer readable medium storing instructions executable by a computer processor to perform a method: (tangible non-transitory “storage” type media, processors, software programming [0045]) and acquiring logs using a gamma ray logging tool and a density logging tool. (tools to an area of interest, measure parameters of the wellbore [0014], MWD, LWD [0015], types of well logging data, porosity logs, density logs [0016], gamma ray logs [0017], processor [0035-0036, 0038, 0058-0065, Fig. 2]).
Gevi is in the same oil and gas industry field characterizing a petroleum reservoir for the purpose of improving estimation of reserves and making decisions regarding the development of the field (see Gevi – [0002, 004-0006]) like Bukar.
Therefore, it would have been obvious to one of ordinary skill in the art at the time when invention is filed before the effective filing date of the current application to modify the teachings of Bukar in view of Gevi to use a non-transitory computer readable medium storing instructions executable by a computer processor to perform a method and acquire logs using a gamma ray logging tool and a density logging tool to accurately estimate the formation property volume and determine a location of hydrocarbons in the geological interest using the formation property volume (Bukar - an improvement in the accuracy of the prediction in hydrocarbon-bearing intervals [pg. 9 par. 1], prediction in hydrocarbon-bearing rocks [pg. 3 par. 2], rock mechanical properties for rock physics, quantitative seismic interpretation and geomechanics interpretation [pg. 1 introduction]) (Gevi - provide better volumetric estimates and can better define "sweet spots" [0033]).
However, the combined prior art is silent regarding assigning, by the computer processor and using a grouping algorithm, a plurality of subsets of the well log data to a plurality of groups based on one or more geological attributes, wherein the grouping algorithm is a k-means clustering algorithm, wherein the plurality of groups comprise a first group of the well log data based on a first subset among the plurality of subsets and a second group of the well log data based on a second subset among the plurality of subsets, and wherein the first group of the well log data corresponds to data at a different depth than the second group of the well log data and determining, by the computer processor and using the plurality of groups, a plurality of well zones for different portions of a respective well among the plurality of wells.
Gkor discloses assigning, by the computer processor and using a grouping algorithm, a plurality of subsets of the well log data to a plurality of groups based on one or more geological attributes, wherein the grouping algorithm is a k-means clustering algorithm, wherein the plurality of groups comprise a first group of the well log data based on a first subset among the plurality of subsets and a second group of the well log data based on a second subset among the plurality of subsets, and wherein the first group of the well log data corresponds to data at a different depth than the second group of the well log data (Facies of a formation are classified from data charactering properties of a portion of the formation as a function of depth [abs, 0008], classification workflow, k-means clustering, partitions logs into uniquely recognizable patterns, discriminate analysis component ‘cluster tagging’ that finds the same pattern in a different data set [0005], depth -based method, depth-by-depth basis [0011, 0050, 0055-0056, Fig. 5, 7, 8F], facies classification as a function of depth [0028-0029, Fig. 9F-9G], k-means clustering [0041], processor [0062, 0065-0066, 0072-0074, 0079, claims 16, 18, Fig. 10]) and
determining, by the computer processor and using the plurality of groups, a plurality of well zones for different portions of a respective well among the plurality of wells; (facies classification based on layer-based properties which are determined from well log data obtained from a plurality of different well logging tools [0008, 0081], processor [0062, Fig. 10], nine gas wells, well logs, clustering results for the … well [0060, Figs. 5, 9A-9F], determine the number of facies along a selected portion of the formation from input well logs, better classification, honoring of different vertical resolution of varying input logs, consistent layer boundaries with high-resolution logs, and better accuracy for formations with thin layers [0061]).
Gkor is also in the same oil and gas industry field evaluating formation and
characterizing a petroleum reservoir properties to improve the chances of success of a well like the combined prior art.
Therefore, it would have been obvious to one of ordinary skill in the art at the time when invention is filed before the effective filing date of the current application to modify the teachings of the combined prior art in view of Gkor to assig, by the computer processor and using a grouping algorithm, a plurality of subsets of the well log data to a plurality of groups based on one or more geological attributes, wherein the grouping algorithm is a k-means clustering algorithm, wherein the plurality of groups comprise a first group of the well log data based on a first subset among the plurality of subsets and a second group of the well log data based on a second subset among the plurality of subsets, and wherein the first group of the well log data corresponds to data at a different depth than the second group of the well log data and determine, by the computer processor and using the plurality of groups, a plurality of well zones for different portions of a respective well among the plurality of wells in order to accurately estimate the formation property volume and determine a location of hydrocarbons in the geological interest using the formation property volume.
Bukar further discloses
determining a plurality of predicted sonic logs for the plurality of wells using a first machine learning model and the well log data, wherein the first machine learning model uses a portion of the plurality of gamma ray logs and a portion of the plurality of density logs as inputs to an input layer of the machine-learning model (A machine learning approach to shear sonic log prediction [abs], Well logs available in the wells include … gamma ray (GR), bulk density (RHOB) … sonic log prediction [pg. 3 par. 2]).
However, Bukar is silent regarding determining interpolated log data using the well log data, the plurality of predicted sonic logs, the plurality of well zones, and an intrawell interpolation process.
AI discloses determining interpolated log data using the well log data, the plurality of well zones, and a statistical interpolation process for both intrawell and interwell (various well logs and a statistical interpolation method [abs, 0003-0005], computer processor [0003-0005], 2D … interpolation method, top depth [0028, Fig. 2], interpolation method [0033-0034, claims 1, 4, 9 and 15]).
AI is in the same oil and gas industry field evaluating the formation of the reservoir by analyzing well logs (see AI – [0001, 0003-0005) like the combined prior art.
Therefore, it would have been obvious to one of ordinary skill in the art at the time when invention is filed before the effective filing date of the current application to modify the teachings of the combined prior art in view of AI to determine, by the computer processor, interpolated log data using the well log data, the plurality of predicted sonic logs, the plurality of well zones, and an intrawell interpolation process and generate, by the computer processor, a formation property volume based on the interpolated log data and the well log data in order to accurately estimate the formation property volume without error (see AI – subjectivity and error [0002]).
Gevi further discloses
generating, by the computer processor, a formation property volume based on the well log data. (initial partition of the scaled raw log data [0032], provide better volumetric estimates and can better define "sweet spots" [0033])
Bukar discloses
determining a location of hydrocarbons in the geological region of interest using the formation property volume (hydrocarbon-bearing intervals [abs, pg. 4 par. 2], hydrocarbon-bearing rocks [pg. 2 par. 2], prediction in hydrocarbon-bearing rocks [pg. 3 par. 2], hydrocarbon-bearing zone [pg. 5 par. 4, pg. 6 par. 1, pg. 8 par. 1, Fig. 8, 9], an improvement in the accuracy of the prediction in hydrocarbon-bearing intervals [pg. 9 par. 1], rock mechanical properties for rock physics, quantitative seismic interpretation and geomechanics interpretation [pg. 1 introduction]).
As per claims 9 and 17, Bukar, Gevi, Gkor and AI disclose claims 1 and 10 set forth above.
Gevirtz discloses the formation property volume corresponds to a three-dimensional region that describes a plurality of geological properties, (produces a 3 dimensional realization of the distribution of log-defined rock types [0030]).
AI discloses the plurality of geological properties are selected from a group consisting of porosity, permittivity, resistivity, density, and water saturation, (porosity, permeability, resistivity, water saturation [0016, Fig. 1]) and
the formation property volume is used to perform hydrocarbon exploration within the geological region of interest. (formation top predictions, hydrocarbon exploration [0001], resistivity may be indicative of the porosity of the formation and the presence of hydrocarbons. More specifically, resistivity may be relatively low for a formation that has high porosity and a large amount of water, and resistivity may be relatively high for a formation that has low porosity or includes a large amount of hydrocarbons [0016, Fig. 1).
Claims 2, 4-5, 11-13 and 19-20 are rejected under 35 U.S.C. 103 as being unpatentable over Bukar, Gevi, Gkor and AI in view of Akkurt (US 20210110280 A1).
As per claims 2, 11 and 19, Bukar, Gevi, Gkor and AI disclose claims 1, 10 and 18 set forth above.
Gevi discloses (predict the rock types [0030], predict the well performance [0033], earth model, earth modeling [0042]).
AI discloses (precited depth [0001], depth of … formation tops … through well log data [0027, 0031, 0049], reservoir model [0018, 0025], a prediction surface map [0034]).
However, the set forth combined prior art is not explicit on determining predicted log data for a predetermined well using a second machine-learning model.
Akkurt discloses determining predicted log data for a predetermined well using a machine-learning model, wherein the predicted log data corresponds to a predetermined well log type that is missing for the predetermined well in the well log data (using Machine Learning ‘ML’ algorithms [0053], machine learning techniques , neural network ‘NN’, [0068], Given an ML model, predict the formation properties, for the test wells [0153, Fig. 3A 312], showing predicted logs, three tracks, the predictions are quantitative and have the accuracy/precision expected from logs [0153, Fig, 3B]).
Therefore, it would have been obvious to one of ordinary skill in the art at the time when invention is filed before the effective filing date of the current application to modify the teachings of the combined prior art in view of Akkurt to determine predicted log data for a predetermined well among the plurality of wells using a second machine-learning model, wherein the predicted log data corresponds to a predetermined well log type that is missing for the predetermined well among in the well log data, and wherein the formation property volume is based on the predicted log data in order to accurately estimate the formation property volume without error.
As per claims 4, 12 and 20, Bukar, Gevi, Gkor and AI disclose claims 1, 10 and 18 set forth above.
The set forth combined prior art is silent regarding determining extrapolated log data using the well log data, the plurality of well zones, and an interwell extrapolation process.
Akkurt discloses determining extrapolated log data using the well log data, the plurality of well zones, and an interwell extrapolation, wherein the extrapolated log data corresponds to one or more interwell regions between a first well and a second well among the plurality of wells (interwell [0195], extrapolation … to the new well data [0197]).
Therefore, it would have been obvious to one of ordinary skill in the art at the time when invention is filed before the effective filing date of the current application to modify the teachings of the combined prior art in view of Akkurt to determine extrapolated log data using the well log data, the plurality of well zones, and an interwell extrapolation process, wherein the extrapolated log data corresponds to one or more interwell regions between a first well and a second well among the plurality of wells, and wherein the formation property volume is based on the extrapolated log data in order to accurately estimate the formation property volume without error.
As per claims 5 and 13, Gevirtz, AI and Akkurt disclose claims 4 and 12 set forth above.
Akkurt discloses obtaining seismic data regarding the geological region of interest,
wherein the interwell extrapolation process uses the seismic data to determine the extrapolated log data (interwell [0195], extrapolation … to the new well data, training data set [0197])
Claims 3 and 15 are rejected under 35 U.S.C. 103 as being unpatentable over Bukar, Gevi, Gkor and AI in view of Akkurt and Anderson (US 20200334577 A1).
As per claims 3 and 15, Bukar, Gevi, Gkor, AI and Akkurt disclose claims 2 and Bukar, Gevi, Gkor and AI disclose claim 10 set forth above.
Akkurt discloses using Deep Neural Network for the prediction of sonic logs (Deep Learning ‘an advanced version of Neural networks [0219], LWD logs … sonic [0144], bad sonic log, predict sonic logs [0158, Fig. 4]).
However, the set forth combined prior art is silent regarding log data related to gamma-ray (GR) log data, true vertical depth (TVD) data, northing data, easting data, Poisson's ratio data, and Young's modulus data.
Anderson discloses use of a deep neural network processing well log including TVD, gamma ray, northing, easting (neural network, deep learning network [0004, 0006, 0008, 0015, 0025, Fig, 14C], drilling data includes TVD, gamma ray, northing, easting [0091]) and the machine learning model provides poison’s ratio and young’s module (rock property measurements including poisons ratio, young's module [0043]).
Therefore, it would have been obvious to one of ordinary skill in the art at the time when invention is filed before the effective filing date of the current application to modify the teachings of the combined prior art in view of Anderson to use a deep neural network as the first machine learning model having gamma-ray (GR) log data, true vertical depth (TVD) data, northing data, and easting data as inputs to the input layer of the deep neural network and providing sonic log data, Poisson's ratio data, and Young's modulus data in order to accurately estimate the formation property volume without error.
Claim 7 is rejected under 35 U.S.C. 103 as being unpatentable over Bukar, Gevi, Gkor and AI in view of Guy (US 20170177764 A1).
As per claim 7, Bukar, Gevi, Gkor and AI disclose claim 1 set forth above.
Gevirtz discloses the one or more geological attributes are selected from a group consisting of a facies classification, a stratigraphy horizon (selection of the type well is based on an analysis of stratigraphic intervals [0019], a stratigraphy in a section of interest [0050, 0060, claims 3 and 12], features, lithofacies classification [0027]), but is silent regarding a group consisting a geological time period.
Guy discloses geological attributes of a region consisting time periods (geo-modelling step, meshed representation, petrophysical properties, facies, based on data acquired seismic campaigns [0012], the meshed representation constructed … for different time periods [0013]).
Therefore, it would have been obvious to one of ordinary skill in the art at the time when invention is filed before the effective filing date of the current application to modify the teachings of the combined prior art in view of Guy to select the one or more geological attributes from a group consisting of a facies classification, a stratigraphy horizon, and a geological time period in order to accurately estimate the formation property volume without error.
Claims 8 and 16 are rejected under 35 U.S.C. 103 as being unpatentable over Bukar, Gevi, Gkor and AI in view of Guevara Diaz (US 20200150305 A1), hereinafter ‘GD’.
As per claims 8 and 16, Bukar, Gevi, Gkor and AI disclose claims 1 and 10 set forth above.
The set forth combined prior art is silent regarding a vertical smoothing and a horizontal smoothing for the well logs of a well zone corresponding to a predetermined depth range.
GD discloses smoothing vertical and horizontal well logs at different depth zone (measurements … actual depths, measured depths [0029-0030], horizontal well logs, vertical well logs, performing a down sampling of a smoothing approximation of the physical measurements to obtain representative values across the path of the well [0030]).
Therefore, it would have been obvious to one of ordinary skill in the art at the time when invention is filed before the effective filing date of the current application to modify the teachings of the combined prior art in view of GD to perform a vertical and a horizontal smoothing to the well logs of a well zone corresponding to a predetermined depth range
in order to accurately estimate the formation property volume without error.
Notes with regard to Prior Art
The prior arts made of record below are considered pertinent to applicant's disclosure.
Al Tammar (US20230003118A1) discloses predicting sonic well logs from gamma ray logs and density logs using machine learning model ([0015—0017, 0027, 0032-0033, 0036, Fig. 2, 0040-0055]).
Onalo (Onalo, David, and et al. "Data driven model for sonic well log prediction." Journal of Petroleum Science and Engineering 170 (2018): 1022-1037) discloses sonic well logs from gamma ray logs and density logs using machine learning model (ANN model, input data … well log data, output data … sonic well log data [pg. 1027, table 2, Fig. 2], 4.1 input parameters … gamma ray log ‘GR’, bulk density log ‘RHOB’, 4.2 output parameters … sonic transit time log [pg. 1027 right col 4.1 and 4.2])
Li (US 20180347354 A1) discloses use of K-means clustering algorithm (rock samples from different locations and depth across a single geologic basin [0068], samples are clustered, Maturity of a new sample is then predicted, K-means clustering algorithm [0097, Figs, 16A and B]).
Horne (US 20200319360 A1) discloses smoothing technique for the vertical and horizontal well logs (seismic wave … measured … for each depth [0070, 0076], smoothing technique, log data, horizontal section, vertical wellbore, combination of data set [0098]).
Al-Ali (Al-Ali, Ali, and et al. "Applications of artificial neural network for seismic facies classification: A case study from the mid-Cretaceous reservoir in supergiant oil field." In SPE Europec featured at EAGE Conference and Exhibition?, p. D031S024R001. SPE, 2020) discloses characterization and evaluation of a reservoir using facies classification. Multilayer feed forward network (MLFN) and probabilistic neural network (PNN) as Artificial Neural Network models were undertaken to classify facies and its distribution. (see [abs]).
Conclusion
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.
Contact Information
Any inquiry concerning this communication or earlier communications from the examiner should be directed to DOUGLAS KAY whose telephone number is (408) 918-7569. The examiner can normally be reached on M, Th & F 8-5, T 2-7, and W 8-1.
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/DOUGLAS KAY/Primary Examiner, Art Unit 2857
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