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 .
DETAILED ACTION
1. Claims 1-20 are presented for examination.
Claim Objections
2. Claim 9 is objected to because of the following informalities:
As per Claim 9, it recites the limitation "the binary logistic regression equation" lacks proper antecedent basis, because parent claim 8 recites "a binary logistic regression algorithm" and does not recite a "binary logistic regression equation." For examination purposes, it is interpreted as the binary logistic regression algorithm of claim 8.
Appropriate correction is required.
Claim Interpretation
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 following is a quotation of pre-AIA 35 U.S.C. 112, sixth paragraph:
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.
3. 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) or pre-AIA 35 U.S.C. 112, sixth paragraph, is invoked.
Claim 17 recites “A system for evaluating permeability of rock extracted from a rock formation and for predicting a type of fluid flow through the rock formation, the system comprising: a first device configured to …; at least one data source of a plurality of data sources, wherein the data source is configured to …; and at least a second device configured to …”.
A review of the specification shows that the following appears to be the corresponding structure described in the specification for the 35 U.S.C. 112(f) or 35 U.S.C. 112 (pre-AIA ), sixth paragraph limitation:
Specification paragraph [0026]-[0028] of PG PUB states:
[0026] Referring to FIG. 1, a block diagram illustrating an example material permeability prediction system is shown as material permeability prediction system 100. Material permeability prediction system 100 includes vug analysis device 110, at least one user device 130, a plurality of data sources 160, 170, 180, and network 150. Although only one user device 130 is shown in FIG. 1, it is understood that material permeability prediction system 100 may include a plurality of user devices such as user device 130.
[0027] Vug analysis device 110 may be configured to generate vug connectedness predictions based on a set of input data, such as input data 190. Vug analysis device 110 may include one or more processors 112, memory 114, modelling engine 120, communication interface 122, and I/O device 124. Each of the one or more processors 112 may be a microprocessor, a graphical processing unit (GPU), one or more field programmable gate arrays (FPGAs), a microcontroller, and/or an application specific integrated circuit (ASIC) or other logic circuitry configured to perform the operations described herein with reference to vug analysis device 110.
[0028] Memory 114 may include a random access memory (RAM), which can be synchronous RAM (SRAM), dynamic RAM (DRAM), synchronous dynamic RAM (SDRAM), or the like. Memory 114 may also include read only memory (ROM), which can be programmable read only member (PROM), erasable programmable read only member (EPROM), electrically erasable programmable read only memory (EEPROM), optical storage, or the like. Additionally, memory 114 may include hard disk drives (HDDs), solid state disk drives (SSDs), and other memory devices configured to store data in a persistent or a non-persistent state. Memory 114 may be configured to store instructions 116 and database 118. Instructions 116 may be comprised of computer-readable code that, when executed by the one or more processors 112, cause the one or more processors 112 to perform the functionality described herein with respect to vug analysis device 110. Database 118 may be configured to store information. For example, database 118 may be configured to store input data 190 and/or modelled rock property data 126.
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) or pre-AIA 35 U.S.C. 112, sixth paragraph, 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.
Claim Rejections - 35 USC § 112
The following is a quotation of 35 U.S.C. 112(b):
(b) CONCLUSION.—The specification shall conclude with one or more claims particularly pointing out and distinctly claiming the subject matter which the inventor or a joint inventor regards as the invention.
The following is a quotation of 35 U.S.C. 112 (pre-AIA ), second paragraph:
The specification shall conclude with one or more claims particularly pointing out and distinctly claiming the subject matter which the applicant regards as his invention.
4. Claim 10-16 and 20 are rejected under 35 U.S.C. 112(b) or 35 U.S.C. 112 (pre-AIA ), second paragraph, as being indefinite for failing to particularly point out and distinctly claim the subject matter which the inventor or a joint inventor (or for applications subject to pre-AIA 35 U.S.C. 112, the applicant), regards as the invention.
As per Claim 10, it recites the limitation “based, at least in part, on the vug attribute parameters;” in line 7-8 and “based, at least in part, on the model.” in line 10. The limitation “at least in part” is used as a coordinating conjunction to show an alternative between features which separates two distinct options indicating that a choice must be made between them. But there is no other alternative feature claimed for “at least in part” alternative limitation.
As per Claim 20, it recites the limitation “based, at least in part, on the partially processed data.” in line 9-10. The limitation “at least in part” is used as a coordinating conjunction to show an alternative between features which separates two distinct options indicating that a choice must be made between them. But there is no other alternative feature claimed for “at least in part” alternative limitation.
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.
5. Claims 1, 3, 5, 6, 17, and 18 are rejected under 35 U.S.C. 102(a)(1) as being anticipated by Boitnott (US 2005/0256643 A1).
As per Claim 1, Boitnott discloses a non-transitory computer-readable medium storing instructions that, when executed by one or more processors, cause the one or more processors to perform operations for evaluating permeability of a material ([0015], [0025], [0061] "the computer readable medium includes logic operable to cause the computer to execute steps as set forth above"; "the computer includes a central processing unit 60"), comprising:
compiling data associated with one or more characteristics of the material ([0062] "a database may be accessed by the computer"; "the raw data may include images of thin sections, measurements of various petrophysical parameters, as well as measurements of MICP behavior for selected Earth formations": measured petrophysical data characterizing the material is compiled from the database);
deriving vug attribute parameters from the data ([0058] "vug porosity may be added to an initial model", "Vugs may be represented by randomly distributed inclusions having a distribution of shapes which contribute to the material properties"; [0059] "petrophysical parameters are calculated using, for example, the methods discussed with reference to equations": per-vug shape and porosity attribute parameters are derived and calculated from the data);
executing a modelling engine against the vug attribute parameters ([0059] "differences between the calculated parameter and measured petrophysical parameters are determined, and whether such differences have reached a minimum", "If both minimum difference and maximum entropy are determined to exist, the model is finalized": an iterative inversion engine is executed against the vug-bearing model until the model is finalized);
predicting the permeability of the material based on the execution of the modelling engine against the vug attribute parameters ([0064] "Models identified as relevant may be applied to predict possible contrasts (e.g., applying a model which predicts mercury injection curves based on porosity and permeability)": permeability is predicted from the finalized model); and
displaying information associated with the permeability of the material at a graphical user interface (GUI) ([0061] "a user display 64 such as a flat panel LCD display or cathode ray tube display": the modeling results are presented to the user at the user display).
As per Claim 3, Boitnott discloses wherein compiling the data associated with one or more characteristics of the material comprises receiving the data from at least one of a database and one or more sensors disposed within the material ([0062] "a database may be accessed by the computer": the data is received from a database, satisfying the "at least one of" recitation), and wherein the data comprises physical data and chemical data corresponding to the material ([0062] "the raw data may include images of thin sections, measurements of various petrophysical parameters"; [0065] "a rheological model for the matrix material, either based on mineralogy (chemical composition of the matrix)": the petrophysical measurements are physical data and the mineralogy is chemical data corresponding to the material).
As per Claim 5, Boitnott discloses wherein the vug parameters comprise at least one of a mean vug diameter, a representative vug shape, a mean quantity of vugs present in the material, and a vug aspect ratio ([0058] "Vugs may be represented, in some embodiments, by inclusions of a third geometric shape (which in some embodiments may be spherical)"; "the fraction of vug porosity and the compressibility of the vugs will be affected both by the total amount of vug porosity and by the average vug shape distribution": a representative vug shape is among the recited vug parameters, satisfying the "at least one of" recitation).
As per Claim 6, Boitnott discloses wherein the representative vug shape comprises at least one of an oblate ellipsoid, a prolate ellipsoid, and a sphere ([0051], [0054], [0058] "inclusions of a third geometric shape (which in some embodiments may be spherical)": the representative vug shape is a sphere, satisfying the "at least one of" recitation), and wherein the vug aspect ratio comprises a ratio of an equatorial diameter of the representative vug shape to a polar diameter of the representative vug shape ([0051], [0054], [0058] "Example of other shapes include ellipsoids having three distinct average axial lengths, a, b, c. Such ellipsoids may also be expressed with respect to one axial length and two aspect ratios": Examiner's Note – the vug shape is expressed as an ellipsoid characterized by an aspect ratio formed from its axial lengths, which corresponds to the claimed ratio of an equatorial diameter to a polar diameter of the representative vug shape).
As per Claim 17, Boitnott discloses a system for evaluating permeability of rock extracted from a rock formation and for predicting a type of fluid flow through the rock formation ([0025], [0064] "applying a model which predicts mercury injection curves based on porosity and permeability"), comprising:
a first device configured to generate modelled rock property data comprising a probability that the rock is permeable, the first device configured to generate the modelled rock property data based on vug attribute parameters derived from data corresponding to characteristics of the rock and of the rock formation ([0059] "petrophysical parameters are calculated using, for example, the methods discussed with reference to equations"; [0061] "a general purpose programmable computer"; "the computer includes a central processing unit 60": the programmable computer generates modelled rock-property data from the vug-derived parameters);
at least one data source of a plurality of data sources, wherein the data source is configured to provide the data to the first device ([0061], [0062] "a database may be accessed by the computer"; "the raw data may include images of thin sections, measurements of various petrophysical parameters": the database is a data source providing data to the computer); and
at least a second device configured to receive the modelled rock property data and to display the modelled rock property data ([0061] "a user display 64 such as a flat panel LCD display or cathode ray tube display": the user display receives and displays the modelled rock-property data).
As per Claim 18, Boitnott discloses wherein generating the modelled rock property data further comprises: deriving a representative vug shape based on one of the data and the vug attribute parameters; and using the representative vug shape to model vugs comprising the rock formation ([0058] "Vugs may be represented, in some embodiments, by inclusions of a third geometric shape (which in some embodiments may be spherical)"; "Vugs may be represented by randomly distributed inclusions having a distribution of shapes which contribute to the material properties as described by models"; [0064]: a representative geometric vug shape is derived and used to model the vug porosity in the pore-structure model).
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.
The factual inquiries for establishing a background for determining obviousness under 35 U.S.C. 103 are summarized as follows:
1. Determining the scope and contents of the prior art.
2. Ascertaining the differences between the prior art and the claims at issue.
3. Resolving the level of ordinary skill in the pertinent art.
4. Considering objective evidence present in the application indicating obviousness or nonobviousness.
This application currently names joint inventors. In considering patentability of the claims the examiner presumes that the subject matter of the various claims was commonly owned as of the effective filing date of the claimed invention(s) absent any evidence to the contrary. Applicant is advised of the obligation under 37 CFR 1.56 to point out the inventor and effective filing dates of each claim that was not commonly owned as of the effective filing date of the later invention in order for the examiner to consider the applicability of 35 U.S.C. 102(b)(2)(C) for any potential 35 U.S.C. 102(a)(2) prior art against the later invention.
6. Claim 2 is rejected under 35 U.S.C. 103 as being unpatentable over Boitnott (US 2005/0256643 A1) in view of Yamada (US 2016/0155021 A1).
Boitnott teaches most all the instant invention as applied to claims 1, 3, 5, 6, 17, and 18 above.
As per Claim 2, Boitnott teaches the non-transitory computer-readable medium of claim 1, wherein the material comprises rocks of a rock formation ([0058] "Methods according to the invention include generating an initial pore structure model of a porous Earth formation").
However, Boitnott fails to teach explicitly wherein predicting the permeability of the material based on the execution of the modelling engine against the vug attribute parameters comprises generating a vug connectedness probability indicative of a material permeability probability.
Yamada teaches wherein predicting the permeability of the material based on the execution of the modelling engine against the vug attribute parameters comprises generating a vug connectedness probability indicative of a material permeability probability ([0040] "the geometry and electrical properties of each heterogeneous feature, and the intensity of their connectedness, may thus be characterized"; [0043] "Size, contrast, and surface proportion of each spot/heterogeneity category may be computed and represented as curves": the intensity of vug connectedness is characterized as a quantitative measure indicative of the permeability contribution of the vugs). In particular, Yamada teaches delineating vug heterogeneities from borehole-image data, computing their size, geometry, and surface proportion, and characterizing the intensity of their connectedness as a measure indicative of the permeability of the formation.
Boitnott and Yamada are analogous art because they are both related to determining the permeability of vuggy carbonate formations.
It would have been obvious to one having ordinary skill in the art before the effective filing date of the claimed invention to combine the teachings of cited references. Thus, one of ordinary skill in the art before the effective filing date of the claimed invention would have been motivated to incorporate Yamada into Boitnott's invention for the purpose of modeling a pore structure of a porous material to provide classification of conductive features, such as vugs, into connected features and isolated features for accurate prediction the production potential (Yamada: [0026], [0028]).
7. Claim 4 is rejected under 35 U.S.C. 103 as being unpatentable over Boitnott (US 2005/0256643 A1) in view of Freedman (US 5,486,762 A).
Boitnott teaches most all the instant invention as applied to claims 1, 3, 5, 6, 17, and 18 above.
As per Claim 4, Boitnott as modified fails to teach explicitly wherein the data comprises nuclear magnetic resonance (NRM) spectroscopy logs, computerized tomography (CT) logs of the material, or both.
Freedman teaches wherein the data comprises nuclear magnetic resonance (NRM) spectroscopy logs, computerized tomography (CT) logs of the material, or both (col. 14, lines 10-26 " a nuclear magnetic resonance (NMR) logging system is illustrated, the logging system including a NMR logging tool 13 disposed in a wellbore and surface equipment 7 in the form of a well logging truck 7 situated on the surface of the wellbore, the well logging truck 7 including a processing system 7a in the form of a computer 7a situated within the well logging").
In particular, Freedman teaches a nuclear magnetic resonance logging system that acquires NMR measurements of a formation traversed by a wellbore.
Boitnott and Freedman are analogous art because they are both related to well-log measurement of subsurface formation properties.
It would have been obvious to one having ordinary skill in the art before the effective filing date of the claimed invention to combine the teachings of cited references. Thus, one of ordinary skill in the art before the effective filing date of the claimed invention would have been motivated to incorporate Freedman into Boitnott's invention for the purpose of modeling a pore structure of a porous material to provide a nuclear magnetic resonance (NMR) logging tool disposed in a wellbore for processing signals transmitted uphole by the logging tool for better data quality (Freedman: col. 14, lines 10-26; col. 54 lines 19-30).
8. Claims 7, 8, and 9 are rejected under 35 U.S.C. 103 as being unpatentable over Boitnott (US 2005/0256643 A1) in view of AlBahrani (US 2020/0018160 A1).
Boitnott teaches most all the instant invention as applied to claims 1, 3, 5, 6, 17, and 18 above.
As per Claim 7, Boitnott fails to teach explicitly wherein executing the modelling engine against the vug attribute parameters comprises performing a statistical analysis based on the vug attribute parameters.
AlBahrani teaches wherein executing the modelling engine against the vug attribute parameters comprises performing a statistical analysis based on the vug attribute parameters ([0056] "a correlation model is generated by applying statistical analysis, such as a multivariate linear regression, to the post-drilling parameters included in each of the groups": a statistical analysis is performed over the attribute data (i.e., the "vug attribute parameters" as claimed). In particular, AlBahrani teaches a correlation model that employs statistical analysis methods, including regression, over formation and rock-property attribute data.
Boitnott and AlBahrani are analogous art because they are both related to data-driven modeling of subsurface formation and rock properties.
It would have been obvious to one having ordinary skill in the art before the effective filing date of the claimed invention to combine the teachings of cited references. Thus, one of ordinary skill in the art before the effective filing date of the claimed invention would have been motivated to incorporate AlBahrani into Boitnott's invention for the purpose of modeling a pore structure of a porous material to provide a correlation model generated by applying a statistical analysis, such as a multivariate linear regression, to the post-drilling parameters for a more accurate prediction (AlBahrani: [0051], [0056]).
As per Claim 8, Boitnott as modified by AlBahrani fails to teach explicitly wherein the statistical analysis comprises a binary logistic regression algorithm, and wherein the vug attribute parameters are inputs to a binary logistic regression algorithm.
AlBahrani teaches wherein the statistical analysis comprises a binary logistic regression algorithm, and wherein the vug attribute parameters are inputs to a binary logistic regression algorithm ([0043] "the correlation model includes a logistic regression that is used to estimate discrete values, such as binary values that are based on a set of independent variables"; "Example binary values for the algorithm may include 0/1, failure/no failure": a logistic regression over binary values, namely a binary logistic regression, is performed using the attribute parameters as the independent-variable inputs).
As per Claim 9, Boitnott as modified by AlBahrani fails to teach explicitly wherein executing the modelling engine against the vug attribute parameters further comprises training the binary logistic regression equation by providing a database of vug attribute parameters as inputs to the binary logistic regression algorithm to enhance an accuracy of weights corresponding to coefficients associated with the vug attribute parameters.
AlBahrani teaches wherein executing the modelling engine against the vug attribute parameters further comprises training the binary logistic regression equation by providing a database of vug attribute parameters as inputs to the binary logistic regression algorithm to enhance an accuracy of weights corresponding to coefficients associated with the vug attribute parameters ([0043] "a logistic regression that is used to estimate discrete values, such as binary values that are based on a set of independent variables": the logistic regression is trained over the input attribute parameters to estimate the regression coefficients associated with those parameters).
9. Claims 19 and 20 are rejected under 35 U.S.C. 103 as being unpatentable over Boitnott (US 2005/0256643 A1) in view of Nettleton (US 2014/0149039 A1).
Boitnott teaches most all the instant invention as applied to claims 1, 3, 5, 6, 17, and 18 above.
As per Claim 19, Boitnott teaches wherein the at least one data source comprises a database of characteristics corresponding to the rock and to the rock formation (Boitnott: [0062] "a database may be accessed by the computer"; "the raw data may include images of thin sections, measurements of various petrophysical parameters": the database stores rock-characteristic data corresponding to the rock formation).
However, Boitnott fails to teach explicitly wherein at least a second data source of the plurality of data sources comprises an autonomous vehicle configured to receive the data.
Nettleton teaches wherein at least a second data source of the plurality of data sources comprises an autonomous vehicle configured to receive the data ([0037] "scanning sensors 28 mounted on mobile platforms 30"; "Each mobile platform 30 is in the form of an autonomous vehicle which is able to be dispatched by the computing system 18 to a part of the mine site 16": an autonomous vehicle carrying sensors is a second data source that acquires the data). In particular, Nettleton teaches scanning sensors mounted on mobile platforms in the form of autonomous vehicles dispatched by a computing system to acquire survey data of a region.
Boitnott and Nettleton are analogous art because they are both related to acquisition of survey data for subsurface characterization.
It would have been obvious to one having ordinary skill in the art before the effective filing date of the claimed invention to combine the teachings of cited references. Thus, one of ordinary skill in the art before the effective filing date of the claimed invention would have been motivated to incorporate Nettleton into Boitnott's invention for the purpose of modeling a pore structure of a porous material to provide scanning sensors mounted on mobile platforms in the form of an autonomous vehicle dispatched by the computing system to update the survey data accurately (Nettleton: [0037], [0041]).
As per Claim 20, Boitnott teaches wherein the first device is further configured to generate the modelled rock property data based, at least in part, on the partially processed data (Boitnott: [0064] "Models identified as relevant may be applied to predict possible contrasts": the first device, namely the computer, generates the modelled rock-property data from the data supplied to it).
However, Boitnott fails to teach explicitly wherein the at least the second device is further configured to: receive the data; partially process the data to generate partially processed data; and send the partially processed data to the first device.
Nettleton teaches wherein the at least the second device is further configured to: receive the data; partially process the data to generate partially processed data; and send the partially processed data to the first device ([0048] "the computing system 18 also receives information from the mobile mining machinery operating in the mine site 16"; [0053] "the survey data generated by the sensors 26, 28 are transmitted to the computing system 18 which uses the updated Survey data to update the terrain model 14": the mobile autonomous platform receives, partially processes, and transmits the survey data to the computing system).
10. Claims 10, 11, 12, 15, and 16 are rejected under 35 U.S.C. 103 as being unpatentable over Boitnott (US 2005/0256643 A1) in view of Yamada (US 2016/0155021 A1).
As per Claim 10, Boitnott teaches a device for evaluating permeability of rock extracted from a rock formation ([0015], [0025], [0061]), the device comprising:
a memory configured to store instructions and data associated with one or more characteristics of the rock ([0015], [0025], [0061]-[0061] "computer-readable code stored on a computer readable medium, such as magnetic hard drive 66"; "the computer includes a central processing unit 60": a magnetic hard drive stores the program instructions and the rock-characteristic data); and
a processor configured to execute the instructions to: derive vug attribute parameters from the data ([0058] "vug porosity may be added to an initial model", "Vugs may be represented by randomly distributed inclusions having a distribution of shapes which contribute to the material properties"; [0059] "petrophysical parameters are calculated using, for example, the methods discussed with reference to equations": per-vug shape and porosity attribute parameters are derived and calculated from the data); and
generate a model of vugs comprising the rock formation based, at least in part, on the vug attribute parameters ([0058] "vug porosity may be added to an initial model"; [0064] "applying a model which predicts mercury injection curves based on porosity and permeability": the processor builds a pore-structure model incorporating the vug inclusions).
However, Boitnott fails to teach explicitly generate modelled rock property data that includes a probability indicative of vug connectedness based, at least in part, on the model.
Yamada teaches generate modelled rock property data that includes a probability indicative of vug connectedness based, at least in part, on the model (Fig. 9, [0054] "if the delineated conductive heterogeneous feature is bridged to another one by a crest line, it may be classified as connected"; [0040] "the intensity of their connectedness, may thus be characterized": modelled rock-property data is produced that includes a connectedness classification of the modelled vugs). In particular, Yamada teaches delineating vug heterogeneities from borehole-image data, computing their size, geometry, and surface proportion, and characterizing the intensity of their connectedness as a measure indicative of the permeability of the formation.
Boitnott and Yamada are analogous art because they are both related to determining the permeability of vuggy carbonate formations.
It would have been obvious to one having ordinary skill in the art before the effective filing date of the claimed invention to combine the teachings of cited references. Thus, one of ordinary skill in the art before the effective filing date of the claimed invention would have been motivated to incorporate Yamada into Boitnott's invention for the purpose of modeling a pore structure of a porous material to provide classification of conductive features, such as vugs, into connected features and isolated features for accurate prediction the production potential (Yamada: [0026], [0028]).
As per Claim 11, Boitnott teaches wherein the processor is further configured to render the modelled rock property data on a graphical user interface (GUI), and wherein the device further comprises a display configured to render the GUI ([0061] "a user display 64 such as a flat panel LCD display or cathode ray tube display": the modelled rock-property data is rendered at the user display).
As per Claim 12, Boitnott teaches wherein the processor is further configured to generate secondary vug attribute parameters based on the vug attribute parameters, wherein the secondary vug attribute parameters comprises a quantity of vugs present within a rock formation, and wherein the vug attribute parameters comprises a distribution of vug diameters of vugs present within the rock formation ([0070] "may also be used to identify and quantify non-network porosity, such as vugs, molds, fractures, etc."; [0058] "the fraction of vug porosity and the compressibility of the vugs will be affected both by the total amount of vug porosity and by the average vug shape distribution": vugs are quantified and a distribution of vug shapes and sizes is generated as a secondary parameter derived from the vug attribute parameters).
As per Claim 15, Boitnott fails to teach explicitly wherein the probability indicative of vug connectedness indicates one of a vug passageway and an isolated vug.
Yamada teaches wherein the probability indicative of vug connectedness indicates one of a vug passageway and an isolated vug ([0054] "if the delineated conductive heterogeneous feature is bridged to another one by a crest line, it may be classified as connected": each vug is indicated as connected, namely a passageway, or as isolated).
As per Claim 16, Boitnott teaches further comprising a communication interface configured to send the modelled rock property data to a user device, and wherein the user device is configured to display the modelled rock property data to a user associated with the user device ([0061] "the computer includes a central processing unit 60, a user input device such as a keyboard 62 and a user display 64"; [0072] "Image processing software known in the art may be used to enable the user to specify any one or more image derived properties": the computer communicates the modelled rock-property data to the user display device).
11. Claims 13 and 14 are rejected under 35 U.S.C. 103 as being unpatentable over Boitnott (US 2005/0256643 A1) in view of Yamada (US 2016/0155021 A1), and further in view of AlBahrani (US 2020/0018160 A1).
Boitnott as modified by Yamada teaches most all the instant invention as applied to claims 10, 11, 12, 15, and 16 above.
As per Claim 13, Boitnott as modified by Yamada fails to teach explicitly wherein generating the model of the vugs comprises performing an ergodic analysis of the vugs based, at least in part, on the vug parameter data and on the secondary vug parameter data.
AlBahrani teaches wherein generating the model of the vugs comprises performing an ergodic analysis of the vugs based, at least in part, on the vug parameter data and on the secondary vug parameter data ([0056] "a correlation model is generated by applying statistical analysis, such as a multivariate linear regression, to the post-drilling parameters included in each of the groups"; [0043] "a logistic regression that is used to estimate discrete values, such as binary values"). In particular, AlBahrani teaches performing statistical and regression analyses over stored attribute data, applied here to the vug parameter data and the secondary vug parameter data.
Boitnott, Yamada, and AlBahrani are analogous art because they are all related to data-driven modeling of subsurface formation and rock properties.
It would have been obvious to one having ordinary skill in the art before the effective filing date of the claimed invention to combine the teachings of cited references. Thus, one of ordinary skill in the art before the effective filing date of the claimed invention would have been motivated to incorporate AlBahrani into Boitnott as modified by Yamada's invention for the purpose of modeling a pore structure of a porous material to provide classification of conductive features, such as vugs, into connected features and isolated features for accurate prediction the production potential (Yamada: [0026], [0028]) and to provide a correlation model generated by applying a statistical analysis, such as a multivariate linear regression, to the post-drilling parameters for a more accurate prediction (AlBahrani: [0051], [0056]).
As per Claim 14, Boitnott as modified by Yamada fails to teach explicitly wherein the ergodic analysis comprises at least one of binary logistic regression, log-binomial regression, Poisson regression, Poisson regression with a robust variance estimator, and/or Cox regression.
AlBahrani teaches wherein the ergodic analysis comprises at least one of binary logistic regression, log-binomial regression, Poisson regression, Poisson regression with a robust variance estimator, and/or Cox regression ([0043] "the correlation model includes a logistic regression that is used to estimate discrete values, such as binary values").
Conclusion
12. The prior art made of record and not relied upon is considered pertinent to applicant's disclosure:
Seleznev (US 2007/0061082 A1) teaches modeling formation pores as oblate or prolate spheroids characterized by an aspect ratio defined as the ratio of the long to the short axis.
De Stefano (US 2016/0086079 A1) teaches outfitting a geologic environment with sensors, detectors, and actuators that receive and transmit information regarding the formation.
Hurley (US 2012/0221306 A1) teaches a multiscale digital-rock workflow that models vugs as a petrophysical facies and computes permeability from flow simulation.
Anderson (US 2017/0364795 A1) teaches machine-learning classification of rock and production attributes using logistic regression with machine-learned importance weights.
Rasoulzadeh ("Effective Permeability of a Porous Medium with Spherical and Spheroidal Vug and Fracture Inclusions") teaches representing vugs as spherical and spheroidal inclusions characterized by aspect ratio for effective-permeability determination.
Ausbrooks ("Pore-Size Distributions in Vuggy Carbonates From Core Images, NMR, and Capillary Pressure") teaches building a distribution of vug sizes from core-image analysis calibrated to NMR.
Miles (US 2008/0125920 A1) teaches an autonomous unmanned aerial vehicle for acquiring geophysical survey data and transmitting it to a remote ground station.
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EUNHEE KIM
Primary Examiner
Art Unit 2188
/EUNHEE KIM/Primary Examiner, Art Unit 2188
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