DETAILED ACTION
This action is responsive to the claims filed on July 28, 2023. Claims 1-20 are under examination.
Claims 1-20 are rejected under 35 USC 101 as ineligible.
Claims 1-20 are rejected under 35 USC 103 as obvious over Kowalska in view of Egozcue.
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 .
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 1-20 are rejected under 35 U.S.C. 101 because the claimed subject matter is directed to an abstract idea without significantly more. The claims recite mental processes that are capable of being performed in the mind and/or with the aid of pen and paper and are also mathematical concepts, abstract ideas.
Independent Claims
Claim 1 (Statutory Category – Machine)
Step 2A – Prong 1: Judicial Exception Recited?
Yes, the claims recite mental processes, which are abstract ideas.
Claim 1 recites:
generate decorrelated well information based on transformation of the well information, the decorrelated well information characterizing the subsurface configuration of the well within the region of interest using decorrelated subsurface properties, wherein the decorrelated subsurface properties are not correlated; and (Mental Process, Mathematical Concept – The generation of decorrelated well data based on transformation of well information is practically performable in the mind or with the aid of pen and paper, so it is an evaluation, a mental process, an abstract idea. Further, the recitation of generation of decorrelated well information based on transformation of well information is a mathematical operation in textual form, so this is a mathematical concept, an abstract idea. See the Applicant’s specification [0067]-[0069] and [0074]-[0081] illustrating that only mathematical transformations were disclosed by the inventor)
generate a subsurface representation for the region of interest based on the decorrelated subsurface properties, wherein the subsurface representation honors a mineralogy proportions constraint on the subsurface properties and correlations between the subsurface properties. (Mental Process, Mathematical Concept – The generation of a subsurface representation based on decorrelated subsurface properties is practically performable in the mind or with the aid of pen and paper, so it is an evaluation, a mental process, an abstract idea. Further, the recitation of generation of a subsurface representation based on decorrelated subsurface properties is a mathematical operation in textual form, so this is a mathematical concept, an abstract idea. See the Applicant’s specification [0057], with the only examples provided that the compositions sum to one and that there are no negative compositions values for any mineral.)
Claim 1 recites mental processes, which are abstract ideas.
Claim 1 recites an abstract idea.
Step 2A – Prong 2: Integrated into a Practical Application?
No.
Claim 1 recites the following additional limitations:
obtain well information, the well information characterizing subsurface configuration of a well within a region of interest using subsurface properties, wherein the subsurface properties are correlated;
This is mere data gathering akin to the MPEP 2106.05(g) examples: “i. Performing clinical tests on individuals to obtain input for an equation” “v. Consulting and updating an activity log, Ultramercial,” “i. Limiting a database index to XML tags” “iii. Selecting information, based on types of information and availability of information in a power-grid environment, for collection, analysis and display.” Accordingly, this is extra-solution activity and fails to integrate the abstract ideas into a practical application.
A system for modeling subsurface regions, the system comprising: one or more physical processors configured by machine-readable instructions to:
The are generic computing elements recited at a high level, which, under MPEP 2106.05(f), fail to integrate the abstract idea into a practical application.
Also, the nature of the data and the context merely limit the abstract idea to a technological environment, which, under MPEP 2106.05(h), fail to integrate the abstract idea into a practical application.
Claim 1 fails to recite any additional limitations that integrate the abstract idea into a practical application.
Claim 1 is directed to the abstract idea.
Step 2B: Claim provides an Inventive Concept?
No.
Claim 1 recites the following additional limitations:
obtain well information, the well information characterizing subsurface configuration of a well within a region of interest using subsurface properties, wherein the subsurface properties are correlated;
This is well-understood, routine, and conventional (WURC) activity akin to the MPEP 2106.05(d) examples: “iii. Electronic recordkeeping” “iv. Storing and retrieving information in memory” “v. Electronically scanning or extracting data from a physical document” “i. Determining the level of a biomarker in blood by any means “ “v. Analyzing DNA to provide sequence information or detect allelic variants” “vi. Arranging a hierarchy of groups, sorting information, eliminating less restrictive pricing information and determining the price.” Because this limitation is WURC and insignificant extra-solution activity, under MPEP 2106.05(d) and 2106.05(g), the limitation fails to combine with the other elements of the claim to provide significantly more than the abstract idea that would confer an inventive concept.
A system for modeling subsurface regions, the system comprising: one or more physical processors configured by machine-readable instructions to:
These are generic computing elements recited at a high level, which, under MPEP 2106.05(f), fail to combine with the other elements of the claim to provide significantly more than the abstract idea that would confer an inventive concept.
Also, the nature of the data and the context merely limit the abstract idea to a technological environment, which, under MPEP 2106.05(h), fail to combine with the other elements of the claim to provide significantly more than the abstract idea that would confer an inventive concept.
The additional limitations fail to combine with the other elements of the claim to provide significantly more than the abstract idea that would confer an inventive concept.
Claim 1 is ineligible.
Claim 11 (Statutory Category – Process)
Claim 11 recites the method of claim 1. The method is rejected for at least the same reasons as claim 1.
Accordingly claim 11 is ineligible.
Dependent Claims
Dependent claims 2-10 and 12-20 are also ineligible for at least the following reasons.
Claims 2 and 12
wherein the region of interest includes an unconventional reservoir.
This merely describes the data that is gathered, so it fails to confer eligibility for at least the same reasons as the obtain step of claim 1.
This also merely limits the abstract idea to a technological environment, so it fails to confer eligibility under MPEP 2106.05(h).
Claims 2 and 12 fail to provide any additional limitations that confer eligibility.
Claims 2 and 12 are ineligible.
Claims 3 and 13
wherein the subsurface properties include compositional subsurface properties and non-compositional subsurface properties.
This merely describes the data that is gathered, so it fails to confer eligibility for at least the same reasons as the obtain step of claim 1.
This also merely limits the abstract idea to a technological environment, so it fails to confer eligibility under MPEP 2106.05(h).
Claims 3 and 13 fail to provide any additional limitations that confer eligibility.
Claims 3 and 13 are ineligible.
Claims 4 and 14
wherein the mineralogy proportions constraint requires the compositional subsurface properties to sum to one.
This merely describes an element of the abstract idea from the second generate step of claim 1 and is an element of the abstract idea for at least the same reasons.
Also, specifically, this is a mathematical operation of summing to one, which is a mathematical concept, an abstract idea.
Claims 4 and 14 fail to provide any additional limitations that confer eligibility.
Claims 4 and 14 are ineligible.
Claims 5 and 15
wherein the transformation of the well information to generate the decorrelated well information includes: a first transformation of the compositional subsurface properties to change distribution of the compositional subsurface properties; and a second transformation of the transformed compositional subsurface properties and the non-compositional subsurface properties to decorrelate the transformed compositional subsurface properties and the non-compositional subsurface properties.
These are mental processes and mathematical concepts, abstract ideas, for the same reasons as the generate step in claim 1 that these elements further describe. These are elements of the abstract idea, so they fail to provide additional limitations.
Claims 5 and 15 fail to provide any additional limitations that confer eligibility.
Claims 5 and 15 are ineligible.
Claims 6 and 16
wherein the generation of the subsurface representation for the region of interest based on the decorrelated subsurface properties includes independent propagation of individual ones of the decorrelated subsurface properties.
These are mental processes and mathematical concepts, abstract ideas, for the same reasons as the second generate step in claim 1 that these elements further describe. These are elements of the abstract idea, so they fail to provide additional limitations.
Claims 6 and 16 fail to provide any additional limitations that confer eligibility.
Claims 6 and 16 are ineligible.
Claims 7 and 17
wherein the generation of the subsurface representation for the region of interest further includes inverse transformation of the propagated, decorrelated subsurface properties.
These are mental processes and mathematical concepts, abstract ideas, for the same reasons as the second generate step in claim 1 that these elements further describe. These are elements of the abstract idea, so they fail to provide additional limitations.
Claims 7 and 17 fail to provide any additional limitations that confer eligibility.
Claims 7 and 17 are ineligible.
Claims 8 and 18
wherein the inverse transformation of the propagated, decorrelated subsurface properties includes: a first inverse transformation of the propagated, decorrelated subsurface properties to correlate the propagated, decorrelated subsurface properties; and a second inverse transformation of compositional subsurface properties in the propagated, correlated subsurface properties to restore distribution of the compositional subsurface properties.
These are mental processes and mathematical concepts, abstract ideas, for the same reasons as the second generate step in claim 1 that these elements further describe. These are elements of the abstract idea, so they fail to provide additional limitations.
Claims 8 and 18 fail to provide any additional limitations that confer eligibility.
Claims 8 and 18 are ineligible.
Claims 9 and 19
wherein the subsurface representation for the region of interest is generated further based on one or more secondary subsurface properties away from the well.
This is mere data gathering that fails to confer eligibility for the same reasons as the obtain step in claim 1.
This merely qualifies the data used in the second generate step of claim 1, so it is an element of the abstract idea.
This merely limits the abstract idea to a technological environment, so it fails to confer eligibility under MPEP 2106.05(h)
Claims 9 and 19 fail to provide any additional limitations that confer eligibility.
Claims 9 and 19 are ineligible.
Claims 10 and 20
wherein the one or more secondary subsurface properties are iteratively modeled in sequence using a weighted average of the one or more secondary subsurface properties and a modeled subsurface property.
These are mental processes and mathematical concepts, abstract ideas, for the same reasons as the second generate step in claim 1 that these elements further describe. These are elements of the abstract idea, so they fail to provide additional limitations.
Claims 10 and 20 fail to provide any additional limitations that confer eligibility.
Claims 10 and 20 are ineligible.
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 1-20: Kowalska and Egozcue
Claim(s) 1-20 is/are rejected under 35 U.S.C. 103 as being unpatentable over NPL: “Downhole Lithological Profile Reconstruction Based on Chemical Composition of Core Samples and Drill Cuttings Measured with Portable X-ray Fluorescence Spectrometer” by Kowalska et al. (Kowalska) in view of NPL: “Isometric Logratio Transformations for Compositional Data Analysis” by Egozcue et al. (Egozcue).
Regarding claim 1, Kowalska teaches:
A system for modeling subsurface regions, the system comprising: one or more physical processors configured by machine-readable instructions to: (Kowalska Page 8, first paragraph “A mineral quantitative composition was calculated by the Rietveld method with the use of the SIROQUANT computer program (version 3) [17,19,21], with documented usefulness for composition analysis of rocks, also including clay minerals [22]. The Rietveld method [17] is applicable in an increasing number of programs for mineral composition analysis using X-ray diffraction. It provides the opportunity for computer modelling with regard to the crystallographic structure of particular minerals occurring in the studied material, and their subsequent use as standards while analysing a qualitative composition. While modelling the atomic structure of particular minerals, it is possible to take into account their real chemical composition, crystallite sizes, the presence of structural defects, or the degree of preparation randomness.” See also the references to software used, the XRF (which itself has processors and memory), and computer rendered images throughout the paper – A computer is used for the methods.)
obtain well information, the well information characterizing subsurface configuration of a well within a region of interest using subsurface properties, wherein the subsurface properties are correlated; (Kowalska Pages 7-8, 2.2.3. XRD Methodology “X-ray measurements were taken on a Panalytical X’Pert Pro equipped with a modern X’Celerator detector. The following set up was applied: 40 kV voltage, 34mAanode current, 0.02_2_ step-width and the angular range from 5_ to 65_2_. Random preparations loaded from the side were prepared in line with the procedure specially recommended for rocks containing a large number of clay minerals [15]. The samples were initially ground to granulation below 20 _m with a Pultverisset 7 mill. Next, in order to refine the granulation to <5 _m and homogenize with an internal standard, the samples were milled again in wet conditions (with methanol) for 5 min in a McCrone micronizing mill. Zinc oxide in the amount of 10% was applied as the internal standard. Measurements were conducted on random preparations loaded from the side, which ensures obtaining real proportions of mineral components occurring in the samples as well as a repeatable density of preparation. - Well information characterizing subsurface configuration of a well within a region of interest using subsurface properties is obtained. The subsurface mineral composition is correlated prior to decorrelation, the correlation including that the total composition must add to 1 or 100%.)
generate well information characterizing the subsurface configuration of the well within the region of interest using subsurface properties, ; and (Kowalska Page 8, First Paragraph “A mineral quantitative composition was calculated by the Rietveld method with the use of the SIROQUANT computer program (version 3) [17,19,21], with documented usefulness for composition analysis of rocks, also including clay minerals [22]. The Rietveld method [17] is applicable in an increasing number of programs for mineral composition analysis using X-ray diffraction. It provides the opportunity for computer modelling with regard to the crystallographic structure of particular minerals occurring in the studied material, and their subsequent use as standards while analysing a qualitative composition. While modelling the atomic structure of particular minerals, it is possible to take into account their real chemical composition, crystallite sizes, the presence of structural defects, or the degree of preparation randomness.” – Subsurface rock composition is determined in the region of interest using subsurface properties.)
generate a subsurface representation for the region of interest based on the decorrelated subsurface properties, wherein the subsurface representation honors a mineralogy proportions constraint on the subsurface properties and correlations between the subsurface properties. (Kowalska Page 15, FIG. 8 (shown below) – This illustrates the constraints that the total composition sums to one or 100%. Page 23, FIG. 14 (shown below) and Equation (1) on Page 21– This illustrates a subsurface representation that honors mineralogy proportions constraints.)
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Kowalska teaches the use of a composite of techniques to best determine the subsurface lithology of a site, including accounting for correlation between mineral compositions (Kowalska Page 11, First Paragraph “The pXRF measurements taken simultaneously for these boreholes allowed to make a lithological reconstruction and a borehole correlation. In both boreholes, drilled Carboniferous rocks have a very diversified mineral composition. Presumably, these are rocks of the Lower Carboniferous Period (the Tournai?), although paleontological documentation is not certain in this case.” Page 15, Last Paragraph “Figure 9 presents main correlations among selected mineralogical and chemical components prepared for Carboniferous rocks from the Kobylin-1 borehole: for the sum of quartz and feldspars as well as SiO2, for the sum of carbonates and CaO, for the sum of micas and illite as well as K2O, and finally, for chlorites and Fe2O3. A weaker correlation between CaO and carbonates results from the presence of siderite and anhydrite in the samples.” See Also the correlations between minerals in the composition determinations on Page 21.), but Kowalska does not appear to teach, but Kowalska in view of Egozcue teaches:
generate decorrelated well information based on transformation of the well information, the decorrelated well information characterizing the subsurface configuration of the well within the region of interest using decorrelated subsurface properties, wherein the decorrelated subsurface properties are not correlated; and (Egozcue Abstract “Geometry in the simplex has been developed in the last 15 years mainly based on the contributions due to J. Aitchison. The main goal was to develop analytical tools for the statistical analysis of compositional data. Our present aim is to get a further insight into some aspects of this geometry in order to clarify the way for more complex statistical approaches. This is done by way of orthonormal bases, which allow for a straightforward handling of geometric elements in the simplex. The transformation into real coordinates preserves all metric properties and is thus called isometric logratio transformation (ilr). An important result is the decomposition of the simplex, as a vector space, into orthogonal subspaces associated with nonoverlapping subcompositions. This gives the key to join compositions with different parts into a single composition by using a balancing element. The relationship between ilr transformations and the centered-logratio (clr) and additive-logratio (alr) transformations is also studied. Exponential growth or decay of mass is used to illustrate compositional linear processes, parallelism and orthogonality in the simplex.” – Egozcue uses mathematical transformations to decorrelate composition data, creating sparser data matrices and reducing complexity of computations.)
It would have been obvious to a person of ordinary skill in the art before the effective filing date of the claims to modify the subsurface composition determinations of Kowalska by the transformations for compositional data of Egozcue because the person of ordinary skill in the art would be motivated by the aim of Kowalska, which uses a combination of data analysis techniques to map the lithology of a site that still presents some discrepancies, to look to Egozcue, which transforms the data in a manner that maps the composition to real space to better represent the composition as orthogonal subspaces. (Kowalska Abstract “The reconstruction of a lithological profile based on geophysical logs of chemical composition provided by geochemical gamma-gamma well logging probes has been increasingly used for geophysical interpretation. A chemical profile, analogous to the measurements mentioned above, can be determined based on measurements made with a portable X-ray fluorescence spectrometer (pXRF). This paper presents a methodology for determining the mineral composition of drilled, clastic, as well as clay-rich rocks on the basis of both inexpensive and timesaving pXRF measurements as well as models combining the results of chemical composition analysis with results of mineral composition analysis (XRD). The results of chemical composition analysis obtained with a portable XRF spectrometer were calibrated based on a detailed analysis produced with ICP-OES and ICP-MS methods. A significant advantage of the proposed method is the possibility to apply it with regard to drill cuttings as well as archival cores. However, considerable discrepancies in the results obtained were identified while comparing the results of chemical composition analysed directly on the core and milled material.”; Egozcue Abstract “Geometry in the simplex has been developed in the last 15 years mainly based on the contributions due to J. Aitchison. The main goal was to develop analytical tools for the statistical analysis of compositional data. Our present aim is to get a further insight into some aspects of this geometry in order to clarify the way for more complex statistical approaches. This is done by way of orthonormal bases, which allow for a straightforward handling of geometric elements in the simplex. The transformation into real coordinates preserves all metric properties and is thus called isometric logratio transformation (ilr). An important result is the decomposition of the simplex, as a vector space, into orthogonal subspaces associated with nonoverlapping subcompositions. This gives the key to join compositions with different parts into a single composition by using a balancing element. The relationship between ilr transformations and the centered-logratio (clr) and additive-logratio (alr) transformations is also studied. Exponential growth or decay of mass is used to illustrate compositional linear processes, parallelism and orthogonality in the simplex.”)
Claims 2 and 12
Regarding claims 2 and 12, Kowalska in view of Egozcue teaches the features of claim 1 and 11, and further teaches:
wherein the region of interest includes an unconventional reservoir. (Kowalska Page 9, 2.3 Research Material “In all the boreholes, Carboniferous rocks were a research object, but they differed significantly among each other in terms of lithological features. In the Kobylin-1 borehole, a drilled rock sequence contained mainly quartzite and claystone with a high degree of diagenesis. However, in the Biesiekierz-1
and -2 boreholes, there was a predominance of strongly arkosic sandstones with a large number of carbonate as well as claystone layers with a considerably lower degree of thermal transformations. Carboniferous rocks of the Fore-Sudetic Monocline and Western Pomerania are treated as source rocks for conventional hydrocarbon deposits occurring in this region. They are part of a Carboniferous-Permian petroleum system extending across entire Europe. Recently, these rocks have also been a research object regarding unconventional gas deposits of a ‘tight gas’ type.” – The methods are applied to an unconventional reservoir.)
Claims 3 and 13
Regarding claims 3 and 13, Kowalska in view of Egozcue teaches the features of claims 1 and 11 and further teaches:
wherein the subsurface properties include compositional subsurface properties and non-compositional subsurface properties. (Kowalska Page 18 “In the case of the Biesiekierz-2 and Biesiekierz -1 boreholes, measurements were made only on a core material with the step-width of ca. 1 m. During the analysis, the authors strove to take into account all drilled rock lithological types occurring in the geological profile. Graphs showing the changeability of particular chemical component contents along with depth were prepared for the Biesiekierz-2 borehole (Figure 13).” – The methods account for composition and depth, which is a non-compositional property)
Claims 4 and 14
Regarding claims 4 and 14, Kowalska in view of Egozcue teaches the features of claims 3 and 13 and further teaches:
wherein the mineralogy proportions constraint requires the compositional subsurface properties to sum to one. (Kowalska See Equation (1) on page 21 – A constraint is that the composition must sum to 1)
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Claims 5 and 15
Regarding claims 5 and 15, Kowalska in view of Egozcue teaches the features of claims 3 and 13 and further teaches:
wherein the transformation of the well information to generate the decorrelated well information includes: a first transformation of the compositional subsurface properties to change distribution of the compositional subsurface properties; and (Egozcue Page 290 “The first step is to get a simplified expression for the orthogonality of two compositions. The clr transformation (5) gives an adequate way. The clr transformation assigns each composition in SD to a rowvector a = clr(x) in RD, satisfying
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.Vectors satisfying such a condition constitute a (D-1)-dimensional subspace of RD, denoted here by VS. We remark that the ratio of the compositional parts to their geometric mean in the clr transformation is convenient because the clr image of the neutral composition e = C[1, 1, … , 1] is clr(e) = [0, 0, … , 0], the neutral element in RD; this is necessary to define an isomorphism of linear spaces between SD and VS. The inner product (4) can be expressed in the clr-transformed space as follows: for a = clr(x) and b = clr(y)”
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– This is a first transformation of the compositional subsurface properties to change distribution of the compositional subsurface properties to change distribution of the compositional subsurface properties.)
a second transformation of the transformed compositional subsurface properties and the non-compositional subsurface properties to decorrelate the transformed compositional subsurface properties and the non-compositional subsurface properties. (Egozcue Page 291 “In order to obtain an orthonormal basis of the linear subspace associated with the eigenvalue D (i.e. VS), we select a set of D-1 linearly independent vectors in that subspace. Let them be v1, v2, … ,vD-1 defined as vi = [0, … , 0 , 1, -1, 0, … , 0], the first non-null element being placed in the i-th column. For any two vectors, a and b in VS, the ordinary Euclidean inner product is <a; b> = D D-1aMb’ as pointed out before. Therefore, the Gram–Schmidt procedure, with respect to the ordinary Euclidean inner product, can be applied to v1, v2, … ,vD-1 and we obtain the following result.
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The vectors ui are orthonormal with respect to the ordinary Euclidean inner
product in <D and constitute a basis of (D ¡ 1)-dimensional linear
subspace VS.”
Page 296, illustrating that the ilr coordinates are logratios.
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– The compositions are represented as ui orthonormal spaces with relative magnitude, representing decorrelated compositional data.)
Claims 6 and 16
Regarding claims 6 and 16, Kowalska in view of Egozcue teaches the features of claims 1 and 11 and further teaches:
wherein the generation of the subsurface representation for the region of interest based on the decorrelated subsurface properties includes independent propagation of individual ones of the decorrelated subsurface properties. (Kowaslka Page 23, Figure 14 – This illustrates that the subsurface representation includes independent propagation of individual ones of the decorrelated subsurface properties)
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Claims 7 and 17
Regarding claims 7 and 17, Kowalska in view of Egozcue teaches the features of claims 6 and 16 and further teaches:
wherein the generation of the subsurface representation for the region of interest further includes inverse transformation of the propagated, decorrelated subsurface properties. (Egozcue Page 295, First Paragraph “Then, the inverse ilr transformation corresponds to the expression of x in the reference basis of SD
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– The inverse transformation is used to render the numbers for the subsurface representation.)
Claims 8 and 18
Regarding claims 8 and 18, Kowalska in view of Egozcue teaches the features of claims 7 and 17 and further teaches:
wherein the inverse transformation of the propagated, decorrelated subsurface properties includes: a first inverse transformation of the propagated, decorrelated subsurface properties to correlate the propagated, decorrelated subsurface properties; and a second inverse transformation of compositional subsurface properties in the propagated, correlated subsurface properties to restore distribution of the compositional subsurface properties. (Egozcue Page 295, First Paragraph “Then, the inverse ilr transformation corresponds to the expression of x in the reference basis of SD
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– The inverse transformations are used to render the numbers for the subsurface representation, relying on the isometry of the system.)
Claims 9 and 19
Regarding claims 9 and 19, Kowalska in view of Egozcue teaches the features of claims 1 and 11 and further teaches:
wherein the subsurface representation for the region of interest is generated further based on one or more secondary subsurface properties away from the well. (Kowalska Abstract “A significant advantage of the proposed method is the possibility to apply it with regard to
drill cuttings as well as archival cores. However, considerable discrepancies in the results obtained were identified while comparing the results of chemical composition analysed directly on the core and milled material. The analysed material comprised Carboniferous rocks derived from three boreholes located in Poland: Kobylin-1 as well as Biesiekierz-1 and -2. It was possible to directly compare the lithological profile obtained based on measurements taken on drill cuttings with the results of the lithological interpretation of a geochemical probe log.” – The methods use composition/XRD data from other boreholes for comparison and also from archived cores that are from different areas.)
Claims 10 and 20
Regarding claims 10 and 20, Kowalska in view of Egozcue teaches the features of claims 9 and 19 and further teaches:
wherein the one or more secondary subsurface properties are iteratively modeled in sequence using a weighted average of the one or more secondary subsurface properties and a modeled subsurface property. (Kowalska “The final stage was to calculate mineral composition resulting logs (single and cumulative) and to illustrate results graphically along with XRD laboratory data. Weighting coefficients for particular components applied during the interpretation were selected by iteration in order to achieve the best compatibility of calculation results with XRD data.” – Secondary subsurface properties are iteratively modeled in sequence using a weighted average of the one or more secondary subsurface properties and a modeled subsurface property.)
Conclusion
The prior art made of record and not relied upon is considered pertinent to applicant's disclosure.
NPL: “Simulation of Decorrelated Multivariate Data in Presence of Auxiliary Information” by Erten et al. (Teaches simulating each uncorrelated primary variable in a sequence, the first uncorrelated primary variable being co-simulated with available secondary information as a covariate, and the second uncorrelated primary variable is co-simulated with using a super secondary variable generated by merging the previously simulated first uncorrelated variable and the secondary information, and so on, to generate a hierarchical simulation framework that preserves the correlation structure between the uncorrelated primary variables, and between the uncorrelated primary values and the secondary information)
NPL: “Feature Compensated Borehole Image Compression for Real-Time Logging While Drilling” by Gelman et al. (Teaches decorrelating measurements in the borehole in a 2D DWT)
NPL: “Simulation of decorrelated factors in presence of secondary data“ by Manchuk et al. (Teaches geostatistical simulation of variables with complex multivariate relationships in presence of exhaustive secondary variables such as remotely sensed geophysical measurements)
NPL: “Volume Decorrelation Effects in TanDEM-X Interferometric SAR Data” by Martone et al. (Teaches decorrelation to increase sparsity calculations from raw sensor data)
NPL: “Identifying Lithology and Matrix for Unconventional Reservoir Based on Geochemical Elements Logs” by Xi et al. (Teaches lithology/composition determinations for unconventional well sites)
NPL: “Prestack Bayesian Linearized Inversion with Decorrelated Prior Information” by Yu et al. (Teaches a decorrelated Bayesian linearized inversion (DBLI) by integrating the BLI with a decorrelation strategy that utilizes principal component analysis to obtain independent model parameters with zero covariances)
US 20240119106 A1 to Edwards et al. (Teaches multivariate normalization of well logs using portability distribution modeling)
US 20230130034 A1 to Emmings et al. (Teaches probability-based determination of stratigraphic anomalies in a subsurface)
US 20170284174 A1 to Ghareeb et al. (Teaches constraint-based well placement using decorrelating techniques on the data)
US 20110083844 A1 to Oppert et al. (Teaches whitening/decorrelating raw seismic data to determine lithology)
US 6263284 B1 to Crider et al. (Teaches whitening/decorrelating raw seismic data to determine lithology)
US 6131071 A to Partyka et al. (Teaches whitening/decorrelating raw seismic data to determine lithology)
US 5870691 A to Partyka et al. (Teaches whitening/decorrelating raw seismic data to determine lithology)
Any inquiry concerning this communication or earlier communications from the examiner should be directed to JAY MICHAEL WHITE whose telephone number is (571) 272-7073. The examiner can normally be reached Mon-Fri 11:00-7:00 EST.
Examiner interviews are available via telephone, in-person, and video conferencing using a USPTO supplied web-based collaboration tool. To schedule an interview, applicant is encouraged to use the USPTO Automated Interview Request (AIR) at http://www.uspto.gov/interviewpractice.
If attempts to reach the examiner by telephone are unsuccessful, the examiner’s supervisor, Ryan Pitaro can be reached at (571) 272-4071. The fax phone number for the organization where this application or proceeding is assigned is 571-273-8300.
Information regarding the status of published or unpublished applications may be obtained from Patent Center. Unpublished application information in Patent Center is available to registered users. To file and manage patent submissions in Patent Center, visit: https://patentcenter.uspto.gov. Visit https://www.uspto.gov/patents/apply/patent-center for more information about Patent Center and https://www.uspto.gov/patents/docx for information about filing in DOCX format. For additional questions, contact the Electronic Business Center (EBC) at 866-217-9197 (toll-free). If you would like assistance from a USPTO Customer Service Representative, call 800-786-9199 (IN USA OR CANADA) or 571-272-1000.
/J.M.W./Examiner, Art Unit 2188
/RYAN F PITARO/Supervisory Patent Examiner, Art Unit 2188
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