DETAILED ACTION
Claims 1-20 are presented for examination.
The present application, filed on or after March 16, 2013, is being examined under the first inventor to file provisions of the AIA .
Information Disclosure Statement
The information disclosure statements filed 30 June 2025 fails to comply with 37 CFR 1.98(a)(2), which requires a legible copy of each cited foreign patent document; each non-patent literature publication or that portion which caused it to be listed; and all other information or that portion which caused it to be listed. It has been placed in the application file, but the information referred to therein has not been considered.
These listings appear to be missing corresponding copies:
Young, III “An essay on the cohesion of fluids” https://royalsocietypublishing.org (1804) [Item 10].
Zhou, et al. “Dynamic capillary pressure curves from pore-scale modeling in mixed-wet-rock images” SPE J. (2013) [Item 17].
Schroeder, et al. “The Visualization Toolkit, An Object-Oriented Approach to 3D Graphics” Edition 4.1, VTK (2018) [Item 23].
Gong, et al. “Parallel dynamic pore-network modeling package for two-phase flow in fractures and solute transport in disordered porous media and rough-walled fractures” Dissertation [Items 9 (2001) and 13 (2021)].
Karpyn, et al. “Prediction of fluid occupancy in fractures using network modeling and x-ray microtomography, I: Data conditioning and model description” Physical Review E, vol. 76, no. 016315 (2007) [Item 36]
An unlabeled dissertation of 122 pages was submitted which appears to be an incomplete second half starting from page 92. This NPL copy missing the front cover information and the first 91 pages may correspond with one of the above missing references.
Note, the IDS listings include a number of typographic errors:
Berg, et al. “Real-time 3D imaging of Haines jumps in porous media flow” Harvard U (2023) is miscited and was actually published (2013).
Brush, et al. “Fluid flow in synthetic rough-walled fractures: Navier-stokes, stokes, and local cubic law simulations” Water Resources Research, vol. 39, no. 4, (2023) is miscited and was actually published (2003).
Gong, et al. “Dynamic pore-scale modeling of residual fluid configurations in disordered porous media” E3S Web of Conferences 366, (2013) is miscited and was actually published (2023).
The included NPL copies clarify these three typographic errors of the IDS.
Drawings
The drawings received on 13 November 2023 are accepted.
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.
Claim 19 is 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 pre-AIA the applicant regards as the invention.
Claim 19 recites “The apparatus of claim, the one or more….” Claim 19 is missing the claim number upon which it depends. Accordingly, it is unclear what dependencies are intended to be incorporated via the claim recitation of “of claim ___” in the preamble. Examiner suggests claim 17 may be the intended dependency.
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.
Claims 1, 2, 6-8, and 11-20
Claims 1, 2, 6-8, and 11-20 are rejected under 35 U.S.C. 103 as being unpatentable over Kohanpur, A. & Valocchi, A. “Pore-network stitching method: A pore-to-core upscaling approach for multiphase flow” arXiv:2004.01523v1 (2020) (cited in IDS dated 30 June 2025) [herein “Kohanpur”] in view of US patent 11,561,215 B2 Nie, et al. [herein “Nie”].
Claim 1 recites “1. A method for generating a heterogeneous plug for a porous media sample by one or more central processing units (CPUs).” Kohanpur title discloses “Pore-network stitching method: A pore-to-core upscaling approach for multiphase flow.” Kohanpur abstract discloses:
This workflow uses micro-CT images of heterogeneous reservoir rock cores at different resolutions to characterize the pore structure in order to select few signature parts of the core and extract their equivalent pore-network models. The space between these signature pore-networks is filled by using their statistics to generate realizations of pore-networks which are then connected together using a deterministic layered stitching method. The output of this workflow is a large pore-network that can be used in any flow and transport solver.
Outputting a large pore-network of heterogeneous rock cores correspond to generating a heterogeneous plug for a porous media sample.
Kohanpur does not explicitly disclose one or more central processing units (CPUs); however, in analogous art of reservoir simulation from core analysis, Nie column 11 lines 62-65 teaches “The computer includes a processor 901 (possibly including multiple processors, multiple cores, multiple nodes, and/or implementing multi-threading, etc.). The computer includes memory 907.” The processor corresponds with a central processing units (CPU).
It would have been obvious to a person having ordinary skill in the art before the effective filing date of the claimed invention to combine Kohanpur and Nie. One having ordinary skill in the art would have found motivation to use computer implementation into the system of pore-network stitching for the advantageous purpose “to perform multiscale modeling with the image data at different resolutions and a digital experiment.” See Nie column 3 lines 44-45.
Claim 1 further recites “comprising: duplicating one or more sample pore elements to fill one or more components of a heterogeneous plug.” Kohanpur page 5 section 2 second paragraph discloses:
The first step is to examine the entire coarse resolution scans of the core to find unique size and shape of solid grains and local pore structure in different locations and identify heterogeneities. Assessment and integration of multiple scales of 2D and 3D micro-CT images can be used to investigate geometry, connectivity, and heterogeneity of pore space (Long et al. 2013). …. The outcome of this analysis is identification of selected locations of the core that are unique in terms of pore size and structure that are defined as signature parts, as shown with red boxes in Fig. 1. The hypothesis is that these signature parts, which might or might not be adjacent, are sufficient to represent the heterogeneity of the core.
Identifying heterogeneities indicates it is of a heterogeneous plug. The signature parts of the core correspond with duplicated sample pore elements which are used as one or more of the components of the pore network model.
Claim 1 further recites “constructing a set of buffer zones between each of the one or more components; populating the set of buffer zones with one or more generated pore elements, the one or more generated pore elements based, at least in part, on the one or more sample pore elements.” Kohanpur page 7 second paragraph discloses:
Since the space between signature PNs in the core can be relatively large and cannot be directly extracted, the next important step of the workflow is to fill the space between signature PNs by new defined pore elements. We accomplish this by using statistics of the signature PNs and a stochastic algorithm to generate new PNs in the empty regions of the domain.
The empty regions of the domain correspond with buffer zones between the signature pore networks (PNs). Using statistics of signature pore networks (PNs) corresponds with using statistics of the sample pore elements.
Claim 1 further recites “and connecting the one or more generated pore elements to the one or more components to generate the heterogeneous plug.” Kohanpur page 8 figure 4 discloses “Fig. 4 Layered stitching of two adjacent pore-networks by generating a layer of pore elements.” Stitching together two adjacent pore-networks corresponds to connecting the generated pore elements (e.g. the new PNs) to the components (e.g. signature pore-networks) to generate the heterogeneous plug.
Kohanpur page 11 second paragraph discloses “Once all empty spaces are filled with generated PNs, we use the layered stitching in longitudinal and lateral directions to connect all PNs together as shown in Fig. 5.”
Claim 2 further recites “2. The method of claim 1, further comprising obtaining a representative pore network having one or more representative pore elements, the representative pore network corresponding to a porous media sample.” Kohanpur page 5 section 2 second paragraph discloses:
The first step is to examine the entire coarse resolution scans of the core to find unique size and shape of solid grains and local pore structure in different locations and identify heterogeneities. Assessment and integration of multiple scales of 2D and 3D micro-CT images can be used to investigate geometry, connectivity, and heterogeneity of pore space (Long et al. 2013). …. The outcome of this analysis is identification of selected locations of the core that are unique in terms of pore size and structure that are defined as signature parts, as shown with red boxes in Fig. 1. The hypothesis is that these signature parts, which might or might not be adjacent, are sufficient to represent the heterogeneity of the core.
Assessment of multiple scales of micro-CT images is obtaining a representative pore network having representative pore elements. The identified signature parts correspond with the representative pore network of the porous media sample.
Claim 4 further recites “4. The method of claim 2, wherein obtaining the representative pore network comprises: generating a search radius for each of the one or more sample pore elements.” Kohanpur page 6 first paragraph disclose “we use the PN extraction code based on [maximal ball (MB)] algorithm from Dong and Blunt (2009). The algorithm was originally introduced by Silin and Patzek (2006) where the entire 3D voxelized pore space is searched to find the largest possible spheres, and subsequently, was extended and modified in later works.” The largest possible spheres correspond with radii of the search generated by searching the sample pore elements.
Claim 4 further recites “and generating an adjacency list for each of the one or more sample pore elements.” Kohanpur page 2-3 across the page-break disclose “Defining a PN requires geometrical (location, size, and shape of pore elements) and topological (connections between pore elements) information of the pore space.” Defining the topological connections between pore-elements corresponds with generating an adjacency list of each of the samples pore elements.
Claim 6 further recites “6. The method of claim 2, wherein duplicating the one or more sample pore elements further comprises: selecting a target pore body from the one or more sample pore elements; adding the target pore body to one of the one or more components of the heterogeneous plug; and replicating one or more pore throat connections from the representative pore network substantially within the one or more components, the one or more pore throat connections disposed between the target pore body and one or more duplicated neighbors of the target pore body.” Kohanpur pages 8-9 across the page-break discloses “a further step of adding or removing some pore-throats in the stitching layer is applied to get the average connection number within a threshold of the arithmetic mean of left and right PNs.” Adding some pore-throats corresponds to replicating one or more pore throat connections substantially within one or more components where the pore throat connections are between the target pore body and one or more duplicated neighbors of the target pore body. Furthermore, the target arithmetic mean of the left and right PNs correspond with at selected target pore bodies of the sample pore elements.
Claim 7 further recites “7. The method of claim 2, wherein the representative pore network has known dimensions.” Kohanpur page 12 section 3 first paragraph and table 1 disclose “the samples are listed in Table 1 with their size, image resolution, porosity, and a chosen label.” Kohanpur page 12 table 1 shows a column “Size (mm3).” Each listed size of the samples is a specific known dimension.
Claim 8 further recites “8. The method of claim 1, further comprising outputting the heterogeneous plug.” Kohanpur abstract discloses:
This workflow uses micro-CT images of heterogeneous reservoir rock cores at different resolutions to characterize the pore structure in order to select few signature parts of the core and extract their equivalent pore-network models. The space between these signature pore-networks is filled by using their statistics to generate realizations of pore-networks which are then connected together using a deterministic layered stitching method. The output of this workflow is a large pore-network that can be used in any flow and transport solver.
Outputting a large pore-network of heterogeneous rock cores correspond to outputting the heterogeneous plug.
Claim 11 further recites “11. The method of claim 1, wherein the heterogeneous plug is generated based on a facies map.” Kohanpur page 5 section 2 second paragraph discloses:
The first step is to examine the entire coarse resolution scans of the core to find unique size and shape of solid grains and local pore structure in different locations and identify heterogeneities. Assessment and integration of multiple scales of 2D and 3D micro-CT images can be used to investigate geometry, connectivity, and heterogeneity of pore space (Long et al. 2013).
Images investigating the geometry, connectivity, and heterogeneity of the pore space corresponds with a mapping of the respective facies of the heterogeneous plug.
Claim 12 further recites “12. The method of claim 1, the method further comprising processing the heterogeneous plug, based on one or more parameters, to align substantially with properties of a representative pore network.” Kohanpur page 10 last paragraph discloses:
One such approach would be to use parameters (e.g., mean, variance) of derived fitting distributions. We accomplish this by concatenating the pore element lists (pore-body radius and volume, connection number, pore-throat radius and volume, pore-throat length, and pore-throat total length) for all signature PNs with a weight for each based on their relative center-to-center distance to the generated PN i.e. the closer to a signature PN, the more it is influenced by its statistics.
Fitting to derived parameters of distance-weighted pore elements corresponds with aligning the properties of the generated heterogeneous plug to parameters of a representative pore network.
Claim 13 further recites “13. The method of claim 12, wherein the one or more parameters comprise at least one of a desired shape, desired dimensions, and a target porosity.” From the above list of alternatives Examiner is selecting “desired dimensions.”
Kohanpur page 7 first paragraph discloses “We use statistics of geometrical and topological information of pore elements in the signature PNs in this workflow.” Kohanpur page 9 third paragraph disclose “The radius, shape factor, and volume of these generated pore-throats are also determined by fitting a proper distribution function.” Fitting a distribution on the radius and shape factor correspond with aligning properties of the heterogeneous plug to a desired shape and desired dimensions.
Claim 14 further recites “14. The method of claim 13, wherein the target porosity comprises: a known porosity value of a representative pore network; or a weighted average porosity value.” From the above list of alternatives Examiner is selecting “a weighted average porosity value.”
Kohanpur page 11 first paragraph disclose “we can relate the weighted average to all signature PNs of the entire domain simultaneously and thus, the footprint of their statistics can be found in every single generated PN of the domain.” Relating the weighted average statistics to every generated PN corresponds to using a weighted average of each respective statistic.
Kohanpur page 22 second paragraph discloses:
Fig. 21 shows a PN representation of this sample and how it is divided into 12 equal-size pieces. We labeled them from 1 to 12, starting from bottom left corner as depicted in Fig. 21, and marked three signature parts (pieces 3, 5, and 10) out of it based on the calculated range of porosity (from 0.152 to 0.221) and absolute permeability (from 219 mD to 1972 mD) across all pieces reported in 3.
Accordingly, Kohanpur teaches considering the porosity and absolute permeability of the signature parts of the pore network. Thus, relating the weighted average of the porosity statistic for every generated PN corresponds with aligning to a weighted average porosity value.
Claim 15 further recites “15. The method of claim 12, further comprising: removing a first portion of one or more generated pore elements from the heterogeneous plug, the first portion of one or more generated pore elements being outside of a target shape, a target domain, or both the target shape and the target domain.” From the above list of alternatives Examiner is selecting “a target shape.”
Kohanpur pages 8-9 across the page-break discloses “a further step of adding or removing some pore-throats in the stitching layer is applied to get the average connection number within a threshold of the arithmetic mean of left and right PNs.”
Kohanpur page 7 first paragraph discloses “We use statistics of geometrical and topological information of pore elements in the signature PNs in this workflow.” Kohanpur page 9 third paragraph disclose “The radius, shape factor, and volume of these generated pore-throats are also determined by fitting a proper distribution function.” Fitting a distribution on the radius and shape factor correspond with aligning properties of the heterogeneous plug to a target shape.
Claim 15 further recites “defining a mid-plane substantially perpendicular to a direction of flow; based on the mid-plane, defining one or more inlet pores and one or more outlet pores.” Kohanpur page 8 section 2.1 second paragraph discloses:
Initially, the algorithm reads in all information of the left and right PNs, represented in yellow in Fig. 4, including: index, location, radius, length, shape factor, volume, connectivity, and inlet and outlet status of all pore-bodies and pore-throats. The next step is to remove outlet pore-throats of left PN and inlet pore-throats of right PN based on the flow direction assumption. This affect the inlet and outlet status of corresponding pore-bodies in those locations.
Defining inlet and outlet status according to an assumed flow direction corresponds with defining inlet and outlets for pores based on the direction of flow.
Claim 15 further recites “and adjusting a first porosity of the heterogeneous plug to substantially align with a second porosity of a representative pore network.” Kohanpur page 10 last paragraph discloses:
One such approach would be to use parameters (e.g., mean, variance) of derived fitting distributions. We accomplish this by concatenating the pore element lists (pore-body radius and volume, connection number, pore-throat radius and volume, pore-throat length, and pore-throat total length) for all signature PNs with a weight for each based on their relative center-to-center distance to the generated PN i.e. the closer to a signature PN, the more it is influenced by its statistics.
Fitting to derived parameters of distance-weighted pore elements corresponds with aligning the properties of the generated heterogeneous plug to parameters of a representative pore network.
Kohanpur page 11 first paragraph disclose “we can relate the weighted average to all signature PNs of the entire domain simultaneously and thus, the footprint of their statistics can be found in every single generated PN of the domain.” Relating the weighted average statistics to every generated PN corresponds to using a weighted average of each respective statistic.
Kohanpur page 22 second paragraph discloses:
Fig. 21 shows a PN representation of this sample and how it is divided into 12 equal-size pieces. We labeled them from 1 to 12, starting from bottom left corner as depicted in Fig. 21, and marked three signature parts (pieces 3, 5, and 10) out of it based on the calculated range of porosity (from 0.152 to 0.221) and absolute permeability (from 219 mD to 1972 mD) across all pieces reported in 3.
Accordingly, Kohanpur teaches considering the porosity and absolute permeability of the signature parts of the pore network. Thus, relating the weighted average of the porosity statistic for every generated PN corresponds with aligning to a weighted average porosity value and is aligning the porosity between the generated heterogeneous plug and the representative pore network.
Claim 16 further recites “16. The method of claim 1, wherein the porous media sample is a rock sample.” Kohanpur abstract disclose “in heterogeneous natural rocks.”
Claim 17 recites “17. An apparatus, comprising: a memory comprising executable instructions, and one or more processors configured to execute the executable instructions.” Kohanpur does not explicitly disclose processor and memory; however, in analogous art of reservoir simulation from core analysis, Nie column 11 lines 62-65 teaches “The computer includes a processor 901 (possibly including multiple processors, multiple cores, multiple nodes, and/or implementing multi-threading, etc.). The computer includes memory 907.”
It would have been obvious to a person having ordinary skill in the art before the effective filing date of the claimed invention to combine Kohanpur and Nie. One having ordinary skill in the art would have found motivation to use computer implementation into the system of pore-network stitching for the advantageous purpose “to perform multiscale modeling with the image data at different resolutions and a digital experiment.” See Nie column 3 lines 44-45.
Claim 17 further recites “and cause the apparatus to: duplicate one or more sample pore elements to fill one or more components of a heterogeneous plug.” Kohanpur page 5 section 2 second paragraph discloses:
The first step is to examine the entire coarse resolution scans of the core to find unique size and shape of solid grains and local pore structure in different locations and identify heterogeneities. Assessment and integration of multiple scales of 2D and 3D micro-CT images can be used to investigate geometry, connectivity, and heterogeneity of pore space (Long et al. 2013). …. The outcome of this analysis is identification of selected locations of the core that are unique in terms of pore size and structure that are defined as signature parts, as shown with red boxes in Fig. 1. The hypothesis is that these signature parts, which might or might not be adjacent, are sufficient to represent the heterogeneity of the core.
Identifying heterogeneities indicates it is of a heterogeneous plug. The signature parts of the core correspond with duplicated sample pore elements which are used as one or more of the components of the pore network model.
Claim 17 further recites “construct a set of buffer zones between each of the one or more components; populate the set of buffer zones with one or more generated pore elements, the one or more generated pore elements based, at least in part, on the one or more sample pore elements.” Kohanpur page 7 second paragraph discloses:
Since the space between signature PNs in the core can be relatively large and cannot be directly extracted, the next important step of the workflow is to fill the space between signature PNs by new defined pore elements. We accomplish this by using statistics of the signature PNs and a stochastic algorithm to generate new PNs in the empty regions of the domain.
The empty regions of the domain correspond with buffer zones between the signature pore networks (PNs). Using statistics of signature pore networks (PNs) corresponds with using statistics of the sample pore elements.
Claim 17 further recites “and connect the one or more generated pore elements to the one or more components to generate the heterogeneous plug.” Kohanpur page 8 figure 4 discloses “Fig. 4 Layered stitching of two adjacent pore-networks by generating a layer of pore elements.” Stitching together two adjacent pore-networks corresponds to connecting the generated pore elements (e.g. the new PNs) to the components (e.g. signature pore-networks) to generate the heterogeneous plug.
Kohanpur page 11 second paragraph discloses “Once all empty spaces are filled with generated PNs, we use the layered stitching in longitudinal and lateral directions to connect all PNs together as shown in Fig. 5.”
Dependent claim 18 is substantially similar to claim 2 above and is rejected for the same reasons.
“Dependent” claim 19 is substantially similar to claim 12 above and is rejected for the same reasons.
Claim 20 recites “20. A non-transitory computer-readable medium comprising executable instructions that, when executed by one or more processors of an apparatus.” Kohanpur does not explicitly disclose processors; however, in analogous art of reservoir simulation from core analysis, Nie column 11 lines 62-65 teaches “The computer includes a processor 901 (possibly including multiple processors, multiple cores, multiple nodes, and/or implementing multi-threading, etc.).
Nie column 12 lines 49-50 disclose “program code/instructions stored in one or more machine-readable media.”
It would have been obvious to a person having ordinary skill in the art before the effective filing date of the claimed invention to combine Kohanpur and Nie. One having ordinary skill in the art would have found motivation to use computer implementation into the system of pore-network stitching for the advantageous purpose “to perform multiscale modeling with the image data at different resolutions and a digital experiment.” See Nie column 3 lines 44-45.
Claim 20 further recites “cause the apparatus to: duplicate one or more sample pore elements to fill one or more components of a heterogeneous plug.” Kohanpur page 5 section 2 second paragraph discloses:
The first step is to examine the entire coarse resolution scans of the core to find unique size and shape of solid grains and local pore structure in different locations and identify heterogeneities. Assessment and integration of multiple scales of 2D and 3D micro-CT images can be used to investigate geometry, connectivity, and heterogeneity of pore space (Long et al. 2013). …. The outcome of this analysis is identification of selected locations of the core that are unique in terms of pore size and structure that are defined as signature parts, as shown with red boxes in Fig. 1. The hypothesis is that these signature parts, which might or might not be adjacent, are sufficient to represent the heterogeneity of the core.
Identifying heterogeneities indicates it is of a heterogeneous plug. The signature parts of the core correspond with duplicated sample pore elements which are used as one or more of the components of the pore network model.
Claim 20 further recites “construct a set of buffer zones between each of the one or more components; populate the set of buffer zones with one or more generated pore elements, the one or more generated pore elements based, at least in part, on the one or more sample pore elements.” Kohanpur page 7 second paragraph discloses:
Since the space between signature PNs in the core can be relatively large and cannot be directly extracted, the next important step of the workflow is to fill the space between signature PNs by new defined pore elements. We accomplish this by using statistics of the signature PNs and a stochastic algorithm to generate new PNs in the empty regions of the domain.
The empty regions of the domain correspond with buffer zones between the signature pore networks (PNs). Using statistics of signature pore networks (PNs) corresponds with using statistics of the sample pore elements.
Claim 20 further recites “and connect the one or more generated pore elements to the one or more components to generate the heterogeneous plug.” Kohanpur page 8 figure 4 discloses “Fig. 4 Layered stitching of two adjacent pore-networks by generating a layer of pore elements.” Stitching together two adjacent pore-networks corresponds to connecting the generated pore elements (e.g. the new PNs) to the components (e.g. signature pore-networks) to generate the heterogeneous plug.
Kohanpur page 11 second paragraph discloses “Once all empty spaces are filled with generated PNs, we use the layered stitching in longitudinal and lateral directions to connect all PNs together as shown in Fig. 5.”
Dependent Claim 3
Claim 3 is rejected under 35 U.S.C. 103 as being unpatentable over Kohanpur and Nie as applied to claim 2 above, and further in view of CN 110320137 A KONG, Qiang-fu et al. (All citations refer to attached Google Patent machine translation) [herein “Kong”].
Claim 3 further recites “3. The method of claim 2, wherein obtaining the representative pore network comprises: defining, for the representative pore network, porosity values, …, a pore body density, and a volume percentage occupied by the one or more sample pore elements.” Kohanpur page 5 section 2 second paragraph discloses:
The first step is to examine the entire coarse resolution scans of the core to find unique size and shape of solid grains and local pore structure in different locations and identify heterogeneities. Assessment and integration of multiple scales of 2D and 3D micro-CT images can be used to investigate geometry, connectivity, and heterogeneity of pore space (Long et al. 2013). …. The outcome of this analysis is identification of selected locations of the core that are unique in terms of pore size and structure that are defined as signature parts, as shown with red boxes in Fig. 1. The hypothesis is that these signature parts, which might or might not be adjacent, are sufficient to represent the heterogeneity of the core.
Assessment of the geometry, connectivity, and heterogeneity of the defined signature parts corresponds with defining for the representative network properties of the sample pore elements.
Kohanpur page 22 second paragraph discloses:
Fig. 21 shows a PN representation of this sample and how it is divided into 12 equal-size pieces. We labeled them from 1 to 12, starting from bottom left corner as depicted in Fig. 21, and marked three signature parts (pieces 3, 5, and 10) out of it based on the calculated range of porosity (from 0.152 to 0.221) and absolute permeability (from 219 mD to 1972 mD) across all pieces reported in 3.
Accordingly, Kohanpur teaches considering the porosity and absolute permeability of the signature parts of the pore network.
Kohanpur page 8 section 2.1 second paragraph disclose “density of pore-bodies (number of elements per the box volume) for each PN is calculated and their arithmetic mean is computed.” The density of pore-bodies corresponds with a pore body density.
Kohanpur page 6 last paragraph discloses “geometrical information of pore elements are stored including the location, radius, volume, length, total length, and shape factor. … V is volume of the voxelized element.” The volume of the voxelized element corresponds with a volume occupied by the one or more sample pore elements. The volume percentage is directly proportional to the volume of the pore elements. Accordingly, the geometric volume of the pore elements corresponds with a volume percentage. Furthermore, Kohanpur page 7 first paragraph discloses “We use statistics of geometrical and topological information of pore elements in the signature PNs in this workflow.”
Claim 3 further recites “clay content values.” Kohanpur does not explicitly disclose clay content values; however, in analogous art of pore network models, Kong page 4 fourteenth paragraph teaches “determine other parameters for generating the small-scale pore network model, including the physical size, porosity, clay content, pore spacing ratio, and minimum pore radius of the pore network model, and randomly distribute the pore bodies in the physical space. According to the setting of the throat parameters, the pore bodies are connected to establish a pore network model.” Determining a clay content for the pore network model corresponds with defining a clay content for the pore network.
It would have been obvious to a person having ordinary skill in the art before the effective filing date of the claimed invention to combine Kohanpur, Nie, and Kong . One having ordinary skill in the art would have found motivation to use clay content characteristics into the system of pore-network stitching for the advantageous purpose of “simultaneously describe the characteristics of macropores and micropores, greatly improves the connectivity representation of the entire carbonate double-pore digital core, and contributes to permeability Numerical simulation of rock physical properties.” See Kong page 3 fifteenth paragraph.
Claim 3 further recites “and removing, from the one or more sample pore elements, a set of surface pore elements to generate the one or more sample pore elements.” Kohanpur abstract discloses:
This workflow uses micro-CT images of heterogeneous reservoir rock cores at different resolutions to characterize the pore structure in order to select few signature parts of the core and extract their equivalent pore-network models. The space between these signature pore-networks is filled by using their statistics to generate realizations of pore-networks which are then connected together using a deterministic layered stitching method. The output of this workflow is a large pore-network that can be used in any flow and transport solver.
Extracting the pore-network models resulting in space between the signature pore-networks corresponds with removing, from the sample pore elements, a set of pore elements. That is, the extraction is a removal of the non-extracted signature pore networks.
Dependent Claims 9 and 10
Claims 9 and 10 are rejected under 35 U.S.C. 103 as being unpatentable over Kohanpur and Nie as applied to claim 1 above, and further in view of Idowu, N. & Blunt, M. “Pore-Scale Modelling of Rate Effects in Waterflooding” Transportation in Porous Media, vol. 83, pp. 151-169 (2010) (cited in IDS dated 30 June 2025) [herein “Idowu”].
Claim 9 further recites “9. The method of claim 1, wherein populating the set of buffer zones comprises populating the buffer zones using stencil based stochastic pore network generation.” Kohanpur page 7 second paragraph discloses:
Since the space between signature PNs in the core can be relatively large and cannot be directly extracted, the next important step of the workflow is to fill the space between signature PNs by new defined pore elements. We accomplish this by using statistics of the signature PNs and a stochastic algorithm to generate new PNs in the empty regions of the domain.
Kohanpur page 9 section 2.2 disclose “This approach allows using available PN generators in the literature, such as the stochastic network generator developed by Idowu and Blunt (2010), which we have used in this workflow.”
Kohanpur does not explicitly disclose that the stochastic network generator of Idowu is stencil based; however, in analogous art of pore-network modeling, Idowu page 164 sixth bullet item teaches “Impose a maximum distance between two connected pores.” The maximum distance between connected pores corresponds with a stencil. Accordingly, the Stochastic Network Algorithm corresponds with a stencil based stochastic pore network generator.
It would have been obvious to a person having ordinary skill in the art before the effective filing date of the claimed invention to combine Kohanpur, Nie, and Idowu. One having ordinary skill in the art would have found motivation to use Idowu’s Stochastic Network Algorithm into the system of pore-network stitching because Kohanpur explicitly cites Idowu as a reference for the stochastic algorithm.
Claim 10 further recites “10. The method of claim 1, wherein connecting the one or more generated pore elements to the one or more components comprises: generating a stitch queue of one or more stitch pore bodies, the one or more stitch pore bodies comprising a portion of one or more generated pore bodies, the portion classified based on a coordination number deficiency.” Kohanpur does not explicitly disclose a stitch queue; however, in analogous art of pore-network modeling, Idowu page 164 bullet items 3-4 teach:
• As the pores are being placed, assign pore index of 1 to the first pore, 2 to the second pore and so on.
• The coordination number and geometrical information (radius, volume and length) for each pore from the original network are randomly assigned to the pores in the stochastic network.
The pore index corresponds with a stitch queue of pore bodies. The assigned coordination number corresponds with an initial coordination number.
Idowu page 164 bullet items 3-4 teach:
• Then, take the pore with the next value of the pore index. If the coordination number is j, connect it to k nearest neighbours. Reject a nearest neighbour if they already have reached their allowed coordination number
Iterating through the pore index and adding nearest neighbors according to the coordination number is ensuring each pore is classified according to a needed coordination number.
It would have been obvious to a person having ordinary skill in the art before the effective filing date of the claimed invention to combine Kohanpur, Nie, and Idowu. One having ordinary skill in the art would have found motivation to use Idowu’s Stochastic Network Algorithm into the system of pore-network stitching because Kohanpur explicitly cites Idowu as a reference for the stochastic algorithm.
Claim 10 further recites “selecting a target stitch pore body from the stitch queue; identifying one or more neighboring stitch pores.” Idowu page 164 bullet items 3-4 teach:
• Then, take the pore with the next value of the pore index. If the coordination number is j, connect it to k nearest neighbours. Reject a nearest neighbour if they already have reached their allowed coordination number
Taking the next pore from the pore index corresponds with selecting a target stitch pore from the stitch queue. Connecting with the k nearest neighbors corresponds with identifying neighboring stitch pores.
Claim 10 further recites “the one or more neighboring stitch pores identified according to a search radius assigned to the target stitch pore.” Idowu page 164 sixth bullet item teaches “Impose a maximum distance between two connected pores.” The maximum distance between connected pores corresponds with a search radius.
Claim 10 further recites “ranking the one or more neighboring stitch pores to classify one of the one or more neighboring stich pores as a highest-ranked neighbor pore; connecting the highest-ranked neighbor pore and the target stitch pore.” Idowu page 164 bullet items 3-4 teach:
• Then, take the pore with the next value of the pore index. If the coordination number is j, connect it to k nearest neighbours. Reject a nearest neighbour if they already have reached their allowed coordination number
Being a nearest neighbor corresponds with a ranking according to proximity to classify respective neighboring stitch pores and connecting the highest ranking (the closest).
Claim 10 further recites “and processing each of the one or more pore stitch bodies in the stitch queue to define the heterogeneous plug.” Idowu page 164 bullet items 9-10 teach “Repeat the above step for the next pore until the last pore with index n has been selected. • Once all the pores in the network have been connected.” Repeating to connect all pores in the network corresponds with processing each of the pore stitch bodies to define the heterogeneous plug.
Examiner Comment
Specification paragraph 61 discloses subject matter relating to an “add queue” for the “stencil replication procedure.” Examiner notes the “add queue” of the Specification is materially different from the “Stochastic Network Algorithm” of Idowu currently cited regarding claim 9 above. Accordingly, incorporation of the “add queue” subject matter in the context of the claimed “stencil based stochastic pore network generation” would overcome the current rejection. However, an updated search and examination would be required based on the specific claim language submitted in any subsequently filed amendment.
Allowable Subject Matter
Claim 5 is objected to as being dependent upon a rejected base claim, but would be allowable if rewritten in independent form including all of the limitations of the base claim and any intervening claims.
The following is a statement of reasons for the indication of allowable subject matter:
Kohanpur, A. & Valocchi, A. “Pore-network stitching method: A pore-to-core upscaling approach for multiphase flow” arXiv:2004.01523v1 (2020) (cited in IDS dated 30 June 2025) [herein “Kohanpur”] teaches stitching together extracted signature pore networks to form a larger pore-network using statistics of the extracted pore-network samples.
US patent 11,561,215 B2 Nie, et al. [herein “Nie”] column 3 lines 11-15 teaches “This pore overlapping contributes errors. Thus, the scale-coupled multiscale model evaluator removes the contributions from pore overlapping. A simulation can then be run with the multiscale model to obtain capillary pressure and relative permeability.
CN 110320137 A KONG, Qiang-fu et al. (All citations refer to attached Google Patent machine translation) [herein “Kong”] abstract teaches extracting a pore network model from a core sample. combining sub-samples of different scales. Generating additional throats to connect adjacent small-scale pore bodies. Kong page 4 teaches:
The specific connection process is shown in Fig. 6a-6c. Large-scale pore bodies (N1 , N2, and N3 regions in Fig. 6a) are within the search radius of the maximum pore spacing (black circle in Fig. The number of digits is constrained to generate additional throats (as shown by the additional round tubes generated around N1, N2, and N3 in Figure 6c) to connect with adjacent fine-scale pore bodies to generate the final pore network model.
Kong fails to teach the search radius is an arithmetic mean of a largest distance and an average distance between the first pore body and connected pore bodies.
Idowu, N. & Blunt, M. “Pore-Scale Modelling of Rate Effects in Waterflooding” Transportation in Porous Media, vol. 83, pp. 151-169 (2010) (cited in IDS dated 30 June 2025) [herein “Idowu”] appendix 1 teaches “Stochastic Network Algorithm.” Idowu page 164 sixth bullet item teaches “Impose a maximum distance between two connected pores.” The maximum distance corresponds with a largest distance between sample pore elements and connected pores. Idowu page 164 seventh bullet item teaches “Take the pore with an index of 1 that has coordination number of j and connect it with j nearest neighbour pores.” But Idowu fails to teach the search radius is an arithmetic mean of a largest distance and an average distance between the first pore body and connected pore bodies.
None of the references taken either alone or in combination with the prior art of record disclose “wherein the search radius is an arithmetic mean of a largest distance and an average distance between a first pore body of the one or more sample pore elements and any other pore body to which the first pore body is connected” in combination with the remaining elements and features of the claimed invention.
Conclusion
Prior art made of record and not relied upon is considered pertinent to applicant's disclosure.
US 10997327 B2 Fredrich; Joanne et al.
teaches
Direct numerical simulation of petrophysical properties of rocks with two or more immicible phases
US 10115188 B2 Roth; Sven
Porous material analysis for hydrology, petroleum and environment engineering
US 8909508 B2 Hurley; Neil F. et al.
Petrographic image analysis for determining capillary pressure in porous media
US 9285301 B2 De Prisco; Giuseppe et al.
Digital rock analysis with reliable multiphase permeability determination
Xu, B., et al. “Use of Pore Network Models to Simulate Laboratory Corefloods in a Heterogeneous Carbonate Sample” Society of Petroleum Engineers, SPE 38879 (1997)
“Pore network modeling offers an approach to improve our analysis of complex flows in such heterogeneous rocks.”
Computed Tomography (CT) data and mercury injection throat size.
Long, H., et al. “Multi-Scale Imaging and Modeling Workflow to Capture and Characterize Microporosity in Sandstone” Int’l Symp. of Society of Core Analysts (2013)
3D Tomographic image analysis to characterize rock samples.
Yakimchuk, I., et al. “Permeability and Porosity Study of Achimov Formation Using Digital Core Analysis” Society of Petroleum Engineers, SPE-196928-MS (2019)
Digital core analysis.
Yakimchuk page 7 calculates properties (porosity, etc.) from scans.
Jackson, S.J., et al. “Representative elementary volumes, hysteresis and heterogeneity in multiphase flow from the pore to continuum scale” Water Resources Research (2020)
Combining experimental coreflooding results with continuum scale Darcy flow modeling.
Wu, Y., et al. “Two-phase flow in heterogeneous porous media: A multiscale digital model approach” Int’l J. Heat & Mass Transfer, vol. 194, no. 123080 (June 2022)
Wu page 2 left column teaches “this study presents a new hybrid method to avoid the overlap of the large and small pores when two-scale pores are fused.” Wu digital models recreate pore/throat size distributions.
Chadwick, E., et al. “Incorporating the Effect of Gravity into Image-Based Drainage Simulations on Volumetric Images of Porous Media” Water Resources Research, vol. 58, e2021WR031509 (2022)
“Simulating drainage in volumetric images of porous materials is a key technique for studying multiphase flow and transport.”
“porous media consisted of randomly generated overlapping spheres.”
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/Jay Hann/Primary Examiner, Art Unit 2186 8 September 2026
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