DETAILED ACTION
Notice of Pre-AIA or AIA Status
The present application, filed on or after March 16, 2013, is being examined under the first inventor to file provisions of the AIA .
Responsive to communications filed on 09/13/2022
Claims 1-20 are pending in the application
Claims 1-20 are rejected
Claims 5,10,12,18, and 20 are objected to
Priority
Application Data Sheet received on 09/04/2022 makes no claim to foreign or domestic priority.
Information Disclosure Statement
The information disclosure statement filed 9/4/2022 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.
All US patents listed in IDS filed 9/4/2022 have been reviewed and considered by the examiner.
Drawings
Drawings received on 9/13/2022 accepted and reviewed by the examiner
Specification
Abstract received on 09/04/2022 contains no legal phraseology and is under 150 words. Abstract is accepted by the examiner.
Specification received on 09/04/2022 accepted by the examiner. U.S. Patent 10,914,140 is incorporated by reference into the disclosure.
Claim Objections
Claims 5, 10, 12, 18, and 20 are objected to because of the following informalities:
Claim 5 states: “The system of claim 4 wherein the function of step (e) includes to represent EDFM format data characterizing subsurface parameters as polygons to produce the output data.” Claim 5 was likely meant to be written as “The system of claim 4 wherein the function of step (e) includes to represent the EDFM format data characterizing subsurface parameters as polygons to produce the output data.”
Claim 10 states: “The system of claim 9 wherein the function to identify geometric relationships between the new created rock discontinuity or hydraulic fracture cells comprises identification of connections between new created rock discontinuity or hydraulic fracture cells corresponding to one or more subsurface rock discontinuities or hydraulic fractures.” This was likely meant to be written as “The system of claim 9 wherein the function to identify geometric relationships between the new created rock discontinuity or hydraulic fracture cells comprises identification of connections between the new created rock discontinuity or hydraulic fracture cells corresponding to one or more subsurface rock discontinuities or hydraulic fractures.”
Claim 12 states “A method for simulating a subterranean region having fracture geometries, comprising: obtaining discrete fracture network digital data produced by a first digital simulator module, the data representing a 3D model of a subterranean region; obtaining hydraulic fracture digital data produced by a second digital simulator module, the data representing a 3D model of the subterranean region;” The limitation “the data” is not limited to hydraulic fracture digital data, since the antecedent basis for the data may also refer to discrete fracture network digital data. The applicant likely intended to write “; obtaining hydraulic fracture digital data produced by a second digital simulator module, the hydraulic fracture digital data representing a 3D model of the subterranean region;”
Claim 18 states: “creating a plurality of new rock discontinuity or hydraulic fracture cells in the computational domain and identifying geometric relationships between the new created rock discontinuity or hydraulic fracture cells by identifying connections between new created rock discontinuity or hydraulic fracture cells” was likely meant to be written as “creating a plurality of new rock discontinuity or hydraulic fracture cells in the computational domain and identifying geometric relationships between the new created rock discontinuity or hydraulic fracture cells by identifying connections between the new created rock discontinuity or hydraulic fracture cells”
Claim 20 states “obtain discrete fracture network digital data produced by a first digital simulator module, the data representing a 3D model of a subterranean region; obtain hydraulic fracture digital data produced by a second digital simulator module, the data representing a 3D model of the subterranean region;” The limitation “the data” is not limited to hydraulic fracture digital data, since the antecedent basis for the data may also refer to discrete fracture network digital data. The applicant likely intended to write “obtain discrete fracture network digital data produced by a first digital simulator module, the data representing a 3D model of a subterranean region; obtain hydraulic fracture digital data produced by a second digital simulator module, the hydraulic fracture digital data representing a 3D model of the subterranean region;”
Appropriate correction is required.
Claim Interpretation
New: Multiple claims use the term “new,” such as in claim 3, “create at least one new rock discontinuity or fracture cell in the computational domain“. The term “New” as the examiner understands it does not mean an actual new fracture being simulated, rather, this is the general and normal workflow used in EDFM modeling, where new matrix cells are added to the computational fracture domain. For example, in par 38-39 of this specification “The computational domain represents three-dimensional interactions between the matrix cells, hydraulic fractures, and rock discontinuities using computational representations thereof. When a rock discontinuity or hydraulic fracture segment intersects a matrix cell, an additional cell is added in the computational domain to represent such interaction, which originally occurs in the physical domain. Newly added cells in the computational domain are different than the original matrix cells and can be called "rock discontinuity cells", for rock discontinuities; and "hydraulic fracture cells", for hydraulic fractures. Depending on the size of the rock discontinuity or hydraulic fracture segment, they can interact with either one or several matrix cells, which generates one or several rock discontinuity or hydraulic fracture cells, respectively. [0039] FIG. 4A represents the interaction between a natural fracture, a bedding layer, and three matrix cells in a reservoir in the physical domain. The natural fracture penetrates three matrix cells, resulting in the creation of three natural fracture segments and three new rock discontinuity cells in the computational domain, shown in FIG. 4B. “ Therefore, when the term “new” in the claims is references, this examiner understands this as simply adding a cell to the computational domain
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 5 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 applications subject to pre-AIA 35 U.S.C. 112, the applicant), regards as the invention.
Claim 5 recites the limitation "The system of claim 4 wherein the function of step (e) includes to represent EDFM format data characterizing subsurface parameters as polygons to produce the output data.". There is insufficient antecedent basis for the limitation of “EDFM format data characterizing subsurface parameters” as the previous claims which this claim depend on only introduce EDFM format data. The previous claims do not state that the EDFM format data characterizes subsurface parameters. The examiner is interpreting this limitation in claim 5 as a new limitation.
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 are rejected under 35 U.S.C. 103 as being unpatentable over “Development of an Efficient Embedded Discrete Fracture Model for 3D Compositional Reservoir Simulation in Fractured Reservoirs” by Ali Moinfar (Moinfar_2013) and further in view of “4. EDFM Demo Mauricio EDFM workshop video 4” by Sim Tech LLC (Sim_2021)
Claim 1:
Moinfar_2013 makes obvious A system for simulating a subterranean region having fracture geometries, comprising: (page viii par 1: “We developed an embedded discrete fracture model (EDFM) for an in-house fully-implicit compositional reservoir simulator.”) at least one processor configured with non-transitory instructions, which when executed cause the processor to perform functions including to: (par 31: “The EOS and chemical compositional modules of GPAS are developed under this framework and thus, multi-processor simulations are feasible using GPAS”).
obtain
page 96 par 3: “To evaluate the impact of grid resolution on accuracy of the EDFM approach, we also performed a mesh sensitivity study for the case in the absence of capillary pressure contrasts. Retaining the same fracture network, we examined three additional EDFM simulations that use uniform 40×40×1, 50×50×1, and 80×80×1 matrix grids, respectively, to check the convergence of the method as the grid is refined.” Examiner note: this passage indicates that a fracture network digital data is used for EDFM simulations. Where the same fracture network being used in different matrix grids shows that this is an independent fracture network from the EDFM simulation module.
Page 110 par 1: “In most simulation studies performed in the past, natural fractures were assumed to be vertical in the subsurface. However, the new implementation of the EDFM approach presented in this research provides an effective and reliable environment to model obliquely dipping fractures. Hence, the third example examines water-flooding into a 3D model reservoir shown in Figure 6.14, comprising 13 inclined fractures. The fractures are extended through different layers. We also investigate the effect of fracture inclination in this example. The dip angle of fractures ranges from 55 to 90 degrees. The matrix grid is 20×20×4, with cell dimensions of 25×25×20 ft in the x, y, and z directions, respectively.” Examiner note: This passage specifies the use of a 3D model
obtain
page 96 par 3: “To evaluate the impact of grid resolution on accuracy of the EDFM approach, we also performed a mesh sensitivity study for the case in the absence of capillary pressure contrasts. Retaining the same fracture network, we examined three additional EDFM simulations that use uniform 40×40×1, 50×50×1, and 80×80×1 matrix grids, respectively, to check the convergence of the method as the grid is refined.” Examiner note: this passage indicates that a fracture network digital data is used for EDFM simulations. Where the same fracture network being used in different matrix grids shows that this is an independent fracture network from the EDFM simulation module.
Page 110 par 1: “In most simulation studies performed in the past, natural fractures were assumed to be vertical in the subsurface. However, the new implementation of the EDFM approach presented in this research provides an effective and reliable environment to model obliquely dipping fractures. Hence, the third example examines water-flooding into a 3D model reservoir shown in Figure 6.14, comprising 13 inclined fractures. The fractures are extended through different layers. We also investigate the effect of fracture inclination in this example. The dip angle of fractures ranges from 55 to 90 degrees. The matrix grid is 20×20×4, with cell dimensions of 25×25×20 ft in the x, y, and z directions, respectively.” Examiner note: This passage specifies the use of a 3D model
c) convert the digital data from
page 43: “The implementation of EDFM includes two parts: [Symbol font/0xB7] Pre-processing of a fracture network with any geometry over an arbitrary grid to provide the required data for reservoir simulation” Examiner note: Where this pre-processing of a fracture network is a conversion of the fracture network into EDFM format.
produce a computational domain separate from the first digital simulator module and the second digital simulator module;
page 50 par 1: “Figure 4.4 specifies all gridblocks containing a segment of a fracture for both layers. Cells containing a segment of the black fracture are marked with black circles and those containing a segment of the red fracture are marked with red squares. In contrast to vertical fractures that cross similar gridblocks in different computational layers, inclined fractures may cross a different number of gridblocks in each layer, as shown in Figure 4.4. For the example under consideration, the black fracture penetrates 14 cells in the top computational layer while penetrating only 9 cells in the bottom one. Unlike the black fracture, the red fracture crosses more gridblocks in the bottom computational layer than in the top one.” (Examiner note: Based on the examiners understanding, figures 4.4 and 4.5 depict a computational domain/layer separate from the first or second digital “physical” simulators)
input the converted digital EDFM format data from step (c) into the computational domain to produce output data;
(page 60 par 3: “We have developed a pre-processing code to provide the required data (Examiners note: output data) for fluid-flow simulations in GPAS. The input of the pre-processing code is the description of the model reservoir including the reservoir dimensions, fracture network, location of wells, structured grid for the matrix domain, aperture and permeability of fractures, and porosity and permeability of matrix (Examiners note: this input as described is the digital EDFM format data). The following calculations are carried out in the pre-processing code:”) Examiners note: Where these calculations are output data. See also table 4.2 in page 66 which outlines an example step of the methodology. As shown, this produces data such as “fracture porosity” and other data.
Examiner note: The examiner would like to make a note to remove confusion from understanding and mapping. Previously, the examiner states that the preprocessing was a conversion of the fracture network into EDFM format. Then the examiner stated that the preprocessing is a conversion of the EDFM format data into output data. As the examiner understands, this pre-processing does do both functions. The pre-processing takes in the fracture network and layers into onto an arbitrary grid (converts to EDFM format) and also produces output data to be simulated.
input the output data of step (e) into a third digital simulator module; and
page 60 par 3: “We have developed a pre-processing code to provide the required data (Examiner note: the required data in this context is the output data of step (e).) for fluid-flow simulations in GPAS (Examiners note: This is the third digital simulator module). … page 62 par 1: “The entire model, including the matrix grid and fracture control volumes, (Examiner note: this is the output of step (e)) is entered into a reservoir simulator, which allows for non-neighboring connections and transmissibility modifiers”
generate a simulation of the subterranean region with the third digital simulator module.
page 62 par 1: “The entire model, including the matrix grid and fracture control volumes, is entered into a reservoir simulator, which allows for non-neighboring connections and transmissibility modifiers” … page 114 gives an example of a simulation “Figure 6.18 shows the pressure profiles after 150 days of production, computed by the EDFM approach. Pressure profiles in all three numerical layers are presented in Figure 6.18, depicting different pressure depletion patterns in various layers because of the presence of randomly-generated fractures. Also, Figure 6.19 shows the oil production rate and average reservoir pressure over two years of production. The computational time of the simulation (Examiner note: this is the third simulation that uses the fracture network information as well as the information computed by the EDFM approach/ format data) was 3.31 hours.”
Moinfar_2013 does not explicitly recite
Sim_2021 however, makes obvious
Minute 5:11, depicts a workflow where a DFN is shown as input and is modeled into EDFM. See also minute 6:30 which depicts the input data as a 3D model.
Minute 5:11, “EDGS platform can integrate explicit hydraulic fractures, natural fractures, and complex fractures with reservoir simulation modeling effectively based on the advanced EDFM technology.” See also minute 6:30 which depicts the input data as a 3D model.
Minute 5:11, depicts a workflow where a DFN is shown as input and is modeled into EDFM. Ending in a DFN+HF (Examiner note: discrete fracture network and hydraulic fractures) model. Where this flow chart makes obvious that the information from the DFN and hydraulic fractures was provided as input and converted into a EDFM format.
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Moinfar_2013 and Sim_2021 are analogous art to the claimed invention because they are from the same field of endeavor called reservoir simulation and modeling, specifically using an EDFM approach. Moinfar_2013 is a research paper that outlines a methodology with research examples, while sim_2021 explains a program that accomplishes that result.
Before the effective filing date, it would have been obvious to a person of ordinary skill in the art to combine Moinfar_2013 and Sim_2021.
The rationale for doing so would have been that it was “Obvious to try.” At the time of the invention, there was a recognized problem for simulating both a discrete fracture network as well as hydraulic fractures. Moinfar_2013 Page 1 par 3: “The importance of fractures in production of oil and gas is not limited to naturally fractured reservoirs. The exploitation of unconventional reservoirs is increasingly a major source of short- and long-term energy in the United States. The economic development of unconventional oil and gas hinges in part on effective stimulation of low-permeability rock through multi-stage hydraulic fracturing of horizontal wells. To achieve this goal, accurate characterization and simulation of production is necessary for selecting the best stimulation strategy.”
This passage shows that Moinfar_2013 understood that it would be necessary to be able to accurately simulate both the natural and hydraulic fracturing of a well. While Moinfar_2013 only describes the invention as using a fracture network, it would have been obvious to try including a discrete fracture network and hydraulic fracture digital data, as predictable solutions to use to model hydraulic and natural fractures in the well.
Therefore, it would have been obvious to combine the methodology and workflow for fracture simulation of Moinfar_2013 with including both discrete fracture network and hydraulic fracture data of Sim_2021 to achieve the goal of accurate characterization and simulation of production for selecting the best stimulation strategy.
Claim 2:
The system of claim 1 wherein:
Moinfar_2013 makes obvious the function of step (d) includes to produce a matrix grid in the produced computational domain; (page 50 par 1: “Figure 4.4 specifies all gridblocks containing a segment of a fracture for both layers. Cells containing a segment of the black fracture are marked with black circles and those containing a segment of the red fracture are marked with red squares. In contrast to vertical fractures that cross similar gridblocks in different computational layers, inclined fractures may cross a different number of gridblocks in each layer, as shown in Figure 4.4. For the example under consideration, the black fracture penetrates 14 cells in the top computational layer while penetrating only 9 cells in the bottom one. Unlike the black fracture, the red fracture crosses more gridblocks in the bottom computational layer than in the top one.” (Examiner note: Where “cells” are the cells of the matrix grid)
the function of step (e) includes to identify geometric relationships between the converted digital EDFM format data and matrix cells in the matrix grid. (page 60 under pre-processing code which is interpreted as the function of step (e), outlines processes including: “Check the intersection of each fracture plane (Examiner note: EDFM format data) with all matrix gridblocks and determine the exact specification of intersections.” )
Claim 3:The system of claim 2
Moinfar_2013 makes obvious wherein the function of step (e) includes to create at least one new rock discontinuity or hydraulic fracture cell in the computational domain (par 44: “The model uses a structured grid to represent the matrix, and introduces additional fracture control volumes by computing the intersection of fractures with the matrix grid” (Examiner note: see claim interpretation, this process as outlined is the creation of a “new” cell ))and identify a geometric relationship between the at least one new created rock discontinuity or hydraulic fracture cell and a cell in the matrix grid. (page 60 under pre-processing step: “Determine the arrangement of fracture control volumes in the fracture domain. Corresponding to each gridblock (Examiner note: cell in matrix grid) containing a segment of a fracture, a fracture 61 control volume should be defined in the fracture domain. Figure 4.5 presented an example of fracture arrangement for a simple 3D model reservoir. Furthermore, arrangement of fracture cells leads to the identification of dead blocks”)
Claim 4:The system of claim 3
Moinfar_2013 makes obvious wherein the function of step (e) includes to identify a non- neighboring connection between the at least one new created rock discontinuity or hydraulic fracture cell and a cell in the matrix grid. (Page 51 fig. 4.5 description: Arrangement of fracture cells in the fracture domain for (a) layer 1, and (b) layer 2 of the model shown in Figure 4.3. Fracture cells are marked and numbered in this figure, which correspond to those in Figure 4.4. The blue dashed lines represent non-neighboring connections (NNC) for the intersection of two fractures. Also, the green dashed line represents only one example of NNC between two cells of an individual fracture. … page 60 under pre-processing code which is interpreted as the function of step (e), outlines processes including: “Calculate the number of NNCs for each computational gridblock either in the matrix domain or in the fracture domain”)
Claim 5:The system of claim 4
Moinfar_2013 makes obvious wherein the function of step (e) includes to represent EDFM format data characterizing subsurface parameters as polygons to produce the output data. (Page 47: “Vertical and inclined fractures are discretized vertically and horizontally by the cell boundaries of the matrix grid. The intersection of a vertical fracture and a matrix gridblock is always a rectangle, as shown in Figure 4.1. However, when an arbitrarily-oriented fracture plane passes through a gridblock, regular intersections are polygons with 3, 4, 5, or 6 corners. Figure 4.2 shows possible intersections of an inclined fracture plane and a matrix gridblock, which can be a triangle, quadrilateral, pentagon, or hexagon. An exact specification of these intersections is important for calculating the connection between matrix and fracture.” Examiner note: Where this polygons are used in the calculations which produces the output data. )
Claim 6:
The system of claim 3
Moinfar_2013 makes obvious wherein the function to identify a geometric relationship between the at least one new created rock discontinuity or hydraulic fracture cell and a cell in the matrix grid comprises identification of a connection between the at least one new created rock discontinuity or hydraulic fracture cell and a matrix grid cell corresponding to one or more subsurface rock discontinuities or hydraulic fractures. (page 60 under pre-processing steps: “Check the intersection of each fracture plane with all matrix gridblocks and determine the exact specification of intersections. • Check if any two fracture planes intersect and identify the gridblocks in which the intersection lies. • Determine the arrangement of fracture control volumes in the fracture domain. Corresponding to each gridblock containing a segment of a fracture, a fracture 61 control volume should be defined in the fracture domain. Figure 4.5 presented an example of fracture arrangement for a simple 3D model reservoir. Furthermore, arrangement of fracture cells leads to the identification of dead blocks”)
Claim 7:The system of claim 3
Moinfar_2013 makes obvious wherein the function of step (e) includes to calculate a fluid flow transmissibility factor between the at least one new created rock discontinuity or hydraulic fracture cell and a cell in the matrix grid. (page 60 under pre-processing code which is interpreted as the function of step (e), outlines processes including: “Calculate the transmissibility factors in the x, y, and z directions for the fracture control volumes in the fracture domain.” … page 64 explains some of the pre-processing steps: “The fourth column presents the transmissibility of NNCs between corresponding matrix and fracture cells.”)
Claim 8:
The system of claim 7
Moinfar_2013 makes obvious wherein the function of step (g) includes to generate the simulation of the subterranean region using the calculated fluid flow transmissibility factor. (Page 62: “The entire model, including the matrix grid and fracture control volumes, is entered into a reservoir simulator, which allows for non-neighboring connections and transmissibility modifiers. The governing equations for fracture control volumes are similar to those described for the matrix medium, implying that Darcy’s law is used in the fracture domain. In order to implement the EDFM approach in GPAS, we have added the NNC term to the mass balance equations, as previously described in Equations 4.2 and 4.3. In doing so, the residual vector and the Jacobian matrix (see Chapter 3) are augmented with additional terms associated with NNCs. Other modifications to GPAS have also been implemented to properly include the parameters calculated in the pre-processing code. These parameters include porosity, permeability, and depth of fracture control volumes, transmissibility factors between NNCs, transmissibility factors between adjacent fracture cells, and well indices for the fractures intercepted by a well. Also, we have made necessary changes to bypass all calculations carried out for the dead blocks in the fracture domain.”
Claim 9:
The system of claim 2
Moinfar_2013 makes obvious wherein the function of step (e) includes to create a plurality of new rock discontinuity or hydraulic fracture cells in the computational domain (page 44: “The model uses a structured grid to represent the matrix, and introduces additional fracture control volumes by computing the intersection of fractures with the matrix grid” (Examiner note: see claim interpretation, this process as outlined is the creation of a “new” cell )) and identify geometric relationships between the new created rock discontinuity or hydraulic fracture cells. (page 60 under pre-processing step: “Determine the arrangement of fracture control volumes in the fracture domain. Corresponding to each gridblock (Examiner note: cell in matrix grid) containing a segment of a fracture, a fracture 61 control volume should be defined in the fracture domain. Figure 4.5 presented an example of fracture arrangement for a simple 3D model reservoir. Furthermore, arrangement of fracture cells leads to the identification of dead blocks”)
Claim 10:The system of claim 9
Moinfar_2013 wherein the function to identify geometric relationships between the new created rock discontinuity or hydraulic fracture cells comprises identification of connections between new created rock discontinuity or hydraulic fracture cells corresponding to one or more subsurface rock discontinuities or hydraulic fractures. (page 60 under pre-processing steps: “Check the intersection of each fracture plane with all matrix gridblocks and determine the exact specification of intersections. [Symbol font/0xB7] Check if any two fracture planes intersect and identify the gridblocks in which the intersection lies. [Symbol font/0xB7] Determine the arrangement of fracture control volumes in the fracture domain. Corresponding to each gridblock containing a segment of a fracture, a fracture 61 control volume should be defined in the fracture domain. Figure 4.5 presented an example of fracture arrangement for a simple 3D model reservoir. Furthermore, arrangement of fracture cells leads to the identification of dead blocks”)
Claim 11:The system of claim 9
Moinfar_2013 makes obvious wherein: the function of step (e) includes to calculate fluid flow transmissibility factors between the new created rock discontinuity or hydraulic fracture cells;
(page 60 under pre-processing code which is interpreted as the function of step (e), outlines processes including: “Calculate the transmissibility factors in the x, y, and z directions for the fracture control volumes in the fracture domain.”)
the function of step (g) includes to generate the simulation of the subterranean region using the calculated fluid flow transmissibility factors.
(Page 62: “The entire model, including the matrix grid and fracture control volumes, is entered into a reservoir simulator, which allows for non-neighboring connections and transmissibility modifiers. The governing equations for fracture control volumes are similar to those described for the matrix medium, implying that Darcy’s law is used in the fracture domain. In order to implement the EDFM approach in GPAS, we have added the NNC term to the mass balance equations, as previously described in Equations 4.2 and 4.3. In doing so, the residual vector and the Jacobian matrix (see Chapter 3) are augmented with additional terms associated with NNCs. Other modifications to GPAS have also been implemented to properly include the parameters calculated in the pre-processing code. These parameters include porosity, permeability, and depth of fracture control volumes, transmissibility factors between NNCs, transmissibility factors between adjacent fracture cells, and well indices for the fractures intercepted by a well. Also, we have made necessary changes to bypass all calculations carried out for the dead blocks in the fracture domain.”)
Claim 12:Moinfar_2013 makes obvious A method for simulating a subterranean region having fracture geometries, comprising: (page viii par 1: “We developed an embedded discrete fracture model (EDFM) for an in-house fully-implicit compositional reservoir simulator.”)
obtaining
page 96 par 3: “To evaluate the impact of grid resolution on accuracy of the EDFM approach, we also performed a mesh sensitivity study for the case in the absence of capillary pressure contrasts. Retaining the same fracture network, we examined three additional EDFM simulations that use uniform 40×40×1, 50×50×1, and 80×80×1 matrix grids, respectively, to check the convergence of the method as the grid is refined.” Examiner note: this passage indicates that a fracture network digital data is used for EDFM simulations. Where the same fracture network being used in different matrix grids shows that this is an independent fracture network from the EDFM simulation module.
Page 110 par 1: “In most simulation studies performed in the past, natural fractures were assumed to be vertical in the subsurface. However, the new implementation of the EDFM approach presented in this research provides an effective and reliable environment to model obliquely dipping fractures. Hence, the third example examines water-flooding into a 3D model reservoir shown in Figure 6.14, comprising 13 inclined fractures. The fractures are extended through different layers. We also investigate the effect of fracture inclination in this example. The dip angle of fractures ranges from 55 to 90 degrees. The matrix grid is 20×20×4, with cell dimensions of 25×25×20 ft in the x, y, and z directions, respectively.” Examiner note: This passage specifies the use of a 3D model
obtaining
page 96 par 3: “To evaluate the impact of grid resolution on accuracy of the EDFM approach, we also performed a mesh sensitivity study for the case in the absence of capillary pressure contrasts. Retaining the same fracture network, we examined three additional EDFM simulations that use uniform 40×40×1, 50×50×1, and 80×80×1 matrix grids, respectively, to check the convergence of the method as the grid is refined.” Examiner note: this passage indicates that a fracture network digital data is used for EDFM simulations. Where the same fracture network being used in different matrix grids shows that this is an independent fracture network from the EDFM simulation module.
Page 110 par 1: “In most simulation studies performed in the past, natural fractures were assumed to be vertical in the subsurface. However, the new implementation of the EDFM approach presented in this research provides an effective and reliable environment to model obliquely dipping fractures. Hence, the third example examines water-flooding into a 3D model reservoir shown in Figure 6.14, comprising 13 inclined fractures. The fractures are extended through different layers. We also investigate the effect of fracture inclination in this example. The dip angle of fractures ranges from 55 to 90 degrees. The matrix grid is 20×20×4, with cell dimensions of 25×25×20 ft in the x, y, and z directions, respectively.” Examiner note: This passage specifies the use of a 3D model
converting the obtained (page 43: “The implementation of EDFM includes two parts: [Symbol font/0xB7] Pre-processing of a fracture network with any geometry over an arbitrary grid to provide the required data for reservoir simulation” Examiner note: Where this pre-processing of a fracture network is a conversion of the fracture network into EDFM format. )
producing a computational domain separate from the first digital simulator module and the second digital simulator module; (page 50 par 1: “Figure 4.4 specifies all gridblocks containing a segment of a fracture for both layers. Cells containing a segment of the black fracture are marked with black circles and those containing a segment of the red fracture are marked with red squares. In contrast to vertical fractures that cross similar gridblocks in different computational layers, inclined fractures may cross a different number of gridblocks in each layer, as shown in Figure 4.4. For the example under consideration, the black fracture penetrates 14 cells in the top computational layer while penetrating only 9 cells in the bottom one. Unlike the black fracture, the red fracture crosses more gridblocks in the bottom computational layer than in the top one.” (Examiner note: Based on the examiners understanding, figures 4.4 and 4.5 depict a computational domain/layer separate from the first or second digital “physical” simulators)
inputting the converted digital EDFM format data into the computational domain to produce output data; (page 60 par 3: “We have developed a pre-processing code to provide the required data (Examiners note: output data) for fluid-flow simulations in GPAS. The input of the pre-processing code is the description of the model reservoir including the reservoir dimensions, fracture network, location of wells, structured grid for the matrix domain, aperture and permeability of fractures, and porosity and permeability of matrix (Examiners note: this input as described is the digital EDFM format data). The following calculations are carried out in the pre-processing code:”) Examiners note: Where these calculations are output data. See also table 4.2 in page 66 which outlines an example step of the methodology. As shown, this produces data such as “fracture porosity” and other data. )
Examiner note: The examiner would like to make a note to remove confusion from understanding and mapping. Previously, the examiner states that the preprocessing was a conversion of the fracture network into EDFM format. Then the examiner stated that the preprocessing is a conversion of the EDFM format data into output data. As the examiner understands, this pre-processing does do both functions. The pre-processing takes in the fracture network and layers into onto an arbitrary grid (converts to EDFM format) and also produces output data to be simulated.
inputting the output data into a third digital simulator module;
(page 60 par 3: “We have developed a pre-processing code to provide the required data (Examiner note: the required data in this context is the output data of step (e).) for fluid-flow simulations in GPAS (Examiners note: This is the third digital simulator module). … page 62 par 1: “The entire model, including the matrix grid and fracture control volumes, (Examiner note: this is the output of step (e)) is entered into a reservoir simulator, which allows for non-neighboring connections and transmissibility modifiers”)
and generating a simulation of the subterranean region with the third digital simulator module.
page 62 par 1: “The entire model, including the matrix grid and fracture control volumes, is entered into a reservoir simulator, which allows for non-neighboring connections and transmissibility modifiers” … page 114 gives an example of a simulation “Figure 6.18 shows the pressure profiles after 150 days of production, computed by the EDFM approach. Pressure profiles in all three numerical layers are presented in Figure 6.18, depicting different pressure depletion patterns in various layers because of the presence of randomly-generated fractures. Also, Figure 6.19 shows the oil production rate and average reservoir pressure over two years of production. The computational time of the simulation (Examiner note: this is the third simulation that uses the fracture network information as well as the information computed by the EDFM approach/ format data) was 3.31 hours.”
Moinfar_2013 does not explicitly recite
Sim_2013 however makes obvious
Minute 5:11, depicts a workflow where a DFN is shown as input and is modeled into EDFM. See also minute 6:30 which depicts the input data as a 3D model.
Minute 5:11, “EDGS platform can integrate explicit hydraulic fractures, natural fractures, and complex fractures with reservoir simulation modeling effectively based on the advanced EDFM technology.” See also minute 6:30 which depicts the input data as a 3D model.
Minute 5:11, depicts a workflow where a DFN is shown as input and is modeled into EDFM. Ending in a DFN+HF (Examiner note: discrete fracture network and hydraulic fractures) model. Where this flow chart makes obvious that the information from the DFN and hydraulic fractures was provided as input and converted into a EDFM format.
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As already stated and outlined in claim 1, it would have been obvious to combine the methodology and workflow for fracture simulation of Moinfar_2013 with including both discrete fracture network and hydraulic fracture data of Sim_2021 to achieve the goal of accurate characterization and simulation of production for selecting the best stimulation strategy.
Claim 13:
The method of claim 12
Moinfar_2013 makes obvious wherein: producing a computational domain comprises producing a matrix grid in the computational domain; (page 50 par 1: “Figure 4.4 specifies all gridblocks containing a segment of a fracture for both layers. Cells containing a segment of the black fracture are marked with black circles and those containing a segment of the red fracture are marked with red squares. In contrast to vertical fractures that cross similar gridblocks in different computational layers, inclined fractures may cross a different number of gridblocks in each layer, as shown in Figure 4.4. For the example under consideration, the black fracture penetrates 14 cells in the top computational layer while penetrating only 9 cells in the bottom one. Unlike the black fracture, the red fracture crosses more gridblocks in the bottom computational layer than in the top one.” (Examiner note: Where “cells” are the cells of the matrix grid)
inputting the converted digital EDFM format data into the computational domain comprises identifying geometric relationships between the EDFM format data and matrix cells in the matrix grid.
(page 60 under pre-processing code which is interpreted as the function of step (e), outlines processes including: “Check the intersection of each fracture plane (Examiner note: EDFM format data) with all matrix gridblocks and determine the exact specification of intersections.” )
Claim 14:The method of claim 13
Moinfar_2013 makes obvious wherein inputting the converted digital EDFM format data into the computational domain comprises creating at least one new rock discontinuity or hydraulic fracture cell in the computational domain (par 44: “The model uses a structured grid to represent the matrix, and introduces additional fracture control volumes by computing the intersection of fractures with the matrix grid” (Examiner note: see claim interpretation, this process as outlined is the creation of a “new” cell )) and identifying a geometric relationship between the at least one new created rock discontinuity or hydraulic fracture cell and a cell in the matrix grid. (page 60 under pre-processing step: “Determine the arrangement of fracture control volumes in the fracture domain. Corresponding to each gridblock (Examiner note: cell in matrix grid) containing a segment of a fracture, a fracture 61 control volume should be defined in the fracture domain. Figure 4.5 presented an example of fracture arrangement for a simple 3D model reservoir. Furthermore, arrangement of fracture cells leads to the identification of dead blocks”)
Claim 15:The method of claim 14
Moinfar_2013 makes obvious wherein inputting the converted digital EDFM format data into the computational domain comprises identifying a non-neighboring connection between the at least one new created rock discontinuity or hydraulic fracture cell and a cell in the matrix grid. (Page 51 fig. 4.5 description: Arrangement of fracture cells in the fracture domain for (a) layer 1, and (b) layer 2 of the model shown in Figure 4.3. Fracture cells are marked and numbered in this figure, which correspond to those in Figure 4.4. The blue dashed lines represent non-neighboring connections (NNC) for the intersection of two fractures. Also, the green dashed line represents only one example of NNC between two cells of an individual fracture. … page 60 under pre-processing code which is interpreted as the function of step (e), outlines processes including: “Calculate the number of NNCs for each computational gridblock either in the matrix domain or in the fracture domain”)
Claim 16:The method of claim 14
Moinfar_2013 makes obvious wherein identifying a geometric relationship between the at least one new created rock discontinuity or hydraulic fracture cell and a cell in the matrix grid comprises identifying a connection between the at least one new created rock discontinuity or hydraulic fracture cell and a matrix grid cell corresponding to one or more subsurface rock discontinuities or hydraulic fractures. (page 60 under pre-processing steps: “Check the intersection of each fracture plane with all matrix gridblocks and determine the exact specification of intersections. • Check if any two fracture planes intersect and identify the gridblocks in which the intersection lies. • Determine the arrangement of fracture control volumes in the fracture domain. Corresponding to each gridblock containing a segment of a fracture, a fracture 61 control volume should be defined in the fracture domain. Figure 4.5 presented an example of fracture arrangement for a simple 3D model reservoir. Furthermore, arrangement of fracture cells leads to the identification of dead blocks”)
Claim 17:
The method of claim 14
Moinfar_2013 makes obvious wherein inputting the converted digital EDFM format data into the computational domain comprises calculating a fluid flow transmissibility factor between the at least one new created rock discontinuity or hydraulic fracture cell and the cell in the matrix grid; (page 60 under pre-processing code which is interpreted as the function of step (e), outlines processes including: “Calculate the transmissibility factors in the x, y, and z directions for the fracture control volumes in the fracture domain.” … page 64 explains some of the pre-processing steps: “The fourth column presents the transmissibility of NNCs between corresponding matrix and fracture cells.”) and generating the simulation of the subterranean region comprises generating the simulation using the calculated fluid flow transmissibility factor. (Page 62: “The entire model, including the matrix grid and fracture control volumes, is entered into a reservoir simulator, which allows for non-neighboring connections and transmissibility modifiers. The governing equations for fracture control volumes are similar to those described for the matrix medium, implying that Darcy’s law is used in the fracture domain. In order to implement the EDFM approach in GPAS, we have added the NNC term to the mass balance equations, as previously described in Equations 4.2 and 4.3. In doing so, the residual vector and the Jacobian matrix (see Chapter 3) are augmented with additional terms associated with NNCs. Other modifications to GPAS have also been implemented to properly include the parameters calculated in the pre-processing code. These parameters include porosity, permeability, and depth of fracture control volumes, transmissibility factors between NNCs, transmissibility factors between adjacent fracture cells, and well indices for the fractures intercepted by a well. Also, we have made necessary changes to bypass all calculations carried out for the dead blocks in the fracture domain.”
Claim 18:
The method of claim 13
Moinfar_2013 makes obvious wherein inputting the converted digital EDFM format data into the computational domain comprises creating a plurality of new rock discontinuity or hydraulic fracture cells in the computational domain (page 44: “The model uses a structured grid to represent the matrix, and introduces additional fracture control volumes by computing the intersection of fractures with the matrix grid” (Examiner note: see claim interpretation, this process as outlined is the creation of a “new” cell )) and identifying geometric relationships between the new created rock discontinuity or hydraulic fracture cells by identifying connections between new created rock discontinuity or hydraulic fracture cells corresponding to one or more subsurface rock discontinuities or hydraulic fractures. (page 60 under pre-processing steps: “Check the intersection of each fracture plane with all matrix gridblocks and determine the exact specification of intersections. [Symbol font/0xB7] Check if any two fracture planes intersect and identify the gridblocks in which the intersection lies. [Symbol font/0xB7] Determine the arrangement of fracture control volumes in the fracture domain. Corresponding to each gridblock containing a segment of a fracture, a fracture 61 control volume should be defined in the fracture domain. Figure 4.5 presented an example of fracture arrangement for a simple 3D model reservoir. Furthermore, arrangement of fracture cells leads to the identification of dead blocks”)
Claim 19:The method of claim 18 wherein:
Moinfar_2013 makes obvious inputting the converted digital EDFM format data into the computational domain comprises calculating fluid flow transmissibility factors between the new created rock discontinuity or hydraulic fracture cells; (page 60 under pre-processing code which is interpreted as the function of step (e), outlines processes including: “Calculate the transmissibility factors in the x, y, and z directions for the fracture control volumes in the fracture domain.”)
generating the simulation of the subterranean region comprises using the calculated fluid flow transmissibility factors. (Page 62: “The entire model, including the matrix grid and fracture control volumes, is entered into a reservoir simulator, which allows for non-neighboring connections and transmissibility modifiers. The governing equations for fracture control volumes are similar to those described for the matrix medium, implying that Darcy’s law is used in the fracture domain. In order to implement the EDFM approach in GPAS, we have added the NNC term to the mass balance equations, as previously described in Equations 4.2 and 4.3. In doing so, the residual vector and the Jacobian matrix (see Chapter 3) are augmented with additional terms associated with NNCs. Other modifications to GPAS have also been implemented to properly include the parameters calculated in the pre-processing code. These parameters include porosity, permeability, and depth of fracture control volumes, transmissibility factors between NNCs, transmissibility factors between adjacent fracture cells, and well indices for the fractures intercepted by a well. Also, we have made necessary changes to bypass all calculations carried out for the dead blocks in the fracture domain.”)
Claim 20:Moinfar_2013 makes obvious A non-transitory computer-readable medium, embodying instructions for simulating a subterranean region having fracture geometries which when executed by a computer cause the computer to perform a plurality of functions, including functions to: (page viii par 1: “We developed an embedded discrete fracture model (EDFM) for an in-house fully-implicit compositional reservoir simulator.” … par 31: “The EOS and chemical compositional modules of GPAS are developed under this framework and thus, multi-processor simulations are feasible using GPAS”).
obtain
page 96 par 3: “To evaluate the impact of grid resolution on accuracy of the EDFM approach, we also performed a mesh sensitivity study for the case in the absence of capillary pressure contrasts. Retaining the same fracture network, we examined three additional EDFM simulations that use uniform 40×40×1, 50×50×1, and 80×80×1 matrix grids, respectively, to check the convergence of the method as the grid is refined.” Examiner note: this passage indicates that a fracture network digital data is used for EDFM simulations. Where the same fracture network being used in different matrix grids shows that this is an independent fracture network from the EDFM simulation module.
Page 110 par 1: “In most simulation studies performed in the past, natural fractures were assumed to be vertical in the subsurface. However, the new implementation of the EDFM approach presented in this research provides an effective and reliable environment to model obliquely dipping fractures. Hence, the third example examines water-flooding into a 3D model reservoir shown in Figure 6.14, comprising 13 inclined fractures. The fractures are extended through different layers. We also investigate the effect of fracture inclination in this example. The dip angle of fractures ranges from 55 to 90 degrees. The matrix grid is 20×20×4, with cell dimensions of 25×25×20 ft in the x, y, and z directions, respectively.” Examiner note: This passage specifies the use of a 3D model
obtain
page 96 par 3: “To evaluate the impact of grid resolution on accuracy of the EDFM approach, we also performed a mesh sensitivity study for the case in the absence of capillary pressure contrasts. Retaining the same fracture network, we examined three additional EDFM simulations that use uniform 40×40×1, 50×50×1, and 80×80×1 matrix grids, respectively, to check the convergence of the method as the grid is refined.” Examiner note: this passage indicates that a fracture network digital data is used for EDFM simulations. Where the same fracture network being used in different matrix grids shows that this is an independent fracture network from the EDFM simulation module.
Page 110 par 1: “In most simulation studies performed in the past, natural fractures were assumed to be vertical in the subsurface. However, the new implementation of the EDFM approach presented in this research provides an effective and reliable environment to model obliquely dipping fractures. Hence, the third example examines water-flooding into a 3D model reservoir shown in Figure 6.14, comprising 13 inclined fractures. The fractures are extended through different layers. We also investigate the effect of fracture inclination in this example. The dip angle of fractures ranges from 55 to 90 degrees. The matrix grid is 20×20×4, with cell dimensions of 25×25×20 ft in the x, y, and z directions, respectively.” Examiner note: This passage specifies the use of a 3D model
convert the obtained
page 43: “The implementation of EDFM includes two parts: [Symbol font/0xB7] Pre-processing of a fracture network with any geometry over an arbitrary grid to provide the required data for reservoir simulation” Examiner note: Where this pre-processing of a fracture network is a conversion of the fracture network into EDFM format.
produce a computational domain separate from the first digital simulator module and the second digital simulator module;
page 50 par 1: “Figure 4.4 specifies all gridblocks containing a segment of a fracture for both layers. Cells containing a segment of the black fracture are marked with black circles and those containing a segment of the red fracture are marked with red squares. In contrast to vertical fractures that cross similar gridblocks in different computational layers, inclined fractures may cross a different number of gridblocks in each layer, as shown in Figure 4.4. For the example under consideration, the black fracture penetrates 14 cells in the top computational layer while penetrating only 9 cells in the bottom one. Unlike the black fracture, the red fracture crosses more gridblocks in the bottom computational layer than in the top one.” (Examiner note: Based on the examiners understanding, figures 4.4 and 4.5 depict a computational domain/layer separate from the first or second digital “physical” simulators)
input the converted digital EDFM format data into the computational domain to produce output data;
(page 60 par 3: “We have developed a pre-processing code to provide the required data (Examiners note: output data) for fluid-flow simulations in GPAS. The input of the pre-processing code is the description of the model reservoir including the reservoir dimensions, fracture network, location of wells, structured grid for the matrix domain, aperture and permeability of fractures, and porosity and permeability of matrix (Examiners note: this input as described is the digital EDFM format data). The following calculations are carried out in the pre-processing code:”) Examiners note: Where these calculations are output data. See also table 4.2 in page 66 which outlines an example step of the methodology. As shown, this produces data such as “fracture porosity” and other data.
Examiner note: The examiner would like to make a note to remove confusion from understanding and mapping. Previously, the examiner states that the preprocessing was a conversion of the fracture network into EDFM format. Then the examiner stated that the preprocessing is a conversion of the EDFM format data into output data. As the examiner understands, this pre-processing does do both functions. The pre-processing takes in the fracture network and layers into onto an arbitrary grid (converts to EDFM format) and also produces output data to be simulated.
input the output data into a third digital simulator module;
page 60 par 3: “We have developed a pre-processing code to provide the required data (Examiner note: the required data in this context is the output data of step (e).) for fluid-flow simulations in GPAS (Examiners note: This is the third digital simulator module). … page 62 par 1: “The entire model, including the matrix grid and fracture control volumes, (Examiner note: this is the output of step (e)) is entered into a reservoir simulator, which allows for non-neighboring connections and transmissibility modifiers”
and generate a simulation of the subterranean region with the third digital simulator module.
page 62 par 1: “The entire model, including the matrix grid and fracture control volumes, is entered into a reservoir simulator, which allows for non-neighboring connections and transmissibility modifiers” … page 114 gives an example of a simulation “Figure 6.18 shows the pressure profiles after 150 days of production, computed by the EDFM approach. Pressure profiles in all three numerical layers are presented in Figure 6.18, depicting different pressure depletion patterns in various layers because of the presence of randomly-generated fractures. Also, Figure 6.19 shows the oil production rate and average reservoir pressure over two years of production. The computational time of the simulation (Examiner note: this is the third simulation that uses the fracture network information as well as the information computed by the EDFM approach/ format data) was 3.31 hours.”
Moinfar_2013 does not explicitly recite
Sim_2021 however, makes obvious
Minute 5:11, depicts a workflow where a DFN is shown as input and is modeled into EDFM. See also minute 6:30 which depicts the input data as a 3D model.
Minute 5:11, “EDGS platform can integrate explicit hydraulic fractures, natural fractures, and complex fractures with reservoir simulation modeling effectively based on the advanced EDFM technology.” See also minute 6:30 which depicts the input data as a 3D model.
Minute 5:11, depicts a workflow where a DFN is shown as input and is modeled into EDFM. Ending in a DFN+HF (Examiner note: discrete fracture network and hydraulic fractures) model. Where this flow chart makes obvious that the information from the DFN and hydraulic fractures was provided as input and converted into a EDFM format.
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This is minute 5:11
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This is minute 6:30
As already stated in claim 1, it would have been obvious to combine the methodology and workflow for fracture simulation of Moinfar_2013 with including both discrete fracture network and hydraulic fracture data of Sim_2021 to achieve the goal of accurate characterization and simulation of production for selecting the best stimulation strategy.
Conclusion
Any inquiry concerning this communication or earlier communications from the examiner should be directed to AHMAD HUSSAM SHALABY whose telephone number is (571)272-7414. The examiner can normally be reached Mon-Fri 7:30am - 5pm.
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/A.H.S./Examiner, Art Unit 2187 /BRIAN S COOK/Primary Examiner, Art Unit 2187