Notice of Pre-AIA or AIA Status
The present application, filed on or after March 16, 2013, is being examined under the first inventor to file provisions of the AIA .
Detailed Action
Claims 1-22 are pending.
Response to Amendment
This action is in response to the Amendment filled on 04/09/2026. The amendment has been entered. Claims 1,9 and 16 have been amended. Claims 1-22 are pending, with claims 1,9 and 16 being independent in the instant application.
Response to Arguments
Applicant's Arguments/Remarks filed on 04/09/2026 on page regarding Objection to Drawing have been fully considered and are found persuasive in view of the attached replacement Drawings sheet and presented Arguments/Remarks by the Applicant. Therefore, the previous Objection regarding Drawing being withdrawn in this current office action.
Applicant's Arguments/Remarks filed on page 8-10 regarding 35 U.S.C. 103
rejections have been fully considered and are found persuasive in view of Applicant's amendments to the claims and amended rejections detailed below. Specifically, Examiner agrees with the Applicant's Arguments/Remarks in page 9: “Bouaouaja fails to disclose that the reservoir flow capacity is "normalized by the bottomhole pressure (BHP)" as recited in amended claims 1, 9, and 16.”
However, a new ground of rejections is necessitated by Applicant's claim amendments. Therefore, the previous rejections regarding 35 U.S.C.103 are being amended in this current office action. (See analysis below Claim Rejections-35 U.S.C. §103).
Examiner Notes
Examiner cites particular columns, paragraphs, figures and line numbers in the references as applied to the claims below for the convenience of the applicant. Although the specified citations are representative of the teachings in the art and are applied to the specific limitations within the individual claim, other passages and figures may apply as well. It is respectfully requested that, in preparing responses, the applicant fully consider the references in their entirety as potentially teaching all or part of the claimed invention, as well as the context of the passage as taught by the prior art or disclosed by the examiner. The entire reference is considered to provide disclosure relating to the claimed invention. The claims & only the claims form the metes & bounds of the invention. Office personnel are to give the claims their broadest reasonable interpretation in light of the supporting disclosure. Unclaimed limitations appearing in the specification are not read into the claim. Prior art was referenced using terminology familiar to one of ordinary skill in the art. Such an approach is broad in concept and can be either explicit or implicit in meaning. Examiner's Notes are provided with the cited references to assist the applicant to better understand how the examiner interprets the applied prior art. Such comments are entirely consistent with the intent & spirit of compact prosecution.
Claim Rejections - 35 USC § 103
In the event the determination of the status of the application as subject to AIA 35 U.S.C. 102 and 103 (or as subject to pre-AIA 35 U.S.C. 102 and 103) is incorrect, any correction of the statutory basis (i.e., changing from AIA to pre-AIA ) for the rejection will not be considered a new ground of rejection if the prior art relied upon, and the rationale supporting the rejection, would be the same under either status.
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 set forth in Graham, v. John Deere Co., 383 U.S.1.148 USPQ 459 (1966), that are applied 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 non-obviousness.
6 Claims 1,3-9,11-15,16 and 18-22 are rejected under 35 U.S.C. 103 as being unpatentable over Bouaouaja et al. (US2020/0095858A1) (IDS provided on 7/6/2023), in view of an NPL paper “A review and evaluation of the methodology for digitising 2D fracture networks and topographic lineaments in GIS” by Romesh Palamakumbura et al. (hereinafter Romesh, NPL published in 2019) and further in view of a Journal Paper “Determining Bottomhole Pressures in Pumping Wells” by A.R. Hasan (hereinafter Hasan, Journal published in 1985).
Regarding Claim 1, Bouaouaja teaches a method for determining a sweet spot in a naturally fractured hydrocarbon reservoir, (Examiner would construe the claim term as “sweet spot” in light of Specification of current Application para [0003] as “natural fracture sweet spots may be identified via the construction of solid 3D fracture models and the integration of reservoir dynamic data”. Bouaouaja disclosed in page 4 para [0057]: “Discrete fracture network realizations formed according to the present invention are constrained by geo-mechanical drivers. The parametrization of the main variable to constrain the fracture presence or position into a 3D geocellular grid includes several physical characteristics of the rock matrix, typically including: fracture density, fracture length, fracture orientation and geometry.” In page 2 para [0015]: “the present invention provides a new and improved method of drilling a well in a subsurface geological structure to a location in a subsurface hydrocarbon reservoir indicated by a natural fracture network model of the reservoir.”).
Bouaouaja teaches the method comprising: obtaining reservoir parameters representing properties of the subsurface reservoir for processing in a data processing system; (Bouaouaja disclosed in page 2 para [0015]: “the present invention provides a new and improved method of drilling a well in a subsurface geological structure to a location in a subsurface hydrocarbon reservoir indicated by a natural fracture network model of the reservoir. Reservoir parameters representing properties of the subsurface reservoir are obtained for processing in a data processing system. The natural fracture network model is then formed by processing the obtained reservoir parameters in the data processing system to identify fracture properties comprising the character, location, and stress conditions of natural fractures at locations in the subsurface hydrocarbon.”).
Bouaouaja teaches forming a natural fracture model by processing the obtained reservoir parameters in the data processing system to identify the presence and extent of natural fractures at locations in the subsurface hydrocarbon reservoir; (Bouaouaja disclosed in page 5 para [0066]: “Critical stress concept criteria are used during critical stress analysis processing 242 in the methodology of the present invention to obtain natural fracture properties. This is done governed by the Coulomb criterion, which depends on the stress magnitude and the orientation of the fracture plane with respect to the “In situ” stress orientation. The orientation impacts the normal and shear stresses on the fracture plane.” In page 6 para [0073]: “A number of discrete fracture network realizations or postulated fracture distributions are formed during processing according to the present invention controlled by several sets of fracture parameters. The parameters for this purpose include fracture length, density, orientation, geometry, aperture and permeability. The individual discrete fracture network realizations so formed are distributed into a 3D geocellular grid to produce reliable stochastic fracture realizations …”
Bouaouaja teaches identifying a fluid flow path using a shear stress, a normal stress, and an aperture of a fracture; (Bouaouaja disclosed in page 7 para [0082]: “FIG. 14A is a schematic diagram of a fracture model F - 2 and 14B is a Mohr’s diagram provided as examples illustrative of the interrelation of fracture aperture orientation angle … to shear stress τϴ; normal stress σnϴ and rock coefficient of friction Φ.” In para [0088-0089]: “Fracture networks usually serve as the major path ways for fluid transport in subsurface rocks, … FIG. 15A is a schematic diagram of a formation model F - 3 of fluid pathways in formation rock in a segment of a subsurface formation. FIG. 15B is a Mohr’s diagram indicating critical stress conditions in certain of the fluid pathways in the formation model F-3. The stress conditions are indicated for the fluid pathways in fracture model in the Mohr’s diagram of FIG. 15B. The critical stress conditions indicated in Mohr’s diagram M-3 (FIG. 15B) for the certain fluid pathways are greater than the coefficient of friction (Φ). This indicates that under the fluid pathway angle and critical stress condition shown in FIG. 15A, …”).
Bouaouaja teaches determining a discrete natural fracture network identifying the presence and extent of natural fractures representing fluid flow paths in the reservoirs; (Bouaouaja disclosed in page 7 para [0088 and 0090]: “Fracture networks usually serve as the major path ways for fluid transport in subsurface rocks, especially if the matrix is almost impermeable compared to the fractures. The partitioning of fluid flow within a fracture population relies on the spatial connectivity of fracture geometries and the transmissivity of individual fractures, both of which can be affected by the geomechanical conditions. … The flow paths 350 based on critical stress (FIG. 15A) which are hydraulically conductive are indicated in red, while flow paths 352 indicated in blue do not conduct fluids. The presence or absence of critical stress which affects hydraulic conductivity in natural fractures is a relationship as follows according to Equation (6) …”.
The disclosure “the spatial connectivity of fracture geometries and the transmissivity of individual fractures based on partitioning of fluid flow within a fracture population” corresponds to claim limitation “determining a discrete natural fracture network”. The claim limitation “identifying the presence and extent of natural fractures representing fluid flow paths in the reservoirs” by disclosure “the flow paths 350 based on critical stress (FIG. 15A) which are hydraulically conductive are indicated in red”).
Bouaouaja teaches obtaining a flow capacity parameter for the reservoir, the flow capacity parameter obtained from a pressure test analysis (PTA); (Bouaouaja disclosed in page 4 para [0052]: “The pressure transient analysis performed as indicated at 224 determines a measure of indicated or estimated actual flow capacity from producing formations in the reservoir. The analysis is based on measurements of the reservoir pressure changes over time during what are known as pressure transient tests. … The indicated or estimated actual flow capacity results from pressure transient analysis are provided as indicated at 225 to the dynamic reservoir model permeability optimization module 260.”).
and Bouaouaja teaches determining a sweet spot from a spatial analysis of the fracture density index and at least one of the flow capacity parameter and the productivity index. (Bouaouaja disclosed in page 6 para [0072-0073]: “The discrete fracture network or DFN processing 250 is a module which according to the present invention forms a computational stochastic discrete fracture network model on a stochastic or statistically random basis from inputs provided. … A number of discrete fracture network realizations or postulated fracture distributions are formed during processing according to the present invention controlled by several sets of fracture parameters. The parameters for this purpose include fracture length, density, orientation, geometry, aperture and permeability.” This disclosure teaches the limitation “determining fracture density from spatial analysis” (e.g., discrete fracture network model on a stochastic or statistically random basis from inputs provided
In page 8 para [0101-0102]: “The difference E1 between the fraction of the total flow capacity attributed to fractures … and the accumulated flow capacity… is determined during step 290 … for well depth intervals between a well depth or PLT point at which production logging tool data are obtained in order to determine the flow contribution at a particular depth in the well, the difference Ei between the portion of flow capacity assigned from the total flow capacity to each PLT point … and the simulated flow capacity … where the parameter PLTFi; represents the percentage of flow contribution at a single point of well depth, and KFSi HFSi represents the flow capacity contribution for the PLT points for fractures simulated; as shown in Figure …”. Further, in para [0104]: “The minimization processing procedure is performed for each realization, and thus E0, E1, Ei are estimated. These are considered the error functions to be optimized (minimized), and minimization processing 300 is performed to select the best parameters to fit the dynamic of the formation rock to the results of the production logging tests.”
The disclosure above teaches the limitation “flow capacity parameter and the productivity index” e.g., production logging tool data are obtained in order to determine the flow contribution at a particular depth in the well, the difference Ei between the portion of flow capacity assigned from the total flow capacity to each PLT point. Further, the disclosure above “the error functions to be optimized (minimized), and minimization processing is performed to select the best parameters to fit the dynamic of the formation rock” teaches the limitation “determining a sweet spot based on the flow capacity parameter and the productivity index” (since the error function is based on the flow capacity and the “the percentage of flow contribution at a single point of well depth” indicates about the “productivity index”)).
Even Bouaouaja teaches the partial claim limitation “determining, discrete natural fracture network” (Bouaouaja disclosed in page 7 para [0088]), however, Bouaouaja does not explicitly teach the limitation “determining, using the discrete natural fracture network, a fracture density index (FDI), wherein determining, using the second discrete natural fracture network, a fracture density index (FDI) comprises generating a raster map from the second discrete natural fracture network, the raster map representing a fracture density per area;
Romesh teaches determining, using the discrete natural fracture network, a fracture density index (FDI), wherein determining, using the discrete natural fracture network, a fracture density index (FDI) comprises generating a raster map from the discrete natural fracture network, the raster map representing a fracture density per area; (Romesh disclosed in page 1 heading ‘Abstract’ (lines 15-19): “The basis of the method is the generation of the vector dataset (shapefile) of a fracture network from a georeferenced photograph of an outcrop in a GIS environment. From that shapefile, key parameters such as fracture density and orientation can be calculated. Furthermore, in the GIS-environment more complex spatial calculations and graphical plots can be carried out such as heat maps of fracture density.” In page 8 section 2.5 lines 167-170: “Fracture density (D) within the circular window 167 can now be calculated using total length of fractures (ΣL) within the area of the circular window (A), … D = ΣL/A (in m-1)”.
Further, in page 9 lines 197-200: “For more evolved analysis of the fracture data the digital traces can be used in fracture analysis software packages … These programs can be used for topological analysis such as deducing node types, and plotting fracture density heat maps illustrating density variations across a fracture zone.” In page 11 Figure 4 (c-d) shown the “heat maps illustrate variations in fracture intersection density”).
Bouaouaja and Romesh are analogous art because they are related in evaluating natural fracture to represent pathways of fluid flow in the reservoir. Before the effective filing date of the claimed invention, it would have been obvious to one of ordinary skill in the art, having the teachings of Bouaouaja and Romesh, to modify identifying the fracture density in natural fracture of Bouaouaja, to include the teaching of Romesh applied generating a series of digital fracture traces in GIS-environment and carried out graphical plots such as heat maps of fracture density. The suggestion/motivation for doing so would have been obvious by Romesh because “The GIS-environment more complex spatial calculations and graphical plots can be carried out such as heat maps of fracture density. There are a number of advantages to using a digital method for gathering fracture data including: time efficiency, generating large fracture network datasets, flexibility during data gathering and consistency of data. The aim of this paper is to review and evaluate the methodology for digitising 2D fracture networks in GIS, and make it more accessible to a broader range of users in both academia and industry. We present a breakdown of the key steps in the methodology, which provides an understanding of how to avoid error and improve the accuracy of the final dataset.” (Romesh disclosed in page 1 heading ‘Abstract’ and page 15 heading ‘Conclusion’).
Even Bouaouaja teaches the claim element “productivity index comprising fluid production”, however Bouaouaja and Romesh do not explicitly teach the whole limitation “obtaining a productivity index for the reservoir, the productivity index comprising a fluid production rate normalized by the bottomhole pressure (BHP);”
Hasan teaches obtaining a productivity index for the reservoir, the productivity index comprising a fluid production rate normalized by the bottomhole pressure (BHP); (Examiner would construe the meaning of claim term “bottomhole pressure” according to Spec. of current Application para [0065], where Applicants stated: “The bottomhole pressure may refer to a shut-in pressure”.
Hasan disclosed in page 826 heading ‘Calculating BHP: Numerical Approach’ (left col.): “the BHP can be calculated by the following equation: … In Eq. 10, γL represents the composite liquid gradient of both oil and water. In other words, the gradients of the two phases must be weighted by the ratio of their production before the well shut-in to estimate the composite liquid gradient, γL. … Therefore, any liquid mixture entering the wellbore after the shut-in is to be added to the pure oil column to obtain the continuous rise in the liquid column.” In page 828 heading ‘Field Example’ (left col.): “We examined pressure buildup data obtained from five wells. Of particular interest are the pressure profiles predicted by the three correlations and the validity of these pressure data to allow meaningful reservoir and well property calculations-such as permeability/thickness (kh) product, skin (s), static reservoir pressure (pR), and well productivity index (J).” This disclosure teaches the limitation “obtaining a productivity index for the reservoir, the productivity index comprising a fluid production”.
Further, in page 834 heading ‘Case 4’ (left col.): “Well 5. This well was experiencing a gas coning phenomenon before the shut-in. This observation is inferred by comparing the stated gas production rate of 48 Mcf/D [1356 m3/d] and no gas production following the well shut-in.” This disclosure teaches the limitation “fluid production rate normalized by the bottomhole pressure (BHP) or shut-in pressure”).
Bouaouaja, Romesh and Hasan are analogous art because they are related in evaluating reservoir production (fluid flow). Before the effective filing date of the claimed invention, it would have been obvious to one of ordinary skill in the art, having the teachings of Bouaouaja, Romesh and Hasan, to modify productivity index comprising fluid production of Bouaouaja, to include fluid production rate (productivity index) normalized by the bottomhole pressure in Hasan’s teaching. The suggestion/motivation for doing so would have been obvious by Hasan because “The field examples studied indicate that both the pumping liquid column height and the superficial gas velocity play a key role in estimating the gas void fraction-an essential element in calculating the BHP. We observe that only the early-time shut-in pressures are affected by the presence of gas bubbles in the liquid column. Because the bottornhole flowing pressure (BHFP) is dependent on the correlation used to predict the fg, both skin and productivity index calculations are affected. A know ledge of the BHFP (pwf) is an essential element in predicting a well's productivity index (J) and its in flow performance relationship (lPR). A pressure buildup test conducted on a pumping well can provide an array of valuable information-such as permeability / thickness product, skin, and static reservoir pressure. The last piece of information is necessary for a meaningful J estimation. (Hasan disclosed in page 823 heading ‘Abstract’ and ‘Introduction’).
Regarding claim 3, Bouaouaja, Romesh and Hasan teach the method of claim 1, wherein Bouaouaja teaches the reservoir parameters comprise seismic attributes from seismic surveys of the subsurface geological structure. (Bouaouaja disclosed in page 3 para [0040]: “As indicated at 102 in FIG. 3, reservoir parameters and properties from a plurality of disciplines of earth science are obtained, assembled and stored in a data processing system D (FIG. 7). As shown at 102, the reservoir parameters include seismic attributes from seismic surveys as indicated at 104;”).
Regarding claim 4, Bouaouaja, Romesh and Hasan teach the method of claim 1, wherein Bouaouaja teaches the reservoir parameters comprise rock and mechanical properties from geological models of the subsurface geological structure. (Bouaouaja disclosed in page 3 para [0040]: “As indicated at 102 in FIG. 3, reservoir parameters and properties from a plurality of disciplines of earth science are obtained, assembled and stored in a data processing system D (FIG. 7). As shown at 102, the reservoir parameters include … rock and mechanical properties from geological modeling as indicated at 106;”).
Regarding claim 5, Bouaouaja, Romesh and Hasan teach the method of claim 1, wherein Bouaouaja teaches the reservoir parameters comprise structural restoration models of the subsurface geological structure. (Bouaouaja disclosed in page 3 para [0040]: “As indicated at 102 in FIG. 3, reservoir parameters and properties from a plurality of disciplines of earth science are obtained, assembled and stored in a data processing system D (FIG. 7). As shown at 102, the reservoir parameters include … measures from structural restoration models as indicated at 108;”).
Regarding claim 6, Bouaouaja, Romesh and Hasan teach the method of claim 1, wherein Bouaouaja teaches the reservoir parameters comprise rock geological characterizations of the subsurface geological structure. (Bouaouaja disclosed in page 3 para [0040]: “As indicated at 102 in FIG. 3, reservoir parameters and properties from a plurality of disciplines of earth science are obtained, assembled and stored in a data processing system D (FIG. 7). As shown at 102, the reservoir parameters include … rock geological characterizations as indicated at 110 obtained from formation core samples and well logs performed in the wellbores such as 32 and 34;”).
Regarding claim 7, Bouaouaja, Romesh and Hasan teach the method of claim 1, wherein Bouaouaja teaches the reservoir parameters comprise reservoir engineering measures obtained from production from the subsurface hydrocarbon reservoir. (Bouaouaja disclosed in page 3 para [0040]: “As indicated at 102 in FIG. 3, reservoir parameters and properties from a plurality of disciplines of earth science are obtained, assembled and stored in a data processing system D (FIG. 7). As shown at 102, the reservoir parameters include … reservoir engineering measures obtained as indicated at 112 from production measures and reservoir simulations of the reservoir layer 10.”).
Regarding claim 8, Bouaouaja, Romesh and Hasan teach the method of claim 1, Bouaouaja teaches drilling a well in a subsurface geological structure to a location in the hydrocarbon reservoir based on the identified sweet spot. (Bouaouaja disclosed in page 3 para [0041]: “The drilling during step 120 is at locations indicated appropriate by the models resulting from processing steps according to the workflow W. Drilling during step 120 is thus directed to regions of the reservoir layer 10 where fractures of the types conducive to increased production are likely to be present.”
It has been disclosed in page 9 para [0107 and 0109]: “The minimization processing workflow during step 300 (FIG. 16) determines the error factors and populates the error matrix for each realization ... Step 304 is performed to determine an error value between the measured flow capacity for the well and the flow capacity determined for each of the realizations. … During step 310 a convergence test is performed to determine if the current estimate of calculated reservoir flow capacity for each realization is within user specified acceptable accuracy limits to actual measured reservoir flow capacity … If during convergence step 310 each realization is within user specified acceptable accuracy limits to actual measured reservoir flow capacity the determined fracture model is stored as indicated at 314 … provided for output analysis in connection with drilling wells during step 120 (FIG. 1).”
The disclosure above discussed about performing test to determine if the current estimate of calculated reservoir flow capacity for each realization (in minimization processing) is within user specified acceptable accuracy limits when compared to actual measured reservoir flow capacity. Thus, when the convergence step is within an acceptable degree accuracy, that is indicated as output analysis in connection with drilling wells, eventually finding a sweet spot for drilling well to regions of the reservoir layer).
Regarding Claim 9, the same ground of rejection is made as discussed in claim 1 for substantially similar rationale, therefore claim 9 is rejected under 35 U.S.C. 103 as being unpatentable over Bouaouaja, Romesh and Hasan as discussed above for substantially similar rationale. In addition, claim 9 recites following limitation:
Bouaouaja teaches a non-transitory computer-readable storage medium having executable code stored thereon for determining a sweet spot in a naturally fractured hydrocarbon reservoir, the executable code comprising a set of instructions that causes a processor to perform operations (Bouaouaja disclosed in page 9 para [0115-0116]: “The data processing system D includes program code 422 stored in non-transitory memory 404 of the computer 400. The program code 422 according to the present invention is in the form of computer operable instructions causing the data processor 402 to form subsurface reservoir 3D geocellular models including natural fracture presence, distribution, and flow parameter properties according to the present invention … It should be noted that program code 422 may be in the form of … symbolic computer operable languages capable of providing a specific set of ordered operations controlling the functioning of the data processing system … The instructions of program code 422 may be stored in memory 404 of the data processing system …”).
Bouaouaja teaches determining a sweet spot from a spatial analysis of the fracture density index and at least one of the flow capacity parameter and the productivity index. (Bouaouaja disclosed in page 6 para [0072-0073]: “The discrete fracture network or DFN processing 250 is a module which according to the present invention forms a computational stochastic discrete fracture network model on a stochastic or statistically random basis from inputs provided. … A number of discrete fracture network realizations or postulated fracture distributions are formed during processing according to the present invention controlled by several sets of fracture parameters. The parameters for this purpose include fracture length, density, orientation, geometry, aperture and permeability.” This disclosure teaches the limitation “determining fracture density from spatial analysis” (e.g., discrete fracture network model on a stochastic or statistically random basis from inputs provided
In page 8 para [0101-0102]: “The difference E1 between the fraction of the total flow capacity attributed to fractures … and the accumulated flow capacity… is determined during step 290 … for well depth intervals between a well depth or PLT point at which production logging tool data are obtained in order to determine the flow contribution at a particular depth in the well, the difference Ei between the portion of flow capacity assigned from the total flow capacity to each PLT point … and the simulated flow capacity … where the parameter PLTFi; represents the percentage of flow contribution at a single point of well depth, and KFSi HFSi represents the flow capacity contribution for the PLT points for fractures simulated; as shown in Figure …”. Further, in para [0104]: “The minimization processing procedure is performed for each realization, and thus E0, E1, Ei are estimated. These are considered the error functions to be optimized (minimized), and minimization processing 300 is performed to select the best parameters to fit the dynamic of the formation rock to the results of the production logging tests.”
The disclosure above teaches the limitation “flow capacity parameter and the productivity index” e.g., production logging tool data are obtained in order to determine the flow contribution at a particular depth in the well, the difference Ei between the portion of flow capacity assigned from the total flow capacity to each PLT point. Further, the disclosure above “the error functions to be optimized (minimized), and minimization processing is performed to select the best parameters to fit the dynamic of the formation rock” teaches the limitation “determining a sweet spot based on the flow capacity parameter and the productivity index” (since the error function is based on the flow capacity and the “the percentage of flow contribution at a single point of well depth” indicates about the “productivity index”)).
Even Bouaouaja teaches the claim element “productivity index comprising a fluid production”, however, Bouaouaja and Romesh do not explicitly teach the whole limitation “obtaining a productivity index for the reservoir, the productivity index comprising a fluid production rate normalized by the bottomhole pressure (BHP);”
Hasan teaches obtaining a productivity index for the reservoir, the productivity index comprising a fluid production rate normalized by the bottomhole pressure (BHP); (Hasan disclosed in page 826 heading ‘Calculating BHP: Numerical Approach’ (left col.): “the BHP can be calculated by the following equation: … In Eq. 10, γL represents the composite liquid gradient of both oil and water. In other words, the gradients of the two phases must be weighted by the ratio of their production before the well shut-in to estimate the composite liquid gradient, γL. … Therefore, any liquid mixture entering the wellbore after the shut-in is to be added to the pure oil column to obtain the continuous rise in the liquid column.” In page 828 heading ‘Field Example’ (left col.): “We examined pressure buildup data obtained from five wells. Of particular interest are the pressure profiles predicted by the three correlations and the validity of these pressure data to allow meaningful reservoir and well property calculations-such as permeability/thickness (kh) product, skin (s), static reservoir pressure (pR), and well productivity index (J).” This disclosure teaches the limitation “obtaining a productivity index for the reservoir, the productivity index comprising a fluid production”.
Further, in page 834 heading ‘Case 4’ (left col.): “Well 5. This well was experiencing a gas coning phenomenon before the shut-in. This observation is inferred by comparing the stated gas production rate of 48 Mcf/D [1356 m3/d] and no gas production following the well shut-in.” This disclosure teaches the limitation “fluid production rate normalized by the bottomhole pressure (BHP) or shut-in pressure”).
Bouaouaja, Romesh and Hasan are analogous art because they are related in evaluating reservoir production (fluid flow). Before the effective filing date of the claimed invention, it would have been obvious to one of ordinary skill in the art, having the teachings of Bouaouaja, Romesh and Hasan, to modify productivity index comprising fluid production of Bouaouaja, to include fluid production rate (productivity index) normalized by the bottomhole pressure in Hasan’s teaching. The suggestion/motivation for doing so would have been obvious by Hasan because “The field examples studied indicate that both the pumping liquid column height and the superficial gas velocity play a key role in estimating the gas void fraction-an essential element in calculating the BHP. We observe that only the early-time shut-in pressures are affected by the presence of gas bubbles in the liquid column. Because the bottornhole flowing pressure (BHFP) is dependent on the correlation used to predict the fg, both skin and productivity index calculations are affected. A know ledge of the BHFP (pwf) is an essential element in predicting a well's productivity index (J) and its in flow performance relationship (lPR). A pressure buildup test conducted on a pumping well can provide an array of valuable information-such as permeability / thickness product, skin, and static reservoir pressure. The last piece of information is necessary for a meaningful J estimation. (Hasan disclosed in page 823 heading ‘Abstract’ and ‘Introduction’).
Regarding claims 11-15, Bouaouaja, Romesh and Hasan teach the non-transitory computer-readable media of claim 9, are incorporating the rejections of claims 3-7, because claims 11-15 have substantially similar claim language as claims 3-7, therefore claims 11-15 are rejected under 35 U.S.C. 103 as being unpatentable over Bouaouaja, Romesh and Hasan as discussed above for substantially similar rationale.
Regarding Claim 16, the same ground of rejection is made as discussed in claims 1 and 9 for substantially similar rationale, therefore claim 16 is rejected under 35 U.S.C. 103 as being unpatentable over Bouaouaja, Romesh and Hasan as discussed above for substantially similar rationale. In addition, claim 16 recites following limitation:
Bouaouaja teaches a system for determining a sweet spot for hydraulic fracturing stimulation in a naturally fractured tight sand hydrocarbon reservoir, comprising: a processor; a non-transitory computer-readable memory accessible by the processor and having executable code stored thereon, the executable code comprising a set of instructions that causes a processor to perform operations. (Bouaouaja disclosed in page 4 para [0057]: “Discrete fracture network realizations formed according to the present invention are constrained by geo-mechanical drivers. The parametrization of the main variable to constrain the fracture presence or position into a 3D geocellular grid includes several physical characteristics of the rock matrix, typically including: fracture density, fracture length, fracture orientation and geometry.” In page 2 para [0015]: “the present invention provides a new and improved method of drilling a well in a subsurface geological structure to a location in a subsurface hydrocarbon reservoir indicated by a natural fracture network model of the reservoir. Reservoir parameters representing properties of the subsurface reservoir are obtained for processing in a data processing system.”).
It has been disclosed in page 9 para [0115-0116]: “The data processing system D includes program code 422 stored in non-transitory memory 404 of the computer 400. The program code 422 according to the present invention is in the form of computer operable instructions causing the data processor 402 to form subsurface reservoir 3D geocellular models including natural fracture presence, distribution, and flow parameter properties according to the present invention … The instructions of program code 422 may be stored in memory 404 of the data processing system D, or on computer diskette, … or other appropriate data storage device having a computer usable non-transitory medium stored thereon.”).
Bouaouaja teaches determining a sweet spot from a spatial analysis of the fracture density index and at least one of the flow capacity parameter and the productivity index. (Bouaouaja disclosed in page 6 para [0072-0073]: “The discrete fracture network or DFN processing 250 is a module which according to the present invention forms a computational stochastic discrete fracture network model on a stochastic or statistically random basis from inputs provided. … A number of discrete fracture network realizations or postulated fracture distributions are formed during processing according to the present invention controlled by several sets of fracture parameters. The parameters for this purpose include fracture length, density, orientation, geometry, aperture and permeability.” This disclosure teaches the limitation “determining fracture density from spatial analysis” (e.g., discrete fracture network model on a stochastic or statistically random basis from inputs provided
In page 8 para [0101-0102]: “The difference E1 between the fraction of the total flow capacity attributed to fractures … and the accumulated flow capacity… is determined during step 290 … for well depth intervals between a well depth or PLT point at which production logging tool data are obtained in order to determine the flow contribution at a particular depth in the well, the difference Ei between the portion of flow capacity assigned from the total flow capacity to each PLT point … and the simulated flow capacity … where the parameter PLTFi; represents the percentage of flow contribution at a single point of well depth, and KFSi HFSi represents the flow capacity contribution for the PLT points for fractures simulated; as shown in Figure …”. Further, in para [0104]: “The minimization processing procedure is performed for each realization, and thus E0, E1, Ei are estimated. These are considered the error functions to be optimized (minimized), and minimization processing 300 is performed to select the best parameters to fit the dynamic of the formation rock to the results of the production logging tests.”
The disclosure above teaches the limitation “flow capacity parameter and the productivity index” e.g., production logging tool data are obtained in order to determine the flow contribution at a particular depth in the well, the difference Ei between the portion of flow capacity assigned from the total flow capacity to each PLT point. Further, the disclosure above “the error functions to be optimized (minimized), and minimization processing is performed to select the best parameters to fit the dynamic of the formation rock” teaches the limitation “determining a sweet spot based on the flow capacity parameter and the productivity index” (since the error function is based on the flow capacity and the “the percentage of flow contribution at a single point of well depth” indicates about the “productivity index”)).
Even Bouaouaja teaches the claim element “productivity index comprising a fluid production”, however, Bouaouaja and Romesh do not explicitly teach the whole limitation “obtaining a productivity index for the reservoir, the productivity index comprising a fluid production rate normalized by the bottomhole pressure (BHP);”
Hasan teaches obtaining a productivity index for the reservoir, the productivity index comprising a fluid production rate normalized by the bottomhole pressure (BHP); (Hasan disclosed in page 826 heading ‘Calculating BHP: Numerical Approach’ (left col.): “the BHP can be calculated by the following equation: … In Eq. 10, γL represents the composite liquid gradient of both oil and water. In other words, the gradients of the two phases must be weighted by the ratio of their production before the well shut-in to estimate the composite liquid gradient, γL. … Therefore, any liquid mixture entering the wellbore after the shut-in is to be added to the pure oil column to obtain the continuous rise in the liquid column.” In page 828 heading ‘Field Example’ (left col.): “We examined pressure buildup data obtained from five wells. Of particular interest are the pressure profiles predicted by the three correlations and the validity of these pressure data to allow meaningful reservoir and well property calculations-such as permeability/thickness (kh) product, skin (s), static reservoir pressure (pR), and well productivity index (J).” This disclosure teaches the limitation “obtaining a productivity index for the reservoir, the productivity index comprising a fluid production”.
Further, in page 834 heading ‘Case 4’ (left col.): “Well 5. This well was experiencing a gas coning phenomenon before the shut-in. This observation is inferred by comparing the stated gas production rate of 48 Mcf/D [1356 m3/d] and no gas production following the well shut-in.” This disclosure teaches the limitation “fluid production rate normalized by the bottomhole pressure (BHP) or shut-in pressure”).
Bouaouaja, Romesh and Hasan are analogous art because they are related in evaluating reservoir production (fluid flow). Before the effective filing date of the claimed invention, it would have been obvious to one of ordinary skill in the art, having the teachings of Bouaouaja, Romesh and Hasan, to modify productivity index comprising fluid production of Bouaouaja, to include fluid production rate (productivity index) normalized by the bottomhole pressure in Hasan’s teaching. The suggestion/motivation for doing so would have been obvious by Hasan because “The field examples studied indicate that both the pumping liquid column height and the superficial gas velocity play a key role in estimating the gas void fraction-an essential element in calculating the BHP. We observe that only the early-time shut-in pressures are affected by the presence of gas bubbles in the liquid column. Because the bottornhole flowing pressure (BHFP) is dependent on the correlation used to predict the fg, both skin and productivity index calculations are affected. A know ledge of the BHFP (pwf) is an essential element in predicting a well's productivity index (J) and its in flow performance relationship (lPR). A pressure buildup test conducted on a pumping well can provide an array of valuable information-such as permeability / thickness product, skin, and static reservoir pressure. The last piece of information is necessary for a meaningful J estimation. (Hasan disclosed in page 823 heading ‘Abstract’ and ‘Introduction’).
Regarding claims 18-22, Bouaouaja, Romesh and Hasan teach the system of claim 16, are incorporating the rejections of claims 3-7, because claims 18-22 have substantially similar claim language as claims 3-7, therefore claims 18-22 are rejected under 35 U.S.C. 103 as being unpatentable over Bouaouaja, Romesh and Hasan as discussed above for substantially similar rationale.
Claims 2, 10 and 17 are rejected under 35 U.S.C. 103 as being unpatentable over Bouaouaja, Romesh and Hasan and further in view of Tabanou et al. (Pub. No. US2010/0191470A1) (IDS provided on 3/6/2023).
Regarding claim 2, Bouaouaja, Romesh and Hasan the method of claim 1, wherein Bouaouaja teaches identifying a fluid flow path using a shear stress, a normal stress, and an aperture associated of a fracture comprises: determining a shear stress associated with the fracture, the shear stress determined from reservoir parameters representing properties of the reservoir; (Bouaouaja disclosed in page 7 para [0082]: “FIG. 14A is a schematic diagram of a fracture model F - 2 and 14B is a Mohr’s diagram provided as examples illustrative of the interrelation of fracture aperture orientation angle … to shear stress τϴ; normal stress σnϴ and rock coefficient of friction Φ.” In para [0088-0089]: “Fracture networks usually serve as the major path ways for fluid transport in subsurface rocks, … FIG. 15A is a schematic diagram of a formation model F - 3 of fluid pathways in formation rock in a segment of a subsurface formation. FIG. 15B is a Mohr’s diagram indicating critical stress conditions in certain of the fluid pathways in the formation model F-3. The stress conditions are indicated for the fluid pathways in fracture model in the Mohr’s diagram of FIG. 15B. The critical stress conditions indicated in Mohr’s diagram M-3 (FIG. 15B) for the certain fluid pathways are greater than the coefficient of friction (Φ). This indicates that under the fluid pathway angle and critical stress condition shown in FIG. 15A, …”).
In page 5 para [0059-0060]: “The formation fracture model so formed has only a determined portion of the discrete fraction network hydraulically open for passage of flow. Determination of such a portion and its distribution in the rock is based whether the fractures are critically stressed. … The fracture orientation with respect to the stress directions has significant impact on determination of normal and shear stresses on a fracture plane. When shear stress exceeds shear stiffness, shearing causes dilation keeps the fracture hydraulically open. Fractures in this stress state are referred to as reactivated or critically stressed.” Further, in para [0062] disclosed “the shear stress τ is expressed according to Equation (2)”).
Bouaouaja teaches determining a normal stress associated with the fracture, the normal stress determined from reservoir parameters representing properties of the reservoir; (Bouaouaja disclosed in page 5 para [0061]: “Critical stress analysis is a function of normal stress σn, shear stress τ and fluid pressure. In the example fracture model F-1 shown in FIG. 11A, normal stress σn is expressed according to Equation (1) as follows: …”. In para [0065]: “In the context of the present invention, the Mohr’s diagrams are graphical representations in two or three dimensions of stress conditions in a rock mass at different planes oriented … The Mohr's circles permit determination at the point of interest of principal normal stresses σmax and σmin, … as well as the orientation of the principal planes … As will be explained, the Mohr’s circles in FIG. 14B of a critical stress fracture model F-2 in FIG. 14A include a plot of rock coefficient of friction Φ to indicate the physical phenomenon of critical stress.”).
and Bouaouaja teaches identifying a fluid flow path using the shear stress, the normal stress, and the aperture. (Bouaouaja disclosed in page 7 para [0082]: “FIG. 14A is a schematic diagram of a fracture model F - 2 and 14B is a Mohr’s diagram provided as examples illustrative of the interrelation of fracture aperture orientation angle … to shear stress τϴ; normal stress σnϴ and rock coefficient of friction Φ.” In para [0088-0089]: “Fracture networks usually serve as the major path ways for fluid transport in subsurface rocks, … FIG. 15A is a schematic diagram of a formation model F - 3 of fluid pathways in formation rock in a segment of a subsurface formation. FIG. 15B is a Mohr’s diagram indicating critical stress conditions in certain of the fluid pathways in the formation model F-3. The stress conditions are indicated for the fluid pathways in fracture model in the Mohr’s diagram of FIG. 15B. The critical stress conditions indicated in Mohr’s diagram M-3 (FIG. 15B) for the certain fluid pathways are greater than the coefficient of friction (Φ). This indicates that under the fluid pathway angle and critical stress condition shown in FIG. 15A, …”).
However, Bouaouaja, Romesh and Hasan do not explicitly teach the limitation “determining the aperture of the fracture in the naturally fractured hydrocarbon reservoir using a resistivity, a drilling fluid resistivity, and an excess current measurement;”
Tabanou teaches determining the aperture of the fracture in the naturally fractured hydrocarbon reservoir using a resistivity, a drilling fluid resistivity, and an excess current measurement; (Tabanou disclosed in page 3 para [0036-0038]: “FIG. 3 shows a schematic illustrating a well 31 penetrating a formation 32, which includes several fractures 33. The fractures may have different apertures. A formation may be characterized by its matrix resistivity Rmatrix. Each Fracture i is characterized by its aperture hi. A given interval of the borehole is characterized by the cumulative aperture of the fractures crossing the borehole per unit length of that borehole or Vhf. Note that the cumulative fracture aperture, Vhf, represents a fraction of the well interval and therefore is less than 1 … If an LMD tool is used to log this interval of the well, the eddy currents induced in the formation will be in planes perpendicular to the well axis, as illustrated in 34. Thus, the horizontal resistivity (Rh) measurements will reflect a summation of currents flowing in the formation layers and the fracture layers, as if these different layers form parallel circuits. On the other hand, if a TMD tool is used to log this interval, the eddy currents will flow in the formation in a direction parallel with the well axis, as illustrated in 35. Because the eddy currents flow through various layers, the resistivity measurements in the vertical direction (Rv) will reflect a summation of resistivities of the various layers in the path of the eddy currents. Assuming that the open natural fracture is filled with mud of resistivity Rmud at the time of the logging,”).
Bouaouaja, Romesh, Hasan and Tabanou are analogous art because they are related in evaluating natural fracture to represent pathways of fluid flow in the reservoir. Before the effective filing date of the claimed invention, it would have been obvious to one of ordinary skill in the art, having the teachings of Bouaouaja, Romesh, Hasan and Tabanou, to modify identifying the fracture density in natural fracture of Bouaouaja, to include the teaching of Tabanou estimated a fracture aperture in a formation penetrated by a well using fluid resistivity, and excess current measurement. The suggestion/motivation for doing so would have been obvious by Tabanou because “The invention relates generally to oil and gas exploration, particularly to methods and systems for estimating fracture apertures in the formations and for assessing fracture aperture changes in response to well stress. Embodiments of the invention are particularly applicable to horizontal or highly deviated well drilled to cross fractures. Methods of the invention allow a user to identify fractured intervals and to quantify fracture apertures.” (Tabanou disclosed in para [0003 and 0025]).
Regarding claims 10 and 17, Bouaouaja, Romesh and Hasan teach non-transitory computer-readable media of claim 9 and the system of claim 16 respectively, are incorporating the rejections of claim 2, because claims 10 and 17 have substantially similar claim language as claim 2, therefore claims 10 and 17 are rejected under 35 U.S.C. 103 as being unpatentable over Bouaouaja, Romesh and Hasan, and further in view of Tabanou as discussed above for substantially similar rationale.
Conclusion
8. Applicant's amendment necessitated the new ground(s) of rejection presented in this Office action. Accordingly, THIS ACTION IS MADE FINAL. See MPEP § 706.07(a). Applicant is reminded of the extension of time policy as set forth in 37 CFR 1.136(a).
A shortened statutory period for reply to this final action is set to expire THREE MONTHS from the mailing date of this action. In the event a first reply is filed within TWO MONTHS of the mailing date of this final action and the advisory action is not mailed until after the end of the THREE-MONTH shortened statutory period, then the shortened statutory period will expire on the date the advisory action is mailed, and any nonprovisional extension fee (37 CFR 1.17(a)) pursuant to 37 CFR 1.136(a) will be calculated from the mailing date of the advisory action. In no event, however, will the statutory period for reply expire later than SIX MONTHS from the mailing date of this final action.
The prior arts made of record and not relied upon is considered pertinent to applicant's disclosure. A Journal “Statistical analysis of geological factors controlling bed-bounded fracture density in heterolithic shale reservoirs: The example of the Woodford Shale Formation (Oklahoma, USA)” by Jing Zhang et al. disclosed a new concept of Fracture Density Index (FDI) is defined and can be further applied to predict fracture density in other unconventional reservoirs, i.e., Fracture Density Index (FDI) can be used as an estimation of area fracture density in unconventional shales. This study aims to integrate and quantify the proposed controlling factors’ (hardness, hard bed ratio, bed frequency) overall impact on 2D fracture area density P20. Since all the controlling factors interact and contribute to the fracture density, it is necessary to quantify and differentiate the contribution from each one of them. An integrated statistical analysis workflow is proposed to evaluate fracture density’s correlations with the controlling factors which can be further applied to other fracture models. Based on the PLS (Partial Least Square) regression model and VIP (Variable Importance in Projection) interpretation result, a more general concept for area fracture density prediction is introduced as the Fracture Density Index (FDI) to evaluate a relative value of area fracture density (Equation (3)). The VIP constants from the model are normalized and used as variable coefficients as controlling factors for both subsurface and shallow surface scenarios to calculate FDI.
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/NUPUR DEBNATH/Examiner, Art Unit 2186
/RENEE D CHAVEZ/Supervisory Patent Examiner, Art Unit 2186
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