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 .
Information Disclosure Statement
The information disclosure statements (IDS) submitted on 12/18/2017 was filed before the mailing of a first Office action on the merits. The IDS is in compliance with the provisions of 37 CFR 1.97. Accordingly, IDS is being considered by the examiner. Applicant has submitted over 260 references. Although a concise explanation of the relevance of the information is not required for English language information, applicants are encouraged to provide a concise explanation of why the English-language information is being submitted and how it is understood to be relevant. Concise explanations (especially those which point out the relevant pages and lines) are helpful to the Office, particularly where documents are lengthy and complex and applicant is aware of a section that is highly relevant to patentability or where a large number of documents are submitted (as in the present case) and applicant is aware that one or more are highly relevant to patentability. Consideration by the examiner of the information submitted in an IDS means that the examiner will consider the documents in the same manner as other documents in Office search files are considered by the examiner while conducting a search of the prior art in a proper field of search. The initials of the examiner placed adjacent to the citations on the PTO/SB/08A and 08B or its equivalent mean that the information has been considered by the examiner to the extent noted above. Due to the large number of NPL references only the abstracts have been reviewed. Applicant should highlight any particularly pertinent NPL they are aware of.
Specification
The disclosure is objected to because of the following informalities:
Paragraphs 5, 7, and 9 of the specification recite: performing a history match between for the bottom hole pressure (BHP). It is not clear what the history matching is “between” or how it is “for” the BHP in context.
Appropriate correction is required.
Claim Objections
Claims 1, 9, and 17 objected to because of the following informalities:
The independent claims first introduce “a hydrocarbon reservoir”, then refer to said reservoir as “the subsurface hydrocarbon reservoir” and “the reservoir”. The dependent claims then return to using “the hydrocarbon reservoir”. The Examiner will interpret all of the recitations to be referring to the same reservoir, however, the claims should be amended so that each instance is the same, for consistency.
Appropriate correction is required.
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.
Claims 1-24 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.
Claims 1, 9, and 17 recites the limitations “the presence and extent” in the first limitation, and "the discrete fracture network" in the second limitation. There is insufficient antecedent basis for this limitation in the claim.
Claims 2-8, 10-16, and 18-24 are rejected for their respective dependencies.
Claim 5 recites: performing a history match between for the bottom hole pressure (BHP). It is not clear what the history matching is “between” or how it is “for” the BHP in context. To promote compact prosecution, the Examiner will interpret the claim to mean history matching between flow rate and BHP as recited in paragraph [0079] of the specification.
Claim Rejections - 35 USC § 101
35 U.S.C. 101 reads as follows:
Whoever invents or discovers any new and useful process, machine, manufacture, or composition of matter, or any new and useful improvement thereof, may obtain a patent therefor, subject to the conditions and requirements of this title.
Claims 1-20 are rejected under 35 U.S.C. 101 because the claimed invention is directed to a judicial exception (i.e., a law of nature, a natural phenomenon, or an abstract idea) without significantly more.
Specifically, representative Claim 1 recites:
A method for developing a hydrocarbon reservoir, the method comprising:
forming, using a mechanical earth model, a fracture network model to identify the presence and extent of natural fractures at locations in the subsurface hydrocarbon reservoir, wherein the mechanical earth model incorporates the principal stress;
determining, using the discrete fracture network, a fracture density index (FDI), wherein determining the fracture density index (FDI) comprises generating a raster map from the discrete fracture network, the raster map representing a fracture density per area;
modifying the fracture density index (FDI) based on a flow capacity response; and
calibrating the modified fracture density index (FDI) using a bottom hole pressure (BHP) and a BHP rate; and
applying the calibrated modified fracture density index (FDI) to a single media matrix permeability for the reservoir to determine an improved matrix permeability.
The claim limitations in the abstract idea have been highlighted in bold above; the remaining limitations are “additional elements”.
Under the Step 1 of the eligibility analysis, we determine whether the claims are to a statutory category by considering whether the claimed subject matter falls within the four statutory categories of patentable subject matter identified by 35 U.S.C. 101: Process, machine, manufacture, or composition of matter. The above claim is considered to be in a statutory category (process).
Under the Step 2A, Prong One, we consider whether the claim recites a judicial exception (abstract idea). In the above claim, the highlighted portion constitutes an abstract idea because, under a broadest reasonable interpretation, it recites limitations that fall into/recite an abstract idea exceptions. Specifically, under the 2019 Revised Patent Subject matter Eligibility Guidance, it falls into the grouping of subject matter when recited as such in a claim limitation, that covers mathematical concepts (mathematical relationships, mathematical formulas or equations, mathematical calculations) and mental processes – concepts performed in the human mind including an observation, evaluation, judgement, and/or opinion.
For example, steps of “forming, using a mechanical earth model, a fracture network model to identify the presence and extent of natural fractures at locations in the subsurface hydrocarbon reservoir, wherein the mechanical earth model incorporates the principal stress (mathematical modeling)” are treated by the Examiner as belonging to mathematical concept grouping, while the steps of “determining, using the discrete fracture network, a fracture density index (FDI), wherein determining the fracture density index (FDI) comprises generating a raster map from the discrete fracture network, the raster map representing a fracture density per area (determination by organizing and visualizing data);
modifying the fracture density index (FDI) based on a flow capacity response (adjustment based on results); and
calibrating the modified fracture density index (FDI) using a bottom hole pressure (BHP) and a BHP rate (adjustment based on data); and
applying the calibrated modified fracture density index (FDI) to a single media matrix permeability for the reservoir to determine an improved matrix permeability (adjustment to data)” are treated as belonging to mental process grouping.
Similar limitations comprise the abstract ideas of Claims 9 and 17.
Next, under the Step 2A, Prong Two, we consider whether the claim that recites a judicial exception is integrated into a practical application.
In this step, we evaluate whether the claim recites additional elements that integrate the exception into a practical application of that exception.
The above claims comprise the following additional elements:
Claim 1: A method for developing a hydrocarbon reservoir;
Claim 9: A non-transitory computer-readable storage medium having executable code stored thereon for developing a hydrocarbon reservoir, the executable code comprising a set of instructions that causes a processor to perform operations;
Claim 17: A system for developing a 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.
The additional element in the preamble of “A method/system for developing a hydrocarbon reservoir” is not qualified for a meaningful limitation because it only generally links the use of the judicial exception to a particular technological environment or field of use. A non-transitory computer-readable storage medium/memory (generic memory) and a processor (generic processor) are generally recited and are not qualified as particular machines.
In conclusion, the above additional elements, considered individually and in combination with the other claim elements do not reflect an improvement to other technology or technical field, and, therefore, do not integrate the judicial exception into a practical application. Therefore, the claims are directed to a judicial exception and require further analysis under the Step 2B.
However, the above claims do not include additional elements that are sufficient to amount to significantly more than the judicial exception (Step 2B analysis).
The claims, therefore, are not patent eligible.
With regards to the dependent claims, claims 2-7, 10-15, and 18-23 provide additional features/steps which are part of an expanded algorithm, so these limitations should be considered part of an expanded abstract idea of the independent claims. Examiner notes that analogous claims 8, 16, and 24 are not rejected under 35 U.S.C. 101 as they integrate the judicial exception into a practical application by incorporating physically drilling the well at the location to access the hydrocarbon reservoir.
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 for establishing a background for determining obviousness under 35 U.S.C. 103 are summarized as follows:
1. Determining the scope and contents of the prior art.
2. Ascertaining the differences between the prior art and the claims at issue.
3. Resolving the level of ordinary skill in the pertinent art.
4. Considering objective evidence present in the application indicating obviousness or nonobviousness.
This application currently names joint inventors. In considering patentability of the claims the examiner presumes that the subject matter of the various claims was commonly owned as of the effective filing date of the claimed invention(s) absent any evidence to the contrary. Applicant is advised of the obligation under 37 CFR 1.56 to point out the inventor and effective filing dates of each claim that was not commonly owned as of the effective filing date of the later invention in order for the examiner to consider the applicability of 35 U.S.C. 102(b)(2)(C) for any potential 35 U.S.C. 102(a)(2) prior art against the later invention.
Claim(s) 1-3, 7-11, 15-19, 23, and 24 is/are rejected under 35 U.S.C. 103 as being unpatentable over Camargo et al. (US 20230333278 A1), hereinafter “Camargo”, in view of Bouaouaja et al. (US 20200095858 A1), hereinafter “Bouaouaja”.
Regarding Claim 1, Camargo teaches a method for developing a hydrocarbon reservoir, the method comprising:
forming, using a mechanical earth model, a fracture network model to identify the presence and extent of natural fractures at locations in the subsurface hydrocarbon reservoir (Camargo [0028] The process may include determining a natural fracture distribution of a 3D fracture model using geomechanics (block 102). See Fig. 1 102 & Fig. 2 214), wherein the mechanical earth model incorporates the principal stress (Camargo [0030] The determination of a natural fracture distribution of a 3D fracture model using geomechanics (block 102) may use rock mechanical properties combined with additional data like seismic, structural restoration and geomechanics determine the natural fractures. Also see [0032] The geomechanics fracture controller (212) may integrate the paleo-stress from structural restoration model (206) obtained for several stages in geological time, and current stress regime conditions obtained through a geomechanical numerical simulation model. See Fig. 2 212 to 214);
determining, using the discrete fracture network, a fracture density index (FDI) (Camargo [0029] As also shown in FIG. 1, a fracture density index (FDI) (block 110) may be determined from the natural fracture distribution of the 3D fracture model (block 102) and the identified fluid flow paths (block 104).), wherein determining the fracture density index (FDI) comprises generating a raster map from the discrete fracture network, the raster map representing a fracture density per area (Camargo [0066] Next, as shown in FIG. 1, a fracture density index (FDI) model may be determined from the natural fracture model and the identified fluid-flow pathways (block 110). The 3D discrete fracture network determined from the geomechanics model may be converted into 2D lines to compute a continuous fracture density property. For example, various geographic information systems (GIS) geoprocessing software may have tools for computing line density. In some embodiments, the conversion of the 3D discrete fracture map to 2D lines may be performed by ArcGIS available from Environmental Systems Research Institute (Ersi), California, USA. In such embodiments, a raster map representing fracture density per area may be generated.);
modifying the fracture density index (FDI) based on a flow capacity response (Camargo [0028] Additionally, a reservoir matrix model may be constructed (block 106) to determine matrix properties such as matrix permeability and matrix flow capacity. Also see [0063] The process 100 may also include the use of reservoir dynamic properties (block 108). The reservoir dynamic properties may be determined by field measurements and operations in well accessing a reservoir. Such measurements may indicate reservoir fluid movement and may include: flow capacity from Pressure Transient Analysis (PTA). And [0068] By way of example, FIGS. 6A- 6C depict a spatial analysis with a total flow capacity (PTA-KH). FIG. 6A depicts total flow capacity (PTA-KH) 600 overlaid on a Fracture Density Index (FDI) map 602 in accordance with an embodiment of the disclosure. The FDI is modified by overlaying it with the flow capacity); and
calibrating the modified fracture density index (FDI) (Camargo [0034] The validation of the fracture model (216) may include cross-checking or validating the model using reservoir production data. […] Several types of reservoir production data can be used to calibrate the fracture models with reservoir engineering data. Examples of such reservoir production data are results of measures obtained from: PTA (Pressure Transient Analysis), tracers, drilling operation events, PLT (production logs), and the like. Also see [0049] The fracture model validation 216 may validate the discrete fracture model (214). The validation may be performed using reservoir production data. As shown in FIG. 2B, Several types of data may be used as fracture dynamic properties (232) to calibrate the fracture model with reservoir engineering measures (210). ) using a bottom hole pressure (BHP) and a BHP rate (Camargo [0049] For example, results from a PTA (Pressure Transient Analysis) test, or measures from tracers, drilling operations, production logs, and the like may be used. For example, pressure transient analysis can estimate permeability contribution due to fracture presence and the capacity for fluid flow due to the fractures presence. Also see [0063] The process 100 may also include the use of reservoir dynamic properties (block 108). The reservoir dynamic properties may be determined by field measurements and operations in well accessing a reservoir. Such measurements may indicate reservoir fluid movement and may include: flow capacity from Pressure Transient Analysis (PTA), productivity index (PI), cumulative fluids, flowmeter logs, and water encroachment. [0064] As known in the art, PTA provides a measurement of the reservoir pressure changes over time. In most well tests, a limited amount of fluid can flow from the formation being tested, and the pressure of the formation is monitored over time. [0065] The productivity index (PI) is an expression of the ability of a reservoir to deliver fluids to the wellbore at a pressure drawdown. In some embodiments, the productivity index may be determined from fluid production rate normalized by the bottomhole pressure (BHP). A variety of parameters and measurements, such as flow capacity from PTA and PI are used to enhance the analysis of the FDI).
Camargo is not relied upon to teach applying the calibrated modified fracture density index (FDI) to a single media matrix permeability for the reservoir to determine an improved matrix permeability.
Bouaouaja teaches applying the calibrated modified fracture density index (FDI) to a single media matrix permeability for the reservoir to determine an improved matrix permeability (Bouaouaja [0054] The matrix permeability model formed at 232 is provided as shown at 233 to the dynamic reservoir model permeability optimization module 260. See Fig. 4 230 input to 260 via 233. The permeability model (230) is optimized (260) by incorporating fracture modeling (250 via 264))).
It would have been obvious to one of ordinary skill in the art, prior to the effective filing date of the instant application, to modify Camargo, in view of Bouaouaja to explicitly teach applying the calibrated modified fracture density index (FDI) to a single media matrix permeability for the reservoir to determine an improved matrix permeability, to ensure that the matrix permeability of Camargo is accurate when used in conjunction with fracture density to determine drilling locations (Camargo [0067] The natural fracture sweet spots may be identified using the fracture density index (FDI), the reservoir matrix model, and the reservoir dynamic properties (block 112). In particular, the identification includes a spatial analysis between the Fracture Density Index (FDI) and matrix permeability versus total flow capacity (PTA-KH) and Productivity Index (PI) to identify sweet spots for natural fractures impacting reservoir fluid behavior. Also see [0069] FIG. 6C depicts total flow capacity (PTA-KH) 600 overlaid on a matrix permeability map 604 in accordance with an embodiment of the disclosure. Fig. 6 is a spatial analysis. Also see [0006] the method includes drilling a well in a subsurface geological structure to a location in the hydrocarbon reservoir based on the identified sweet spot.).
Regarding Claim 2, Camargo in view of Bouaouaja (as stated above) further teaches wherein the flow capacity response comprises a flow capacity from a pressure transient analysis (PTA) (Camargo [0063] The process 100 may also include the use of reservoir dynamic properties (block 108). The reservoir dynamic properties may be determined by field measurements and operations in well accessing a reservoir. Such measurements may indicate reservoir fluid movement and may include: flow capacity from Pressure Transient Analysis (PTA).).
Regarding Claim 3, Camargo in view of Bouaouaja (as stated above) further teaches wherein the flow capacity response comprises a flow capacity from a matrix permeability model (Camargo Additionally, a reservoir matrix model may be constructed (block 106) to determine matrix properties such as matrix permeability and matrix flow capacity.).
Regarding Claim 7, Camargo in view of Bouaouaja (as stated above) further teaches determining a location for a well to access the hydrocarbon reservoir using the improved matrix permeability (Camargo [0005] determining a sweet spot based on the fracture density index and at least one of the flow capacity parameter and the productivity index.).
Regarding Claim 8, Camargo in view of Bouaouaja (as stated above) further teaches drilling the well at the location to access the hydrocarbon reservoir (Camargo [0006] drilling a well in a subsurface geological structure to a location in the hydrocarbon reservoir based on the identified sweet spot).
Regarding Claim 9, Camargo teaches a non-transitory computer-readable storage medium having executable code stored thereon for developing a hydrocarbon reservoir, the executable code comprising a set of instructions that causes a processor to perform operations (Camargo [0076] The data processing system 800 includes executable code 820 stored in non-transitory memory 806 of the computer 802. The executable code 820 according to the present disclosure is in the form of computer operable instructions causing the data processor 804 to determine geomechanical components, determine a discrete natural fracture model, identify fluid flow paths, determine a fracture density index (FDI) map, and determine sweet spots, according to the present disclosure in the manner set forth.) comprising:
forming, using a mechanical earth model, a fracture network model to identify the presence and extent of natural fractures at locations in the subsurface hydrocarbon reservoir (Camargo [0028] The process may include determining a natural fracture distribution of a 3D fracture model using geomechanics (block 102). See Fig. 1 102 & Fig. 2 214), wherein the mechanical earth model incorporates the principal stress (Camargo [0030] The determination of a natural fracture distribution of a 3D fracture model using geomechanics (block 102) may use rock mechanical properties combined with additional data like seismic, structural restoration and geomechanics determine the natural fractures. Also see [0032] The geomechanics fracture controller (212) may integrate the paleo-stress from structural restoration model (206) obtained for several stages in geological time, and current stress regime conditions obtained through a geomechanical numerical simulation model. See Fig. 2 212 to 214);
determining, using the discrete fracture network, a fracture density index (FDI) (Camargo [0029] As also shown in FIG. 1, a fracture density index (FDI) (block 110) may be determined from the natural fracture distribution of the 3D fracture model (block 102) and the identified fluid flow paths (block 104).), wherein determining the fracture density index (FDI) comprises generating a raster map from the discrete fracture network, the raster map representing a fracture density per area (Camargo [0066] Next, as shown in FIG. 1, a fracture density index (FDI) model may be determined from the natural fracture model and the identified fluid-flow pathways (block 110). The 3D discrete fracture network determined from the geomechanics model may be converted into 2D lines to compute a continuous fracture density property. For example, various geographic information systems (GIS) geoprocessing software may have tools for computing line density. In some embodiments, the conversion of the 3D discrete fracture map to 2D lines may be performed by ArcGIS available from Environmental Systems Research Institute (Ersi), California, USA. In such embodiments, a raster map representing fracture density per area may be generated.);
modifying the fracture density index (FDI) based on a flow capacity response (Camargo [0028] Additionally, a reservoir matrix model may be constructed (block 106) to determine matrix properties such as matrix permeability and matrix flow capacity. Also see [0063] The process 100 may also include the use of reservoir dynamic properties (block 108). The reservoir dynamic properties may be determined by field measurements and operations in well accessing a reservoir. Such measurements may indicate reservoir fluid movement and may include: flow capacity from Pressure Transient Analysis (PTA). And [0068] By way of example, FIGS. 6A- 6C depict a spatial analysis with a total flow capacity (PTA-KH). FIG. 6A depicts total flow capacity (PTA-KH) 600 overlaid on a Fracture Density Index (FDI) map 602 in accordance with an embodiment of the disclosure. The FDI is modified by overlaying it with the flow capacity); and
calibrating the modified fracture density index (FDI) (Camargo [0034] The validation of the fracture model (216) may include cross-checking or validating the model using reservoir production data. […] Several types of reservoir production data can be used to calibrate the fracture models with reservoir engineering data. Examples of such reservoir production data are results of measures obtained from: PTA (Pressure Transient Analysis), tracers, drilling operation events, PLT (production logs), and the like. Also see [0049] The fracture model validation 216 may validate the discrete fracture model (214). The validation may be performed using reservoir production data. As shown in FIG. 2B, Several types of data may be used as fracture dynamic properties (232) to calibrate the fracture model with reservoir engineering measures (210). ) using a bottom hole pressure (BHP) and a BHP rate (Camargo [0049] For example, results from a PTA (Pressure Transient Analysis) test, or measures from tracers, drilling operations, production logs, and the like may be used. For example, pressure transient analysis can estimate permeability contribution due to fracture presence and the capacity for fluid flow due to the fractures presence. Also see [0063] The process 100 may also include the use of reservoir dynamic properties (block 108). The reservoir dynamic properties may be determined by field measurements and operations in well accessing a reservoir. Such measurements may indicate reservoir fluid movement and may include: flow capacity from Pressure Transient Analysis (PTA), productivity index (PI), cumulative fluids, flowmeter logs, and water encroachment. [0064] As known in the art, PTA provides a measurement of the reservoir pressure changes over time. In most well tests, a limited amount of fluid can flow from the formation being tested, and the pressure of the formation is monitored over time. [0065] The productivity index (PI) is an expression of the ability of a reservoir to deliver fluids to the wellbore at a pressure drawdown. In some embodiments, the productivity index may be determined from fluid production rate normalized by the bottomhole pressure (BHP). A variety of parameters and measurements, such as flow capacity from PTA and PI are used to enhance the analysis of the FDI).
Camargo is not relied upon to teach applying the calibrated modified fracture density index (FDI) to a single media matrix permeability for the reservoir to determine an improved matrix permeability.
Bouaouaja teaches applying the calibrated modified fracture density index (FDI) to a single media matrix permeability for the reservoir to determine an improved matrix permeability (Bouaouaja [0054] The matrix permeability model formed at 232 is provided as shown at 233 to the dynamic reservoir model permeability optimization module 260. See Fig. 4 230 input to 260 via 233. The permeability model (230) is optimized (260) by incorporating fracture modeling (250 via 264))).
It would have been obvious to one of ordinary skill in the art, prior to the effective filing date of the instant application, to modify Camargo, in view of Bouaouaja to explicitly teach applying the calibrated modified fracture density index (FDI) to a single media matrix permeability for the reservoir to determine an improved matrix permeability, to ensure that the matrix permeability of Camargo is accurate when used in conjunction with fracture density to determine drilling locations (Camargo [0067] The natural fracture sweet spots may be identified using the fracture density index (FDI), the reservoir matrix model, and the reservoir dynamic properties (block 112). In particular, the identification includes a spatial analysis between the Fracture Density Index (FDI) and matrix permeability versus total flow capacity (PTA-KH) and Productivity Index (PI) to identify sweet spots for natural fractures impacting reservoir fluid behavior. Also see [0069] FIG. 6C depicts total flow capacity (PTA-KH) 600 overlaid on a matrix permeability map 604 in accordance with an embodiment of the disclosure. Fig. 6 is a spatial analysis. Also see [0006] the method includes drilling a well in a subsurface geological structure to a location in the hydrocarbon reservoir based on the identified sweet spot.).
Regarding Claim 10, Camargo in view of Bouaouaja (as stated above) further teaches wherein the flow capacity response comprises a flow capacity from a pressure transient analysis (PTA) (Camargo [0063] The process 100 may also include the use of reservoir dynamic properties (block 108). The reservoir dynamic properties may be determined by field measurements and operations in well accessing a reservoir. Such measurements may indicate reservoir fluid movement and may include: flow capacity from Pressure Transient Analysis (PTA).).
Regarding Claim 11, Camargo in view of Bouaouaja (as stated above) further teaches wherein the flow capacity response comprises a flow capacity from a matrix permeability model (Camargo Additionally, a reservoir matrix model may be constructed (block 106) to determine matrix properties such as matrix permeability and matrix flow capacity.).
Regarding Claim 15, Camargo in view of Bouaouaja (as stated above) further teaches determining a location for a well to access the hydrocarbon reservoir using the improved matrix permeability (Camargo [0005] determining a sweet spot based on the fracture density index and at least one of the flow capacity parameter and the productivity index.).
Regarding Claim 16, Camargo in view of Bouaouaja (as stated above) further teaches drilling the well at the location to access the hydrocarbon reservoir (Camargo [0006] drilling a well in a subsurface geological structure to a location in the hydrocarbon reservoir based on the identified sweet spot).
Regarding Claim 17, Camargo teaches a system for developing a hydrocarbon reservoir, comprising:
a processor (Camargo [0073] FIG. 8 depicts a data processing system 800 that includes a computer 802 having a master node processor 804);
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 (Camargo [0073] FIG. 8 depicts a data processing system 800 that includes a computer 802 having a master node processor 804 and memory 806 coupled to the processor 804 to store operating instructions, control information and database records therein in accordance with an embodiment of the disclosure.) comprising:
forming, using a mechanical earth model, a fracture network model to identify the presence and extent of natural fractures at locations in the subsurface hydrocarbon reservoir (Camargo [0028] The process may include determining a natural fracture distribution of a 3D fracture model using geomechanics (block 102). See Fig. 1 102 & Fig. 2 214), wherein the mechanical earth model incorporates the principal stress (Camargo [0030] The determination of a natural fracture distribution of a 3D fracture model using geomechanics (block 102) may use rock mechanical properties combined with additional data like seismic, structural restoration and geomechanics determine the natural fractures. Also see [0032] The geomechanics fracture controller (212) may integrate the paleo-stress from structural restoration model (206) obtained for several stages in geological time, and current stress regime conditions obtained through a geomechanical numerical simulation model. See Fig. 2 212 to 214);
determining, using the discrete fracture network, a fracture density index (FDI) (Camargo [0029] As also shown in FIG. 1, a fracture density index (FDI) (block 110) may be determined from the natural fracture distribution of the 3D fracture model (block 102) and the identified fluid flow paths (block 104).), wherein determining the fracture density index (FDI) comprises generating a raster map from the discrete fracture network, the raster map representing a fracture density per area (Camargo [0066] Next, as shown in FIG. 1, a fracture density index (FDI) model may be determined from the natural fracture model and the identified fluid-flow pathways (block 110). The 3D discrete fracture network determined from the geomechanics model may be converted into 2D lines to compute a continuous fracture density property. For example, various geographic information systems (GIS) geoprocessing software may have tools for computing line density. In some embodiments, the conversion of the 3D discrete fracture map to 2D lines may be performed by ArcGIS available from Environmental Systems Research Institute (Ersi), California, USA. In such embodiments, a raster map representing fracture density per area may be generated.);
modifying the fracture density index (FDI) based on a flow capacity response (Camargo [0028] Additionally, a reservoir matrix model may be constructed (block 106) to determine matrix properties such as matrix permeability and matrix flow capacity. Also see [0063] The process 100 may also include the use of reservoir dynamic properties (block 108). The reservoir dynamic properties may be determined by field measurements and operations in well accessing a reservoir. Such measurements may indicate reservoir fluid movement and may include: flow capacity from Pressure Transient Analysis (PTA). And [0068] By way of example, FIGS. 6A- 6C depict a spatial analysis with a total flow capacity (PTA-KH). FIG. 6A depicts total flow capacity (PTA-KH) 600 overlaid on a Fracture Density Index (FDI) map 602 in accordance with an embodiment of the disclosure. The FDI is modified by overlaying it with the flow capacity); and
calibrating the modified fracture density index (FDI) (Camargo [0034] The validation of the fracture model (216) may include cross-checking or validating the model using reservoir production data. […] Several types of reservoir production data can be used to calibrate the fracture models with reservoir engineering data. Examples of such reservoir production data are results of measures obtained from: PTA (Pressure Transient Analysis), tracers, drilling operation events, PLT (production logs), and the like. Also see [0049] The fracture model validation 216 may validate the discrete fracture model (214). The validation may be performed using reservoir production data. As shown in FIG. 2B, Several types of data may be used as fracture dynamic properties (232) to calibrate the fracture model with reservoir engineering measures (210). ) using a bottom hole pressure (BHP) and a BHP rate (Camargo [0049] For example, results from a PTA (Pressure Transient Analysis) test, or measures from tracers, drilling operations, production logs, and the like may be used. For example, pressure transient analysis can estimate permeability contribution due to fracture presence and the capacity for fluid flow due to the fractures presence. Also see [0063] The process 100 may also include the use of reservoir dynamic properties (block 108). The reservoir dynamic properties may be determined by field measurements and operations in well accessing a reservoir. Such measurements may indicate reservoir fluid movement and may include: flow capacity from Pressure Transient Analysis (PTA), productivity index (PI), cumulative fluids, flowmeter logs, and water encroachment. [0064] As known in the art, PTA provides a measurement of the reservoir pressure changes over time. In most well tests, a limited amount of fluid can flow from the formation being tested, and the pressure of the formation is monitored over time. [0065] The productivity index (PI) is an expression of the ability of a reservoir to deliver fluids to the wellbore at a pressure drawdown. In some embodiments, the productivity index may be determined from fluid production rate normalized by the bottomhole pressure (BHP). A variety of parameters and measurements, such as flow capacity from PTA and PI are used to enhance the analysis of the FDI).
Camargo is not relied upon to teach applying the calibrated modified fracture density index (FDI) to a single media matrix permeability for the reservoir to determine an improved matrix permeability.
Bouaouaja teaches applying the calibrated modified fracture density index (FDI) to a single media matrix permeability for the reservoir to determine an improved matrix permeability (Bouaouaja [0054] The matrix permeability model formed at 232 is provided as shown at 233 to the dynamic reservoir model permeability optimization module 260. See Fig. 4 230 input to 260 via 233. The permeability model (230) is optimized (260) by incorporating fracture modeling (250 via 264))).
It would have been obvious to one of ordinary skill in the art, prior to the effective filing date of the instant application, to modify Camargo, in view of Bouaouaja to explicitly teach applying the calibrated modified fracture density index (FDI) to a single media matrix permeability for the reservoir to determine an improved matrix permeability, to ensure that the matrix permeability of Camargo is accurate when used in conjunction with fracture density to determine drilling locations (Camargo [0067] The natural fracture sweet spots may be identified using the fracture density index (FDI), the reservoir matrix model, and the reservoir dynamic properties (block 112). In particular, the identification includes a spatial analysis between the Fracture Density Index (FDI) and matrix permeability versus total flow capacity (PTA-KH) and Productivity Index (PI) to identify sweet spots for natural fractures impacting reservoir fluid behavior. Also see [0069] FIG. 6C depicts total flow capacity (PTA-KH) 600 overlaid on a matrix permeability map 604 in accordance with an embodiment of the disclosure. Fig. 6 is a spatial analysis. Also see [0006] the method includes drilling a well in a subsurface geological structure to a location in the hydrocarbon reservoir based on the identified sweet spot.).
Regarding Claim 18, Camargo in view of Bouaouaja (as stated above) further teaches wherein the flow capacity response comprises a flow capacity from a pressure transient analysis (PTA) (Camargo [0063] The process 100 may also include the use of reservoir dynamic properties (block 108). The reservoir dynamic properties may be determined by field measurements and operations in well accessing a reservoir. Such measurements may indicate reservoir fluid movement and may include: flow capacity from Pressure Transient Analysis (PTA).).
Regarding Claim 19, Camargo in view of Bouaouaja (as stated above) further teaches wherein the flow capacity response comprises a flow capacity from a matrix permeability model (Camargo Additionally, a reservoir matrix model may be constructed (block 106) to determine matrix properties such as matrix permeability and matrix flow capacity.).
Regarding Claim 23, Camargo in view of Bouaouaja (as stated above) further teaches determining a location for a well to access the hydrocarbon reservoir using the improved matrix permeability (Camargo [0005] determining a sweet spot based on the fracture density index and at least one of the flow capacity parameter and the productivity index.).
Regarding Claim 24, Camargo in view of Bouaouaja (as stated above) further teaches drilling the well at the location to access the hydrocarbon reservoir (Camargo [0006] drilling a well in a subsurface geological structure to a location in the hydrocarbon reservoir based on the identified sweet spot).
Claim(s) 4, 12, and 20 is/are rejected under 35 U.S.C. 103 as being unpatentable over Camargo in view of Bouaouaja (as stated above), further in view of Alwehaibi et al. (US 20210349226 A1), hereinafter “Alwehaibi”.
Regarding Claim 4, Camargo in view of Bouaouaja (as stated above) is not relied upon to explicitly teach wherein calibrating the modified fracture density index (FDI) using a bottom hole pressure (BHP) and a BHP rate comprises using a pressure transient analysis simulation to determine a permeability multiplier.
Alwehaibi teaches wherein calibrating the modified fracture density index (FDI) using a bottom hole pressure (BHP) and a BHP rate comprises using a pressure transient analysis simulation to determine a permeability multiplier (Alwehaibi [0079] The permeability obtained from the pressure-transient analysis is valid within the radius of investigation during the build-up period. […] The permeability within that region can be calibrated by applying multipliers on the permeability of the model to match the pressure-transient permeability.).
It would have been obvious to one of ordinary skill in the art, prior to the effective filing date of the instant application, to modify Camargo in view of Bouaouaja (as stated above), further in view of Alwehaibi, to explicitly teach wherein calibrating the modified fracture density index (FDI) using a bottom hole pressure (BHP) and a BHP rate comprises using a pressure transient analysis simulation to determine a permeability multiplier, to enhance matching between intermediate zones between wells and zones of various sizes (Alwehaibi [0079] The permeability obtained from the pressure-transient analysis is valid within the radius of investigation during the build-up period. Beyond that radius, the permeability from pressure transient data is unknown. Therefore, conditioning the permeability in the model should be performed only within that radius, which is performed in this workflow by creating a composite region around the subject well. […] These scenarios can be evaluated during the last step of this workflow, which is the multi-well transient match. Long bottom-hole pressure trends of the wells can be matched during the multi-well matching process, and the most likely scenario can be distinguished by providing the best match.).
Regarding Claim 12, Camargo in view of Bouaouaja (as stated above) is not relied upon to explicitly teach wherein calibrating the modified fracture density index (FDI) using a bottom hole pressure (BHP) and a BHP rate comprises using a pressure transient analysis simulation to determine a permeability multiplier.
Alwehaibi teaches wherein calibrating the modified fracture density index (FDI) using a bottom hole pressure (BHP) and a BHP rate comprises using a pressure transient analysis simulation to determine a permeability multiplier (Alwehaibi [0079] The permeability obtained from the pressure-transient analysis is valid within the radius of investigation during the build-up period. […] The permeability within that region can be calibrated by applying multipliers on the permeability of the model to match the pressure-transient permeability.).
It would have been obvious to one of ordinary skill in the art, prior to the effective filing date of the instant application, to modify Camargo in view of Bouaouaja (as stated above), further in view of Alwehaibi, to explicitly teach wherein calibrating the modified fracture density index (FDI) using a bottom hole pressure (BHP) and a BHP rate comprises using a pressure transient analysis simulation to determine a permeability multiplier, to enhance matching between intermediate zones between wells and zones of various sizes (Alwehaibi [0079] The permeability obtained from the pressure-transient analysis is valid within the radius of investigation during the build-up period. Beyond that radius, the permeability from pressure transient data is unknown. Therefore, conditioning the permeability in the model should be performed only within that radius, which is performed in this workflow by creating a composite region around the subject well. […] These scenarios can be evaluated during the last step of this workflow, which is the multi-well transient match. Long bottom-hole pressure trends of the wells can be matched during the multi-well matching process, and the most likely scenario can be distinguished by providing the best match.).
Regarding Claim 20, Camargo in view of Bouaouaja (as stated above) is not relied upon to explicitly teach wherein calibrating the modified fracture density index (FDI) using a bottom hole pressure (BHP) and a BHP rate comprises using a pressure transient analysis simulation to determine a permeability multiplier.
Alwehaibi teaches wherein calibrating the modified fracture density index (FDI) using a bottom hole pressure (BHP) and a BHP rate comprises using a pressure transient analysis simulation to determine a permeability multiplier (Alwehaibi [0079] The permeability obtained from the pressure-transient analysis is valid within the radius of investigation during the build-up period. […] The permeability within that region can be calibrated by applying multipliers on the permeability of the model to match the pressure-transient permeability.).
It would have been obvious to one of ordinary skill in the art, prior to the effective filing date of the instant application, to modify Camargo in view of Bouaouaja (as stated above), further in view of Alwehaibi, to explicitly teach wherein calibrating the modified fracture density index (FDI) using a bottom hole pressure (BHP) and a BHP rate comprises using a pressure transient analysis simulation to determine a permeability multiplier, to enhance matching between intermediate zones between wells and zones of various sizes (Alwehaibi [0079] The permeability obtained from the pressure-transient analysis is valid within the radius of investigation during the build-up period. Beyond that radius, the permeability from pressure transient data is unknown. Therefore, conditioning the permeability in the model should be performed only within that radius, which is performed in this workflow by creating a composite region around the subject well. […] These scenarios can be evaluated during the last step of this workflow, which is the multi-well transient match. Long bottom-hole pressure trends of the wells can be matched during the multi-well matching process, and the most likely scenario can be distinguished by providing the best match.).
Claim(s) 5, 13, and 21 is/are rejected under 35 U.S.C. 103 as being unpatentable over Camargo in view of Bouaouaja (as stated above), further in view of Jayasinghe et al. (US 20250068804 A1), hereinafter “Jayasinghe”.
Regarding Claim 5, Camargo in view of Bouaouaja (as stated above) is not relied upon to teach performing a history match between for the bottom hole pressure (BHP).
Jayasinghe teaches performing a history match between for the bottom hole pressure (BHP) (Jayasinghe [0038] In an initial stage, termed “history matching”, past (“historic”) production, i.e., flow rates and downhole pressures, may be simulated and reservoir properties adjusted, iteratively, until simulated values match the historic measured values of flow rates and downhole pressures.).
It would have been obvious to one of ordinary skill in the art, prior to the effective filing date of the instant application, to modify Camargo in view of Bouaouaja (as stated above), further in view of Jayasinghe, to explicitly teach performing a history match between for the bottom hole pressure (BHP), to validate the accuracy if the models (Jayasinghe [0053] One of the purposes of simulating past production history is to validate the reservoir simulation model. The reservoir simulation results (210) may be compared to observed reservoir and individual well production history. When the simulated and observed historical production match to within a satisfactory tolerance the reservoir simulation model may be considered to be validated, and future production histories may be predicted with some confidence. […] This process of comparing simulated and observed production history and updating the reservoir simulation model (206) may be termed “history matching”).
Regarding Claim 13, Camargo in view of Bouaouaja (as stated above) is not relied upon to teach performing a history match between for the bottom hole pressure (BHP).
Jayasinghe teaches performing a history match between for the bottom hole pressure (BHP) (Jayasinghe [0038] In an initial stage, termed “history matching”, past (“historic”) production, i.e., flow rates and downhole pressures, may be simulated and reservoir properties adjusted, iteratively, until simulated values match the historic measured values of flow rates and downhole pressures.).
It would have been obvious to one of ordinary skill in the art, prior to the effective filing date of the instant application, to modify Camargo in view of Bouaouaja (as stated above), further in view of Jayasinghe, to explicitly teach performing a history match between for the bottom hole pressure (BHP), to validate the accuracy if the models (Jayasinghe [0053] One of the purposes of simulating past production history is to validate the reservoir simulation model. The reservoir simulation results (210) may be compared to observed reservoir and individual well production history. When the simulated and observed historical production match to within a satisfactory tolerance the reservoir simulation model may be considered to be validated, and future production histories may be predicted with some confidence. […] This process of comparing simulated and observed production history and updating the reservoir simulation model (206) may be termed “history matching”).
Regarding Claim 21, Camargo in view of Bouaouaja (as stated above) is not relied upon to teach performing a history match between for the bottom hole pressure (BHP).
Jayasinghe teaches performing a history match between for the bottom hole pressure (BHP) (Jayasinghe [0038] In an initial stage, termed “history matching”, past (“historic”) production, i.e., flow rates and downhole pressures, may be simulated and reservoir properties adjusted, iteratively, until simulated values match the historic measured values of flow rates and downhole pressures.).
It would have been obvious to one of ordinary skill in the art, prior to the effective filing date of the instant application, to modify Camargo in view of Bouaouaja (as stated above), further in view of Jayasinghe, to explicitly teach performing a history match between for the bottom hole pressure (BHP), to validate the accuracy if the models (Jayasinghe [0053] One of the purposes of simulating past production history is to validate the reservoir simulation model. The reservoir simulation results (210) may be compared to observed reservoir and individual well production history. When the simulated and observed historical production match to within a satisfactory tolerance the reservoir simulation model may be considered to be validated, and future production histories may be predicted with some confidence. […] This process of comparing simulated and observed production history and updating the reservoir simulation model (206) may be termed “history matching”).
Claim(s) 6, 14, and 22 is/are rejected under 35 U.S.C. 103 as being unpatentable over Camargo in view of Bouaouaja (as stated above), further in view of Camargo et al. (US 20240060418 A1), hereinafter “Camargo2”.
Regarding Claim 6, Camargo in view of Bouaouaja (as stated above) Camargo in view of Bouaouaja (as stated above) is not relied upon to teach wherein the modified fracture density index (FDI) comprises an effective permeability tensor.
Camargo2 teaches wherein the modified fracture density index (FDI) comprises an effective permeability tensor (Camargo2 [0078] By way of example, FIG. 7 depicts fracture porosity 700 and an effective permeability tensor 702, 707, and 707 determined using scale-up of fracture model properties in accordance with an embodiment of the present disclosure. [0079] Next, the fracture properties of the fracture model may be calibrated using the fracture properties (for example, fracture porosity and fracture permeability) derived from the X-ray MicroCT workflow (block 120). The fracture permeability tensor information is used in calibrating the fracture model that quantifies fracture density ([0046]).).
Regarding Claim 14, Camargo in view of Bouaouaja (as stated above) Camargo in view of Bouaouaja (as stated above) is not relied upon to teach wherein the modified fracture density index (FDI) comprises an effective permeability tensor.
Camargo2 teaches wherein the modified fracture density index (FDI) comprises an effective permeability tensor (Camargo2 [0078] By way of example, FIG. 7 depicts fracture porosity 700 and an effective permeability tensor 702, 707, and 707 determined using scale-up of fracture model properties in accordance with an embodiment of the present disclosure. [0079] Next, the fracture properties of the fracture model may be calibrated using the fracture properties (for example, fracture porosity and fracture permeability) derived from the X-ray MicroCT workflow (block 120). The fracture permeability tensor information is used in calibrating the fracture model that quantifies fracture density ([0046]).).
Regarding Claim 22, Camargo in view of Bouaouaja (as stated above) Camargo in view of Bouaouaja (as stated above) is not relied upon to teach wherein the modified fracture density index (FDI) comprises an effective permeability tensor.
Camargo2 teaches wherein the modified fracture density index (FDI) comprises an effective permeability tensor (Camargo2 [0078] By way of example, FIG. 7 depicts fracture porosity 700 and an effective permeability tensor 702, 707, and 707 determined using scale-up of fracture model properties in accordance with an embodiment of the present disclosure. [0079] Next, the fracture properties of the fracture model may be calibrated using the fracture properties (for example, fracture porosity and fracture permeability) derived from the X-ray MicroCT workflow (block 120). The fracture permeability tensor information is used in calibrating the fracture model that quantifies fracture density ([0046]).).
Conclusion
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/CHRISTIAN T BRYANT/Primary Examiner, Art Unit 2857
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