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
The Office Action is in response to the application filed on 10/28/2022. Claims 1-20 are pending in
the application. Claims 1, 13 and 17 are independent claims.
Information Disclosure Statement
The information disclosure statement (IDS) submitted on 10/28/2022 in compliance with the provisions of 37 CFR 1.97. Accordingly, the information disclosure statement is being considered by the examiner.
Specification
The abstract dated 10/28/2022 has been reviewed. It has 110 words and 8 lines and no legal
phraseology. It is accepted.
Claim Objections
Claims 2, 8, 9, 12, 14, 17 and 18 are objected to because of the following informalities:
Claim 2 recites “forming a predicted drainage function comprises …” should read “forming the predicted drainage function comprises …”
Claim 14 also recites element “a predicted drainage function”, is objected for similar reason.
Claim 8 recites “a preferred drainage model based on a reduction of the misfit value comprises …” should read “the preferred drainage model based on the reduction of the misfit value comprises …”
Claim 9 recites “performing full-physics pressure-transient/rate-transient simulation comprises …” should read “performing the full-physics pressure-transient/rate-transient simulation comprises …”
Claim 12 recites “generating hydraulic fractures at the spacing” should read “generating the hydraulic fractures at the spacing.”
Claim 18 also recites element “hydraulic fractures”, is objected for similar reason.
Claim 17 recites “ … to determine a measured drainage function; and a computer processor, configured to: receive a measured drainage function for a well …” should read “… to determine a measured drainage function; and a computer processor, configured to: receive the measured drainage function for a well …”.
Appropriate correction is required.
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.
The claims 1-20 are rejected under 35 USC § 101 because the claimed invention is directed to
judicial exception, an abstract idea, it has not been integrated into practical application and the claims further do not recite significantly more than the judicial exception. Examiner has evaluated the claims under the framework provided in the 2019 Patent Eligibility Guidance published in the Federal Register 01/07/2019 and has provided such analysis below.
Step 1: Are the claims to a process, machine, manufacture or composition of matter?"
Yes, Claims 1-12 are directed to method and fall within the statutory category of process;
Yes, Claims 13-16 are directed to non-transitory computer-readable medium and fall within the statutory category of article of manufacture;
Yes, Claims 17-20 are directed to system and fall within the statutory category of machine.
In order to evaluate the Step 2A inquiry "Is the claim directed to a law of nature, a natural phenomenon or an abstract idea?" we must determine, at Step 2A Prong 1, whether the claim recites a law of nature, a natural phenomenon or an abstract idea and further whether the claim recites additional elements that integrate the judicial exception into a practical application.
Step 2A Prong 1: The claim 1 does recite a mental process.
As explained in MPEP 2106.04(a)(2)(III): Nor do the courts distinguish between claims that recite mental processes performed by humans and claims that recite mental processes performed on a computer. As the Federal Circuit has explained, "[c]ourts have examined claims that required the use of a computer and still found that the underlying, patent-ineligible invention could be performed via pen and paper or in a person’s mind." Versata Dev. Group v. SAP Am., Inc., 793 F.3d 1306, 1335, 115 USPQ2d 1681, 1702 (Fed. Cir. 2015). See also Intellectual Ventures I LLC v. Symantec Corp., 838 F.3d 1307, 1318, 120 USPQ2d 1353, 1360 (Fed. Cir. 2016) (‘‘[W]ith the exception of generic computer-implemented steps, there is nothing in the claims themselves that foreclose them from being performed by a human, mentally or with pen and paper.’’); Mortgage Grader, Inc. v. First Choice Loan Servs. Inc., 811 F.3d 1314, 1324, 117 USPQ2d 1693, 1699 (Fed. Cir. 2016) (holding that computer-implemented method for "anonymous loan shopping" was an abstract idea because it could be "performed by humans without a computer").
Further, as explain in MPEP 2106.04(a)(2)(III)(A): In contrast, claims do recite a mental process when they contain limitations that can practically be performed in the human mind, including for example, observations, evaluations, judgments, and opinions.
Further, as explain in MPEP 2106.04(a)(2)(III)(C): 1. Performing a mental process on a generic computer. An example of a case identifying a mental process performed on a generic computer as an abstract idea is Voter Verified, Inc. v. Election Systems & Software, LLC, 887 F.3d 1376, 1385, 126 USPQ2d 1498, 1504 (Fed. Cir. 2018) … 2. Performing a mental process in a computer environment. An example of a case identifying a mental process performed in a computer environment as an abstract idea is Symantec Corp., 838 F.3d at 1316-18, 120 USPQ2d at 1360 … 3. Using a computer as a tool to perform a mental process. An example of a case in which a computer was used as a tool to perform a mental process is Mortgage Grader, 811 F.3d. at 1324, 117 USPQ2d at 1699.
Claim 1: The limitations of “determining a misfit value based, at least in part, on the predicted drainage function and the measured drainage function; and
determining a set of candidate drainage models based, at least in part, on the misfit value for each drainage model, wherein each candidate drainage model comprises a candidate reservoir model and a candidate fracture model,” as drafted, is a process that, but for the recitation of generic computing components, under its broadest reasonable interpretation (BRI) in light of specification, covers performance of the limitation in the mind. For example a person is capable of observing the measured drainage behavior and predicted drainage behavior, mentally comparing/evaluating both to determine a difference representing a misfit value, and define which drainage models qualify as candidate drainage models based on the magnitude of the misfit value of each drainage model (The courts consider a mental process (thinking) that "can be performed in the human mind, or by a human using a pen and paper" to be an abstract idea. CyberSource Corp. v. Retail Decisions, Inc., 654 F.3d 1366, 1372, 99 USPQ2d 1690, 1695 (Fed. Cir. 2011)) – MPEP 2106.04(a)(2)(III).
If a claim limitation, under its broadest reasonable interpretation, covers performance of the limitation in the mind but for the recitation of generic computer components, then it falls within the “Mental Processes” grouping of abstract ideas. Accordingly, the claim recites an abstract idea under step 2A Prong I.
In MPEP 2106.04(II)(B): A claim may recite multiple judicial exceptions. For example, claim 4 at issue in Bilski v. Kappos, 561 U.S. 593, 95 USPQ2d 1001 (2010) recited two abstract ideas, and the claims at issue in Mayo Collaborative Servs. v. Prometheus Labs. Inc., 566 U.S. 66, 101 USPQ2d 1961 (2012) recited two laws of nature. However, these claims were analyzed by the Supreme Court in the same manner as claims reciting a single judicial exception, such as those in Alice Corp., 573 U.S. 208, 110 USPQ2d 1976.
The claim 1 does recite a mathematical concepts.
MPEP 2106.4(a)(2)(I): “The mathematical concepts grouping is defined as mathematical
relationships, mathematical formulas or equations, and mathematical calculations”.
MPEP 2106.04(a)(2)(I)(A), “A mathematical relationship is a relationship between variables or numbers. A mathematical relationship may be expressed in words or using mathematical symbols.”
Further, MPEP recites: “For example, a step of "determining" a variable or number using mathematical methods or "performing" a mathematical operation may also be considered mathematical calculations when the broadest reasonable interpretation of the claim in light of the specification encompasses a mathematical calculation.
Claim 1, The limitation recites “obtaining a measured drainage function for a well; …
for each drainage model: forming a predicted drainage function based, at least in part, on the drainage model, ” as drafted, is a process that, under its broadest reasonable interpretation (BRI) in light of specification, can be reasonably considered to represent mathematical concept, as described in the instance specification, [0048] … a diagnostic plot may feature DTOF (τ) on the horizontal axis and w(τ) on the vertical axis. In one or more embodiments, w(τ) refers to the derivative of pore volume (also known as drainage volume) with respect to DTOF and is defined by Equation 1:
w(τ)=dVp(τ)/dτ, Equation 1
where τ is the diffusive time of flight (DTOF) physically associated with the propagation of the peak of a pressure pulse for an impulse source and Vp is the pore volume at a given τ. In one or more embodiments, a w(τ) model may also be referred to as a drainage model. [0075] In step S504, a production based w(τ) may be calculated from the production data, which may or may not be filtered. This may include plotting w(τ) vs. τ, or by employing other production data analysis techniques. From the diagnostic plot, key parameters may be extracted. In one or more embodiments, the key parameters may include w(τ)LF, time of flight at the onset of fracture interference (τFl), and stimulated rock volume (SRV). In one or more embodiments, production based w(τ) may also be referred to as a measured drainage function. [0076] In step S506, a model based w(τ) may be calculated. In one or more embodiments, a model for the well may be selected, where the model includes a fracture model and a reservoir model. The selected reservoir model may be converted to DTOF. In one or more embodiments, this may be accomplished by solving the Eikonal equation, as shown above in Equation 6. Further FMM can be run to calculate τ contour lines originating from selected fracture models. In one or more embodiments, the key parameters may include w(τ)LF, τFl, and SRV. [0082] Next, for each drainage model, a predicted drainage function based, at least in part, on the drainage model may be formed in step S604. In one or more embodiments, the predicted drainage function may be defined as w(τ). Further, forming a predicted drainage function may include simulating a connected volume using a FMM solution to an eikonal equation. Therefore, the limitation discloses mathematical relationships, mathematical formulas or equations, mathematical calculations – MPEP 2106.04(a)(2)(I).
The elements of claims 13 and 17 are substantially the same as those of claim 1. Therefore, the elements of claims 13 and 17 are rejected due to the same reasons as outlined above for claim 1. For limitation of claim 17, “determine a measured drainage function” as drafted, is a process that, under its broadest reasonable interpretation (BRI) in light of specification, can be reasonably considered to represent mathematical concept, similar to the analysis of claim 1.
Therefore, claims 1, 13 and 17 recite judicial exceptions. The claims have been identified to recite judicial exceptions, Step 2A Prong 2 will evaluate whether the claim as a whole integrates the exception into a practical application of that exception.
Step 2A Prong 2: Claims 1, 19 and 20: The judicial exception is not integrated into a practical application.
In particular, the claims recite the following additional elements – “A non-transitory computer-readable medium storing instructions, the instructions, when executed on a processor, comprising functionality for: " and “A system, comprising: a well testing system, … and a computer processor, configured to:” which are merely recitations of instructions to implement an abstract idea on a computer, or merely uses a computer as a tool to implement the judicial exception with the broad reasonable interpretation in light of specification, which does not integrate judicial exception into a practical application (see MPEP § 2106.05(f)).
Further, the following additional element – “obtaining a measured …; obtaining a set of drainage models, wherein each drainage model comprises a reservoir model and a fracture model” and “receiving a measured drainage function for a well,” which are merely adding a recitation of insignificant extra-solution activities such as data gathering (i.e., obtaining …), which does not integrate a judicial exception into practical application (see MPEP 2106.05(g)).
Therefore, "Do the claims recite additional elements that integrate the judicial exception into a practical application? No, these additional elements do not integrate the abstract idea into a practical application and they do not impose any meaningful limits on practicing the abstract idea. The claims are directed to an abstract idea.
After having evaluated the inquires set forth in Steps 2A Prong 1 and 2, it has been concluded that claims 1, 13 and 17 not only recite a judicial exception but that the claims are directed to the judicial exception as the judicial exception has not been integrated into practical application.
Step 2B: Claims 1, 13 and 17: The claims do not include additional elements, alone or in combination, that are sufficient to amount to significantly more than the judicial exception. As discussed above with respect to integration of the abstract idea into a practical application, the additional elements amount to no more than generic computing components which do not amount to significantly more than the abstract idea. Limitations that the courts have found not to be enough to qualify as "significantly more" when recited in a claim with a judicial exception include:
i. Adding the words "apply it" (or an equivalent) with the judicial exception, or mere instructions to implement an abstract idea on a computer, e.g., a limitation indicating that a particular function such as creating and maintaining electronic records is performed by a computer, as discussed in Alice Corp., 573 U.S. at 225-26, 110 USPQ2d at 1984 (see MPEP § 2106.05(f));
ii. Simply appending well-understood, routine, conventional activities previously known to the industry, specified at a high level of generality, to the judicial exception, e.g., a claim to an abstract idea requiring no more than a generic computer to perform generic computer functions that are well-understood, routine and conventional activities previously known to the industry, as discussed in Alice Corp., 573 U.S. at 225, 110 USPQ2d at 1984 (see MPEP § 2106.05(d));
iii. Adding insignificant extra-solution activity to the judicial exception, e.g., mere data gathering in conjunction with a law of nature or abstract idea such as a step of obtaining information about credit card transactions so that the information can be analyzed by an abstract mental process, as discussed in CyberSource v. Retail Decisions, Inc., 654 F.3d 1366, 1375, 99 USPQ2d 1690, 1694 (Fed. Cir. 2011) (see MPEP § 2106.05(g)); or
iv. Generally linking the use of the judicial exception to a particular technological environment or field of use, e.g., a claim describing how the abstract idea of hedging could be used in the commodities and energy markets, as discussed in Bilski v. Kappos, 561 U.S. 593, 595, 95 USPQ2d 1001, 1010 (2010) or a claim limiting the use of a mathematical formula to the petrochemical and oil-refining fields, as discussed in Parker v. Flook, 437 U.S. 584, 588-90, 198 USPQ 193, 197-98 (1978) (MPEP § 2106.05(h)).
Further, The courts have recognized the following computer functions as well‐understood, routine, and conventional functions when they are claimed in a merely generic manner (e.g., at a high level of generality) or as insignificant extra-solution activity. i. Receiving or transmitting data over a network, e.g., using the Internet to gather data, Symantec, 838 F.3d at 1321, 120 USPQ2d at 1362 (utilizing an intermediary computer to forward information); TLI Communications LLC v. AV Auto. LLC, 823 F.3d 607, 610, 118 USPQ2d 1744, 1745 (Fed. Cir. 2016) (using a telephone for image transmission); OIP Techs., Inc., v. Amazon.com, Inc., 788 F.3d 1359, 1363, 115 USPQ2d 1090, 1093 (Fed. Cir. 2015) (sending messages over a network); buySAFE, Inc. v. Google, Inc., 765 F.3d 1350, 1355, 112 USPQ2d 1093, 1096 (Fed. Cir. 2014) (computer receives and sends information over a network); … ii. Performing repetitive calculations, Flook, 437 U.S. at 594, 198 USPQ2d at 199 (recomputing or readjusting alarm limit values); …; iii. Electronic recordkeeping, Alice Corp. Pty. Ltd. v. CLS Bank Int'l, 573 U.S. 208, 225, 110 USPQ2d 1984 (2014) (creating and maintaining "shadow accounts"); Ultramercial, 772 F.3d at 716, 112 USPQ2d at 1755 (updating an activity log); iv. Storing and retrieving information in memory, Versata Dev. Group, Inc. v. SAP Am., Inc., 793 F.3d 1306, 1334, 115 USPQ2d 1681, 1701 (Fed. Cir. 2015); OIP Techs., 788 F.3d at 1363, 115 USPQ2d at 1092-93; v. Electronically scanning or extracting data from a physical document, Content Extraction and Transmission, LLC v. Wells Fargo Bank, 776 F.3d 1343, 1348, 113 USPQ2d 1354, 1358 (Fed. Cir. 2014) (optical character recognition); …
The additional limitations do not provide significantly more than the judicial exception. In particular, the additional limitations merely describe using generic computer components (e.g., a processor and software) to perform data gathering and processing, which are described at a high level of generality and corresponds to well-understood, routine, and conventional computer functions. The claims do not recite any specialized hardware, unconventional computer architecture, or particular technological mechanism for performing the recited computer functions in an unconventional manner. Therefore, the additional elements, individually or in combination, amount to no more than applying computer components to perform well-understood, routine and conventional functions in the field of numerical modeling, which is insufficient to qualify as “significantly more” than the abstract idea under Step 2B.
Therefore, "Do the claims recite additional elements that amount to significantly more than the judicial exception? No, these additional elements, alone or in combination, do not amount to significantly more than the judicial exception. Having concluded analysis within the provided framework, claims 1, 13 and 17 do not recite patent eligible subject matter under 35 U.S.C. § 101.
Dependent claims 2-12, 14-16 and 18-20 are also similar rejected under same rationale as cited above wherein these claims do not include additional elements that are sufficient to amount to significantly more than the judicial exception. These claims are merely further elaborate the mental process and/or mathematical concepts, or providing additional definition of process which does not impose any meaningful limits on practicing the abstract idea. Claims 2-12, 14-16 and 18-20 are also rejected for incorporating the deficiency of their independent claims 1, 13 and 17.
Claim 2 recites “The method of claim 1, wherein forming a predicted drainage function comprises simulating a connected volume using a fast marching method solution to an eikonal equation.”
The limitation specifies simulating a connected volume using a fast marching method solution to an eikonal equation to generate the predicted drainage function; therefore, it merely a mathematical concept such as using a fast marching method solution to an eikonal equation as mathematical formulations used to compute propagation or arrival times), and merely adding the words "apply it" (or an equivalent) with the judicial exception, or instructions to implement an abstract idea on a computer, or merely uses a computer as a tool to perform simulation function at high level of generality, is simply the act of instructing a computer to perform the generic simulation function, which is merely an instruction to apply a computer to the judicial exception does not integrate a judicial exception into a practical application or provide significantly more (see MPEP 2106.05(f)). Therefore, the office finds that the claim 2 is ineligible under 35 USC 101.
Claim 3 recites “The method of claim 1, further comprising determining, for each candidate model, a preferred drainage model based on a reduction of the misfit value caused by perturbing of the candidate fracture model.”
The limitation further defines determination of a preferred drainage model by adjusting the candidate fracture model to reduce misfit value; therefore, it merely an extension of mental process (e.g., mentally determine a preferred drainage model based on reviewed and evaluated a reduction of the misfit value), and merely adding the words "apply it" (or an equivalent) with the judicial exception, or instructions to implement an abstract idea on a computer, or merely uses a computer as a tool to perform optimization function to perturbing/adjusting of the candidate fracture model at high level of generality, is simply the act of instructing a computer to perform the generic optimization function, which is merely an instruction to apply a computer to the judicial exception does not integrate a judicial exception into a practical application or provide significantly more (see MPEP 2106.05(f)). Therefore, the office finds that the claim 3 is ineligible under 35 USC 101.
Claim 4 recites “The method of claim 3, further comprising performing full-physics pressure-transient/rate-transient simulation using the preferred drainage model.”
The limitation further defines performing full-physics pressure-transient/rate-transient simulation using the preferred drainage model; therefore, it merely adding the words "apply it" (or an equivalent) with the judicial exception, or instructions to implement an abstract idea on a computer, or merely uses a computer as a tool to perform simulation function using the preferred drainage model at high level of generality, is simply the act of instructing a computer to perform the generic simulation functions, which is merely an instruction to apply a computer to the judicial exception does not integrate a judicial exception into a practical application or provide significantly more (see MPEP 2106.05(f)). Therefore, the office finds that the claim 4 is ineligible under 35 USC 101.
Claim 5 recites “The method of claim 1, wherein determining the set of candidate drainage models comprises comparing the misfit value of each drainage model with a tolerance value.”
The limitation specifies the determination of candidate drainage models comprises comparing the misfit value of each drainage model with a predefined threshold; therefore, it merely an extension of mental process (e.g., mentally comparing misfit value with predefined threshold, then mentally determining the set of candidate drainage models). Therefore, the office finds that the claim 5 is ineligible under 35 USC 101.
Claim 6 recites “The method of claim 5, wherein the tolerance value is a predetermined value.”
The limitation specifies the tolerance value is a predetermined value; therefore, it merely described the tolerance refers to claim 5 as an extension of mental process. Therefore, the office finds that the claim 6 is ineligible under 35 USC 101.
Claim 7 recites “The method of claim 5, wherein the tolerance value is selected based on a range of the misfit values.”
The limitation further defines the tolerance value is selected based on a range of the misfit values; therefore, it merely a mental process (e.g., mentally selecting tolerance value from observed range of the misfit values). Therefore, the office finds that the claim 7 is ineligible under 35 USC 101.
Claim 8 recites “The method of claim 3, wherein determining, for each candidate model, a preferred drainage model based on a reduction of the misfit value comprises applying a machine learning network.”
The limitation specifies determination of a preferred drainage model by applying a machine learning network to iteratively adjust candidate fracture model to reduce the misfit value; therefore, it merely an extension of mental process refers to claim 3 and, merely adding the words "apply it" (or an equivalent) with the judicial exception, or instructions to implement an abstract idea on a computer, or merely uses a computer component (i.e., machine learning network) as a tool to reduce misfit value at high level of generality, is simply the act of instructing a computer to perform the generic data processing functions, which is merely an instruction to apply a computer to the judicial exception does not integrate a judicial exception into a practical application or provide significantly more (see MPEP 2106.05(f)). Therefore, the office finds that the claim 8 is ineligible under 35 USC 101.
Claim 9 recites “The method of claim 4, wherein performing full-physics pressure-transient/rate-transient simulation comprises determining an uncertainty for the drainage model.”
The limitation specifies performing full-physics pressure-transient/rate-transient simulation using the preferred drainage model; therefore, it merely adding the words "apply it" (or an equivalent) with the judicial exception, or instructions to implement an abstract idea on a computer, or merely uses a computer as a tool to perform simulation function using the preferred drainage model at high level of generality, is simply the act of instructing a computer to perform the generic simulation functions, which is merely an instruction to apply a computer to the judicial exception does not integrate a judicial exception into a practical application or provide significantly more (see MPEP 2106.05(f)). Therefore, the office finds that the claim 9 is ineligible under 35 USC 101.
Claim 10 recites “The method of claim 1, wherein the fracture model comprises a plurality of fracture models each describing one of a plurality of hydraulic fractures intersecting the well.”
The limitation specifies the fracture model as one of a plurality of hydraulic fractures intersecting the well; therefore, it merely a description of the fracture model refers to claim 1 as insignificant extra-solution activities such as data gathering (i.e., obtaining …), which does not integrate a judicial exception into practical application (see MPEP 2106.05(g)). Therefore, the office finds that the claim 10 is ineligible under 35 USC 101.
Claim 11 recites “The method of claim 1, wherein the well comprises one or more wells.”
The limitation specifies the well can be one or more wells; therefore, it merely a description of the well refers to claim 1; Therefore, the office finds that the claim 11 is ineligible under 35 USC 101.
Claim 12 recites “The method of claim 1, further comprising: determining a spacing of hydraulic fractures in an adjacent well; and generating hydraulic fractures at the spacing.”
The limitation further defines determination of a spacing of hydraulic fractures in an adjacent well and generating hydraulic fractures at the spacing; therefore, it merely a mental process (e.g., mentally defining a spacing of hydraulic fractures based on obtained data, and merely adding the words "apply it" (or an equivalent) with the judicial exception, or instructions to implement an abstract idea on a computer, or merely uses a computer as a tool to perform generic generation function to generate hydraulic fractures at the spacing at high level of generality, is simply the act of instructing a computer to perform the generic data processing functions, which is merely an instruction to apply a computer to the judicial exception does not integrate a judicial exception into a practical application or provide significantly more (see MPEP 2106.05(f)). Therefore, the office finds that the claim 12 is ineligible under 35 USC 101.
The elements of claims 14-16 are substantially the same as those of claims 2-4. Therefore, the elements of claims 14-16 are rejected due to the same reasons as outlined above for claims 2-4.
The elements of claims 18-20 are substantially the same as those of claims 3-4 and 12. Therefore, the elements of claims 18-20 are rejected due to the same reasons as outlined above for claims 3-4 and 12.
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.
Claim(s) 1-3, 10-15 and 17-19 are rejected under 35 U.S.C. 103 as being unpatentable over
King US20150120255A1 in view of Xue (“Reservoir and Fracture-Flow Characterization Using Novel Diagnostic Plots,” published on 2019) and Vignau US20190339415A1.
Claim 1, King teaches A method, comprising:
obtaining a set of drainage models, wherein each drainage model comprises a reservoir model and a fracture model ([0011], “obtaining a heterogeneous multi-dimensional model of at least a portion of the hydrocarbon reservoir divided into cells…” [0213], “The reference SRV versus four updated models are shown in FIG. 64.” [0214], “… a single shale gas horizontal well in a reservoir with 22 hydraulically fractured stages.” Examiner note: the reference teaches obtaining a plurality of drainage models (e.g., a reference model and multiple updated models) showing a hydrocarbon reservoir using a heterogeneous, multi-dimensional gridded reservoir representation. Each drainage model includes representation of hydraulic fractures associated with the well (e.g., fractured stages), such that the drainage model includes both a reservoir model and a fracture representation integrated with the modeled system);
for each drainage model:
forming a predicted drainage function based, at least in part, on the drainage model ([0009], “The diffusive time of flight can be calculated … may be used to calculate the drainage volume as a function of the diffusive time of flight. This drainage volume function provides a complete characterization of the reservoir heterogeneity … The equations for the prediction of well pressures and rates follow the same transformation.” Examiner note: the reference teaches forming a drainage function by calculating a drainage volume as a function of diffusive time of flight derived from the reservoir mode, and using the same transformation to predict well pressures and rates, indicating that the drainage function is a predicted function formed from the drainage model), and
determining a misfit value ([0007], “Calibration of a dynamic reservoir characterization is essentially solving an inverse problem, i.e. finding the “best” model(s) under historical production data constraints, which produces (by forward simulation) the closest calculated results compared to the observed dynamic data,…” FIG. 70 shows a tornado diagram of the objective function—logarithm of cumulative gas misfit); and
(See Figs.16A-16E, [0011], [0213]-[0214]; Examiner note: the reference teaches obtaining a set of drainage models, wherein each drainage model comprises a reservoir model that incorporates fracture characteristics associated with the well. Determining a set of candidate drainage model merely designates a subset of the previously obtained drainage models based on evaluation or ranking, without changing the structure of the models. Therefore, each drainage model continues to comprise a (candidate) reservoir model and a (candidate) fracture model).
However, King fails to teach obtaining a measured drainage function for a well.
Xue teaches obtaining a measured drainage function for a well (Page.1250, “instead, we can calculate the drainage volume using the pressure and rate data: … w(τ) is the function we are trying to determine. We invert for the w(τ) function using a piecewise constant representation … Given pressure and rate data, the procedure involves converting the given pressure to bottomhole pressure (BHP) if the measured pressure is tubinghead pressure or casing pressure; using Eq. 10 to calculate the drainage volume Vd(τ); using Eq. 12 to calculate the IRR; and using Eq. 7 to link the drainage volume Vd(t) with the w(τ) function …”).
It would have been obvious for a person of ordinary skill in the art before the effective filing date of the claimed invention to have modified King to incorporate the teachings of Xue, and apply calculating a drainage volume and a drainage function from well pressure and rate data in order to incorporate drainage behavior derived from measured well data into reservoir and fracture analysis, thereby allowing the drainage analysis to reflect observed well behavior rather than relying on model assumptions only.
However, King and Xue fail to teach determining a misfit value based, at least in part, on the predicted drainage function and the measured drainage function; and determining a set of candidate drainage models based, at least in part, on the misfit value for each drainage model.
Vignau teaches determining a misfit value based, at least in part, on the predicted drainage function and the measured drainage function ([0156], “… The result is an expression of the drained volume as a function of diffusive time of flight …” [0158], “Simulated well tests on geomodels using the methods described above can be compared against each other and with the measured well test on the actual geological reservoir in order to rank the geomodels and select those which reproduce most closely the measured well test data. This ranking can be performed by computing a distance between different simulated well tests and between one or more simulated well tests and one or more measured well tests …” Examiner note: the reference teaches generating simulated well test results from geomodels, where the simulation produces an expression of drained volume as a function of diffusive time of flight as predicted drainage behavior, and comparing the simulated well test results with measured well test data from the actual geological reservoir by computing a distance between the simulated and measured results; the computed distance quantitatively represents the distance between the predicted related drainage results and the measure related drainage result is interpreted as determining a misfit value based, at least in part, on the predicted drainage function and the measured drainage function); and determining a set of candidate drainage models based, at least in part, on the misfit value for each drainage model. ([0158], “Simulated well tests on geomodels using the methods described above can be compared against each other and with the measured well test on the actual geological reservoir in order to rank the geomodels and select those which reproduce most closely the measured well test data.”).
It would have been obvious for a person of ordinary skill in the art before the effective filing date of the claimed invention to have modified King and Xue to incorporate the teachings of Vignau, and apply comparing simulated well test results with measured well test results by computing a distance between the simulated and measured results in order to evaluate how closely drainage behavior predicted by different drainage models matches drainage behavior derived from well measurements, thereby enabling objective ranking and selection of drainage models based on the consistency with measure well performance.
Claim 2, King teaches The method of claim 1, wherein forming a predicted drainage function comprises simulating a connected volume using a fast marching method solution to an eikonal equation ([0072], “(4) calculating the diffusive time of flight based upon the heterogeneous multi-dimensional model using a Fast Marching Method;” [0074], “… defining an Eikonal equation obtained from an asymptotic solution to a pressure diffusivity equation, solving the defined Eikonal equation, and transforming the pressure diffusivity equation into an equivalent one-dimensional form based on the diffusive time of flight as the spatial variable.” [0081], “Vp(τ) Drainage pore volume obtained from the Eikonal solution. See also [0082]. Examiner note: the reference teaches solving an eikonal equation using a Fast Marching Method to calculate diffusive time of flight within a heterogeneous, multi-dimensional reservoir model and deriving a drainage pore volume Vp(τ) from the eikonal solution. The drainage pore volume represents a connected volume of the reservoir contributing to flow as a function diffusive time of flight. Therefore, a predicted drainage function is formed from the drainage pore volume derived from the eikonal simulation, for example as a function of the rate of change of the drainage pore volume with respect to diffusive time of flight).
Claim 3, King and Xue fail to teach, but Vignau teaches The method of claim 1, further comprising determining, for each candidate model, a preferred drainage model based on a reduction of the misfit value caused by perturbing of the candidate fracture mode ([0158], “Simulated well tests on geomodels using the methods described above can be compared against each other and with the measured well test on the actual geological reservoir in order to rank the geomodels and select those which reproduce most closely the measured well test data. This ranking can be performed by computing a distance between different simulated well tests and between one or more simulated well tests and one or more measured well tests.” Examiner note: the reference teaches evaluating multiple geomodels by comparing simulated well test responses with measure well test data, computing a distance as a misfit metric, and ranking and selecting geomodels that most closely reproduce the measured data. The evaluated geomodels differ in their fracture representations, such that the comparative evaluation reflects variation in the candidate fracture models. Therefore, selecting a geomodel that shows a reduced misfit relative to other geomodels is interpreted as determining a preferred drainage model based on a reduction of the misfit value caused by perturbing or variations in the candidate fracture model).
It would have been obvious for a person of ordinary skill in the art before the effective filing date of the claimed invention to have modified King and Xue to incorporate the teachings of Vignau, and apply comparing simulated well test results with measured well test results by computing a distance between the simulated and measure responses in order to evaluate model accuracy, and providing objective selection of a preferred drainage model based on a reduction of the misfit value relative to other candidate models, thereby improving the reliability and accuracy of drainage model selection.
Claim 10, King teaches The method of claim 1, wherein the fracture model comprises a plurality of fracture models each describing one of a plurality of hydraulic fractures intersecting the well ([0157], “FIGS. 16A-16E depict a synthetic example of a heterogeneous reservoir with five transverse hydraulic fractures: (a) permeability field, (b) the geometry of five transverse fractures, (c) calculated diffusive time of flight, (d) drainage volume in 1 month, and (e) drainage volume in 30 years. Examiner note: the reference teaches a plurality of hydraulic fractures intersecting the well, each having a modeled geometric representation within the reservoir model (see FIGs 16A-16E). A POSITA would understand that modeling multiple distinct hydraulic fractures requires corresponding fracture representations, such that the fracture model comprises a plurality of fracture representations, each describing a respective one of the plurality of hydraulic fractures intersecting the well).
Claim 11, King teaches The method of claim 1, wherein the well comprises one or more wells ([0008], “Performance predictions can be used to assess, calibrate and optimize multi-stage fracture design in tight and unconventional reservoirs and to optimize well placement (spacing and timing) in tight and conventional reservoirs.” [0065], “The performance data may include a reservoir drainage volume, a reservoir pressure, a reservoir flow rate, a well pressure, a well flow rate, a bottom hole flowing pressure for each well in the hydrocarbon reservoir during a draw-down, and a well rate as a function of the bottom hole flowing pressure during a routine production …” Examiner note: applying performance prediction and optimization in a hydrocarbon reservoir context involving multiple wells, such as determine performance data for each well and optimizing well placement, including spacing and timing).
Claim 12, King teaches The method of claim 1, further comprising: determining a spacing of hydraulic fractures in an adjacent well ([0213], “The reference SRV versus four updated models are shown in FIG. 64.” [0214], “… a single shale gas horizontal well in a reservoir with 22 hydraulically fractured stages.” Figs 16A-16E depict a reservoir model with multiple transverse hydraulic fractures distributed along a horizontal well bore. Examiner note: A POSITA would understand that modeling a horizontal well having multiple hydraulic fracture stages distributed along the wellbore inherently requires defining and using spacing between adjacent hydraulic fracture as part of the modeled well configuration. The reference also teaches reservoir models involving multiple wells within a common reservoir context. Under BRI, an “adjacent well” is interpreted as another well within the same reservoir model to which similar spacing of hydraulic fractures design considerations); and
generating hydraulic fractures at the spacing ([0011], “… obtaining a heterogeneous multi-dimensional model of at least a portion of the hydrocarbon reservoir divided into cells …” [0214], “… a single shale gas horizontal well in a reservoir with 22 hydraulically fractured stages.” Figs. 16A-16E shows the reservoir model including hydraulic fracture geometries. Examiner note: the reference teaches generating reservoir and fracture models that include multiple hydraulic fractures positioned along the well at defined locations. The figures and descriptions show a modeled reservoir including hydraulic fracture geometries corresponding to discrete fracture stages along the wellbore. A POSITA would understand that, once fracture spacing has been defined as part of the modeled well configuration, generating the reservoir and fracture model includes generating modeled hydraulic fracture at the spacing, i.e., placing fracture geometries at the spaced locations along the well).
Claims 13-15 recite substantially the same elements as claims 1-3, and are rejected for the same reasons under 35 U.S.C. 101. Further for additional limitation of claim 13, “A non-transitory computer-readable medium storing instructions, the instructions, when executed on a processor, comprising functionality for:” (see King [0011] In addition, the present disclosure provides a non-transitory computer readable medium encoded with a computer program for determining performance data for a hydrocarbon reservoir that, when executed by one or more processors perform the following steps: …”.
Claims 17-19 recite substantially the same elements as claims 1, 3 and 12, and are rejected for the same reasons under 35 U.S.C. 101. Further, the additional limitation of claim 17, “A system, comprising: a well testing system, to determine a measured drainage function; and a computer processor, configured to:” (see King, [0012] Moreover, the present disclosure provides an apparatus for determining performance data for a hydrocarbon reservoir. The apparatus includes data storage, an output device, and one or more processors communicably coupled to the data storage and the output device.” See also Xue (Page 1250) as discuss for claim 1).
Claim(s) 4, 8, 9, 16 and 20 are rejected under 35 U.S.C. 103 as being unpatentable over
King and Xue and Vignau as applied to claims 3, 15 and 19 above, and further in view of Lino (“ Efficient Modeling and History Matching of Shale Oil Reservoirs Using the Fast Marching Method: Field Application and Validation,” published in 2017).
Claim 4, King and Xue fail to teach, but Vignau teaches The method of claim 3, further comprising (see Vignau [0158] and Examiner note discussed above).
It would have been obvious for a person of ordinary skill in the art before the effective filing date of the claimed invention to have modified King and Xue to incorporate the teachings of Vignau, and apply comparing simulated well test results with measured well test results by computing a distance between the simulated and measure responses in order to evaluate model accuracy, and providing objective selection of a preferred drainage model based on a reduction of the misfit value relative to other candidate models, thereby improving the reliability and accuracy of drainage model selection.
However, King and Xue and Vignau fail to teach performing full-physics pressure-transient/rate-transient simulation.
Lino teaches performing full-physics pressure-transient/rate-transient simulation (page.1, “The use of FMM-based simulation also enables systematic history matching and uncertainty analysis using population-based techniques that require substantial simulation runs. We first validate the accuracy and computational efficiency of the FMM-based multi-phase simulation using synthetic reservoir models and comparison with a commercial finite-difference simulator … The 3-D heterogeneous reservoir model was built and history matched for oil, gas and water production using the Genetic Algorithm with the FMM-based flow simulation. Multiple history-matched models were obtained to examine uncertainties in the production forecast associated with respect to the properties related to hydraulic fractures, microfractures and the matrix.” page.2, “On the other hand, analytical methods such as pressure transient analysis (PTA) and rate transient analysis (RTA) are based on the physical theory described by the diffusivity equation. In these approaches, the parameters in the well and reservoir models will be calibrated such that the model replicates the observed rate/ pressure history … Numerical simulation is also widely used to evaluate the performance of multi-stage hydraulically fractured wells in unconventional reservoirs (Wang 2015). The advantage over the empirical and analytical approaches is the capability of incorporating complex underlying physics.”).
It would have been obvious for a person of ordinary skill in the art before the effective filing date of the claimed invention to have modified King and Xue and Vignau to incorporate the teachings of Lino, and apply full physics pressure transient and rate transient numerical simulation using selected and history matched reservoir models in order to simulate drainage behavior and well performance under realistic reservoir conditions thereby enabling subsequent simulations to be performed using models that are calibrated to measure pressure and production data and improving the accuracy of the drainage behavior simulation.
Claim 8, King and Xue and Vignau fail to teach, but Lino teaches The method of claim 3, wherein determining, for each candidate model, a preferred drainage model based on a reduction of the misfit value comprises applying a machine learning network (page.1, “The 3-D heterogeneous reservoir model was built and history matched for oil, gas and water production using the Genetic Algorithm (examiner note: machine learning technique) with the FMM-based flow simulation. Multiple history-matched models were obtained to examine uncertainties in the production forecast associated with respect to the properties related to hydraulic fractures, microfractures and the matrix.” Examiner note: the reference teaches applying a Generic Algorithm, which is a machine learning technique, to perform history matching of simulated oil, gas, and water production using an FMM-based flow simulation. The Genetic Algorithm is applied to iteratively generate and evaluate multiple reservoir modes during the history-matching process. Therefore, the application of a Generic Algorithm is interpreted as applying a machine learning network).
It would have been obvious for a person of ordinary skill in the art before the effective filing date of the claimed invention to have modified King and Xue and Vignau to incorporate the teachings of Lino, and apply using a Generic Algorithm to perform history matching and obtain multiple history-matched models in order to provide evaluating and selecting drainage models based on comparisons between simulated production results and measured production data, thereby enabling systematic selection of a preferred drainage model consistent with observed well performance.
Claim 9, King and Xue and Vignau fail to teach, but Lino teaches The method of claim 4, wherein performing full-physics pressure-transient/rate-transient simulation comprises determining an uncertainty for the drainage model (page.1, “The use of FMM-based simulation also enables systematic history matching and uncertainty analysis using population-based techniques that require substantial simulation runs ... Multiple history-matched models were obtained to examine uncertainties in the production forecast associated with respect to the properties related to hydraulic fractures, microfractures and the matrix.” Page.2, “… analytical methods such as pressure transient analysis (PTA) and rate transient analysis (RTA) are based on the physical theory described by the diffusivity equation. In these approaches, the parameters in the well and reservoir models will be calibrated such that the model replicates the observed rate/ pressure history.” Examiner note: the reference teaches full-physics pressure and rate transient reservoir simulations calibrated to observed pressure and rate history, generating multiple history-marched drainage models, and examining uncertainty in production forecasts associated with fracture and reservoir properties. A POSITA would understand that evaluating variation across multiple history-matched modes constitutes determining uncertainty for the drainage model).
It would have been obvious for a person of ordinary skill in the art before the effective filing date of the claimed invention to have modified King and Xue and Vignau to incorporate the teachings of Lino, and apply performing full-physics pressure transient and rate transient simulations to generate multiple history-matched drainage models and evaluate uncertainty associated with reservoir and fracture properties in order to assess variability in drainage model behavior predicted under different simulated conditions, thereby enabling informed evaluation and selection of drainage modes based on quantified uncertainty in predicted well performance.
Claims 16 and 20 recite substantially the same elements as claim 4, and are rejected for the same reasons under 35 U.S.C. 101.
Claim(s) 5-6 are rejected under 35 U.S.C. 103 as being unpatentable over King and Xue and Vignau
as applied to claim 1 above, and further in view of Maschio (“Analysis of history matching and production forecast using a theoretical reservoir model,” published in 2016).
Claim 5, King and Xue fail to teach, but Vignau teaches The method of claim 1, wherein determining the set of candidate drainage models comprises ([0158], “Simulated well tests on geomodels using the methods described above can be compared against each other and with the measured well test on the actual geological reservoir in order to rank the geomodels and select those which reproduce most closely the measured well test data.”).
It would have been obvious for a person of ordinary skill in the art before the effective filing date of the claimed invention to have modified King and Xue to incorporate the teachings of Vignau, and apply comparing simulated well test results with measured well test results by computing a distance between the simulated and measured results in order to evaluate how closely drainage behavior predicted by different drainage models matches drainage behavior derived from well measurements, thereby enabling objective ranking and selection of drainage models based on the consistency with measure well performance.
However, King and Xue and Vignau fail to teach comparing the misfit value with a tolerance value.
Maschio teaches comparing the misfit value with a tolerance value (page.1, “The quality match is measured by the Normalized Quadratic Distance with Signal (NQDS), which represents an acceptable misfit based on a tolerance applied to the observed data … For Prod_Qo and Inj_Qw, a tolerance (Tol) of 0.05 was used and for the others, Tol was set to 0.1 … Figure 2 shows, in gray, all combinations and, in green, the combinations with |NQDS|<1 for all well data. Examiner note: the reference teaches evaluating model realizations using a misfit metric (NQDS) and determining acceptable models by comparing the misfit value to a tolerance value (e.g., Tol = 0.05 or 0.1), such that only models whose misfit satisfies the tolerance criterion are selected).
It would have been obvious for a person of ordinary skill in the art before the effective filing date of the claimed invention to have modified King and Xue and Vignau to incorporate the teachings of Maschio, and apply comparing a misfit metric for model realizations against a specified tolerance value to identify acceptable models in order to use an objective acceptance criterion when determining which drainage models are selected as candidate drainage models, thereby improving consistency and repeatability in candidate model selection by excluding modes whose misfit exceeds the tolerance.
Claim 6, King and Xue and Vignau fail to teach, but Maschio teaches The method of claim 5, wherein the tolerance value is a predetermined value (page.1, “… For Prod_Qo and Inj_Qw, a tolerance (Tol) of 0.05 was used and for the others, Tol was set to 0.1).
It would have been obvious for a person of ordinary skill in the art before the effective filing date of the claimed invention to have modified King and Xue and Vignau to incorporate the teachings of Maschio, and apply comparing a misfit metric for model realizations against a predetermined tolerance value to identify acceptable models in order to use an objective acceptance criterion when determining which drainage models are selected as candidate drainage models, thereby improving consistency and repeatability in candidate model selection by excluding modes whose misfit exceeds the tolerance.
Claim(s) 7 is rejected under 35 U.S.C. 103 as being unpatentable over King and Xue and Vignau and
Maschio as applied to claim 5 above, and further in view of Davolio (“Probabilistic seismic history matching using binary images,” published in 2018).
Claim 7, King and Xue and Vignau and Maschio fail to teach, but Davolio teaches The method of claim 5, wherein the tolerance value is selected based on a range of the misfit values (page.263, “… (3) where Tol is the tolerance given by a percentage of the observed data (Hist).” page.264, “Through a visual inspection of the binary images, we set a maximum value of OFbin so that the matching quality was considered acceptable.” page.267, “After a careful evaluation, we set the acceptance limits as OFbin < 100 for S31 and S41, and OFbin < 240 for S61.” Examiner note: the reference teaches computing misfit values for a plurality of model realizations and selecting tolerance or acceptance limits after evaluating the magnitude and spread of the misfit values is interpreted as selecting a tolerance value based on a range of misfit values).
It would have been obvious for a person of ordinary skill in the art before the effective filing date of the claimed invention to have modified King and Xue and Vignau and Maschio to incorporate the teachings of Davolio, and apply selecting a tolerance value based on an evaluated range of misfit values obtained from multiple model realizations in order to establish acceptance limits that reflect the observed distribution of misfit values rather than a predetermined fixed threshold, thereby improving the reliability of candidate drainage model selection by using tolerance criteria informed by the spread of misfit values across models.
Conclusion
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/YI . HAO/
Examiner, Art Unit 2187
/EMERSON C PUENTE/Supervisory Patent Examiner, Art Unit 2187