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 .
Response to Amendment
The amendment filed 04/02/2026 has been entered. As directed, claims 1-3, 7-10, 17 and 19 have
been amended, claims 4, 11-16, 18 and 20 have been canceled, no claim has been added. Thus claims 1-3, 5-10, 17 and 19 remain pending in the application.
Response to Arguments
With respect to the Applicant’s argued rejection under 35 U.S.C 101 in “Applicant Arguments/Remarks Made in an Amendment”:
Applicant argues:
…
Claims 1-3, 7-10, 17, and 19 are amended. To the extent that the rejection under 35 U.S.C. § 101 may still apply to the amended claims, the rejection is respectfully traversed as follows.
The Manual of Patent Examining Procedure (MPEP) outlines a subject matter eligibility (SME) test that Examiners use to determine whether, under its broadest reasonable interpretation, a claim as a whole is patent eligible under 35 U.S.C. § 101. See MPEP § 2106. The SME test includes two steps. "Step 1 relates to the statutory categories and ensures that the first criterion is met by confirming that the claim falls within one of the four statutory categories of invention." MPEP § 2106(III). "Step 2, which is the Supreme Court's Alice/Mayo test, is a two-part test to identify claims that are directed to a judicial exception (Step 2A) and to then evaluate if additional elements of the claim provide an inventive concept (Step 2B) (also called "significantly more" than the recited judicial exception)" to render the claims patent eligible. Id. "Step 2A is a two- prong inquiry, in which Examiners determine in Prong One whether a claim recites a judicial exception, and if so, then determine in Prong Two if the recited judicial exception is integrated into a practical application of that exception" to render the claims patent eligible. MPEP § 2106.04(II)(A).
Amended independent claim 1 recites, in part, the following:
(a) performing, using a hydraulic fracturing system, a hydraulic fracturing operation on
the hydrocarbon-bearing formation based on the hydraulic fracture geometries and the spacings.
Amended independent claim 17 recites similar subject matter.
Applicant submits that limitation (a) integrates any alleged judicial exceptions into a
practical application by improving the technical field of hydraulic fracturing. See MPEP § 2106.04(d)(I). The technical field of hydraulic fracturing is improved because the hydraulic fracture geometries and spacing induced by the hydraulic fracturing system when performing the hydraulic fracturing operation results in the fractured hydrocarbon-bearing formation being better able to drain hydrocarbons than if a different hydraulic fracturing operation was performed.
In view of the above, amended independent claims 1 and 17 and, by virtue of their dependence, dependent claims 2-3, 5-10, and 19 are patent eligible under Step 2A Prong Two of the SME test. Withdrawal of the § 101 rejection is thus respectfully requested.
(see Response filed 04/02/2026 [pages 6-8]).
In response to applicant's argument, the examiner disagrees that “limitation (a) integrates any alleged judicial exceptions into a practical application by improving the technical field of hydraulic fracturing.”
In order to determine if additional element is integrating the abstract idea into a practical application, See MPEP 2106.04(d)(1), “first the specification should be evaluated to determine if the disclosure provides sufficient details such that one of ordinary skill in the art would recognize the claimed invention as providing an improvement. The specification need not explicitly set forth the improvement, but it must describe the invention such that the improvement would be apparent to one of ordinary skill in the art. Conversely, if the specification explicitly sets forth an improvement but in a conclusory manner (i.e., a bare assertion of an improvement without the detail necessary to be apparent to a person of ordinary skill in the art), the examiner should not determine the claim improves technology. Second, if the specification sets forth an improvement in technology, the claim must be evaluated to ensure that the claim itself reflects the disclosed improvement. That is, the claim includes the components or steps of the invention that provide the improvement described in the specification. The claim itself does not need to explicitly recite the improvement described in the specification (e.g., "thereby increasing the bandwidth of the channel").” In other words, the specification should describe the improvement over the background invention or existing technology, and the claimed improvement should be reflected at least in the additional elements (emphasis added) by specifying how the improvement perform the additional element different from existing technology, functioning of a computer or existing technical field.
However, the additional elements – “… “performing, using a hydraulic fracturing system, a hydraulic fracturing operation on the hydrocarbon-bearing formation based on the hydraulic fracture geometries and the spacings,” and “a hydraulic-fracturing system configured to perform a hydraulic fracturing operation on the hydrocarbon-bearing formation based on the hydraulic fracture geometries and the spacings,” which are 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 an abstract idea. (See MPEP 2106.05(f)). In particular, the limitations merely recite using the calculated hydraulic fracture geometries and spacings as input for performing a generic hydraulic fracturing operation using a generic hydraulic fracturing system. The limitations do not recite any specific manner of controlling or performing the hydraulic fracturing operation based on the calculated geometries and spacings, nor do the limitation recite any particular technological implementation for applying the calculated results. Rather, the limitations merely state the desired result of performing a hydraulic fracturing operation “based on” the determined hydraulic fracture geometries and spacings while reciting the hydraulic fracturing system and operation at a high level of generality. Thus, the limitations merely use ordinary hydraulic fracturing equipment to implement the results of the mathematical analysis, without specifying how the hydraulic fracturing system is technologically improved or operated differently from conventional hydraulic fracturing system. Therefore, these additional limitations do not integrate the judicial exception into a practical application.
Further, as explained in MPEP 2106.05(a), II.: "it is important to keep in mind that an improvement in the abstract idea itself (e.g. a recited fundamental economic concept) is not an improvement in technology." (emphasis added). The alleged benefit/improvement, “the hydraulic fracture geometries and spacing induced by the hydraulic fracturing system when performing the hydraulic fracturing operation results in the fractured hydrocarbon-bearing formation being better able to drain hydrocarbons than if a different hydraulic fracturing operation was performed” only reflect the improvement to the abstract idea itself, the performing step merely apply the calculated result by using the hydraulic fracturing system with its ordinary capacities to perform recited hydraulic fracturing operation at high level of generality.
Further, for analysis under step 2B, the well testing system, computer processor, reservoir simulator and hydraulic fracturing system are recited only a generic functional level and are used in their ordinary capacities to obtain data, process/model data, perform simulation, and apply the resulting calculated information. The claims do not recite any unconventional arrangement of these components, any particular technical implementation, or any specific control technique for carrying out the hydraulic fracturing operation based on the calculated geometries and spacings. Rather, the combination merely places conventional data gathering, computer simulation and hydraulic-fracturing components around the abstract mathematical concepts and/or mental process workflow. Therefore, the additional limitations, considered individually and in combination, do not amount to significantly more than the judicial exception
Therefore, as discussed above, the claim does not integrate the judicial exception into a practical application under Step 2A, Pong Two, and nor does it amount to significantly more than the recited judicial exception under Step 2B. Therefore, the rejection under 35 U.S.C. 101 for claims 1-3, 5-10, 17 and 19 is maintained.
Applicant's arguments filed “Applicant Arguments/Remarks Made in an Amendment,” on
04/02/2026, pages 8-16, have been fully considered but they are not persuasive.
In response to Applicant’s argument that Vignau fails to supply what King and Xue allegedly lack because Vignau compares simulated well tests determined from geomodels with measured well tests after simulation, whereas amended claim 1 requires determining a misfit value, determining a set of candidate drainage models, and determining a preferred drainage model prior to performing the full-physics pressure-transient/rate-transient simulation using the preferred drainage model. The Examiner respectfully disagrees because the Applicant’s argument conflates Vignau’s simulated well test results, which are used for model comparison and ranking, with the later full-physics pressure-transient/rate-transient simulation recited in limitation (e). The rejection does not rely on Vignau’s simulated well tests as the full-physics pressure-transient/rate-transient simulation of limitation (e). Rather, Vignau is used to teach comparing simulated well test results with measured well test data, computing a distance or misfit between the simulated and measured results, ranking models based on the computed distance or misfit, and selecting models that most closely reproduce the measured data. Further, in the current rejection, Vignau teaches misfit based ranking and selection technique is applied to King’s drainage model framework to determine candidate drainage models and a preferred drainage model before the later full-physics pressure-transient/rate-transient simulation using the preferred drainage model. King teaches the drainage model framework and for determining hydraulic fracture geometries and spacings by performing the full-physics pressure-transient/rate-transient simulation. Xue teaches obtaining the measured drainage function. Iino further suggests using selected or best models in a simulation and history matching workflow.
Applicant also argues that Vignau’s comparison is of simulation outputs from geomodels, not a predicted drainage function and a measured drainage function. This argument is not persuasive because the rejection does not require Vignau to use the same terminology as the claim. King provides the predicted drainage function context, Xue provides the measured drainage function context, and Vignau supplies the comparison technique by which simulated model results are compared with measured well test data and a distance or misfit is computed. Thus, Vignau’s computed distance between predicted model behavior and measured behavior corresponds to the claimed misfit value when applied in the combined workflow.
Applicant further argues that a person of ordinary skill in the art would have had no motivation to repurpose Vignau’s geomodels as drainage models, candidate drainage models, or a preferred drainage model. This argument is not persuasive because the rejection does not require bodily repurposing Vignau’s geomodels as the claimed drainage models. King teaches the drainage models. Vignau is relied upon for its model evaluation technique of computing distance or misfit, ranking models, and selecting closest matching models. Applying Vignau’s ranking and selection technique to King’s drainage models would have predictably allowed objective identification of drainage models that more closely match measured well performance.
With respect to claims 5, 6, and 7, Applicant argues that these claims are not obvious for at least the same reasons presented with respect to amended independent claim 1, and further argues that Maschio and Davolio fail to supply the limitations allegedly missing from King, Xue, and Vignau. These arguments are not persuasive because Maschio and Davolio are not relied upon to teach the entirety of limitations (b) through (e) of amended claim 1. Rather, Maschio is relied upon for the limitations of claims 5 and 6, and Davolio is relied upon for the limitation of claim 7. Accordingly, Applicant’s arguments directed to whether Maschio or Davolio cure all alleged deficiencies of amended claim 1 are not commensurate with the current rejection.
With respect to claims 8 and 9, Applicant argues that these claims are not obvious by virtue of their dependence from amended independent claim 1, and further argues that Iino fails to supply the limitations allegedly missing from King, Xue, and Vignau. These arguments are not persuasive because Iino is not relied upon to teach the entirety of limitations (b) through (e). For claim 8, the current rejection relies on newly cited Kim (“Iterative learning-based many-objective history matching using deep neural network with stacked autoencoder,” published in 2021). For claim 9, Iino is relied upon for the uncertainty feature, because Iino teaches performing uncertainty analysis using population based simulation/history matching and obtaining multiple history matched/selected models to examine uncertainty in production forecasts associated with hydraulic fracture, microfracture, and matrix properties, and further teaches selecting models based on an objective function and identifying a best model. The uncertainty evaluated across the selected/history matched model population, including the best/preferred model, corresponds to determining an uncertainty for the preferred drainage model.
Applicant also argues that Iino teaches away from full-physics pressure-transient/rate-transient simulation because Iino discusses a computational time bottleneck when high resolution models are used to accurately describe hydraulic fracture geometry. This argument is not persuasive. Iino’s discussion of computational burden does not criticize, discredit, or discourage the claimed combination. Rather, Iino identifies the computational burden as a problem addressed by its FMM based simulation and history matching workflow (See Iino, e.g., pages 2-3, last three paragraphs under INTRODUCTION). Therefore, Iino supports using simulation and history matching to evaluate model uncertainty for the selected or best models.
Applicant’s assertion of impermissible hindsight is also not persuasive. The rejection is based on the express teachings of the applied references and the articulated reasons for combining those teachings. The references are relied upon for their respective teachings as set forth in the current rejection, and the rejection does not require any one reference to teach every feature of the amended claim. It must be recognized that any judgment on obviousness is in a sense necessarily a reconstruction based upon hindsight reasoning. But so long as it takes into account only knowledge which was within the level of ordinary skill at the time the claimed invention was made, and does not include knowledge gleaned only from the applicant's disclosure, such a reconstruction is proper. See In re McLaughlin, 443 F.2d 1392, 170 USPQ 209 (CCPA 1971).
For at least the reasons discussed above, Applicant’s arguments do not overcome the rejection of amended independent claims 1 and 17. The combined teachings teach or suggest the amended limitations of claims 1 and 17. Therefore, the rejection of claims 1 and 17, and the claims dependent thereon, under 35 U.S.C. 103 is maintained.
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 1-3, 5-10, 17 and 19 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 Revised Patent Subject Matter Eligibility Guidance
published in the Federal Register 01/07/2019, as well as subsequent USPTO eligibility guidance updates,
and has provided such analysis below.
Step 1: Are the claims to a process, machine, manufacture or composition of matter?"
Yes, Claims 1-3 and 5-10 are directed to method and fall within the statutory category of process;
Yes, Claims 17 and 19 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 set of candidate drainage models among the set of drainage models based on the misfit value for each drainage model, wherein each candidate drainage model among the set of candidate drainage models comprises a candidate reservoir model and a candidate fracture model;
determining, from the set of candidate drainage models, a preferred drainage 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 human mind. For example a person is capable of observing a misfit value, mentally comparing/evaluating misfit value with a threshold or range of tolerance to determine a set of candidate drainage models, and mentally selecting/choosing a best or preferred drainage model. The steps include observation, evaluation, judgment, and reasoning processes that can be performed mentally or with the aid of pen and paper (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 within a hydrocarbon-bearing formation; …
for each drainage model: forming a predicted drainage function based on each drainage model; determining a misfit value between the predicted drainage function and the measured drainage function; determining hydraulic fracture geometries and spacings within the hydrocarbon-bearing formation by performing a full-physics pressure-transient/rate-transient simulation using the preferred drainage model,” as drafted, under its broadest reasonable interpretation (BRI) in light of specification, can be reasonably considered to represent mathematical concept, as recites in the 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. [0077], “In one or more embodiments, a misfit value may refer to the difference between production based w(τ) and model based w(τ). The misfit value may be calculated and then iteratively minimized.” [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. [0084], “In one or more embodiments, the misfit value may be determined by calculating the difference between the measured drainage function and the predicted drainage function.” [0086], “In one or more embodiments, a full-physics pressure-transient/rate-transient simulation may be performed using the preferred drainage model. Further, performing a full-physics pressure-transient/rate-transient simulation may include determining an uncertainty for the drainage model.” [0087], “… to determine a probabilistic range of hydraulic fracture geometries and spacing under different geologic scenarios.” See also eq. (2)-(9). Examiner note: the preferred drainage model, which the instant specification describes as a drainage function model (e.g., a w(τ) model) based on mathematical relationships and equations, is utilized to perform full-physics pressure-transient/rate-transient simulation to determine the hydraulic fracture geometries and spacings. In particular, the specification describes w(τ) as a mathematical function representing a derivative of pore volume with respect to diffusive time of flight (DTOF), and further describes the drainage model as being associated with DTOF calculations, pressure propagation modeling, and equation based reservoir characterization. Under the broadest reasonable interpretation in light of the specification, the recited a full-physics pressure-transient/rate-transient simulation using the preferred drainage model reasonably encompasses mathematical and numerical modeling techniques (e.g., differential equation based reservoir flow modeling). Therefore, the limitations disclose mathematical relationships, mathematical formulas or equations, mathematical calculations – MPEP 2106.04(a)(2)(I).
The elements of claim 17 is substantially the same as those of claim 1. Therefore, the elements of claim 17 is rejected due to the same reasons as outlined above for claim 1. For limitation of claim 17, “determine a measured drainage function” as drafted, 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 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 and 17: The judicial exception is not integrated into a practical application.
In particular, the claims recite the following additional elements – “a well testing system configured to … a computer processor configured to: … a reservoir simulator 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 set of drainage models, wherein each drainage model comprises a reservoir model and a fracture model” and “receive the 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)).
Further, the following additional element – “performing, using a hydraulic fracturing system, a hydraulic fracturing operation on the hydrocarbon-bearing formation based on the hydraulic fracture geometries and the spacings,” and “a hydraulic-fracturing system configured to perform a hydraulic fracturing operation on the hydrocarbon-bearing formation based on the hydraulic fracture geometries and the spacings,” which are 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 an abstract idea. (See MPEP 2106.05(f)). In particular, the limitations merely recite using the calculated hydraulic fracture geometries and spacings as input for performing a generic hydraulic fracturing operation using a generic hydraulic fracturing system. The limitations do not recite any specific manner of controlling or performing the hydraulic fracturing operation based on the calculated geometries and spacings, nor do the limitation recite any particular technological implementation for applying the calculated results. Rather, the limitations merely state the desired result of performing a hydraulic fracturing operation “based on” the determined hydraulic fracture geometries and spacings while reciting the hydraulic fracturing system and operation at a high level of generality. Therefore, this additional limitations merely use of a computer or other machinery in its ordinary capacity for simply adding a general purpose computer or other machinery after the fact to an abstract idea (e.g., mental process and/or mathematical concepts) does not integrate a judicial exception into a practical application or provide significantly more.
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 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 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); …
In particular, the well testing system, computer processor, reservoir simulator and hydraulic fracturing system are recited only a generic functional level and are used in their ordinary capacities to obtain data, process/model data, perform simulation, and apply the resulting calculated information. The claims do not recite any unconventional arrangement of these components, any particular technical implementation, or any specific control technique for carrying out the hydraulic fracturing operation based on the calculated geometries and spacings. Rather, the combination merely places conventional data gathering, computer simulation and hydraulic fracturing components around the abstract mathematical concepts and/or mental process workflow. Therefore, the additional limitations, considered individually and in combination, do not amount to significantly more than the judicial exception.
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 and 17 do not recite patent eligible subject matter under 35 U.S.C. § 101.
Dependent claims 2-3, 5-10, and 19 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 2-3, 5-10, and 19 are also rejected for incorporating the deficiency of their independent claims 1 and 17.
Claim 2 recites “The method of claim 1, wherein forming the 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, from the set of candidate drainage models, the preferred drainage model based on a reduction of the misfit value caused by perturbing 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 by perturbing the candidate fracture model), 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 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 value for the set of drainage models.”
The limitation further defines the tolerance value is selected based on a range of the misfit values for the set of drainage models; 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, from the set of candidate drainage models, the preferred drainage model comprises predicting the preferred drainage model using a machine learning network.”
The limitation specifies determination of a preferred drainage model by applying a machine learning network to predict/determine the preferred drainage model; 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 or other machinery (i.e., machine learning network) as a tool to predicting drainage model as its ordinary capacity, which is recited at high level of generality, is simply the act of instructing a computer to perform the generic 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 1, wherein performing the full-physics pressure-transient/rate-transient simulation comprises determining an uncertainty for the preferred drainage model.”
The limitation specifies performing full-physics pressure-transient/rate-transient simulation to determine an uncertainty for 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 to determine an uncertainty 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)) and generally linking the use of the judicial exception to a particular technological environment (i.e., full-physics transient simulation) or field of use (see MPEP 2106.05(h). 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 describes a hydraulic fracture intersecting the well.”
The limitation specifies the information content of the fracture model represents/describes a hydraulic fracture intersecting the well; therefore, it merely an information 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.
The elements of claim 19 is substantially the same as those of claim 3. Therefore, the elements of claim 19 is rejected due to the same reasons as outlined above for claim 3.
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, 9-10, 17 and 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 and Iino (“Efficient Modeling and History Matching of Shale Oil Reservoirs Using the Fast Marching Method: Field Application and Validation,” published in 2017) and Roussel US20120325462A1.
Claim 1, King teaches A method, comprising:
obtaining a set of drainage models, wherein each drainage model among the set of
drainage models 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, each cell having a volume and one or more other attributes;” [0213], “The genetic algorithm will vary reservoir and hydraulic fracture characteristics (permeability (fracture, EPA, matrix), fracture half-length, EPA minor axis) and calibrate against one year of production data (well gas rate) … The reference SRV versus four updated models are shown in FIG. 64.” [0214], “The model is shown in FIGS. 65A-65C. The grid is 41×77×16 with a single shale gas horizontal well in a reservoir with 22 hydraulically fractured stages.” Examiner note: the reference model and four updated models correspond to the claimed set of drainage models; the heterogeneous multi-dimensional hydrocarbon reservoir model corresponds to the reservoir model; the varied hydraulic fracture characteristics and hydraulically fractured stages correspond to the fracture model);
for each drainage model:
forming a predicted drainage function based on each 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.” [0197], “… The diffusive time of flight map calculated by the FMM is shown in FIG. 26B. FIG. 27 illustrates the drainage pore volume as a function of τ and its first derivative (the w(τ) function).” Examiner note: the drainage volume as a function of diffusive time of flight and its first derivative, i.e., the w(τ) function, correspond to the predicted drainage function form from the model based FMM/DTOF calculation), and
(See Figs.16A-16E, [0011], [0213]-[0214]; Examiner note: the reference teaches a set of drainage models, wherein each drainage model comprises a reservoir model that incorporates fracture characteristics associated with the well. A candidate drainage model is a selected subset of the previously obtained drainage models; therefore, when the models are selected as candidates, each candidate drainage model continues to comprise the same reservoir model and fracture model structure);
determining, ([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, e.g. production rate, gas oil ratio, and wellbore pressure.” [0064], “ … to rank and/or calibrate reservoir models against field performance data.” [0094], “the disclosed method may include the steps of ranking or calibrating the model of the hydrocarbon reservoir against a field performance data of the hydrocarbon reservoir based the performance data using the processor, and providing the ranked or calibrated model of the hydrocarbon reservoir to the output device.” Examiner note: the “best” model(s) or ranked/calibrated model corresponds to the preferred drainage model selected from the models being evaluated);
determining hydraulic fracture geometries and spacings within the hydrocarbon-bearing
formation by performing a full-physics pressure-transient/rate-transient simulation([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, e.g. production rate, gas oil ratio, and wellbore pressure.” [0008], “Because the one-dimensional equation is solved numerically, all relevant process physics can be included … 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. For unconventional reservoirs, the approach provides for the prediction and interpretation of bottom hole flowing pressures for wells with rate measurements and more importantly the prediction and interpretation of well rates as a function of the bottom hole pressure during routine production.” [0121], “The pressure/rate transient analysis incorporates simplified completion and reservoir models, such as homogeneous reservoirs with fully-penetrated, equally-spaced, symmetric rectangular and planar hydraulic fractures. A small number of model parameters, e.g. fracture permeability and fracture half length, can easily be calibrated from the observed flow regimes and then well production can be predicted based on the calibrated model.” [0122], “The FMM approach may serve as a bridge for this transition and a screening tool to select models for the more expensive traditional reservoir simulation … Second, it is a numerical model capable of handling the same degree of geometrical complexity and reservoir heterogeneity as in traditional reservoir simulation.” Examiner note: the tight and unconventional reservoirs correspond to the hydrocarbon-bearing formation. The multi-stage fracture design, well placement/spacing, equally-spaced planar hydraulic fractures, fracture permeability, and fracture half-length correspond to hydraulic fracture geometries and spacings. The forward simulation, numerical solution including all relevant process physics, and pressure/rate transient analysis, and numerical model capable of handling geometrical complexity and reservoir heterogeneity correspond to performing full-physics pressure-transient/rate-transient simulation. The “best model(s), calibrate model, and screening tool to select models correspond to the preferred drainage model); and
However, King fails to teach obtaining a measured drainage function for a well within a hydrocarbon-bearing formation.
Xue teaches obtaining a measured drainage function for a well within a hydrocarbon-bearing
formation (Page.1248, Introduction, “Unconventional reservoirs such as shale oil and shale gas play a significant role in the US and worldwide energy market (Holditch 2013). For these low-permeability reservoirs, long horizontal wells with multistage hydraulic fracturing have proved to be an effective
way to stimulate the formation in most cases.”. Page.1250, “If we do not have the reservoir and well model, we cannot directly determine τ, Vp (τ), and w(τ). Instead, we can calculate the drainage volume using the pressure and rate data: … For unconventional reservoirs … we can use the rate-normalized pressure (RNP) to calculate the drainage volume. This RNP approximation represents the production behavior that would be observed if the well were produced at a constant reference rate … The drainage volume Vd (t) and w(τ) can be related using Eq. 7 … 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 using the methodology provided in Appendix B.” Examiner note: the reference teaches shale oil/shale gas unconventional reservoirs correspond to the hydrocarbon-bearing formation, and long horizontal wells with multistage hydraulic fracturing corresponds to the well, and further teaches calculating drainage volume from measure pressure rate data and linking the drainage volume Vd(t) with the w(τ) function corresponds to obtaining a measured drainage 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 within Unconventional reservoirs 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 between the predicted drainage function and the measured drainage function; and determining a set of candidate drainage models among the set of drainage models based on the misfit value for each drainage model.
Vignau teaches determining a misfit value between 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 simulated well test results generated from geomodels, where the result includes an expression of drained volume as a function of diffusive time of flight, and further teaches comparing the simulated well test results with measured well test data and computing a distance between the simulated and measured results; the computed distance corresponds to the misfit value between predicted drainage related results and measured drainage related results); and determining a set of candidate drainage models among the set of drainage models based 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. 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: ranking the geomodels based on the computed distance and selecting the geomodels that reproduce the measured well test data most closely corresponds to determining a set of candidate drainage models based on the misfit value.); determining, from the set of candidate drainage models, a preferred drainage model (see [0158]; [0164], “This distance is used to characterise the difference in shape of the plot of data points of first well test 600 a and the plot of data points of second well test 600 b.” Examiner note: the reference teaches ranking geomodels based on a computed distance and selecting the geomodels that reproduce the measure well test data most closely. A POSITA would understand that, within a ranked set of selected geomodels, the geomodel having the smallest computed distance/highest ranking is the preferred geomodel because it most closely reproduces the measured well test data. Thus, the highest-ranked/lowest-distance selected geomodel corresponds to the preferred drainage model determined from the set of candidate drainage models).
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 and ranking geomodels based on the computed distance, selecting geomodels that reproduce the measure well test data most closely, and identifying the highest-ranked geomodel in order to objectively evaluate how closely drainage behavior predicted by different drainage models matches drainage behavior derived from well measurements, select candidate drainage models based on the evaluated misfit, and determine a preferred drainage model based on the closet match to measured well performance. The combination of teaches would predictably provide benefit of improve model calibration and selection by using measured well test data to identify drainage models that most accurately represent actual reservoir behavior.
Examiner note: as discussed above with respect to King, each drainage model in the set includes a reservoir mode and a fracture model; therefore, when a subset of those drainage models is selected as candidate drainage modes based on Vignau’s ranking/distance, each selected candidate drainage model continues to include the same reservoir model and fracture model structure, corresponding to each candidate drainage model among the set of candidate drainage models comprises a candidate reservoir model and a candidate fracture model.
However, King and Xue and Vignau fail to teach performing simulation by using the preferred drainage model.
Iino teaches performing simulation by using the preferred drainage model (Page.1, Abstract, last paragraph, “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.19, “The well performances simulated with 50 models selected based on the objective function were compared with the observed data … where the best model is highlighted … The fracture height, stage length and SRV width were also altered in the history matching.” Examiner note: the reference teaches performing the FMM based flow simulation/history matching using multiple selected models and identifying a best model from the simulated selected models. The “best model” corresponds to the preferred model, and the simulated well performance using the selected models show that the simulation is performed using the model set from which the best/preferred model is identified).
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 Iino, and apply Lino’s teaching of using selected models in simulation/history matching with Vignau's ranking/selection of geomodels in King's reservoir simulation workflow in order to carry forward the model that best matches measure well performance for subsequent reservoir performance simulation and fracture design evaluation. The combination would predictably provide benefit of improve model calibration and reduce uncertainty in the simulated reservoir/fracture response by using a model selected based on consistency with measured well test or production data.
However, King and Xue and Vignau and Iino fail to teach performing, using a hydraulic fracturing system, a hydraulic fracturing operation on the hydrocarbon-bearing formation based on the hydraulic fracture geometries and the spacings.
Roussel teaches performing, using a hydraulic fracturing system, a hydraulic fracturing operation on the hydrocarbon-bearing formation based on the hydraulic fracture geometries and the spacings ([0006], “… The method includes propagating an initial fracture, measuring pressure associated with propagating the initial fracture, determining a minimum spacing required to prevent a second fracture from intersecting the initial fracture, and propagating the second fracture at least the minimum spacing distance away from the initial fracture.” [0032], “FIG. 1 illustrates an example schematic of a gas well 100 configured to extract natural gas from a gas rich shale formation 102. Well 100 may be drilled using horizontal drilling methods to create a wellbore 104 that runs within and along shale formation 102.” [0034], “The number and/or size of fractures in shale formation 102 may be increased using hydraulic fracturing (“fracing”). Fracing may refer to any process used to initiate and propagate a fracture in a rock formation … Fracing may include forcing a hydraulic fluid in a fracture of a rock formation to increase the size of the fracture and introducing proppant (e.g., sand) in the newly induced fracture to keep the fracture open …” [0036], “… the spacing of performing fracturing operations for a hydrocarbon well, such as gas well 100, may be determined using net pressure measurements to determine a minimum frac spacing that also reduces the likelihood of subsequent fractures intersecting and interfering with previous fractures.” [0042], “… Therefore, as described in further detail below, by mapping the angle of stress reorientation and the horizontal stress in multiple fractured horizontal wells, the trajectory of each fracture may be estimated. By mapping the trajectory of each induced fracture, the induced fracture spacing may be determined such that it may be minimized without compromising the efficiency of each frac stage.” Examiner note: the reference teaches gas well 100 and wellbore 104 in gas-rich shale formation 102, together with the disclosed fracing operation of forcing hydraulic fluid into the rock formation and introducing proppant, correspond to the hydraulic fracturing system used to perform the hydraulic fracturing operation. The reference further teaches determining minimum fracture spacing and propagating a subsequence fracture at the determined spacing, and teaches estimating fracture trajectory to determine induced fracture spacing. Therefore, Roussel teaches performing a hydraulic fracturing operation based on hydraulic fracture geometry/trajectory and spacing information).
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 Iino to incorporate the teachings of Roussel, and apply performing hydraulic fracturing/fracing by forcing hydraulic fluid into a rock formation and introducing proppant, determining fracture trajectory and induced fracture spacing, determining a minimum fracture spacing required to reduce the likelihood of a subsequent fracture intersecting a previous fracture and propagating the subsequent fracture at the determined spacing in order to implement fracture spacing and trajectory information during an actual hydraulic fracturing operation whole reducing fracture interference. The combination of teaches would predictably provide benefit of carrying the determined hydraulic fracture geometry/trajectory and spacing information into the physical hydraulic fracturing operation, thereby improving fracturing efficiency and reducing the likelihood that adjacent fracture intersect or interfere with each other.
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 teaches drainage model and fracture model (see King, [0011], [0213] and [0214]). However, King and Xue fail to teach, but Vignau teaches determining, from the set of candidate models, the preferred model based on a reduction of the misfit value([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.” [0164], “This distance is used to characterise the difference in shape of the plot of data points of first well test 600 a and the plot of data points of second well test 600 b.” Examiner note: the reference teaches ranking geomodels based on a computed distance and selecting the geomodels that reproduce the measure well test data most closely. A POSITA would understand that, within a ranked set of selected geomodels, the geomodel having the smallest computed distance/highest ranking is the preferred geomodel because it most closely reproduces the measured well test data. Thus, the highest-ranked/lowest-distance selected geomodel corresponds to the preferred drainage model determined from the set of candidate drainage models).
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 and ranking geomodels based on the computed distance, selecting geomodels that reproduce the measure well test data most closely, and identifying the highest-ranked geomodel in order to objectively evaluate how closely drainage behavior predicted by different drainage models matches drainage behavior derived from well measurements, select candidate drainage models based on the evaluated misfit, and determine a preferred drainage model based on the closet match to measured well performance. The combination of teaches would predictably provide benefit of improve model calibration and selection by using measured well test data to identify drainage models that most accurately represent actual reservoir behavior.
However, King and Xue and Vignau fail to teach, but Iino teaches determining the preferred model based on a reduction of the misfit value caused by perturbing the fracture model (Page.17, Uncertain Parameters and Sensitivity, “Due to the limited data availability, the significant uncertainty lies in the fracture properties and dimensions of hydraulic fractures and SRV. Table 3 lists the uncertain parameters with the base values and ranges used for the sensitivity study … For the sensitivity and history matching purpose, the 10-stage hydraulic fractures and SRVs were divided into three groups that have uniform properties.”Page.18, “the objective function was defined as the summation of misfits in the cumulative production for each of the three phases as: …”. Page.19, History Matching, “The history matching was carried out following the method outlined by Zhang et al (2016), which utilized the Genetic Algorithm (GA) facilitated by use of experimental design and response surface modelling (Yin et al. 2011) … The well performances simulated with 50 models selected based on the objective function were compared with the observed data as illustrated in Figure 25 where the best model is highlighted by a purple line … The fracture height, stage length and SRV width were also altered in the history matching.” Examiner note: the reference teaches hydraulic fracture properties/dimensions are uncertain parameters in the model and are varied/altered during sensitivity/history matching. The alteration of fracture height, stage length , and SRV width corresponds to perturbing the fracture model portion of the candidate mode. The reference further teaches comparing selected model simulations with observed data based on an objective function and identifying a best model, which corresponds to determining a preferred model based on reduced misfit after perturbing the fracture model parameters).
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 Iino, and apply alternating hydraulic fracture parameters, including fracture height, stage length and SRV width, during history matching with Vignau’s teaching of computing a distance/misfit between simulated and measure well test results and raking/selecting models based on the computed distance in King’s drainage model workflow in order to refine the fracture model portion of the candidate drainage models and selected a model that more closely matches measured well performance. The combination of teaching would predictably provide the benefit of improving model calibration by reducing the misfit between simulated or model predicted behavior and measure well behavior through adjustment o hydraulic fracture model parameters. 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 9, King teaches performing the full-physics pressure- transient/rate-transient simulation (See King, [0121]-[0122]). However, King and Xue and Vignau fail to teach, but Iino teaches determining an uncertainty for the preferred 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.19, “The well performances simulated with 50 models selected based on the objective function were compared with the observed data as illustrated in Figure 25 where the best model is highlighted by a purple line. The selected models showed a good agreement with the three-phase production data with some variations … As expected, model parameters were not well constrained due to the non-uniqueness.” Examiner note: the reference teaches performing uncertainty analysis using population based simulation/history matching and obtaining multiple history matched/selected models to examine uncertainty in production forecasts associated with hydraulic fracture, microfracture, and matrix properties, and further teaches selecting models based on an objective function and identifying a best model. The uncertainty evaluated across the selected/history matched model population, including the best/preferred model, corresponds to determining an uncertainty for the preferred 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 Iino, and apply performing uncertainty analysis using FMM-based simulation/history matched models to examine uncertainties in production forecasts associated with hydraulic fracture, microfracture, and matrix properties, and comparing selected models with observed data while identifying a best model in order to evaluate the reliability and variability of reservoir/fracture model predictions under uncertain reservoir and fracture property conditions. The combination of teaches would predictably provide benefit of improving confidence in the selected drainage model by quantifying uncertainty in predicted well performance and fracture related reservoir behavior.
Claim 10, King teaches The method of claim 1, wherein the fracture model describes a hydraulic fracture 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 … There are 5 transverse hydraulic fractures intersecting a horizontal well as shown in FIG. 16B. These fractures have different half length, height and location in the reservoir. Examiner note: the reference teaches a reservoir/fracture modeling example in which transverse hydraulic fractures intersect a horizontal well, and the geometry of the fractures in modeled. The modeled geometry of a hydraulic fracture intersecting the horizontal well corresponds to the fracture mode describing a hydraulic fracture intersecting the well).
The elements of claims 17 and 19 are substantially the same as those of claims 1 and 3. Therefore, the elements of claims 17 and 19 are rejected due to the same reasons as outlined above for claims 1 and 3. 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: … a reservoir simulator 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 [0010], [0064]. Examiner note: the disclosed commercial software/computerized apparatus with processors for determining hydrocarbon reservoir performance data corresponds to the reservoir simulator, and the software/apparatus predicts reservoir pressures and rates, optimizes multi-stage fracture design, and optimizes well spacing/time; therefore, the reservoir simulator is configure to determine hydraulic fracture geometries and spacings. Additionally, See also Xue (Page 1250) regarding limitation of claim 17“determine a measured drainage function” as discuss for claim 1).
Claim(s) 5-6 are rejected under 35 U.S.C. 103 as being unpatentable over King and Xue and Vignau
and Iino and Roussel 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 and Iino and Roussel 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 and Iino and Roussel 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 and Iino and Roussel 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 and Iino and Roussel 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
Iino and Roussel 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 teaches the set of drainage models (see King, [0011], [0213]-[0214]), However, King and Xue and Vignau and Iino and Roussel and Maschio fail to teach, but Davolio teaches the tolerance value is selected based on a range of the misfit value for the set of models (Page.262, “Step 2: generation of model samples using DLHC with geostatistical realizations (DLHG). Step 3: run flow simulations for the set of generated models. Step 4: compute the normalized misfit of all data considering well data, or well data and 4DS data. Step 5: select the best models according to the match quality using a correlation matrix and use these models to update the pdf for attributes.” Page.263, “… (3) where Tol is the tolerance given by a percentage of the observed data (Hist) … The NQDS range of [−1 + 1] represents an excellent matching quality, i.e., the simulated data provide values within the user defined tolerance.” 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.265, “(b) Find the NQDS and OFmap limits so that Nm models present all OFs … within these limits.” Page.267, 3.4. Tolerances and stop criteria, “we set the range [−10 + 10] to define good models … we set the acceptance limits as OFbin < 100 for S31 and S41, and OFbin < 240 for S61.” Examiner note: the reference teaches generating a set of model samples, running simulations for the generated models, computing normalized misfit/objective function values for the generated models, and selecting best models according to match quality, and further teaches user defined tolerance/acceptance limits, including NQDS and OFmap/OFbin limits, and expressly sets ranges or limits of the misfit/objective function values to define acceptable or good models. Thus, Davolio teaches selected NQDS and OFmap/OFbin limits corresponds to a tolerance value selected based on a range of misfit values of the evaluated model set).
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 Iino and Roussel and Maschio to incorporate the teachings of Davolio, and apply computing normalized misfit/objective function values for generated models, setting NQDS and OFmap/OFbin limits or ranges to define acceptable or good models, and selecting best models according to matching quality in order to provide an objective and quantitative thresholding framework for evaluating model match quality. The combination of teachings would predictably provide benefit of improving the reliability of model screening and selection by using quantitative tolerance limits with observed data matching performance.
Claim(s) 8 is rejected under 35 U.S.C. 103 as being unpatentable over King and Xue and Vignau
and Iino and Roussel as applied to claim 1 above, and further in view of Kim (“Iterative learning-based many-objective history matching using deep neural network with stacked autoencoder,” published in 2021).
Claim 8, King and Xue and Vignau and Iino and Roussel fail to teach, but Kim teaches predicting the preferred drainage model using a machine learning network (Page.1465, Abstract, “This paper presents an innovative data-integration that uses an iterative-learning method, a deep neural network (DNN) coupled with a stacked autoencoder (SAE) to solve issues encountered with many objective history matching. The proposed method consists of a DNN-based inverse model with SAE encoded static data and iterative updates of supervised-learning data are based on distance-based clustering schemes. DNN functions as an inverse model and results in encoded flattened data, while SAE, as a pre-trained neural network, successfully reduces dimensionality and reliably reconstructs geomodels. The iterative-learning method can improve the training data for DNN by showing the error reduction achieved with each iteration step … This confirms the proposed workflow constructs more plausible geo-models.” Page.1469, 3.1. DNN-SAE: DNN-based inverse modeling with SAE encoding/decoding process, “Training neural networks (DNN and SAE) is a process that adjusts weights to find a nonlinear function to minimize the prediction error between actual values and neural network predictions.” 3.1.2. Training the DNN-based inverse model, “The DNN-based inverse model in this study is a multiple hidden-layered neural network consisting of two hidden layers, one input, and one output layer.” Examiner note: the references teaches using a deep neural network and stacked autoencoder, including neural network layers, weights, and activation functions, to perform inverse modeling/history matching and reconstruct more plausible geomodels with reduced error. The DNN/SAE corresponds to the claimed machine learning network, and the reconstructed /plausible history matched geomodel corresponds to predicting the preferred drainage model using the 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 and Iino and Roussel to incorporate the teachings of Kim, and apply using a deep neural network coupled with a stacked autoencoder to perform inverse modeling/history matching and reconstruct more plausible geomodels with reduced error in order to improve the accuracy and reliability of reservoir model calibration based on observed production behavior. The combination of teachings would predictably provide benefit of improving model prediction and calibration by using a neural network based history matching workflow to generate a drainage model that more accurately presents observed reservoir performance.
Conclusion
THIS ACTION IS MADE FINAL. Applicant is reminded of the extension of time policy as set forth in 37
CFR 1.136(a).
A shortened statutory period for reply to this final action is set to expire THREE MONTHS from the mailing date of this action. In the event a first reply is filed within TWO MONTHS of the mailing date of this final action and the advisory action is not mailed until after the end of the THREE-MONTH shortened statutory period, then the shortened statutory period will expire on the date the advisory action is mailed, and any nonprovisional extension fee (37 CFR 1.17(a)) pursuant to 37 CFR 1.136(a) will be calculated from the mailing date of the advisory action. In no event, however, will the statutory period for reply expire later than SIX MONTHS from the mailing date of this final action.
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/YI . HAO/
Examiner, Art Unit 2187
/EMERSON C PUENTE/Supervisory Patent Examiner, Art Unit 2187
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