DETAILED ACTION
Notice of Pre-AIA or AIA Status
The present application, filed on or after March 16, 2013, is being examined under the first inventor to file provisions of the AIA .
Responsive to communications on 04/05/2024
Claims 1-15 Pending
Claims 1-15 Rejected
Priority
Application data sheet received on 03/20/2023 does not claim foreign or domestic priority. Application Data Sheet accepted by the examiner.
Information Disclosure Statement
Responsive to IDS received on 04/05/2024. IDS accepted by the examiner and all references considered.
Drawings
Responsive to drawings received on 03/20/2023. Drawings are accepted by the examiner.
Specification
Abstract received on 03/20/2023 is less than 150 words and contains no legal or implied phraseology. Abstract is accepted by the examiner.
Specification received on 03/20/2023 accepted by the examiner.
Claim Objections
Claim 8 is objected to because of the following informalities: Claim 8 states “does includes” instead of “does include” or “includes”. Appropriate correction is required.
Claim Rejections - 35 USC § 112
The following is a quotation of 35 U.S.C. 112(b):
(b) CONCLUSION.—The specification shall conclude with one or more claims particularly pointing out and distinctly claiming the subject matter which the inventor or a joint inventor regards as the invention.
The following is a quotation of 35 U.S.C. 112 (pre-AIA ), second paragraph:
The specification shall conclude with one or more claims particularly pointing out and distinctly claiming the subject matter which the applicant regards as his invention.
Claim 13 is rejected under 35 U.S.C. 112(b) or 35 U.S.C. 112 (pre-AIA ), second paragraph, as being indefinite for failing to particularly point out and distinctly claim the subject matter which the inventor or a joint inventor (or for applications subject to pre-AIA 35 U.S.C. 112, the applicant), regards as the invention.
Claim 13 recites the limitation "the method”. There is insufficient antecedent basis for this limitation in the claim.
Claim Rejections - 35 USC § 101
35 U.S.C. 101 reads as follows:
Whoever invents or discovers any new and useful process, machine, manufacture, or composition of matter, or any new and useful improvement thereof, may obtain a patent therefor, subject to the conditions and requirements of this title.
Claims 1-15 are rejected under 35 U.S.C. 101 because the claimed invention recites a judicial exception, an abstract idea, which has not been integrated into practical application and the claims further do not recite significantly more than the judicial exception.
Claim 1
Step 1: Is the claimed invention one of the four statutory categories? :
YES. The claim recites A method comprising: which is a process.
Step 2A Prong 1, inquiry "Is the claim directed to a law of nature, a natural phenomenon or an abstract idea?":
YES. Claim 1 recites: generating, using a radial based four-dimensional model, simulated PVT data for the plurality of hydraulically connected wells not in the subset;
The radial based four-dimensional model is a mathematic model. Simulated PVT data is numeric data which is calculated by the radial based four-dimensional model. MPEP 2106.04(a)(2)(I)(C) states “A claim that recites a mathematical calculation, when the claim is given its broadest reasonable interpretation in light of the specification, will be considered as falling within the "mathematical concepts" grouping. A mathematical calculation is a mathematical operation (such as multiplication) or an act of calculating using mathematical methods to determine a variable or number, e.g., performing an arithmetic operation such as exponentiation. There is no particular word or set of words that indicates a claim recites a mathematical calculation. That is, a claim does not have to recite the word "calculating" in order to be considered a mathematical calculation. 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.” This claim limitation recites a mathematic calculation (generating PVT data using a radial based four-dimensional model) and therefore recites an abstract idea.
determining, based on the simulated PVT data, respective fluid compositions for the plurality of hydraulically connected wells;
This claim pertains to a determination (fluid compositions) made based on received information (the simulated PVT data). As informed by the specification, this is done through a workflow which involves calculations and determinations, see figure 4 and equations 2-4 in the specifications.
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The MPEP 2106.04(a)(2)(III) states “Accordingly, the "mental processes" abstract idea grouping is defined as concepts performed in the human mind, and examples of mental processes include observations, evaluations, judgments, and opinions “and MPEP 2106.04(a)(2)(III)(B) states “If a claim recites a limitation that can practically be performed in the human mind, with or without the use of a physical aid such as pen and paper, the limitation falls within the mental processes grouping, and the claim recites an abstract idea.” As outlined, the workflow involves evaluations performed by a user to solve for fluid compositions based on PVT data through a workflow, which can be done by an individual with a pen and paper.
aggregating, based on one or more factors, the respective fluid compositions into an aggregated composition;
An aggregated composition is generated by combinign fluid compositions based on weights of individual fluid compositions. See par 43: “At step 422, the workflow 400 involves aggregating compositions. More specifically, step 422 involves aggregating the compositions based on their production contribution to the overall system (e.g., weighted based on each stream's contribution to the overall fluid stream).” This is an observation and detemrination made by an individual, (ie: if one stream contributes 20% and another contributes 80%, the aggregated composition will be .2 (stream 1) + .8(stream 2). ) The MPEP 2106.04(a)(2)(III) states “Accordingly, the "mental processes" abstract idea grouping is defined as concepts performed in the human mind, and examples of mental processes include observations, evaluations, judgments, and opinions. This is an evaluation from a user, and therefore the claim recites a mental process.
and flashing the aggregated composition to a desired pressure and temperature.
As understood in light of the specification, flashing the aggregated composition in this context refers to a mathematic calculation being performed, not a physical action performed on a fluid so that it reaches a desired pressure and temperature. See par 43: At step 430, the workflow 400 involves flashing compositions to a desired pressure and temperature. That is, the outcome will be an aggregate fluid composition per each time step that can be extrapolated to any specific desired pressure and temperature (P&T).” The MPEP 2106.04(a)(2)(III)(B) states “If a claim recites a limitation that can practically be performed in the human mind, with or without the use of a physical aid such as pen and paper, the limitation falls within the mental processes grouping, and the claim recites an abstract idea.” This claim limitations refers to an individual extrapolating data based on other data, which is a recitation of a mental process.
Step 2A Prong 2, Does the claim recite additional elements that integrate the judicial exception into a practical application?
NO. Claim 1 additionally recites receiving input data comprising:
This claim limitations pertain the usage of an ordinary computing device to receive data, which is then applied to the judicial exceptions above. The MPEP 2106.05(f)(2) states “Use of a computer or other machinery in its ordinary capacity for economic or other tasks (e.g., to receive, store, or transmit data) or simply adding a general purpose computer or computer components after the fact to an abstract idea (e.g., a fundamental economic practice or mathematical equation) does not integrate a judicial exception into a practical application or provide significantly more.” Therefore, the receiving data does not integrate the judicial exception into a practical application or provide significantly more.
(i) production data from a plurality of hydraulically connected wells, and
(ii) measured pressure, volume, temperature (PVT) data for a subset of the plurality of hydraulically connected wells, wherein the measured PVT data comprises gas samples and oil samples;
These claim limitations specify the type of data received. The MPEP 2106.05(h) states “limitations that amount to merely indicating a field of use or technological environment in which to apply a judicial exception do not amount to significantly more than the exception itself, and cannot integrate a judicial exception into a practical application” ... “[with] Examples of limitations that the courts have described as merely indicating a field of use or technological environment in which to apply a judicial exception include ... vi. Limiting the abstract idea of collecting information, analyzing it, and displaying certain results of the collection and analysis to data related to the electric power grid, because limiting application of the abstract idea to power-grid monitoring is simply an attempt to limit the use of the abstract idea to a particular technological environment” This claim limitation limits the abstract ideas above that collects and analyzes information to the field of petroleum engineering, and therefore does not integrate the judicial exception into a practical application or provide significantly more.
Step 2B, does the claim recites additional elements that amount to significantly more than the judicial exception.
NO. As stated in Step 2A Prong 2, the above additional elements do not integrate the judicial exception into a practical application or provide significantly more.
Based on the above facts, the office concludes that claim 1 is not eligible under 35 USC 101.
Claim 2:The method of claim 1,
wherein the input data further comprises operating times for the plurality of hydraulically connected wells,
This claim limitation further specifies the input data above which was determined to be field of use and the usage of a generic computing device to receive data. Therefore, this claim limitation does not integrate the judicial exception into a practical application or provide significantly more.
and wherein aggregating, based on one or more factors, the respective fluid compositions into an aggregated composition comprises: applying to the respective compositions respective weights that correspond to the respective operating times.
The aggregation above was determined to be a mental process. Applying weights that corresponds to the respective operating time is a user observing the operating times (well 1 operates 20% of the time and well 2 operates 80% of the time) and then passing a judgment for the weights based on that information (well 1 is weighted at .2 and well 2 is weighted at .8). Therefore, this claim limitation is a further recitation of the above abstract ideas.
Claim 3:The method of claim 1, wherein the production data comprises at least one of: respective daily oil production rates for the plurality of hydraulically connected wells or respective daily gas production rates for the plurality of hydraulically connected wells.
This claim limitation further specifies the input data above which was determined to be field of use and the usage of a generic computing device to receive data. Therefore, this claim limitation does not integrate the judicial exception into a practical application or provide significantly more.
Claim 4:The method of claim 1, wherein the PVT data comprises composition data for the subset of the plurality of hydraulically connected wells.
This claim limitation further specifies the input data above which was determined to be field of use and the usage of a generic computing device to receive data. Therefore, this claim limitation does not integrate the judicial exception into a practical application or provide significantly more.
Claim 5:The method of claim 1, wherein the input data further comprises deviation surveys and completion configurations of the plurality of hydraulically connected wells, and wherein the method further comprises:
This claim limitation further specifies the input data above which was determined to be field of use and the usage of a generic computing device to receive data. Therefore, this claim limitation does not integrate the judicial exception into a practical application or provide significantly more.
detecting well placement based on the deviation surveys and the completion configurations.
This process involves receiving /observing information (deviation surveys and completion configurations) and evaluating the information to detect a well placement. The MPEP 2106.04(a)(2)(III) states “Accordingly, the "mental processes" abstract idea grouping is defined as concepts performed in the human mind, and examples of mental processes include observations, evaluations, judgments, and opinions. Therefore the claim recites an abstract idea.
Claim 6:The method of claim 1, wherein determining, based on the simulated PVT data, the respective fluid compositions comprises:
determining, for a first well of the plurality of hydraulically connected wells, whether the respective composition of the first well includes free gas.
As stated above in claim 1, This claim pertains to a determination (fluid compositions) made based on received information (the simulated PVT data). As informed by the specification, this is done through a workflow which involves calculations and determinations, see figure 4 and equations 2-4 in the specifications.
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The MPEP 2106.04(a)(2)(III) states “Accordingly, the "mental processes" abstract idea grouping is defined as concepts performed in the human mind, and examples of mental processes include observations, evaluations, judgments, and opinions “and MPEP 2106.04(a)(2)(III)(B) states “If a claim recites a limitation that can practically be performed in the human mind, with or without the use of a physical aid such as pen and paper, the limitation falls within the mental processes grouping, and the claim recites an abstract idea.” As outlined, the workflow involves evaluations performed by a user to solve for fluid compositions based on PVT data through a workflow, which can be done by an individual with a pen and paper.
Claim 7:The method of claim 6, wherein determining whether the respective composition of the first well includes free gas comprises:
determining, from the production data, a production gas-oil-ratio (GORproduction) for the first well;
This claim pertains to determining a mathematic value (gas oil ratio) based on numeric information (production data). As understood by one ordinarily skilled in the art, this includes dividing the amount of oil by total oil and gas weight/volume/moles (ie: a 2 to 1 gas oil ratio). MPEP 2106.04(a)(2)(III)(B) states “If a claim recites a limitation that can practically be performed in the human mind, with or without the use of a physical aid such as pen and paper, the limitation falls within the mental processes grouping, and the claim recites an abstract idea.” Therefore this claim is a further recitation of the abstract idea of determining the composition of the first well.
calculating, based on the simulated PVT data, a predicted GOR (GORpredicted) for the first well;
The MPEP 2106.04(a)(2)(I)(C) states “A claim that recites a mathematical calculation, when the claim is given its broadest reasonable interpretation in light of the specification, will be considered as falling within the "mathematical concepts" grouping. A mathematical calculation is a mathematical operation (such as multiplication) or an act of calculating using mathematical methods to determine a variable or number, e.g., performing an arithmetic operation such as exponentiation. There is no particular word or set of words that indicates a claim recites a mathematical calculation. That is, a claim does not have to recite the word "calculating" in order to be considered a mathematical calculation. 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.” This claim recites a calculation of a mathematic value (predicted GOR) from mathematic data (PVT data). Therefore, this claim is a further recitation of the abstract idea of determining the composition of the well.
and determining whether GORproduction <= (1 + tol) * GORpredicted, wherein tol is a predetermined acceptable tolerance.
This claim recites an evaluation of whether one side of an equation is greater than the other. The MPEP 2106.04(a)(2)(III) states “Accordingly, the "mental processes" abstract idea grouping is defined as concepts performed in the human mind, and examples of mental processes include observations, evaluations, judgments, and opinions. “This claim recites an evaluation of an inequality which can be performed in the mind. Therefore, this claim is a further recitation of the abstract idea of determining the composition of the well.
Claim 8:The method of claim 7, wherein determining whether the respective composition of the first well includes free gas comprises:
in response to determining that GORproduction is <=(1 + tol) * GORpredicted, determining that the respective composition of the first well does not include free gas;
This claim limitation is a judgement (does not include free gas) made in response to an observation (GORproduction is <=(1 + tol) * GORpredicted,) . The MPEP 2106.04(a)(2)(III) states “Accordingly, the "mental processes" abstract idea grouping is defined as concepts performed in the human mind, and examples of mental processes include observations, evaluations, judgments, and opinions. “ Therefore this claim recites a mental process.
or in response to determining that GORproduction is not <=(1 + tol) * GORpredicted, determining that the respective composition of the first well does includes free gas.
This claim limitation is a judgement (does include free gas) made in response to an observation (GORproduction is >(1 + tol) * GORpredicted,) . The MPEP 2106.04(a)(2)(III) states “Accordingly, the "mental processes" abstract idea grouping is defined as concepts performed in the human mind, and examples of mental processes include observations, evaluations, judgments, and opinions. “Therefore this claim recites a mental process.
Claims 9-15:Claims 9-15 are effectively similar to claims 1-7 except that they are regarding a product of manufacturing rather than a method, and are rejected under the same rational as above. Additionally, Regarding the additional limitations A non-transitory computer storage medium encoded with instructions that, when executed by one or more computers, cause the one or more computers to perform operations comprising:
This limitation is a recitation of generic computer machinery which implements the abstract ideas in the claim. The MPEP 2106.05(f)(2) states “Use of a computer or other machinery in its ordinary capacity for economic or other tasks (e.g., to receive, store, or transmit data) or simply adding a general purpose computer or computer components after the fact to an abstract idea (e.g., a fundamental economic practice or mathematical equation) does not integrate a judicial exception into a practical application or provide significantly more.” Therefore this limitation does not integrate a judicial exception into a practical application or provide significantly more and claims 9-15 are directed to an abstract idea.
Claim Rejections - 35 USC § 103
The following is a quotation of 35 U.S.C. 103 which forms the basis for all obviousness rejections set forth in this Office action:
A patent for a claimed invention may not be obtained, notwithstanding that the claimed invention is not identically disclosed as set forth in section 102, if the differences between the claimed invention and the prior art are such that the claimed invention as a whole would have been obvious before the effective filing date of the claimed invention to a person having ordinary skill in the art to which the claimed invention pertains. Patentability shall not be negated by the manner in which the invention was made.
Claims 1, 4, 9, and 12 are rejected under 35 U.S.C. 103 as being unpatentable over Wong_2017 (US 20170009558 A1) , Fath_2020 (“Implementation of multilayer perceptron (MLP) and radial basis function (RBF) neural networks to predict solution gas-oil ratio of crude oil systems”) and Raman_2020 (US 10859730 B2)
Claim 1:Wong_2017 makes obvious A method comprising: (abstract: “System and methods”)
receiving input data comprising (par 57: “Method 400 begins in step 402, which includes determining pressure-volume- temperature (PVT) data for multiple fluids in the common surface network. In an embodiment, the PVT data in step 402 may be based on existing raw laboratory data determined to match the fluid behavior at points in the common surface network where is fluids produced from different reservoirs may mix or commingle together.”) :
production data from a plurality of hydraulically connected wells, (par 26: “While production wells 100A and 100B are described in the context of a single reservoir, it should be noted that the embodiments disclosed herein are not limited thereto and that the disclosed embodiments may be applied to fluid production from multiple reservoirs in a multi-reservoir production system with a common surface or gathering network, as will be described in further detail below with respect to FIG. 3. Thus, a plurality of surface control units similar to control unit 132 may be used to send production system data from the respective wellsites of different reservoirs in the production system to processing system 140. In addition to the above-described SCADA functionality, processing system 140 may be used to process the received data and simulate fluid production in the multi-reservoir system, as will be described in further detail below.)
and (ii) measured pressure, volume, temperature (PVT) data for a subset of the plurality of hydraulically connected wells, (par 35: “Production data 222 may include, for example, actual and/or simulated production system measurements. Actual production system measurements may include, for example, surface and downhole well measurements from various production wells in the multi-reservoir system. Such measurements may include, but are not limited to, pressure, volume, temperature and fluid flow measurements taken downhole near the well perforations, along the production string, at the wellhead and within the gathering network prior to the point where the fluids mix with fluids from other reservoirs. Likewise, the simulated measurements may include, for example and without limitation, estimates of pressure, temperature and fluid flow. Such estimates may be determined based on, for example, simulation results from one or more previous time-steps.” Examiner note: Where “actual and simulated” makes obvious a process where some are measured and some are simulated )
wherein the measured PVT data comprises gas samples and oil samples; (par 19: “In an embodiment, the simulation may be based in part on production system data including various measurements collected downhole from a well drilled within each hydrocarbon reservoir, e.g., in the form of a production well for an oil and gas reservoir. Further, multiple production wells may be drilled for providing access to the reservoir fluids underground. Measured well data may be collected regularly from each production well to track changing conditions in the reservoir, as will be described in further detail below with respect to the production well examples illustrated in FIGS. 1A and 1B.)
generating,par 35: " Production data 222 may include, for example, actual and/or simulated production system measurements. Actual production system measurements may include, for example, surface and downhole well measurements from various production wells in the multi-reservoir system. Such measurements may include, but are not limited to, pressure, volume, temperature and fluid flow measurements taken downhole near the well perforations, along the production string, at the wellhead and within the gathering network prior to the point where the fluids mix with fluids from other reservoirs. Likewise, the simulated measurements may include, for example and without limitation, estimates of pressure, temperature and fluid flow. Such estimates may be determined based on, for example, simulation results from one or more previous time-steps.” ... par 57: “Alternatively, in cases where such data may not be available, step 402 may include generating artificial or synthetic PVT data in the desired range of pressure and temperature conditions based on previously established EOS characterizations for the reservoir fluids, as described above.” )
determining, based on the simulated PVT data, respective fluid compositions for the plurality of hydraulically connected wells; (par 37: “is In an embodiment, fluid model generator 212 may generate a fluid model for each reservoir in the multi-reservoir system based on corresponding production data 222 and fluid data 224. For example, fluid model generator 212 may determine parameters for each fluid component or group of components of the reservoir based on actual and simulated production system measurements (e.g., from one or more prior simulation time-steps) and fluid component characterizations associated with each reservoir”)
aggregating, based on one or more factors, the respective fluid compositions into an aggregated composition; (par 18: “As noted above, the disclosed embodiments relate to using a shared EOS characterization of multiple fluids during a simulation of fluid production in a multi- reservoir system with a common surface network. As will be described in further detail below, reservoir fluids from multiple hydrocarbon reservoirs may be produced through a common gathering point or shared facility of the common surface network. Thus, heterogeneous fluids from different reservoirs that flow into the common gathering point may combine or mix together. Thus, heterogeneous fluids from different reservoirs that flow into the common gathering point may combine or mix together. In an example, the disclosed embodiments may be used to calculate properties of the mixed fluids at the common gathering point or other points within the common surface network during a simulation of fluid production in the multi-reservoir system.”) Examiner note: Where properties of fluids makes obvious compositions according to par 40: “Further, the equations may be used by flow simulator 214 to determine updated fluid properties (e.g., updated fluid component mass and volume values for each gridblock) at the end of the simulation time-step.” )
and par 57: “Method 400 begins in step 402, which includes determining pressure-volume- temperature (PVT) data for multiple fluids in the common surface network. In an embodiment, the PVT data in step 402 may be based on existing raw laboratory data determined to match the fluid behavior at points in the common surface network where is fluids produced from different reservoirs may mix or commingle together. For example, step 402 may include selecting existing PVT data in a desired range of temperature and pressure conditions used in common or shared points at which mixed fluids are produced in the common surface network. Alternatively, in cases where such data may not be available, step 402 may include generating artificial or synthetic PVT data in the desired range of pressure and temperature conditions based on previously established EOS characterizations for the reservoir fluids, as described above.” Examiner note: Where the established EOS is for the multiple fluids at shared points.)
Wong_2017 does not expressly recite using a radial based four-dimensional model and
flashing
Fath_2020 however makes obvious using a radial based four-dimensional model (abstract: “Exact determination of pressure-volume-temperature (PVT) properties of the reservoir oils is necessary for reservoir calculations, reservoir performance prediction, and the design of optimal production conditions. The objective of this study is to develop intelligent and reliable models based on multilayer perceptron (MLP) and radial basis function (RBF) neural networks for estimating the solution gas–oil ratio as a function of bubble point pressure, reservoir temperature, oil gravity (API), and gas specific gravity.” ... section 2.2 par 3-4 : “This parameter is a multidimensional radial basis function describing the difference between an input vector and a pre-defined center vector. Different types of radial basis functions are applied in the literature, with the most common applications referring to the Gaussian Function which is defined as follows:(4) Where σj is the width of the jth hidden neuron, Finding the centers, widths, and the weights connecting hidden neurons to the output is the key for constructing and training the RBF-NN. Both the dimensionality and the distribution of the input patterns affect the number of the hidden neurons. If the dimensionality is reduced, the number of hidden neurons will also be decreased [58].”)
(Examiner note: Where bubble point pressure, reservoir temperature, oil gravity (API), and gas specific gravity” make obvious four dimensions of the model. Where the passage also makes obvious various dimensionality for the model))
Wong_2017 and Fath_2020 are analogous art to the claimed invention because they are from the same field of endeavor called petroleum engineering modeling. Before the effective filing date, it would have been obvious to a person ordinarily skilled in the art to combine Wong_2017 and Fath_2020.
The rational for doing so would have been to follow a teaching proposed in the prior art. Wong_2017 simulates/calculates PVT properties using an EOS. See abstract: “ A shared equation of state (EOS) characterization representing each of the fluids across the plurality of reservoirs is generated based on the corresponding PVT data. Data representing properties of the fluids in each reservoir is calculated based on the shared EOS characterization of the fluids. When the calculated data is determined not to match the PVT data associated with the fluids in each reservoir, to the shared EOS characterization is adjusted based on a difference between the calculated data and the PVT data. Fath_2020 proposes the radial basis function as a better option for the same goal. See page 1 col 2 par 1: “EOSs are poor predictive tools unless they are tuned using the fluid composition and some experimentally measured properties, such as saturation pressure of a given hydrocarbon system. In addition, EOSs involve numerous numeric computations and exhibit poor accuracy when calculating the viscosity of crude oil [5]. On the contrary, empirical correlations require neither any tuning process, nor the complete set of fluid data. In most cases, empirical correlations use field data such as reservoir pressure, reservoir temperature, oil gravity and gas specific gravity for predicting PVT properties.” Further stating in the abstract “Performance of the developed MLP and RBF models were evaluated and investigated against a number of well-known empirical correlations using statistical and graphical error analyses. The results indicated that the proposed models outperform the considered empirical correlations, providing a strong agreement between predicted and experimental values, However, the developed RBF exhibited higher accuracy and efficiency compared to the proposed MLP model.” Therefore it would have been obvious to combine the modeling workflow of Wong_2017 with the use of a four dimensional radial basis function of Fath_2020 for the benefit of higher accuracy modeling to obtain the invention as specified in the claims.
Wong_2017 and Fath_2020 do not expressly recite flashing
Raman_2020 makes obvious flashing (par 32: “The reservoir-specific compositional database can be used to train the machine-learning algorithm for making predictions on phase stability and perform flash calculations for compositional reservoir simulations. “)
Wong_2017 and Raman_2020 are analogous art to the claimed invention because they are from the same field of endeavor called petroleum engineering modeling. Before the effective filing date, it would have been obvious to a person ordinarily skilled in the art to combine Wong_2017 and Raman_2020. The rational for doing so would have been the use of a known technique to improve similar devices in the same way. The prior art of Wong_2017 teaches a base device which modifies pressure and temperature for fluid analysis, see par 57: “For example, step 402 may include selecting existing PVT data in a desired range of temperature and pressure conditions used in common or shared points at which mixed fluids are produced in the common surface network. Alternatively, in cases where such data may not be available, step 402 may include generating artificial or synthetic PVT data in the desired range of pressure and temperature conditions based on previously established EOS characterizations for the reservoir fluids, as described above.” Raman_2020 teaches a comparable device, which also uses PVT data and EOS experiments, which also includes performing flash calculations afterwards for compositional simulation (abstract “One or more machine-learning algorithms are trained using the compositional database, and the trained one or more machine-learning algorithms are used to predict phase stability and perform flash calculations for compositional reservoir simulation.”) . One ordinarily skilled in the art could have applied the known improvement of including flash calculations in the same way to the prior art of Wong_2017 after generating an EOS using PVT data, which would have provided a predictable result of allowing for phase equilibrium calculations such as demonstrated in Raman_2020 par 36: “ flash calculation is performed using machine-learning to calculate phase equilibrium for compositional modeling” Therefore it would have been obvious to combine the PVT and EOS fluid modeling workflow of Wong_2017 with the usage of Flash calculations by Raman_2020 for the benefit of allowing predictable phase equilibrium calculations to be used in composition modeling to obtain the invention as specified in the claims.
Claim 4:The method of claim 1,
Wong_2017 makes further obvious wherein the PVT data comprises composition data for the subset of the plurality of hydraulically connected wells. (par 36: “Fluid data 224 may represent different reservoir fluid components (e.g., heavy crude, light crude, methane, etc.) and related properties including, for example, their proportions, fluid density and viscosity for various compositions, pressures and temperatures, or other data.”)
Claim 9:
Claim 9 is effectively similar to claim 1 and is rejected under a similar rational. Wong_2017 further makes obvious the additional limitations of A non-transitory computer storage medium encoded with instructions that, when executed by one or more computers, cause the one or more computers to perform operations comprising: (par 80: “In addition, certain aspects of the disclosed embodiments, as outlined above, may be embodied in software that is executed using one or more processing units/components. Program aspects of the technology may be thought of as “products” or “articles of manufacture” typically in the form of executable code and/or associated data that is carried on or embodied in a type of machine readable medium. Tangible non-transitory “storage” type media include any or all of the memory or other storage for the computers, processors or the like, or associated modules thereof, such as various semiconductor memories, tape drives, disk drives, optical or magnetic disks, and the like, which may provide storage at any time for the software programming.”)
Claim 12:Claim 12 is effectively similar to claim 4 except that it is dependent on claim 9 and is therefore rejected under a similar rational to claims 4 and 9.
Claims 3 and 11 are rejected under Wong_2017 , Fath_2020, Raman_2020, and Admin_2017 (“PVT Properties and Correlations”)
Claim 3:The method of claim 1, wherein the production data comprises at least one of:
Wong_2017 makes obvious respective wells. (par 25: “In an example, data storage device 144 may be used to store historical production data including a record of actual and simulated production system measurements obtained or calculated over a period of time, e.g., multiple simulation time-steps, as will be described in further detail below.” … par 35: “Production data 222 may include, for example, actual and/or simulated production system measurements. Actual production system measurements may include, for example, surface and downhole well measurements from various production wells in the multi-reservoir system. Such measurements may include, but are not limited to, pressure, volume, temperature and fluid flow measurements taken downhole near the well perforations, along the production string, at the wellhead and within the gathering network prior to the point where the fluids mix with fluids from other reservoirs. Likewise, the simulated measurements may include, for example and without limitation, estimates of pressure, temperature and fluid flow. Such estimates may be determined based on, for example, simulation results from one or more previous time-steps.”) … par 53: “Maximizing fluid production in the multi-reservoir production system of FIG. 3 may involve controlling the production of each individual well such that the combined production of the wells, or a selected group of the wells, provides the greatest possible amount of hydrocarbon (e.g., oil and/or gas) production within the operating limits of processing facility 300 and without exceeding any production system constraints.”
Wong_2017 does not expressly recite daily oil … or daily gas
Admin_2017 however makes obvious daily oil … or daily gas (page 8: “Q = Gas volumetric flow rate at standard conditions (scf/d or Sm3/d)”
Wong_2017 and Admin_2017 are analogous art to the claimed invention because they are from the same field of endeavor called petroleum engineering modeling. Before the effective filing date, it would have been obvious to a person ordinarily skilled in the art to combine Wong_2017 and Admin_2017. The rational for doing so would have been applying a known technique to a known device ready for improvement to yield predictable results. The prior art of Wong_2017 contains a base modeling device which tracks production data, which includes fluid flow measurements at wellheads and throughout the system. The prior art of Wong_2017 is silent that this data is taken as daily measurements. The prior art of Admin_2017 shows a known technique, which is measuring flow rate in units of day. One ordinarily skilled in the art would represent the flow rate measurements of Wong_2017 daily as outlined by Admin_2017 which would provide a predictable result to one ordinarily skilled in the art of tracking the daily oil or gas production. Therefore it would have been obvious to combine the volumetric flow modeling of Wong_2017 with the units of measurement to track flow by units of day of Admin_2017 for the predictable result of tracking daily production to obtain the invention as specified in the claims.
Claim 11:
Claim 11 is an effective duplicate to claim 3 except that it depends on claim 9 and is therefore rejected under a similar rational to claims 3 and 9.
Claims 6 and 14 are rejected under Wong_2017 , Fath_2020, Raman_2020, and Admin_2017 and as motivated by Schlumberger_2023 (“free gas”)
Claim 6:The method of claim 1, wherein determining, based on the simulated PVT data, the respective fluid compositions comprises:
Wong_2017 makes obvious determining, for a first well of the plurality of hydraulically connected wells, whether the respective composition of the first well includes (abstract: “A shared equation of state (EOS) characterization representing each of the fluids across the plurality of reservoirs is generated based on the corresponding PVT data”) Examiner note: Where one ordinarily skilled in the art understands the EOS to include gas.
Wong_2017 does not expressly recite free
Admin_2017 however makes obvious free gas. (2017 page 8 par 1: “Unlike oil and water formation volume factors, the gas formation volume factor does not include the effect of gas dissolved in the oil or water. This means we must subtract the volume of gas dissolved in the oil or water, before applying the gas formation volume factor. The following equation can be used to calculate the free gas volume at down hole conditions:”)
Wong_2017 and Admin_2017 are analogous art to the claimed invention because they are from the same field of endeavor called petroleum engineering. Before the effective filing date, it would have been obvious to a person ordinarily skilled in the art to combine Wong_2017 and Admin_2017. The rational for doing so would have been to follow a teaching proposed in the prior art of Schlumberger_2023. The prior art of Schlumberger_2023 provides a definition of the term free gas to one ordinarily skilled in the art. Schlumberger_2023 defines free gas as “The gaseous phase present in a reservoir or other contained area. Gas may be found either dissolved in reservoir fluids or as free gas that tends to form a gas cap beneath the top seal on the reservoir trap. Both free gas and dissolved gas play important roles in the reservoir-drive mechanism” One ordinarily skilled in the art would recognize that free gas is an understood term in the art and is an important state to model for reservoir modeling. Therefore it would have been obvious to combine the EOS modeling of Wong_2017 which includes gas with the free gas equations of Admin_2017 for the benefit of modeling the gas present in the EOS to obtain the invention as specified in the claims.
Claim 14:Claim 14 is effectively similar to claim 6 except that it depends from claim 9 and is therefore rejected under a similar rational to claims 6 and 9.
Claims 7-8 and 15 are rejected under Wong_2017 , Fath_2020, Raman_2020, Admin_2017 as motivated by Schlumberger_2023, and Pomerantz_2011 (US 20110246143 A1)
Claim 7:The method of claim 6, wherein determining whether the respective composition of the first well includes free gas comprises:
Wong_2017 does not expressly recite determining, from the production data, a production gas-oil-ratio (GORproduction) for the first well;
calculating, based on the simulated PVT data, a predicted GOR (GORpredicted) for the first well; and
determining whether GORproduction (1 + tol) * GORpredicted,wherein tol is a predetermined acceptable tolerance.
Pomeranctz_2011 however makes obvious determining, from the production data, a production gas-oil-ratio (GORproduction) for the first well; (par 56: “The operations begin in step 201 by employing the downhole fluid analysis (DFA) tool of FIGS. 1A and 1B to obtain a sample of the formation fluid at the reservoir pressure and temperature (a live oil sample) at a measurement station in the wellbore (for example, a reference station). The sample is processed by the fluid analysis module 25. In the preferred embodiment, the fluid analysis module 25 performs spectrophotometry measurements that measure absorption spectra of the sample and translates such spectrophotometry measurements into concentrations of several alkane components and groups in the fluids of interest. In an illustrative embodiment, the fluid analysis module 25 provides measurements of the concentrations (e.g., weight percentages) of carbon dioxide (CO.sub.2), methane (CH.sub.4), ethane (C.sub.2H.sub.6), the C3-C5 alkane group including propane, butane, and pentane, the lump of hexane and heavier alkane components (C6+), and asphaltene content. The tool 10 also preferably provides a means to measure temperature of the fluid sample (and thus reservoir temperature at the station), pressure of the fluid sample (and thus reservoir pressure at the station), live fluid density of the fluid sample, live fluid viscosity of the fluid sample, gas-oil ratio (GOR) of the fluid sample, optical density, and possibly other fluid parameters (such as API gravity, formation volume fraction (FVF), etc.) of the fluid sample.”)
calculating, based on the simulated PVT data, a predicted GOR (GORpredicted) for the first well; (par 9: “Computer-based modeling and simulation techniques have been developed for estimating the properties and/or behavior of petroleum fluid in a reservoir of interest. Typically, such techniques employ an equation of state (EOS) model that represents the phase behavior of the petroleum fluid in the reservoir. Once the EOS model is defined, it can be used to compute a wide array of properties of the petroleum fluid of the reservoir, such as: gas-oil ratio (GOR)”)
and
Wong_2017 and Pomerantz_2011 are analogous art to the claimed invention because they are from the same field of endeavor called reservoir modeling. Before the effective filing date, it would have been obvious to a person ordinarily skilled in the art to combine Wong_2017 and Pomerantz_2011. The rational for doing so would have been applying a known technique to a known device ready for improvement to yield a predictable result. The prior art of Wong_2017 contains a base device which uses an EOS model. The prior art of Wong_2017 is silent that the EOS model is specifically used to calculate an GOR. Wong_2017 abstract states “Data representing properties of the fluids in each reservoir is calculated based on the shared EOS characterization of the fluids.” One ordinarily skilled in the art in light of Pomerantz_2011 would reasonably conclude that the EOS model of Wong_2017 would then also be able to be used to calculate GOR values as that is an example of fluid characterization which could be performed with an EOS model which would then be used to validate obtained GOR data. See abstract : “When the calculated data is determined not to match the PVT data associated with the fluids in each reservoir, to the shared EOS characterization is adjusted based on a difference between the calculated data and the PVT data.” Therefore it would have been obvious to combine the workflow using EOS and PVT data of Wong_2017 with the calculation of GOR by Pomerantz_2011 for the predictable result of validating and calculating the fluid property of GOR to obtain the invention as specified in the claims.
Wong_2017 and Pomerantz_2011 do not expressly recite production (1 + tol) * GORpredicted,wherein tol is a predetermined acceptable tolerance.
Admin_2017 however makes obvious determining whether GORproduction (1 + tol) * GORpredicted,wherein tol is a predetermined acceptable tolerance.
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Admin_2017 page 8
Where GOR is the producing gas oil ratio (GOR production) and Rs is the solution gas oil ratio (GOR predicted). Where this equation makes obvious to one ordinarily skilled in the art the determination if GOR production is less than GOR predicted. Where tol = 0 is the mathematic tolerance above.
Where as already stated, it would have been obvious to combine the EOS modeling of Wong_2017 which includes gas with the free gas equations of Admin_2017 for the benefit of modeling the gas present in the EOS to obtain the invention as specified in the claims.
Claim 8:wherein determining whether the respective composition of the first well includes free gas comprises:
Admin_2017 makes obvious in response to determining that GORproduction is <=(1 + tol) * GORpredicted, determining that the respective composition of the first well does not include free gas;
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Examiner note: If R (GOR predicted) is larger than GOR (GOR production) then the value of q (free gas) will be less than zero, meaning the well does not include free gas.
or in response to determining that GORproduction is not <=(1 + tol) * GORpredicted, determining that the respective composition of the first well does includes free gas.
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Examiner note: If R (GOR predicted) is smaller than GOR (GOR production) then the value of q (free gas) will be greater than zero, meaning the well does include free gas.
Claim 15:Claim 15 is effectively similar to claim 7 except that it depends on claim 14 and is therefore rejected under a similar rational to claims 7 and 14.
Claims 2 and are rejected under Wong_2017 , Fath_2020, Raman_2020, Admin_2017 as motivated by Schlumberger_2023, and Baker_2015 (“Practical Reservoir Engineering and Characterization Chapter 4.2”)
Claim 2:The method of claim 1,
Wong_2017 makes obvious wherein the input data further comprises
and wherein aggregating, based on one or more factors, the respective fluid compositions into an aggregated composition comprises:
applying to the respective compositions respective weights that correspond to the (par 41: “With the state of the fluids known throughout the production system, the flow of fluid can be simulated using mass/volume balance equations representative of the reservoir, of perforations in the wellbore and of the gathering network. In an embodiment, the facility equations representing the gathering network include molar balance equations at the nodes, hydraulic equations, constraint equations, and composition equations. The independent variables for the facility equations include pressure and composition for the nodes, and molar flow rates for the connections.”)
Wong_2017 does not expressly recite operating times for the plurality of hydraulically connected wells,
to the respective operating times.
Baker_2015 however makes obvious operating times for the plurality of hydraulically connected wells,
to the respective operating times. (Section 4.2 production history “Production history refers to the recorded volumes of produced and injected oil, gas, water, or other injection fluids over time. These volumes are measured in surface facilities at the operating temperature and pressure of those facilities. The volumes are corrected to standard oilfield conditions (60 °F, 14.7 psia; 15.6 °C, 101 kPa). Production is reported as monthly volumes of oil, gas, and water. The number of hours a well was on production is also reported. These data are manipulated into a variety of amounts and ratios, some of which are listed in Table 4.2.1.” … Section 4.2.1 Table “
Monthly hours
Number of hours a well was on production or injection
Well count
Number of wells producing and/or injecting
Producing day oil rate
Monthly oil volume divided by hours on production times 24 h per day
“ … par 2: “The production from an individual well is measured at the test separator, while the production from the group of wells is measured after the group separator. Each well is tested individually in the test separator for 2–3 days. The test time and frequency for each well in each field may vary dramatically, depending on the number of wells attached to a header and the flow rates into the test seperators. Wells with high flow rates tend to have better tests because there are shorter purge times and larger volumes associated with these wells. Group production is reported on a monthly basis. Group production volumes are assumed to be accurate. Production is allocated to each well based on prorated test separator production rates.” … Section 4.2.3 par 1: “Composite plots: Production plots are used for single wells, groups of wells, and for entire pools. The most common format for viewing oil production data is a composite plot, that is, combined plots of oil rate, GOR, and water cut versus time or cumulative production. “
Wong_2017 and Baker_2015 are analogous art to the claimed invention because they are from the same field of endeavor called petroleum engineering. Before the effective filing date, it would have been obvious to a person ordinarily skilled in the art to combine Wong_2017 and Baker_2015. The rational for doing so would have been a simple substitution. Wong_2017 aggregates fluids at nodes through flow rates at the connections. Baker_2015 tracks production of wells based on operating times. One reasonably skilled in the art would understand that the prior art of Wong_2017 which measures molar balance based on mass and volume could also utilize operating time and flow rate to infer volume. Therefore it would have been obvious to substitute the molar balancing of Wong_2017 with calculating volume through operating time of Baker_2015 for simple substitution to obtain the invention as specified in the claims.
Claim 10:
Claim 10 is effectively similar to claim 2 except that it depends from claim 9 and is therefore rejected under a similar rational to claims 2 and 9.
Claim(s) 5 is/are rejected under 35 U.S.C. 103 as being unpatentable over Wong_2017, Fath_2020, Raman_2020, and Stishenko_2021 (US 20210332690 A1), and Suter_2013 (US 20130332125 A1)
Claim 5:The method of claim 1, wherein the input data further comprises
Wong_2017 makes obvious par 26: “disclosed embodiments may be applied to fluid production from multiple reservoirs in a multi-reservoir production system with a common surface or gathering network,”) Examiner note: see also claim 1.
Wong_2017 does not expressly recite deviation surveys and completion configurations
and wherein the method further comprises: detecting well placement based on the deviation surveys and the completion configurations.
Stishenko_2021 however makes obvious deviation surveys and
and wherein the method further comprises: detecting well placement based on the deviation surveys par 91: “Geosteering (geological steering, well placement) is controlled changing of wellbore position in the stratum, based on the analysis of geological, geophysical, and deviation survey data collected while drilling.”)
Wong_2017 and Stishenko_2021 are analogous art to the claimed invention because they are from the same field of endeavor called petroleum engineering. Before the effective filing date, it would have been obvious to a person ordinarily skilled in the art to combine Wong_2017 and Stishenko_2021. The rational for doing so would have been to follow a motivation proposed in the prior art. The prior art of Wong_2017 contains wellbores. Stishenko_2021 teaches the fact that well placement is done based on the analysis of deviation survey data. Par 78: “ Deviation survey data are used to drill the well in a predetermined direction, to determine actual depth of geological object occurrence, to plot maps and sections, when logging and drilling materials are used.” Therefore it would have been obvious to combine the wells of Wong_2017 with the well placement with deviation surveys by Stishenko_2021 for the benefit determining actual depth of geological object occupancy and to plot maps which are then used for fluid modeling to obtain the invention as specified in the claims.
Wong_2017 and Stishenko_2021 do not expressly recite completion configurations
and wherein the method further comprises: detecting well placement based on the completion configurations
Suter_2013 however makes obvious completion configurations
and wherein the method further comprises: detecting well placement based on the completion configurationspar 4: “ Well placement decisions are made under large uncertainties, short timeframes and involve multiple objectives such as drilling risks and costs, wellbore completion configuration and future reservoir production”
Wong_2017, Stishenko_2021, and Suter_2013 are analogous art to the claimed invention because they are from the same field of endeavor called petroleum engineering. Before the effective filing date, it would have been obvious to a person ordinarily skilled in the art to combine Wong_2017, Stishenko_2021, and Suter_2013. The rational for doing so would have been to follow a teaching and motivation proposed in the prior art. The prior art of Wong_2017 teaches wells used for drilling. Suter_2013 builds a model and states par 4: “During drilling, geological structures and petrophysical and formation properties are often found to diverge from the geological interpretation represented in the earth model which was constructed prior to drilling. This comes as a result of new measurements acquired during the drilling operation, and increases the difficulty of placing the well optimally within the pay zone. Well placement decisions are made under large uncertainties, short timeframes and involve multiple objectives such as drilling risks and costs, wellbore completion configuration and future reservoir production. Real-time interpretation and modification of the existing earth model, based on data obtained during the drilling process, would be extremely useful when pursuing an optimized well placement while drilling.” Therefore it would have been obvious to combine the wells and fluid modeling of Wong_2017 which uses input data with completion configurations of Suter_2013 for the benefit of pursuing and optimized well placement while drilling to obtain the invention as specified in the claims.
Claim 13:
Claim 13 is effectively similar to claim 5 except that it depends from claim 9 and is therefore rejected under a similar rational to claims 5 and 9.
Conclusion
Any inquiry concerning this communication or earlier communications from the examiner should be directed to AHMAD HUSSAM SHALABY whose telephone number is (571)272-7414. The examiner can normally be reached Mon-Fri 7:30am - 5pm.
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/A.H.S./Examiner, Art Unit 2187
/EMERSON C PUENTE/Supervisory Patent Examiner, Art Unit 2187
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