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 .
Notice to Applicant
The following is a Final Office action. In response to Examiner’s Non-Final Rejection of 11/03/2025, Applicant, on 02/13/2026, amended claims 1-21. Claims 1-21 are pending in this application and have been rejected below.
Response to Arguments
Applicant's arguments filed 02/13/2026 have been fully considered, but they are not fully persuasive. The updated 35 USC § 103 and 101 rejection of claims 1-21 are applied in light of Applicant's amendments.
The Applicant argues “Applicant submits that (i) the claims are not directed to an abstract idea; and ii) even if the claims were directed to an abstract idea, they integrate the alleged abstract idea into a practical application.” (Remarks 02/13/2026)
In response, the Examiner respectfully disagrees. The claimed subject matter, is directed to an abstract idea by reciting concepts performed in the human mind (including an observation, evaluation, judgment, opinion), which falls into the “Mental Process” group; and by reciting mathematical relationships, mathematical formulas or equations, mathematical calculations which falls into the “Mathematical concepts” within the enumerated groupings of abstract ideas. The mere nominal recitation of a generic computer does not take the claim limitation out of mathematical concepts or the mental processes grouping. Thus, the claim recites a mental process for performing certain mathematical concepts.
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.
The claimed subject matter is merely claims a method for calculating and analyzing information regarding reservoir data. Although it may be intended to be performed in a digital environment, the claimed subject matter (as currently claimed in the independent claim) speaks to the calculating and analyzing data. Such steps are not tied to the technological realm, but rather utilizing technology to perform the abstract ideas (mathematical concepts). Additionally, the claimed subject matter can also be categorized as a Mental Process as it recites concepts performed in the human mind (observation and evaluation). The steps of calculating data, training/updating models, and generating a model/trend line can be performed by a human (mental process/pen and paper). The practice of calculating information and constructing models with set parameters and timelines can be performed without computers, and thus are not tied to technology nor improving technology.
The solution mentioned in the amended limitation is not implemented/integrated into technology and thus not an improvement to the technical field. Further, there is no integration into a practical application as the claims can be interpreted as humans per se, as the claims fail to tie the steps to technology; insignificant extra solution activities (which are merely calculating and/or analyzing data).
The additional elements have been evaluated, but fail to integrate the abstract idea into a practical application because they amount to using generic computing elements or instructions (software) to perform the abstract idea, similar to adding the words “apply it” (or an equivalent), which merely serves to link the use of the judicial exception to a particular technological environment (generic computing environment). See MPEP 2106.05(f) and 2106.05(h).
The steps relied upon by the Applicant as recited does not improve upon another technology, the functioning of the computer itself, or allow the computer to perform a function not previously performable by a computer. The claims do not mention to any use of a specialized computer and/or processor. The Applicant is using generic computing components (processors) to perform in a generic/expected way (obtaining and analyzing data).The abstract idea is not particular to a technological environment, but is merely being applied to a computer realm. The process of calculating and analyzing data specifically for reservoir information, and performing additional analysis can be done without a computer, and thus the claims are not “necessarily rooted", but rather they are utilizing computer technology to perform the abstract idea. The Examiner does not recognize any elements of the Applicant's claims and/or specification that would improve or allow the computer to perform a function(s) not previously performable by the computer, or improve the functioning of the computer itself. It is insufficient to indicate that the claims are novel and non-obvious, and thus contain “something more.” Just because the components may perform a specialized function does not mean that that the computer components are specialized. As such the application of the abstract idea of collecting and analyzing data regarding reservoir information, and performing correlation analysis is insufficient to demonstrate an improvement to the technology.
Applicant’s arguments with respect to the rejection to the claims for the 35 U.S.C. 103 have been considered but are moot because the arguments do not apply to the current combination of references being used in the current rejection. In light of Applicants amendments and arguments the Examiner updated the search and provided new art to reject the claim limitations.
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 1-21 are rejected under 35 U.S.C. 101 because the claimed invention is directed to non-patentable subject matter. The claims are directed to an abstract idea without significantly more.
Claims 1-21 are rejected under 35 U.S.C. 101 because the claimed invention is directed to an abstract idea without significantly more. The judicial exception is not integrated into a practical application. The claims do not include additional elements that are sufficient to amount to significantly more than the judicial exception.
With respect to Step 1 of the eligibility inquiry (as explained in MPEP 2106), it is first noted that the method (claims 1-7), computer program product (claims 15-21), and system (claims 8-14) are directed to potentially eligible categories of subject matter (i.e., process, machine, and article of manufacture respectively). Thus, Step 1 is satisfied.
With respect to Step 2, and in particular Step 2A Prong One, it is next noted that the claims recite an abstract idea by reciting concepts performed in the human mind (including an observation, evaluation, judgment, opinion), which falls into the “Mental Process” group; ; and by reciting mathematical relationships, mathematical formulas or equations, mathematical calculations which falls into the “Mathematical concepts” within the enumerated groupings of abstract ideas. The mere nominal recitation of a generic computer does not take the claim limitation out of mathematical concepts or the mental processes grouping. Thus, the claim recites a mental process for performing certain mathematical concepts.
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.
The limitations reciting the abstract idea(s), as set forth in exemplary claim 1, are: obtaining a historical amount of produced fluid from wells in the unconventional reservoir …petrophysical characteristics and geochemical characteristics as a function of position within the unconventional reservoir …; obtaining an initial forecasted amount of produced fluid as a function of position within the unconventional reservoir …wherein the initial forecasted amount of produced fluid correlates to the petrophysical characteristics and the geochemical characteristics of the unconventional reservoir; adjusting…the initial forecasted amount of produced fluids as the function of position within the unconventional reservoir using the historical amount of produced fluid from the wells in the unconventional reservoir and the petrophysical characteristics and the geochemical characteristics as the function of position within the unconventional reservoir to determinethe effective storage capacity of the unconventional reservoir as a function of position. Independent claims 8 and 15 recite the CRM and system for performing the method of independent claim 1 without adding significantly more. Thus, the same rationale/analysis is applied.
With respect to Step 2A Prong Two, the judicial exception is not integrated into a practical application. The additional elements are directed to: a computer system that includes a physical computer processor, a graphical user interface, and a non-transitory storage medium…generating, with the physical computer processor, a graphical representation of the effective storage capacity of the unconventional reservoir as a function of position using visual effects to depict at least a portion of the effective storage capacity and displaying the graphical representation in the graphical user interface; a non-transitory storage medium; a graphical user interface; and a physical computer processor configured by machine-readable instructions to…; A non-transitory computer-readable medium storing instructions for determining an effective storage capacity of an unconventional subsurface volume of interest as a function of position, the instructions configured to, when executed…; (as recited in claims 1, 8, and 15). However, these elements fail to integrate the abstract idea into a practical application because they fail to provide an improvement to the functioning of a computer or to any other technology or technical field, fail to apply the exception with a particular machine, fail to apply the judicial exception to effect a particular treatment or prophylaxis for a disease or medical condition, fail to effect a transformation of a particular article to a different state or thing, and fail to apply/use the abstract idea in a meaningful way beyond generally linking the use of the judicial exception to a particular technological environment.
Accordingly, because the Step 2A Prong One and Prong Two analysis resulted in the conclusion that the claims are directed to an abstract idea, additional analysis under Step 2B of the eligibility inquiry must be conducted in order to determine whether any claim element or combination of elements amount to significantly more than the judicial exception.
With respect to Step 2B of the eligibility inquiry, it has been determined that the claims do not include additional elements that are sufficient to amount to significantly more than the judicial exception. The additional limitation(s) is/are directed to: a computer system that includes a physical computer processor, a graphical user interface, and a non-transitory storage medium…generating, with the physical computer processor, a graphical representation of the effective storage capacity of the unconventional reservoir as a function of position using visual effects to depict at least a portion of the effective storage capacity and displaying the graphical representation in the graphical user interface; a non-transitory storage medium; a graphical user interface; and a physical computer processor configured by machine-readable instructions to…; A non-transitory computer-readable medium storing instructions for determining an effective storage capacity of an unconventional subsurface volume of interest as a function of position, the instructions configured to, when executed…; (as recited in claims 1, 8, and 15) for implementing the claim steps/functions. These elements have been considered, but merely serve to tie the invention to a particular operating environment (i.e., computer-based implementation), though at a very high level of generality and without imposing meaningful limitation on the scope of the claim.
In addition, Applicant’s Specification (paragraph [0030]) describes generic off-the-shelf computer-based elements for implementing the claimed invention, and which does not amount to significantly more than the abstract idea, which is not enough to transform an abstract idea into eligible subject matter. Such generic, high-level, and nominal involvement of a computer or computer-based elements for carrying out the invention merely serves to tie the abstract idea to a particular technological environment, which is not enough to render the claims patent-eligible, as noted at pg. 74624 of Federal Register/Vol. 79, No. 241, citing Alice, which in turn cites Mayo. See, e.g., 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).
In addition, when taken as an ordered combination, the ordered combination adds nothing that is not already present as when the elements are taken individually. There is no indication that the combination of elements integrate the abstract idea into a practical application. Their collective functions merely provide conventional computer implementation. Therefore, when viewed as a whole, these additional claim elements do not provide meaningful limitations to transform the abstract idea into a practical application of the abstract idea or that the ordered combination amounts to significantly more than the abstract idea itself. Further, the courts have found the presentation of data to be a well-understood, routine, conventional activity, OIP Techs., 788 F.3d at 1362-63, 115 USPQ2d at 1092-93 (see MPEP 2106.05(d)).
The additional elements have been evaluated, but fail to integrate the abstract idea into a practical application because they amount to using generic computing elements or instructions (software) to perform the abstract idea, similar to adding the words “apply it” (or an equivalent), which merely serves to link the use of the judicial exception to a particular technological environment (generic computing environment). See MPEP 2106.05(f) and 2106.05(h).
The dependent claims (2-7, 9-14, and 16-21) are directed to the same abstract idea as recited in the independent claims, and merely incorporate additional details that narrow the abstract idea via additional details of the abstract idea. For example claims 2-7 “wherein the initial storage capacity model is based on mobility, buoyancy, reservoir heterogeneity, water saturation, or any combination thereof; wherein the subsurface reservoir data correlates to petrophysical and geochemical characteristics as a function of position; wherein the subsurface characterization parameters comprise core-based data, petrophysical data, geochemical data, geophysical data, or any combination thereof; wherein the reservoir characteristics comprise rock fluid data, pressure data, chemical property data, physical property data, or any combination thereof; wherein the effective storage capacity data correlates to an available storage volume in the unconventional subsurface volume of interest as a function of position for carbon dioxide; wherein the effective storage capacity data correlates to an available storage volume in the unconventional subsurface volume of interest as a function of position for a fluid storable in the unconventional subsurface volume of interest ”, without additional elements that integrate the abstract idea into a practical application and without additional elements that amount to significantly more to the claims. The remaining dependent claims (9-14 and 16-21) recite the CRM and system for performing the method of claims 2-7. Thus, the same rationale/analysis is applied. Thus, all dependent claims have been fully considered, however, these claims are similarly directed to the abstract idea itself, without integrating it into a practical application and with, at most, a general purpose computer that serves to tie the idea to a particular technological environment, which does not add significantly more to the claims.
The ordered combination of elements in the dependent claims (including the limitations inherited from the parent claim(s)) add nothing that is not already present as when the elements are taken individually. There is no indication that the combination of elements improves the functioning of a computer or improves any other technology. Their collective functions merely provide conventional computer implementation. Accordingly, the subject matter encompassed by the dependent claims fails to amount to significantly more than the abstract idea itself.
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 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.
Claims 1-5, 7-12, 14-19, and 21 are rejected under 35 U.S.C. 103 as being unpatentable over U.S. PGPub 20240402383 (hereinafter “Tang”) et al., in view of WO 2022170358 to (hereinafter “Mustapha”) et al., in further view of U.S. PGPub 20180313807 to (hereinafter “Michael”) et al.
As per claim 1, Tang teaches a method for determining an effective storage capacity of an unconventional subsurface volume of interest as a function of position, the method being implemented in a computer system that includes a physical computer processor, a graphical user interface, and a non-transitory storage medium, the method comprising:
obtaining a historical amount of produced fluid from wells in the unconventional reservoir from the non-transitory storage medium within the unconventional reservoir from the non-transitory storage medium…generating, with the physical computer processor, a graphical representation of the effective storage capacity of the unconventional reservoir as a function of position using visual effects to depict at least a portion of the effective storage capacity and displaying the graphical representation in the graphical user interface;
Tang 0043-0099: “FIG. 4 illustrates an example flow diagram 400 for controlling well operations in unconventional reservoirs. Input data 410 for one or more wells in an unconventional reservoir may be obtained. The input data 410 may include operation characteristics 412 and/or production characteristics 414 of the well(s). Input data 410 may include historical data of the well(s) and/or forecasted data of the well(s). The input data 410 may include other characteristics of the well(s)…The input data 410 (the operation characteristics 412 and/or the production characteristics 414) may be used to determine one or more characteristics of the well(s) in the unconventional reservoir. For example, at step 422, the input data 410 may be used to determine historical flowing bottom hole pressure of the well(s). The input data 410 may be input into one or more machine learning models trained to output flowing bottom hole pressure. For example, the machine learning model(s) may be trained to receive as input the operation characteristics 412 and/or the production characteristics 414 of a well corresponding to particular moment(s) in time, and output the flowing bottom hole pressure of the well at the particular moment(s) in time. Historical input data may be input into the machine learning model(s) to obtain historical flowing bottom hole pressure of the well. Present input data may be input into the machine learning model(s) to obtain present flowing bottom hole pressure of the well. Forecasted input data may be input into the machine learning model(s) to obtain forecasted flowing bottom hole pressure of the well. At step 422, historical input data may be input into the machine learning model(s) to obtain historical flowing bottom hole pressure of the well(s). The machine learning model(s) may be trained based on fluid flow simulation of the well(s) and/or other information. For example, a physics-based modeling tool may be used to perform fluid flow simulation of a well, and convert operation characteristics and/or production characteristics of the well into the flowing bottomhole pressure of the well. The inputs (e.g., operation characteristics, production characteristics) and outputs (e.g., flowing bottom hole pressure) of the conversion using the physics-based modeling may be used as input-output pairs for training the machine-learning model(s)… the production parameters for the well and/or information determined from the production parameters for the well may be visually provided and/or presented to one or more users in controlling the operation of the well. For example, optimized setpoints/parameters for the wells may be communicated to an operator and/or presented on the electronic display 14. Other related information may be communicated to an operator and/or presented on the electronic display 14.”
Tang may not explicitly teach the following. However, Mustapha teaches:
obtaining petrophysical characteristics…; adjusting, with the physical computer processor, the initial forecasted amount of produced fluids as the function of position within the unconventional reservoir using the historical amount of produced fluid from the wells in the unconventional reservoir and the petrophysical characteristics …to determine the effective storage capacity of the unconventional reservoir as a function of position;Mustapha 0104: “The method 900 may receive, as input, a reservoir model representing a subsurface volume or other rock properties, as at 902. The method 900 may also receive, as input, reservoir data, also representing the subsurface volume or rock properties, as at 904, such as well measurement data, such as production and injection history (e.g. rates, volumes produced, pressures), well location (e.g. distance to injectors, distance to other production wells), log data and core data providing insights on reservoir properties (e.g. permeability, porosity, saturation, pressure), and/or reservoir properties derived from the reservoir model when measured data is lacking… 0121-0124: Advanced machine learning methods (deep learning methods) may integrate different types of data such as time series (oil, gas, water production and injection rates and cumulative volumes; bottomhole, tubing head and reservoir pressure) and tabular data (e.g., geological, petrophysical as described earlier) to predict future performance of existing as well as new wells…The workflow may be able to produce accurate real-time production forecast for existing wells and production prediction faster and able to screen thousands of locations to generate the most optimal location to drill a well. [0125] In other words, well performance forecast of existing wells may be used to generate "live and evergreen model" that continuously updates and provides up to date well forecasts based on historical data. This data then can then be used to optimize field development planning. Furthermore, well performance forecast of a new well may be used to generate rapid results that allows to screen thousands of well locations to select the most optimal one - complimenting or replacing current technologies used for the same purposes.”
Tang and Mustapha are deemed to be analogous references as they are reasonably pertinent to each other and directed towards measuring, collecting, and analyzing information with a series of inputs to solve similar problems in the similar environments. Before the effective filing date of the claimed invention, it would have been obvious for one of ordinary skill in the art to have modified Tang with the aforementioned teachings from Mustapha with a reasonable expectation of success, by adding steps that allow the software to update/adjust data with the motivation to more efficiently and accurately organize and analyze data [Mustapha 0124].
Tang and Mustapha may not explicitly teach the following. However, Michael teaches:
and geochemical characteristics as a function of position within the unconventional reservoir from the non-transitory storage medium…and the geochemical characteristics as the function of position within the unconventional reservoir… wherein the initial forecasted amount of produced fluid correlates to the petrophysical characteristics and the geochemical characteristics of the unconventional reservoir; 0018-0033: “ the reservoir ultimately may record a significant amount of both vertical and lateral geochemical heterogeneity. This geochemical heterogeneity represents temporal and spatial variability in organic facies, depositional conditions, and thermal maturity across and through the reservoir… time-series reservoir geochemistry is analyzed using a spatial mapping on the x, y, and z axes in a known reservoir. Additionally changes over time may be monitored.… Location information may include depth and lateral placement (x, y and z axes). A reservoir map may be generated in the form of tables including fingerprint data organized by location (x, y and z axes) and time.”
Tang, Mustapha, and Michael are deemed to be analogous references as they are reasonably pertinent to each other and directed towards measuring, collecting, and analyzing information with a series of inputs to solve similar problems in the similar environments. Before the effective filing date of the claimed invention, it would have been obvious for one of ordinary skill in the art to have modified Tang and Mustapha with the aforementioned teachings from Michael with a reasonable expectation of success, by adding steps that allow the software to update/adjust geochemical data with the motivation to more efficiently and accurately organize and analyze information [Michael 0024].
As per claim 2, Tang, Mustapha, and Michael teach all the limitations of claim 1.
In addition, Mustapha teaches:
wherein the initial forecasted amount of produced fluid storage capacity model is based on mobility, buoyancy, reservoir heterogeneity, water saturation, or any combination thereof.
Mustapha 0104: “As mentioned above, machine learning techniques may be employed, e.g., for model realization selection in the context of uncertainty analysis and elsewhere. Figure 9 illustrates an example of one type of machine learning method that may be used, showing a flowchart of a method 900 for building a machine learning model to generate reservoir quality map. The method 900 may receive, as input, a reservoir model representing a subsurface volume or other rock properties, as at 902. The method 900 may also receive, as input, reservoir data, also representing the subsurface volume or rock properties, as at 904, such as well measurement data, such as production and injection history (e.g. rates, volumes produced, pressures), well location (e.g. distance to injectors, distance to other production wells), log data and core data providing insights on reservoir properties (e.g. permeability, porosity, saturation, pressure), and/or reservoir properties derived from the reservoir model when measured data is lacking.”
Tang and Mustapha are deemed to be analogous references as they are reasonably pertinent to each other and directed towards measuring, collecting, and analyzing information with a series of inputs to solve similar problems in the similar environments. Before the effective filing date of the claimed invention, it would have been obvious for one of ordinary skill in the art to have modified Tang with the aforementioned teachings from Mustapha with a reasonable expectation of success, by adding steps that allow the software to update/adjust data with the motivation to more efficiently and accurately organize and analyze data [Mustapha 0124].
As per claim 3, Tang, Mustapha, and Michael teach all the limitations of claim 1.
In addition, Mustapha teaches:
wherein the petrophysical characteristics comprise fracture closure stress, mineralogy, porosity, hydrocarbon thermal maturity, pore saturation, or any combination thereof.; Mustapha 0055-0121: “The subterranean structure 204 has a plurality of geological formations 206a-206d. As shown, this structure has several formations or layers, including a shale layer 206a, a carbonate layer 206b, a shale layer 206c and a sand layer 206d. A fault 207 extends through the shale layer 206a and the carbonate layer 206b. The static data acquisition tools are adapted to take measurements and detect characteristics of the formations… While a specific subterranean formation with specific geological structures is depicted, it will be appreciated that oilfield 200 may contain a variety of geological structures and/or formations, sometimes having extreme complexity. In some locations, typically below the water line, fluid may occupy pore spaces of the formations. Each of the measurement devices may be used to measure properties of the formations and/or its geological features… Through this methodology, future well performance may be predicted without relying on analytical methods, decline curve analysis or numerical methods such as reservoir simulation. The methodology may produce faster results to perform short-term production forecasts, operationalize insights, provide another perspective on performance prediction and/or decision making in the context of field development planning. Advanced machine learning methods (deep learning methods) may integrate different types of data such as time series (oil, gas, water production and injection rates and cumulative volumes; bottomhole, tubing head and reservoir pressure) and tabular data (e.g., geological, petrophysical as described earlier) to predict future performance of existing as well as new wells.”
Tang and Mustapha are deemed to be analogous references as they are reasonably pertinent to each other and directed towards measuring, collecting, and analyzing information with a series of inputs to solve similar problems in the similar environments. Before the effective filing date of the claimed invention, it would have been obvious for one of ordinary skill in the art to have modified Tang with the aforementioned teachings from Mustapha with a reasonable expectation of success, by adding steps that allow the software to update/adjust data with the motivation to more efficiently and accurately organize and analyze data [Mustapha 0124].
As per claim 4, Tang, Mustapha, and Michael teach all the limitations of claim 1.
In addition, Mustapha teaches:
wherein the geochemical characteristics comprise water chemistry, total dissolved solids, PH values, or any combination thereof; Michael 0094: “ Chemical and isotopic analyses of constituents dissolved in the water samples were also conducted. Dissolved metals were analyzed using inductively coupled plasma emission spectrometry and mass spectrometry (ICPES, ICPMS), anions were measured by ion chromatography (IC), and pH, temperature, filtration, and preservation were procedures conducted at the well site. Stable isotopic analyses of key indicator constituents were measured by mass spectrometry. wells.”
Tang, Mustapha, and Michael are deemed to be analogous references as they are reasonably pertinent to each other and directed towards measuring, collecting, and analyzing information with a series of inputs to solve similar problems in the similar environments. Before the effective filing date of the claimed invention, it would have been obvious for one of ordinary skill in the art to have modified Tang and Mustapha with the aforementioned teachings from Michael with a reasonable expectation of success, by adding steps that allow the software to update/adjust geochemical data with the motivation to more efficiently and accurately organize and analyze information [Michael 0024].
As per claim 5, Tang, Mustapha, and Michael teach all the limitations of claim 1.
In addition, Tang teaches:
wherein the effective storage capacity correlates to an available storage volume in the unconventional reservoir as a function of position for hydrocarbons; Mustapha 0026-0028: “T A hybrid modeling approach incorporates both physics-based reservoir modeling and machine learning technique to capture dynamic behavior of unconventional wells. Shut-in bottom hole pressure for unconventional wells are simulated for use as proxy for reservoir pressure in unconventional reservoirs. Production parameters for unconventional wells (e.g., gas/oil ratio, water cut, flowing bottom hole pressure, shut-in bottom hole pressure, productivity index) are determined for use in controlling the operations of unconventional wells… Historical flowing bottom hole pressure of the well may be determined by the processor 11 based on the operation characteristics of the well, the production characteristics of the well, and/or other information. Forecasted flowing bottom hole pressure of the well may be determined by the processor 11 based on the historical flowing bottom hole pressure of the well and/or other information. Forecasted production characteristics of the well may be determined by the processor 11 based on the operation characteristics of the well, the production characteristics of the well, and/or other information.”
As per claim 7, Tang, Mustapha, and Michael teach all the limitations of claim 1.
In addition, Tang teaches:
wherein the effective storage capacity [[data]] correlates to an available storage volume in the unconventional reservoir subsurface volume of interest as a function of position for a fluid storable in the unconventional reservoir; Mustapha 0026-0028: “To locate the potential areas within the reservoir to drill such new wells, engineers may rely on reservoir simulation models to identify the areas where the most oil volume remains with favorable pressure and reservoir rock quality to facilitate hydrocarbon extraction. Reservoir simulation model quality and the quality of the model calibration, if applicable, may play impact the accuracy of such identification. Additionally, to identify which areas identified would lead to favorable production performance, multiple simulation scenarios are generally evaluated to determine the potential performance for each individual area.”
Claims 8-12, 14-19, and 21 are directed to the system and CRM for performing the method of claims 1-5 and 7 above. Since Tang, Mustapha, and Michael teach the system and CRM, the same art and rationale apply.
Claims 6, 13, and 20 are rejected under 35 U.S.C. 103 as being unpatentable over U.S. PGPub 20240402383 (hereinafter “Tang”) et al., in view of WO 2022170358 to (hereinafter “Mustapha”) et al., in further view of U.S. PGPub 20180313807 to (hereinafter “Michael”) et al. and in further view of U.S. PGPub 20220091291 (hereinafter “Zhang”) et al.
As per claim 6, Tang, Mustapha, and Michael teach all the limitations of claim 1.
Tang, Mustapha, and Michael may not explicitly teach the following. However, Zhang teaches:
wherein the effective storage capacity correlates to an available storage volume in the unconventional reservoir as a function of position for carbon dioxide; Zhang 0104-0108: “Figure 9 illustrates an example of one type of machine learning method that may be used, showing a flowchart of a method 900 for building a machine learning model to generate reservoir quality map. The method 900 may receive, as input, a reservoir model representing a subsurface volume or other rock properties, as at 902. The method 900 may also receive, as input, reservoir data, also representing the subsurface volume or rock properties, as at 904, such as well measurement data, such as production and injection history (e.g. rates, volumes produced, pressures), well location (e.g. distance to injectors, distance to other production wells), log data and core data providing insights on reservoir properties (e.g. permeability, porosity, saturation, pressure), and/or reservoir properties derived from the reservoir model when measured data is lacking…The subsurface volume of interest, the hydrocarbons, or any combination thereof may also include non-hydrocarbon items. For example, non-hydrocarbon items may include connate water, brine, tracers, carbon dioxide, items used in enhanced oil recovery or other hydrocarbon recovery processes, items from other types of treatments (e.g., gels used in conformance control), etc.”
Tang, Mustapha, Michael, and Zhang are deemed to be analogous references as they are reasonably pertinent to each other and directed towards measuring, collecting, and analyzing information with a series of inputs to solve similar problems in the similar environments. Before the effective filing date of the claimed invention, it would have been obvious for one of ordinary skill in the art to have modified Tang, Mustapha, and Michael with the aforementioned teachings from Zhang with a reasonable expectation of success, by adding steps that allow the software to utilize carbon data with the motivation to more efficiently and accurately organize and analyze data [Zhang 0050].
Claims 13 and 20 are directed to the system and CRM for performing the method of claim 6 above. Since Tang, Mustapha, Michael, and Zhang teach the system and CRM, the same art and rationale apply.
Conclusion
The prior art made of record and not relied upon is considered pertinent to applicant's disclosure:
Crews; James B.. Diagnostic Lateral Wellbores And Methods Of Use, .U.S. PGPub 20160326859 The present invention relates to methods of obtaining information about subterranean formations and features therein using multiple wellbores, and more particularly relates, in one non-limiting embodiment, to methods of obtaining information about unconventional shale subterranean formations and features thereof using multiple wellbores comprising at least one primary lateral wellbore and at least one diagnostic lateral wellbore adjacent thereto.
Applicant's amendment necessitated the new ground(s) of rejection presented in this Office action. 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 extension fee 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 date of this final action.
Any inquiry concerning this communication or earlier communications from the examiner should be directed to Arif Ullah, whose telephone number is (571) 270-0161. The examiner can normally be reached from Monday to Friday between 9 AM and 5:30 PM.
If any attempt to reach the examiner by telephone is unsuccessful, the examiner’s supervisor, Beth Boswell, can be reached at (571) 272-6737. The fax telephone numbers for this group are either (571) 273-8300 or (703) 872-9326 (for official communications including After Final communications labeled “Box AF”).
/Arif Ullah/
Primary Examiner, Art Unit 3625
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