DETAILED ACTION
Claims 21 – 40 have been presented for examination. Claims 1 – 20 are cancelled.
This Office Action is in response to the amendments dated 07/14/2025.
Response to Rejections under 35 U.S.C. § 101
Applicant’s arguments have been fully considered. However, the Office does not consider them to be persuasive.
Applicant argues: “The new claims are directed to a specific technological improvement in predicting and updating fracture-network geometry during hydraulic-stimulation treatment of subterranean formations. This is not an abstract idea but rather a concrete technological process that addresses specific challenges in reservoir engineering.
The claims recite specific technical elements including: Receiving … Generating … Analyzing” (emphasis)
Examiner notes that the recited “prediction and updating” cover steps that are reasonably performed in the mind are implemented using a general-purpose computer (i.e., “computer-implemented” and “a processor”), and performed in the field of oil and gas exploration. Further, the hydraulic-stimulation treatment itself is not implemented since the claim merely recites “at least on reservoir management recommendation … includes adjusting at least one parameter of the hydraulic-stimulation treatment”. Although Applicant lists specific technical elements (e.g., “Receiving” and “Generating” and “Analyzing”), they substantially repeat the claim language without any further explanation which amounts to a conclusory argument.
Applicant argues: “Even if the Office considers the machine learning aspects of the claims to be abstract, the claims integrate any such concept into a practical application that provides meaningful limitations beyond generally linking the use of an abstract idea to a particular technological environment.
The claims recite a specific, structured process that:
1. Operates on real-time sensor data from physical equipment at a well site
2. Processes this data using computational algorithms to generate reservoir conditions
3. Identifies pressure events that have physical significance in the reservoir
4. Uses these pressure events with a specifically trained model that incorporates fracture geometry simulation
5. Produces actionable predictions about physical fracture geometry
6. Generates specific recommendations for adjusting hydraulic-stimulation treatment parameter
This represents a practical application that improves the technical field of hydraulic fracturing by providing real-time analysis and prediction capabilities that enable dynamic adjustment of treatment parameters”
Applicant argues substantially all the recited limitations as amounting to a practical application. Examiner notes that an abstract idea cannot be a practical application of itself. Further, all the listed process steps appear to map to claim limitations which are either part of the abstract idea itself, or amount to insignificant data gathering or outputting. Examiner further notes that limiting the claimed invention to a field of use or technological environment does not amount to a practical application (see MPEP 2106.05(h)).
Applicant argues: “The claims include numerous technical features that amount to significantly more than any alleged abstract idea:
1. Technical Data Integration: The claims require receiving and processing "raw sensor information about the reservoir acquired from sensors positioned at or near the target well," representing integration with physical measurement systems.
2. Real-Time Processing: The claims specify processing "real-time pressure data and flow-rate data acquired during the hydraulic-stimulation treatment," requiring the system to operate contemporaneously with physical operations
3. Specific Technical Analysis: The identification of "pressure events comprise changes
in pressure conditions that impact reservoir behavior" represents technical analysis of physical phenomena, not mere data gathering.
4. Specialized Model Training: The machine learning model is trained using "a fracture
geometry simulator to generate a fracture model and comparing the fracture model against measured reservoir performance data," showing domain-specific technical implementation
5. Technical Output: The predicted reservoir geometry includes specific technical parameters (fracture proppant density, length, complexity, width, orientation, and spacing) that have direct physical meaning in reservoir engineering.
6. Actionable Technical Recommendations: The generation of "reservoir management recommendation[s]" that include "adjusting at least one parameter of the hydraulic-stimulation treatment" demonstrates the technical effect of the claimed invention.”
As previously remarked, all the listed process steps appear to map to claim limitations which are either part of the abstract idea itself, or amount to insignificant data gathering or outputting. For example, the “Technical Data Integration” does not require anything other than receiving specific data from generic sensor, and the integration itself does not amount to significantly more especially when the disclosure is silent with regard to how the integration is accomplished (see the instant application Paragraph 15 “As used in this disclosure, a "sensor" is a device that is configured to detect a phenomenon and transmit information related to the detection of the phenomenon”). Applicant argues “Real-Time Processing”, however, the claim itself merely receives “real-time pressure data and flow-rate data” with further recited processing, where the processing itself is not recited as being “real-time”. The “Specific Technical Analysis” covers the recited “analyzing … to determine pressure events …, wherein the pressure events comprise changes in pressure conditions that impact reservoir behavior” which covers steps that are reasonably performed in the mind. The “Specialized Model Training” covers the recited “training a reservoir machine learning model” which amounts to the idea of an outcome since it uses any algorithm to train a generic machine learning model, and since the “fracture geometry simulator” covers a generic simulator. The “Technical Output” appears to be part of the abstract idea itself (i.e., “predicted reservoir geometry”). The “Actionable Technical Recommendations” covers insignificant data outputting since recommendations merely include potential adjustments to at least one parameters of the hydraulic-stimulation treatment.
Applicant argues: “The specification and claims make clear that the invention addresses the technical problem of predicting fracture geometry during hydraulic stimulation treatments. This is a complex technical challenge in the oil and gas industry that requires real-time analysis of sensor data, understanding of reservoir mechanics, and the ability to make dynamic adjustments to treatment parameters.
The claimed solution provides technical improvements including:
• Enhanced accuracy in predicting fracture geometry during treatment
• Real-time adaptation to changing reservoir conditions
• Integration of multiple data types (rock properties, treatment parameters, fluid properties)
• Automated generation of actionable recommendations for treatment adjustment”
Applicant argues that the claimed invention address a complex technical problem/challenge. Examiner notes that merely addressing a complex problem using abstract idea steps does not transform the claimed invention into eligible subject matter.
Response to Rejections under 35 U.S.C. § 103
Applicant’s arguments have been fully considered. However, the Office does not consider them to be persuasive.
Applicant argues: “Representative independent claim 21 requires "predicting and updating fracture-network geometry during a hydraulic-stimulation treatment" with "real-time pressure data and flow-rate data acquired during the hydraulic-stimulation treatment." None of the prior art references disclose this specific real-time integration of machine learning prediction with ongoing hydraulic fracturing operations.”
Applicant argues that the prior art references Jaaskelainen and Rose and Lehmann do not disclose “this specific real-time integration of machine learning prediction with ongoing hydraulic fracturing operations”. Examiner notes that all the prior art references explicitly disclose real-time fracture modeling in the context of hydraulic fracturing time operations. (Jaaskelainen Abstract) (Rose Paragraph 18 “Aspects of the present disclosure generally relate to analyzing high frequency acoustic or vibration signals in a well to assess well activity in real-time via time domain and/or spectral analysis of said signals”) (Lehmann Paragraph 20 “The concept of direct hits vs. fluid front pressure was identified in these two case studies and could be seen directly in both the real-time growth of the microseismic ESVms and the overlap between the ESVms’s of adjacent wells.”)
Applicant argues: “The combination of (1) real-time sensor data processing, (2) machine learning model training using fracture geometry simulators, and (3) immediate operational parameter adjustment during active treatment creates an unexpected synergistic effect that enables previously impossible real-time fracture network optimization.
The prior art addresses different technical problems: Jaaskelainen solves well interference detection; Rose solves offset well communication prediction; and Lehmann analyzes posttreatment connectivity. None address the claimed problem of real-time fracture geometry prediction and updating during active hydraulic stimulation to enable immediate treatment optimization.
A person of ordinary skill would not be motivated to combine the post-treatment analysis
techniques of Lehmann with the well interference detection of the other references to arrive at the claimed real-time fracture geometry prediction system. The references teach away from real-time geometry updating during active treatment, instead focusing on post-treatment analysis or interference prevention.”
Applicant argues that the claimed invention recites a combination of features that creates “unexpected synergistic effect[s] that enables previously impossible real-time fracture network optimization” without specifically detailing said synergistic effects, and further arguing that the prior art addresses different technical problems. Examiner notes that the primary reference Jaaskelainen explicitly teaches a real-time monitoring and analysis of treatment and monitoring wells by a fracturing treatment optimization system (see Abstract “The fracturing treatment optimization system may enable real-time monitoring and analysis of treatment and monitoring wells. The fracturing treatment optimization system may suggest and effect modifications to optimize treatment of the treatment and monitoring wells.”). Therefore, Applicant’s arguments are not persuasive.
Allowable Subject Matter
The following is a statement of reasons for the indication of allowable subject matter, subject to overcoming the 101 and 112(a) rejection.
None of the prior art of record taken individually or in combination discloses the claim 25 method, comprising: “correlating the detected pressure events with production data to determine a value of each pressure event, wherein the value indicates a relevance of the pressure event to reservoir performance”, in combination with the remaining elements and features of the claim. It is for these reasons that the applicant’s invention defines over the prior art of record.
Mu, Nan et al. (WO 2020/097060) teaches detecting an abnormal pressure trend or deviation. However, does not appear to explicitly disclose: correlating detected pressure events with production data to determine a value of each pressure event, wherein the value indicates a relevance of the pressure event to reservoir performance.
Any comments considered necessary by applicant must be submitted no later than the payment of the issue fee and, to avoid processing delays, should preferably accompany the issue fee. Such submissions should be clearly labeled “Comments on Statement of Reasons for Allowance.”
Claim Rejections - 35 USC § 112
The following is a quotation of 35 U.S.C. 112(b):
(b) CONCLUSION.—The specification shall conclude with one or more claims particularly pointing out and distinctly claiming the subject matter which the inventor or a joint inventor regards as the invention.
The following is a quotation of 35 U.S.C. 112 (pre-AIA ), second paragraph:
The specification shall conclude with one or more claims particularly pointing out and distinctly claiming the subject matter which the applicant regards as his invention.
Claims 21 – 40 are 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.
With regard to claim 21 (and similarly claim 29 and 37), it recites “generating … a plurality of reservoir conditions associated with the target well as a function of the condition data, wherein the plurality of reservoir conditions includes real-time pressure data and flow-rate data acquired during the hydraulic-stimulation treatment, and wherein the generating comprises calculating the reservoir conditions from the raw sensor information using computational algorithms”. It is unclear how the recited “reservoir conditions” can be both acquired as real-time data and also be calculated using computational algorithms. Further, it is unclear if the “acquired” is a separate data gathering step, or the generating is the acquiring itself (i.e., inferring conditions in real-time as contrasted with directly measuring them). The limitation is interpreted for examination purposes as the generating relies on real-time pressure data and flow-rate data acquired during the hydraulic-stimulation, and the generating calculates the requires conditions from raw sensor information using computational algorithms.
With regard to claim 28 (and similarly claim 36), it recites “wherein pressure increases indicate positive pressure events caused by injection operations, fluid expansion, and wherein pressure decreases indicate negative pressure events caused by fluid withdrawal, fluid migration, or reservoir compaction”. It is unclear if all the positive pressure event causes are required, or if only one is required as with the negative pressure events. The limitation is interpreted for the prior art search as the positive pressure events requiring at least one of the listed elements.
With regard to claims 22 – 27, 30 – 35 and 38 – 40, they are rejected by virtue of depending from a rejected parent claim, and without reciting further limitations to overcome the unclarity.
The following is a quotation of the first paragraph of 35 U.S.C. 112(a):
(a) IN GENERAL.—The specification shall contain a written description of the invention, and of the manner and process of making and using it, in such full, clear, concise, and exact terms as to enable any person skilled in the art to which it pertains, or with which it is most nearly connected, to make and use the same, and shall set forth the best mode contemplated by the inventor or joint inventor of carrying out the invention.
The following is a quotation of the first paragraph of pre-AIA 35 U.S.C. 112:
The specification shall contain a written description of the invention, and of the manner and process of making and using it, in such full, clear, concise, and exact terms as to enable any person skilled in the art to which it pertains, or with which it is most nearly connected, to make and use the same, and shall set forth the best mode contemplated by the inventor of carrying out his invention.
Claims 21 – 40 are rejected under 35 U.S.C. 112(a) or 35 U.S.C. 112 (pre-AIA ), first paragraph, as failing to comply with the written description requirement. The claim(s) contains subject matter which was not described in the specification in such a way as to reasonably convey to one skilled in the relevant art that the inventor or a joint inventor, or for applications subject to pre-AIA 35 U.S.C. 112, the inventor(s), at the time the application was filed, had possession of the claimed invention.
With regard to claim 21 (and similarly claim 29 and claim 37), it recites “real-time pressure data and flow-rate data acquired during the hydraulic-stimulation treatment”. Examiner notes that it is merely disclosed that a prediction and simulation are real-time (see the instant application Paragraph 27 and 40 “As used in the current disclosure, "Real-time" refers to the capability of a system to process and respond to events or inputs immediately as they occur, with minimal or no delay.”). Further, there is not explicitly disclosed real-time pressure data and flow-rate data acquired during a hydraulic-stimulation treatment.
Further, it recites “training a reservoir machine learning model using reservoir training data, wherein the reservoir training data comprises a plurality of pressure events correlated to examples of reservoir geometry, and wherein the training comprises using a fracture geometry simulator to generate a fracture model and comparing the fracture model against measured reservoir performance data”. Examiner notes that it is disclosed incorporating user feedback into the training of a machine learning model which learns correlations or flags data (see the instant application Paragraph 31 – 34). Further, it is disclosed simulations using a reservoir model for pressure events (see the instant application Paragraph 29 – 30). However, training the machine learning model by pressure events correlated to reservoir geometry and using a fracture geometry simulator does not appear to be explicitly disclosed.
With regard to claim 25, it recites “correlating the detected pressure events with production data to determine a value of each pressure event, wherein the value indicates a relevance of the pressure event to reservoir performance”. Examiner notes that it is merely disclosed flagging data using a trained classifier based on user feedback (see the instant application Paragraph 34). However, it does not appear to be explicitly disclosed determine a value for pressure events indicating a relevance of the pressure event to reservoir performance.
With regard to claim 28, it recites “wherein the changes in pressure conditions comprise pressure increases or decreases that exceed a predetermined threshold value”. Examiner notes that it is merely disclosed data sanitization by comparing to a threshold (see the instant application Paragraph 15), and a data compatibility threshold (see the instant application Paragraph 72), and a probability is compared to a threshold to determine a match in a fuzzy set (see the instant application Paragraph 75). However, it does not appear to explicitly disclose that the changes in pressure conditions are limited to those that exceed a predetermined threshold.
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 21 – 40 are rejected under 35 U.S.C. 101 because the claimed invention is directed to a judicial exception (i.e., an abstract idea) without significantly more.
Independent claim 21 recites at Step 1 a statutory category (i.e. a process) method for predicting and updating fracture-network geometry during a hydraulic-stimulation treatment of a subterranean formation, the method comprising: generating, using the at least a processor, a plurality of reservoir conditions associated with the target well as a function of the condition data, wherein the generating comprises calculating the reservoir conditions from the raw sensor information using computational algorithms; analyzing the plurality of reservoir conditions to determine pressure events within the reservoir, wherein the pressure events comprise changes in pressure conditions that impact reservoir behavior; predicting reservoir geometry as a function of the pressure events using a trained reservoir machine learning model; generating at least one reservoir management recommendation as a function of the predicted reservoir geometry, wherein the reservoir management recommendation includes adjusting at least one parameter of the hydraulic-stimulation treatment. At Step 2A, Prong I the recited limitations, alone or in combination, amount to steps that, under its broadest reasonable interpretation, cover performance of the limitations in the mind in combination with using a pen and paper (see MPEP 2106.04(a)(2)(III)). For example, the “analyzing” and “generating” amounts to modeling actions recited at a high-level of generality. The “predicting” uses a previously trained machine learning model, where the mere use of a previously trained machine learning model does not preclude performance in the mind since the form of the machine learning model itself is not recited. Accordingly, the claim recites an abstract idea.
At Step 2A, Prong II this judicial exception is not integrated into a practical application since the claimed invention further claims: that the method is computer-implemented; that the “analyzing” and “generating” are using a processor; receiving, using at least a processor, condition data associated with a target well, wherein the condition data comprises raw sensor information about the reservoir acquired from sensors positioned at or near the target well; wherein the plurality of reservoir conditions includes real-time pressure data and flow-rate data acquired during the hydraulic-stimulation treatment, and training a reservoir machine learning model using reservoir training data, wherein the reservoir training data comprises a plurality of pressure events correlated to examples of reservoir geometry, and wherein the training comprises using a fracture geometry simulator to generate a fracture model and comparing the fracture model against measured reservoir performance data; displaying, using a display device, the predicted reservoir geometry on a graphical user interface. The “computer-implemented” and “processor” and “display device” are recited at a high-level of generality such that they amount to no more than mere application of the judicial exception using generic computer components which does not amount to an improvement in computer functionality (see MPEP 2106.04(a)(I)). The “receiving” and “acquired” amounts to insignificant data gathering since it is recited at a high-level of generality, and since the “generating” step relies on the received elements in a generic manner (see MPEP 2106.04(d)). The “training” amounts to the idea of an outcome since it uses any algorithm to train a generic machine learning model, and since the “fracture geometry simulator” covers a generic simulator. The “displaying” amounts to insignificant data outputting since it is recited at a high-level of generality (see MPEP 2106.04(d)). The claim is directed to an abstract idea.
At Step 2B the claim does not recite additional elements that, alone or in an ordered combination, are sufficient to amount to significantly more than the judicial exception. As discussed above with respect to the integration of the abstract idea into a practical application, the recited “computer-implemented” and “processor” and “display device” amount to no more than mere instructions to apply the judicial exception using generic computer components. The recited “receiving” and “acquired” and “displaying” covers well-understood, routine, and conventional activity since it is generic and covers receiving and outputting data by any electronics means (see MPEP 2106.05(d)(II) “i. Receiving or transmitting data over a network”). The “training” amounts to reciting the words “apply-it”. Considering the additional elements in combination does not add anything more than when considering them individually since the “receiving” and “acquired” and “displaying” and “training” require no more than generic computer functions. For at least these reasons, the claim is not patent eligible.
Dependent claim 22 – 28 recite(s) at Step 1 the same statutory category as the parent claim(s), and further recite(s): Claim 25 wherein the pressure events include positive pressure events indicating sudden increases in reservoir pressure and negative pressure events indicating sudden decreases in reservoir pressure, and further comprising correlating the detected pressure events with production data to determine a value of each pressure event, wherein the value indicates a relevance of the pressure event to reservoir performance; Claim 26 wherein the reservoir geometry comprises fracture proppant density, fracture length, fracture complexity, fracture width, fracture orientation, and fracture spacing of fractures created within the reservoir rock; Claim 28 wherein the changes in pressure conditions comprise pressure increases or decreases that exceed a predetermined threshold value, wherein pressure increases indicate positive pressure events caused by injection operations, fluid expansion, and wherein pressure decreases indicate negative pressure events caused by fluid withdrawal, fluid migration, or reservoir compaction. At Step 2A, Prong I the recited limitations, alone or in combination, amount to steps that, under its broadest reasonable interpretation, cover performance of the limitations in the mind in combination with using a pen and paper (see MPEP 2106.04(a)(2)(III)). For example, the “pressure events include” and “changes in pressure conditions comprise” further limits the parent claim “analyzing” without precluding performance in the mind. The “correlating” amounts to modeling and predicting actions recited at a high-level of generality. The “reservoir geometry comprises” further limits the parent claim “predicting” without precluding performance in the mind. Accordingly, the claim(s) recite(s) an abstract idea.
At Step 2A, Prong II this judicial exception is not integrated into a practical application since the claimed invention further claims: Claim 22 wherein the condition data comprises rock properties including porosity and permeability of underground rock formations targeted for oil and gas extraction; Claim 23 wherein the condition data comprises treatment parameters including injection rate, injection pressure, and proppant concentration controlled during the hydraulic-stimulation treatment; Claim 24 wherein the condition data comprises fluid properties including viscosity and density of fracturing fluid used in the hydraulic-stimulation treatment; Claim 27 wherein displaying the predicted reservoir geometry comprises displaying a three-dimensional representation of the target well along a set of Cartesian coordinates including an XYZ axis. For example, the “condition data comprises” amounts to insignificant data gathering since it further limits the received condition data (see MPEP 2106.04(d)). For example, the “displaying” amounts to insignificant data outputting since it is recited at a high-level of generality. The claim is directed to an abstract idea.
At Step 2B the claim(s) do not recite additional elements that, alone or in an ordered combination, are sufficient to amount to significantly more than the judicial exception. The “condition data comprises” and “displaying” amount(s) to well-understood, routine, conventional activity since it reasonably encompasses using any electronic means for the receiving data (see MPEP 2106.05(d)(II)(i) “Receiving or transmitting data over a network”). For at least these reasons, the claim(s) are not patent eligible.
Independent claim 29 recites at Step 1 a statutory category (i.e. a machine) apparatus for predicting and updating fracture-network geometry during a hydraulic-stimulation treatment of a subterranean formation, to implement the same steps as claim 21. Accordingly, the claim recites an abstract idea for the same reasons as in claim 21.
At Step 2A, Prong II this judicial exception is not integrated into a practical application since the claimed invention further claims the same additional elements as in claim 21, and further recites: at least a processor; and memory communicatively connected to the at least a processor, wherein the memory contains instructions configuring the at least a processor to. The claim is directed to an abstract idea for the same reasons as in claim 21.
At Step 2B the claim does not recite additional elements that, alone or in an ordered combination, are sufficient to amount to significantly more than the judicial exception. For at least the reasons in claim 21, the claim is not patent eligible.
Dependent claim 30 – 36 recite(s) at Step 1 the same statutory category as the parent claim(s). Further, they may be compared to claim 22 – 28, respectively. Accordingly, the claim(s) recite(s) an abstract idea for the same reasons as in claim 22 – 28, respectively
At Step 2A, Prong II this judicial exception is not integrated into a practical application since the claimed invention further claims the same additional elements in claim 22 – 24 and 27. The claim is directed to an abstract idea for the same reasons as in claim 22 – 24 and 27.
At Step 2B the claim(s) do not recite additional elements that, alone or in an ordered combination, are sufficient to amount to significantly more than the judicial exception. For at least the reasons as in claim 22 – 24 and 27, the claim(s) are not patent eligible.
Independent claim 37 recites at Step 1 a statutory category (i.e. a manufacture) non-transitory computer-readable medium storing instructions to perform the same steps as in claim 21. Accordingly, the claim recites an abstract idea for the same reasons as in claim 21.
At Step 2A, Prong II this judicial exception is not integrated into a practical application since the claimed invention further claims the same additional elements as in claim 21, and further recites: a non-transitory computer-readable medium storing instructions that, when executed by at least a processor, cause the at least a processor to perform a method for predicting and updating fracture-network geometry during a hydraulic-stimulation treatment of a subterranean formation. The claim is directed to an abstract idea for the same reasons as in claim 21.
At Step 2B the claim does not recite additional elements that, alone or in an ordered combination, are sufficient to amount to significantly more than the judicial exception. For at least the reasons in claim 21, the claim is not patent eligible.
Dependent claim 38 - 40 recite(s) at Step 1 the same statutory category as the parent claim(s). Further, they may be compared to claim 22 – 23 and 25 – 28. Specifically, claim 38 is a combination of claim 22 and 23, claim 39 is a combination of claim 25 and 28, and claim 40 is a combination of claim 26 and 27. Accordingly, the claim(s) recite(s) an abstract idea for the same reasons as in claim 22 – 23 and 25 – 28.
At Step 2A, Prong II this judicial exception is not integrated into a practical application since the claimed invention further claims the same additional elements in claim 22 – 23 and 25 – 28. The claim is directed to an abstract idea for the same reasons as in claim 22 – 23 and 25 – 28.
At Step 2B the claim(s) do not recite additional elements that, alone or in an ordered combination, are sufficient to amount to significantly more than the judicial exception. For at least the reasons as in claim 22 – 23 and 25 – 28, the claim(s) are not patent eligible.
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.
The factual inquiries set forth in Graham v. John Deere Co., 383 U.S. 1, 148 USPQ 459 (1966), that are applied for establishing a background for determining obviousness under 35 U.S.C. 103 are summarized as follows:
Determining the scope and contents of the prior art.
Ascertaining the differences between the prior art and the claims at issue.
Resolving the level of ordinary skill in the pertinent art.
Considering objective evidence present in the application indicating obviousness or nonobviousness.
Claims 21 – 23, 27, 29 - 30, 35 and 37 - 38 are rejected under 35 U.S.C. 103 as being unpatentable over Jaaskelainen et al. (US 2021/0189874) (henceforth “Jaaskelainen (874)”) in view of Rose et al. (US 2023/0417940) (henceforth “Rose (940)”), and further in view of Lehmann et al. “Expanding Interpretation of Interwell Connectivity and Reservoir Complexity through Pressure Hit Analysis and Microseismic Integration” (henceforth “Lehmann”). Jaaskelainen (874) and Rose (940) and Lehmann are analogous art because they solve the same problem of generating a reservoir model, and because they are in the same field of oil and gas exploration.
With regard to claim 21, Jaaskelainen (874) teaches a computer-implemented method for predicting and updating fracture-network geometry during a hydraulic-stimulation treatment of a subterranean formation, the method comprising: (Abstract)
receiving, using at least a processor, condition data associated with a target well, wherein the condition data comprises raw sensor information about the reservoir acquired from sensors positioned at or near the target well; (Figure 1 configuration of target and offset well is received, and Figure 2 measured and collected subsurface data is used, and Figure 5 using a computer)
generating, using the at least a processor, a plurality of reservoir conditions associated with the target well as a function of the condition data, (Figure 2 and Paragraph 43 subsurface conditions of a fracture network are modeled and matched with measured data, not necessarily including the interwell fluid interaction data “In one or more embodiments, the system may comprise a fracture network model. A fracture model may comprise a set of equations expressed as a mathematical model implemented in software that corresponds to the subsurface physics”)
wherein the plurality of reservoir conditions includes real-time pressure data and flow-rate data acquired during the hydraulic-stimulation treatment, and (Paragraph 67 measurements for matching with the model includes specific data “In one or more embodiments, sensors may be used to measure well interactions by placing them along either the treatment well, along the monitoring well, or along both wells. One or more parameters, including, for example, the treatment well pressure, rate”, and Paragraph 68 measurements are for real-time analytics “For example, the measurement data captured by these sensors may be combined with a subsurface fracture network model to improve real-time analytics and make predictions about how to optimize oil and gas production”)
wherein the generating comprises calculating the reservoir conditions from the raw sensor information using computational algorithms; (Figure 2 and Paragraph 43 subsurface conditions of a fracture network are modeled and matched with measured data)
analyzing, using the at least a processor, the plurality of reservoir conditions to determine pressure events within the reservoir, wherein the pressure events comprise changes in pressure conditions that impact reservoir behavior; (Paragraph 67 “Pressure changes due to one or more interwell fluid interaction effects may be measured in the monitoring well. Pressure data may be measured in the treatment well and correlated to formation responses.”)
training a reservoir model using reservoir training data, wherein the reservoir training data comprises a plurality of pressure events correlated to examples of reservoir geometry, and wherein the training comprises using a fracture geometry simulator to generate a fracture model and comparing the fracture model against measured reservoir performance data (Paragraph 43 a fracture network model is trained based on data comprising fluid interaction effects “In step 207, subsurface data may be analyzed. In one or more embodiments, the fracture network model fracture network model may be updated based, at least in part, on this analysis. The fracture network model may incorporate data measured by one or more sensing systems, including data from the fiber optic sensors including one or more interwell fluid interaction effects, microseismic, temperature, and strain data. These data may be used to constrain the solution”, and Paragraph 72 the fracture network model can be in a reservoir simulator (training comprises using a fracture geometry simulator) “System files, such as an ASCII text file may be used to store the instructions, data input, or both for the reservoir simulator as may be required in, for example, one or more steps of FIG. 2 discussed herein”)
generating, using the at least a processor, at least one reservoir management recommendation as a function of the predicted reservoir geometry, wherein the reservoir management recommendation includes adjusting at least one parameter of the hydraulic-stimulation treatment. (Paragraph 40 fracturing treatment optimizations are suggested based on the fracture network model and its related results, where one of ordinary skill in the art would be motivated to use the most accurate model for the suggestions “FIG. 2 is a flow diagram illustrating one embodiment of a process for suggesting fracturing treatment optimization actions based, at least in part, on a fracture network model incorporating subsurface sensor data and well treatment data.”)
Jaaskelainen (874) does not appear to explicitly disclose: training a reservoir machine learning model using reservoir training data, wherein the reservoir training data comprises a plurality of pressure events correlated to examples of reservoir geometry, and wherein the training comprises using a fracture geometry simulator to generate a fracture model and comparing the fracture model against measured reservoir performance data.
However, Rose (940) teaches:
training a reservoir machine learning model using reservoir training data, wherein the reservoir training data comprises a plurality of pressure events correlated to examples of reservoir geometry, (Paragraph 117 a machine learning model is used to predict fracture communication (pressure events correlated to examples of reservoir geometry) “In some embodiments, a plurality of distinct machine-learning algorithms may operate in parallel, which may serve to enhance the accuracy of predicting fracture communication between wells, or for preventing fracture communication between wells”)
It would have been obvious for one of ordinary skill in the art before the filing date of the claimed invention to have combined the modeling of subsurface reservoir using a physics based model with interwell fluid interaction data disclosed by Jaaskelainen (874) with the fracture detection and prediction system disclosed by Rose (940). One of ordinary skill in the art would have been motivated to make this modification in order to improve the fracturing of wells (Rose (940) Abstract).
Jaaskaelainen (874) in view of Rose (940) does not appear to explicitly disclose: predicting reservoir geometry as a function of the pressure events using a trained reservoir machine learning model; and displaying, using a display device, the predicted reservoir geometry on a graphical user interface.
However, Lehmann teaches:
predicting reservoir geometry as a function of the pressure events using a trained reservoir machine learning model; and displaying, using a display device, the predicted reservoir geometry on a graphical user interface; (Page 13, Bottom – 14, Top and Figure 15 measured pressure transients and microseismic data are combined with iso surfaces to estimate the stimulated volume (predicting reservoir geometry) which is displayed (display a reservoir geometry) “By identifying the time at which a pressure hit occurred (Figure 14) we are able to constrain the microseismic data to events that occur prior to a pressure hit. Once this subset of data is identified, a density based iso surface calculation is performed to obtain the microseismic estimated stimulated volume (ESVms) encompassed by these events. This volume is then plotted in 3D to visualize its location”)
It would have been obvious for one of ordinary skill in the art before the filing date of the claimed invention to have combined the modeling of subsurface reservoir using a physics based model with interwell fluid interaction disclosed by Jaaskelainen (874) in view of Rose (940) with the computing and displaying an estimated stimulated volume disclosed by Lehmann. One of ordinary skill in the art would have been motivated to make this modification in order to visualize a stimulated volume relative to one or more wells (Lehmann Page 14, Top).
With regard to claim 29, it recites the same steps as claim 21, which is taught by Jaaskelainen (874) in view of Rose (940), and further in view of Lehmann. Claim 29 further recites: an apparatus for predicting and updating fracture-network geometry during a hydraulic-stimulation treatment of a subterranean formation, the apparatus comprising: at least a processor; and memory communicatively connected to the at least a processor, wherein the memory contains instructions configuring the at least a processor to perform the steps.
Jaaskelainen (874) teaches:
an apparatus for predicting and updating fracture-network geometry during a hydraulic-stimulation treatment of a subterranean formation, the apparatus comprising: at least a processor; and memory communicatively connected to the at least a processor, wherein the memory contains instructions configuring the at least a processor to perform the steps (Figure 5 using a computer).
With regard to claim 37, it recites the same steps as claim 21, which is taught by Jaaskelainen (874) in view of Rose (940), and further in view of Lehmann. Claim 37 further recites: a non-transitory computer-readable medium storing instructions that, when executed by at least a processor, cause the at least a processor to perform the steps.
Jaaskelainen (874) teaches:
a non-transitory computer-readable medium storing instructions that, when executed by at least a processor, cause the at least a processor to perform the steps (Figure 5 using a computer).
With regard to claim 22 and 30, Jaaskelainen (874) in view of Rose (940), and further in view of Lehmann teaches all the elements of the parent claim 21 and 29, and further teaches:
wherein the condition data comprises rock properties including porosity and permeability of underground rock formations targeted for oil and gas extraction. (Jaaskelainen (874) Paragraph 64 “It may also be possible to determine formation properties like permeability, poroelastic responses, and leak off rates based, at least in part, on changes in the measured strain data over time and the rate at which the measured strain data changes over time.”, and Paragraph 42 “Treatment parameters used in the fracture network model may be collected from numerous sources such as historical and regional data including, for example, permeability, porosity, in situ stresses, and the existence of natural fractures in the area”)
With regard to claim 23 and 31, Jaaskelainen (874) in view of Rose (940), and further in view of Lehmann teaches all the elements of the parent claim 21 and 29, and further teaches:
wherein the condition data comprises treatment parameters including injection rate, injection pressure, and proppant concentration controlled during the hydraulic-stimulation treatment. (Jaaskelainen (874) Paragraph 67 measurements for matching with the model includes specific data “In one or more embodiments, sensors may be used to measure well interactions by placing them along either the treatment well, along the monitoring well, or along both wells. One or more parameters, including, for example, the treatment well pressure, rate”, and Paragraph 42 “actual treatment data including, for example, surface rates, pressures, concentrations, chemicals, proppants, and volumes”)
With regard to claim 27 and 35, Jaaskelainen (874) in view of Rose (940), and further in view of Lehmann teaches all the elements of the parent claim 21 and 29, and further teaches:
wherein displaying the predicted reservoir geometry comprises displaying a three-dimensional representation of the target well along a set of Cartesian coordinates including an XYZ axis. (Lehmann Page 13, Bottom – 14, Top and Figure 15 measured pressure transients and microseismic data are combined with iso surfaces to estimate the stimulated volume)
It would have been obvious for one of ordinary skill in the art before the filing date of the claimed invention to have combined the modeling of subsurface reservoir using a physics based model with interwell fluid interaction disclosed by Jaaskelainen (874) in view of Rose (940) with the computing and displaying an estimated stimulated volume disclosed by Lehmann. One of ordinary skill in the art would have been motivated to make this modification in order to visualize a stimulated volume relative to one or more wells (Lehmann Page 14, Top).
With regard to claim 38, Jaaskelainen (874) in view of Rose (940), and further in view of Lehmann teaches all the elements of the parent claim 37, and further teaches:
wherein the condition data comprises rock properties including porosity and permeability of underground rock formations targeted for oil and gas extraction. (Jaaskelainen (874) Paragraph 64 “It may also be possible to determine formation properties like permeability, poroelastic responses, and leak off rates based, at least in part, on changes in the measured strain data over time and the rate at which the measured strain data changes over time.”, and Paragraph 42 “Treatment parameters used in the fracture network model may be collected from numerous sources such as historical and regional data including, for example, permeability, porosity, in situ stresses, and the existence of natural fractures in the area”)
wherein the condition data comprises treatment parameters including injection rate, injection pressure, and proppant concentration controlled during the hydraulic-stimulation treatment. (Jaaskelainen (874) Paragraph 67 measurements for matching with the model includes specific data “In one or more embodiments, sensors may be used to measure well interactions by placing them along either the treatment well, along the monitoring well, or along both wells. One or more parameters, including, for example, the treatment well pressure, rate”, and Paragraph 42 “actual treatment data including, for example, surface rates, pressures, concentrations, chemicals, proppants, and volumes”)
Claims 24 and 32 are rejected under 35 U.S.C. 103 as being unpatentable over Jaaskelainen (874) in view of Rose (940), and further in view of Lehmann, and further in view of Dalamarinis et al. (WO 2021/016212) (henceforth “Dalamarinis (212)”). Jaaskelainen (874) and Rose (940) and Lehmann and Dalamarinis (212) are analogous art because they solve the same problem of generating a reservoir model, and because they are in the same field of oil and gas exploration.
With regard to claim 24 and 32, Jaaskelainen (874) in view of Rose (940), and further in view of Lehmann teaches all the elements of the parent claim 21 and 29, and does not appear to explicitly disclose: wherein the condition data comprises fluid properties including viscosity and density of fracturing fluid used in the hydraulic-stimulation treatment.
However, Dalamarinis (212) teaches:
wherein the condition data comprises fluid properties including viscosity and density of fracturing fluid used in the hydraulic-stimulation treatment. (Paragraph 5 “In some embodiments, the at least one parameter comprises at least one of pad volume, fracture fluid viscosity, fracture fluid density, propping agent type, propping agent volume, fracturing fluid Injection rate and fracture fluid volume.”)
It would have been obvious for one of ordinary skill in the art before the filing date of the claimed invention to have combined the modeling of subsurface reservoir using a physics based model with interwell fluid interaction disclosed by Jaaskelainen (874) in view of Rose (940), and further in view of Lehmann with the hydraulic fracturing treatment planning and optimization disclosed by Dalamarinis (212). One of ordinary skill in the art would have been motivated to make this modification in order to optimize a hydraulic fracturing treatment (Dalamarinis (212) Paragraph 1).
Claims 26, 34 and 40 are rejected under 35 U.S.C. 103 as being unpatentable over Jaaskelainen (874) in view of Rose (940), and further in view of Lehmann, and further in view of Almulhim, A. “FLUID FLOW MODELING IN MULTI-STAGE HYDRAULIC FRACTURING PATTERNS FOR PRODUCTION OPTIMIZATION IN SHALE RESERVOIRS” (henceforth “Almulhim (Thesis)”). Jaaskelainen (874) and Rose (940) and Lehmann and A