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 . This action is made non-final.
Claims 1-20 filed on 09/22/2023 have been reviewed and considered by this office action.
Specification
The disclosure is objected to because of the following informalities: in paragraph 30, line 4, the reference number 103 is used to designate the "drilled hole comprising the well" where 103 designates "production fluids" elsewhere. In paragraph 52 line 4, "SMV" should be "SVM".
Appropriate correction is required.
Claim Objections
Claim 8 is objected to because of the following informalities: claim 8 recites the limitation "predictive maintenance action". There is insufficient antecedent basis for this limitation in the claim. The limitation will be interpreted to recite "preventative maintenance action" (as seem in claim 3). Appropriate correction is required.
Claim Rejections - 35 USC § 101
35 U.S.C. 101 reads as follows:
Whoever invents or discovers any new and useful process, machine, manufacture, or composition of matter, or any new and useful improvement thereof, may obtain a patent therefor, subject to the conditions and requirements of this title.
Claims 1-20 are rejected under 35 U.S.C. 101 because the claimed invention is directed to an abstract idea without significantly more.
Step 1: Claims 1-8 are directed to a process. Claims 9-20 are directed to a machine or an article of manufacture.
Regarding claim 1:
Step 2A Prong One: The claim recites an abstract idea. Specifically:
“Detecting, with a computational model, a gas migration event based on the process data;”
“determining a maintenance action based on the gas migration event;”
The limitations of detecting a gas migration event and determining a maintenance action can be reasonably performed using the human mind/with pen and paper and thus fall under the “Mental Processes” grouping of abstract ideas.
Step 2A Prong Two: The judicial exception is not integrated into a practical application. Claim 1 includes the additional limitations:
“Obtaining process data from a gas-lifted, hydrocarbon production well, wherein the production well is controlled by a set of operation parameters;”
“adjusting one or more operation parameters in the set of operation parameters to mitigate the gas migration event based on the gas migration event and process data;”
“generating an alert for the detected gas migration event;”
“and performing the maintenance action on the production well.”
The limitation of obtaining process data adds insignificant extra-solution activity in the form of mere data gathering (MPEP 2106.05(g)). The limitations of generating an alert for the detected gas migration event and performing the maintenance action on the production well are post-solution activities that do not integrate the abstract idea into a practical application and are insignificant extra-solution activities to the judicial exception (MPEP 2106.05(g)). The limitation of adjusting one or more operation parameters amounts to no more than mere instructions to apply a judicial exception or abstract idea to a particular technological environment of mitigating gas migration by adjusting an operation parameter (MPEP 2106.05(f)&(h)).
Step 2B: The claim does not include additional elements that are sufficient to amount to significantly more than the judicial exception. The limitations of obtaining process data, adjusting one or more operation parameters, generating an alert for the detected gas migration event, and performing the maintenance action represent functions that are well-understood, routine, and conventional when they are claimed in a merely generic manner within the industry.
With regards to “Obtaining process data from a gas-lifted, hydrocarbon production well, wherein the production well is controlled by a set of operation parameters;”, US Patent No. 20230186218 to Bestman et al. (“Bestman”) describes “An example process includes obtaining production data about the well” (abstract) and “the automated response is uniquely operable to adjust or affect flow conditions of the hydrocarbon (e.g., gas) well and can be configured to cause a total shut-in of the well” (paragraph 26). Australian Patent Publication AU 2013296746 A1 to Querales et al. (“Querales”) describes “collecting measured data” (abstract) and “control settings” (page 7 line 27).
With regards to “adjusting one or more operation parameters in the set of operation parameters to mitigate the gas migration event based on the gas migration event and process data;”, Bestman describes “automated response actions to eliminate or mitigate occurrence of the deviation” (paragraph 38). Querales describes “Software executing within the system may automatically detect the mismatch” and “determines the most likely cause of the measured conditions and suggests recommended actions to resolve said conditions” (page 7 lines 5-12).
With regards to “generating an alert for the detected gas migration event;” Bestman describes: “For example, depending on the magnitude of a variance in performance of the well, the system can select from actions that include an automatic warning, an automatic alarm…” (paragraph 26). Querales describes “When an advisory is generated by the monitoring, diagnosis and optimizing system during data collection (e.g., because a measured value has exceeded a threshold limit or is outside an allowable range of values), a notification is also generated…” (page 8 line 34, page 9 lines 1-3).
With regards to “and performing the maintenance action on the production well.”, Querales describes “Such action may include assignment of personnel to address the underlying condition (block 456), any required authorizations, equipment corrections, repairs 10 and/or replacements…” (page 9 lines 8-10). US Patent Publication US 11017321 B1 to Mishra et al. (“Mishra”) describes “analyze and categorize events associated with an equipment asset, such as industrial machinery” and “Output that indicates the maintenance actions may be displayed to a user or used to automatically initiate performance of one or more of the maintenance actions.” (Abstract)
In view of the foregoing, in accord with MPEP 2106.05(d), simply appending well-understood, routine, conventional activities previously known to the industry, specified at a high level of generality, to the judicial exception does not qualify the claim as reciting “significantly more”. Therefore, the additional claimed features do not amount to significantly more and the claim is not patent eligible.
Regarding claim 2: the additional limitations do not integrate the judicial exception into practical application or add significantly more to the judicial exception. Determining a gas lift efficiency falls under a mental process and is thus an abstract idea. Adjusting operation parameters to optimize production falls under mere instructions to apply an exception (MPEP 2106.05(f)).
Regarding claim 3: the additional limitations do not integrate the judicial exception into practical application or add significantly more to the judicial exception. Obtaining environmental data falls under data gathering and is thus insignificant extra-solution activity. Predicting a future gas migration event, determining a first expected cost, determining a preventative maintenance action, and determining whether a second expected cost is less than the first expected cost fall under a mental process and are thus abstract ideas. Generating an alert for the predicted gas migration event falls under mere instructions to apply an exception (MPEP 2106.05(f)). Performing the preventative maintenance action falls under post-solution activities that do not integrate the abstract idea into a practical application and are insignificant extra-solution activities to the judicial exception (MPEP 2106.05(g)).
Regarding claim 4: the additional limitations do not integrate the judicial exception into practical application or add significantly more to the judicial exception. Adjusting one or more operation parameters to prevent a future gas migration event falls under mere instructions to apply an exception (MPEP 2106.05(f)).
Regarding claim 5: the additional limitations do not integrate the judicial exception into practical application or add significantly more to the judicial exception. The process data and surface data fall under data gathering and are thus insignificant extra-solution activity (MPEP 2106.05(g)).
Regarding claim 6: the additional limitations do not integrate the judicial exception into practical application or add significantly more to the judicial exception. An artificial intelligence (AI) model and a preventative maintenance model both fall under mere instructions to apply an exception (MPEP 2106.05(f)). Detecting a gas migration model by determining with the AI model a gas migration status falls under a mental process and is thus an abstract idea.
Regarding claim 7: the additional limitations do not integrate the judicial exception into practical application or add significantly more to the judicial exception. The AI model being an anomaly detection model falls under mere instructions to apply an exception (MPEP 2106.05(f)).
Regarding claim 8: the additional limitations do not integrate the judicial exception into practical application or add significantly more to the judicial exception. The maintenance action being one of repairing cement/valves and replacing valves, and the predictive maintenance action being one of improving cement condition, replacing valves, and updating hydrocarbon production equipment fall under mere instructions to apply an exception (MPEP 2106.05(f)).
Regarding claim 9:
Step 2A Prong One: The claim recites an abstract idea. Specifically:
“detect, with a computational model, a gas migration event based on the process data;”
“determine a maintenance action based on the gas migration event;”
The limitations of detecting a gas migration event and determining a maintenance action can be reasonably performed using the human mind/with pen and paper and thus fall under the “Mental Processes” grouping of abstract ideas.
Step 2A Prong Two: The judicial exception is not integrated into a practical application. Claim 9 includes the following limitations:
“a hydrocarbon production well, controlled by a set of operation parameters;”
“a gas-lift system, coupled to the hydrocarbon production well, comprising: a gas source; a gas pump that pumps gas from the gas source into a wellbore of the hydrocarbon production well;”
“a data acquisition system that collects process data from a plurality of sensors disposed on the hydrocarbon production well;”
“and a computer comprising one or more computer processors and a user interface, the computer communicatively connected to the data acquisition system and configured to: receive process data from the data acquisition system;”
“adjust one or more operation parameters in the set of operation parameters to mitigate the gas migration event based on the gas migration event and the process data;”
“wherein the maintenance action is performed on the production well,”
“wherein the user interface is configured to communicate, to a user, the detected gas migration event, the one or more adjusted parameters, and the determined maintenance action.”
The hydrocarbon production well, gas-lift system, and computer + user interface are all generic systems and thus fall under mere instructions to apply an exception (MPEP 2106.05(f)). The data acquisition system and the computer connected to the data acquisition system fall under data gathering and are thus insignificant extra-solution activity (MPEP 2106.05(g)).
Step 2B: The claim does not include additional elements that are sufficient to amount to significantly more than the judicial exception. The limitations of a hydrocarbon production well, a gas-lift system, a data acquisition system, a computer, adjusting one or more operation parameters, performing the maintenance action on the production well, and wherein the user interface is configured to communicate to a user are well-understood, routine, and conventional when they are claimed in a merely generic manner within the industry.
With regards to “a hydrocarbon production well, controlled by a set of operation parameters;”, Bestman describes “In some implementations, the well is a gas or hydrocarbon producing well” (paragraph 11). Querales describes “recovery of hydrocarbons from reservoirs” and “monitoring of wells” (page 1 lines 15-20) and “control settings” (page 7 line 27).
With regards to “a gas-lift system, coupled to the hydrocarbon production well, comprising: a gas source; a gas pump that pumps gas from the gas source into a wellbore of the hydrocarbon production well;”, Querales describes a “gas lift (GL) system” (abstract) and “Gas is injected” (page 4 line 9). US Patent Publication US 20220403721 A1 to Jaaskelainen et al. (“Jaaskelainen”) describes “Once a gas lift valve opens and gas is injected into the tubing…” (paragraph 29).
With regards to “and a computer comprising one or more computer processors and a user interface, the computer communicatively connected to the data acquisition system and configured to: receive process data from the data acquisition system;”, Bestman describes “The sensors 116 can be geophone-receivers that produce electrical output signals transmitted as input data, for example, to a computer 118” (paragraph 0031). Querales describes “Referring again to FIG. 1A…for telemetry signals received by control panel 132 from the devices” and “The devices may be controlled and monitored locally by field personnel using a user interface built into control panel 132, or may be controlled and monitored by a computer system 45".
With regards to “adjust one or more operation parameters in the set of operation parameters to mitigate the gas migration event based on the gas migration event and the process data;”, Bestman describes “automated response actions to eliminate or mitigate occurrence of the deviation” (paragraph 38). Querales describes “Software executing within the system may automatically detect the mismatch” and “determines the most likely cause of the measured conditions and suggests recommended actions to resolve said conditions” (page 7 lines 5-12).
With regards to “wherein the maintenance action is performed on the production well,”, Querales describes “Such action may include assignment of personnel to address the underlying condition (block 456), any required authorizations, equipment corrections, repairs 10 and/or replacements…” (page 9 lines 8-10). US Patent Publication US 11017321 B1 to Mishra et al. (“Mishra”) describes “analyze and categorize events associated with an equipment asset, such as industrial machinery” and “Output that indicates the maintenance actions may be displayed to a user or used to automatically initiate performance of one or more of the maintenance actions.” (Abstract)
With regards to “wherein the user interface is configured to communicate, to a user, the detected gas migration event, the one or more adjusted parameters, and the determined maintenance action.”, Bestman describes “More specifically, the detection engine 205 can be used to provide an early warning and mitigation system that generates automated warnings to the user of a deviation in performance of a gas producing well as well as automated response actions to eliminate or mitigate occurrence of the deviation”(paragraph 38) and “The illustrated computer 602 is intended to encompass any computing device” (paragraph 74). Querales describes “In at least some illustrative embodiments, a rule-based expert system determines the most likely cause of the measured conditions and suggests recommended actions to resolve said conditions. Both the most likely cause and the recommended actions to resolve the issue are generated by the expert system and presented at the bottom of the display as analysis system results 228.” (page 7 lines 10-14) and “Such action may include…each time the task ticket is updated.” (page 9 lines 8-12).
In view of the foregoing, in accord with MPEP 2106.05(d), simply appending well-understood, routine, conventional activities previously known to the industry, specified at a high level of generality, to the judicial exception does not qualify the claim as reciting “significantly more”. Therefore, the additional claimed features do not amount to significantly more and the claim is not patent eligible.
Regarding claim 10: the additional limitations do not integrate the judicial exception into practical application or add significantly more to the judicial exception. Claim 10 is similar to claim 2 and is similarly rejected.
Regarding claim 11: the additional limitations do not integrate the judicial exception into practical application or add significantly more to the judicial exception. Claim 11 is similar to claim 3 and is similarly rejected.
Regarding claim 12: the additional limitations do not integrate the judicial exception into practical application or add significantly more to the judicial exception. The plurality of sensors falls under mere instructions to apply the judicial exception.
Regarding claim 13: the additional limitations do not integrate the judicial exception into practical application or add significantly more to the judicial exception. Claim 13 is similar to claim 5 and is similarly rejected.
Regarding claim 14: the additional limitations do not integrate the judicial exception into practical application or add significantly more to the judicial exception. Claim 14 is similar to claim 6 and is similarly rejected.
Regarding claim 15: the additional limitations do not integrate the judicial exception into practical application or add significantly more to the judicial exception. Claim 15 is similar to claim 7 and is similarly rejected.
Regarding claim 16:
Step 2A Prong One: The claim recites a judicial exception. Specifically:
“detecting, with a computational model, a gas migration event based on the process data;”
“determining a maintenance action based on the gas migration event;”
The limitations of detecting a gas migration event and determining a maintenance action can be reasonably performed using the human mind/with pen and paper and thus fall under the “Mental Processes” grouping of abstract ideas.
Step 2A Prong Two: The judicial exception is not integrated into a practical application. Claim 16 includes the following limitations:
“obtaining process data from a gas-lifted, hydrocarbon production well, wherein the production well is controlled by a set of operation parameters;”
“adjusting one or more operation parameters in the set of operation parameters to mitigate the gas migration event based on the gas migration event and process data;”
“and generating an alert for the detected gas migration event,”
“wherein the maintenance action is performed on the production well.”
The limitation of obtaining process data adds insignificant extra-solution activity in the form of mere data gathering (MPEP 2106.05(g)). The limitations of generating an alert for the detected gas migration event and performing the maintenance action on the production well are post-solution activities that do not integrate the abstract idea into a practical application and are insignificant extra-solution activities to the judicial exception (MPEP 2106.05(g)). The limitation of adjusting one or more operation parameters amounts to no more than mere instructions to apply a judicial exception or abstract idea to a particular technological environment of mitigating gas migration by adjusting an operation parameter (MPEP 2106.05(f)&(h)).
Step 2B: The claim does not include additional elements that are sufficient to amount to significantly more than the judicial exception.
With regards to “Obtaining process data from a gas-lifted, hydrocarbon production well, wherein the production well is controlled by a set of operation parameters;”, US Patent No. 20230186218 to Bestman et al. (“Bestman”) describes “An example process includes obtaining production data about the well” (abstract) and “the automated response is uniquely operable to adjust or affect flow conditions of the hydrocarbon (e.g., gas) well and can be configured to cause a total shut-in of the well” (paragraph 26). Australian Patent Publication AU 2013296746 A1 to Querales et al. (“Querales”) describes “collecting measured data” (abstract) and “control settings” (page 7 line 27).
With regards to “adjusting one or more operation parameters in the set of operation parameters to mitigate the gas migration event based on the gas migration event and process data;”, Bestman describes “automated response actions to eliminate or mitigate occurrence of the deviation” (paragraph 38). Querales describes “Software executing within the system may automatically detect the mismatch” and “determines the most likely cause of the measured conditions and suggests recommended actions to resolve said conditions” (page 7 lines 5-12).
With regards to “and generating an alert for the detected gas migration event,” Bestman describes: “For example, depending on the magnitude of a variance in performance of the well, the system can select from actions that include an automatic warning, an automatic alarm…” (paragraph 26). Querales describes “When an advisory is generated by the monitoring, diagnosis and optimizing system during data collection (e.g., because a measured value has exceeded a threshold limit or is outside an allowable range of values), a notification is also generated…” (page 8 line 34, page 9 lines 1-3).
With regards to “wherein the maintenance action is performed on the production well”, Querales describes “Such action may include assignment of personnel to address the underlying condition (block 456), any required authorizations, equipment corrections, repairs 10 and/or replacements…” (page 9 lines 8-10). US Patent Publication US 11017321 B1 to Mishra et al. (“Mishra”) describes “analyze and categorize events associated with an equipment asset, such as industrial machinery” and “Output that indicates the maintenance actions may be displayed to a user or used to automatically initiate performance of one or more of the maintenance actions.” (Abstract)
In view of the foregoing, in accord with MPEP 2106.05(d), simply appending well-understood, routine, conventional activities previously known to the industry, specified at a high level of generality, to the judicial exception does not qualify the claim as reciting “significantly more”. Therefore, the additional claimed features do not amount to significantly more and the claim is not patent eligible.
Regarding claim 17: the additional limitations do not integrate the judicial exception into practical application or add significantly more to the judicial exception. Claim 17 is similar to claim 10 and claim 2 and is rejected similarly.
Regarding claim 18: the additional limitations do not integrate the judicial exception into practical application or add significantly more to the judicial exception. Claim 18 is similar to claim 11 and claim 3 and is rejected similarly.
Regarding claim 19: the additional limitations do not integrate the judicial exception into practical application or add significantly more to the judicial exception. Claim 19 is similar to claim 14 and claim 6 and is rejected similarly.
Regarding claim 20: the additional limitations do not integrate the judicial exception into practical application or add significantly more to the judicial exception. Claim 20 is similar to claim 15 and claim 7 and is rejected similarly.
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.
Claim(s) 1-2, 5, 9-10, 12-13, and 16 is/are rejected under 35 U.S.C. 103 as being unpatentable over Querales (WO 2014022320 A2) in light of Coates (CA 2837193 A).
Regarding claim 1, Querales teaches a method, comprising: obtaining process data (abstract “collecting measured data”) from a gas-lifted, hydrocarbon production well (page 1, lines 17-25, 29), wherein the production well is controlled by a set of operation parameters (page 7 line 27, “control settings”); detecting, with a computational modelan anomaly based on the process data (page 7 lines 3-10); adjusting one or more operation parameters in the set of operation parameters to mitigate the anomaly based on the anomaly and the process data (page 7 lines 10-12); determining a maintenance action based on the anomaly (page 7 lines 10-12, page 9 lines 8-11); generating an alert for the detected anomaly (page 8 line 34, page 9 lines 1-3); and performing the maintenance action on the production well (page 9 lines 8-10).
While Querales teaches controlling a well by adjusting operation parameters to mitigate anomalies, Querales does not explicitly teach that these anomalies are specifically a “gas migration event”.
However, Coates teaches detecting abnormalities (anomalies) indicative of a gas influx (gas migration event) (page 1 Line 30 – Page 2, Line 7, wherein a “gas kick” is equivalent to the claimed gas migration event).
Both Querales and Coates are analogous to the claimed invention because both are in the field of hydrocarbon extraction. It would have been obvious to a person of ordinary skill before the effective filing date of the claimed invention to incorporate the specific detecting a gas migration event of Coates as part of the broad anomaly detection and mitigation system of Querales to maintain operational efficiency or prevent potential damage caused by the gas migration (page 2, lines 3-7).
Regarding claim 2, the combination of Querales and Coates teaches the method of claim 1. Querales also teaches the method of claim 1, further comprising: determining, with the computational model, a gas lift efficiency of the production well based on the process data (page 8 line 15 “GL system performance curves”); and adjusting one or more operation parameters in the set of operation parameters to optimize a production rate of the production well based on the gas lift efficiency (page 7 lines 5-17).
Regarding claim 5, the combination of Querales and Coates teaches the method of claim 1. Querales also teaches the method of claim 1, wherein the process data comprises downhole data and surface data (page 3 line 33, page 4 lines 1-2), the downhole data comprising one of: a downhole gas flow rate, a downhole gas injection rate, a downhole liquid production rate, a downhole pressure (page 10 line 4, “bottom hole pressure”), a downhole temperature (page 10 line 6, “bottom hole temperature”), a downhole gas concentration, and a subsurface equipment failure and maintenance report; and the surface data comprising one of: a surface pressure, a surface temperature, a gas injection pressure (page 10 line 6, “injected gas pressure”), a gas injection temperature (page 10 line 6, “injected gas temperature”), and a surface equipment failure and maintenance report.
Regarding claim 9, Querales teaches a system (abstract), comprising: a hydrocarbon production well, controlled by a set of operation parameters (page 7 line 27, “control settings”); a gas-lift system, coupled to the hydrocarbon production well (abstract), comprising: a gas source; a gas pump that pumps gas from the gas source into a wellbore of the hydrocarbon production well (page 4 line 9 “injected”); a data acquisition system (page 5, lines 16-18) that collects process data from a plurality of sensors disposed on the hydrocarbon production well (page 3 lines 27-33, page 4 lines 1-2); and a computer comprising one or more computer processors and a user interface (page 4 lines 27-29), the computer communicatively connected to the data acquisition system and configured to: receive process data from the data acquisition system (page 4 lines 20-29); detect, with a computational model, a an anomaly based on the process data (page 7 lines 3-10); adjust one or more operation parameters in the set of operation parameters to mitigate the anomaly based on the anomaly and the process data (page 7 lines 5-12); determine a maintenance action based on the anomaly (page 7 lines 10-12); and generate an alert for the anomaly (page 8 line 34, page 9 lines 1-3); wherein the maintenance action is performed on the production well, wherein the user interface is configured to communicate, to a user, the anomaly, the one or more adjusted parameters, and the determined maintenance action (page 7 lines 5-12, page 9 lines 6-16).
While Querales teaches controlling a well by adjusting operation parameters to mitigate anomalies, Querales does not explicitly teach that these anomalies are specifically a “gas migration event”.
However, Coates teaches detecting abnormalities (anomalies) indicative of a gas influx (gas migration event) (page 1 Line 30 – Page 2, Line 7, wherein a “gas kick” is equivalent to the claimed gas migration event).
Both Querales and Coates are analogous to the claimed invention because both are in the field of hydrocarbon extraction. It would have been obvious to a person of ordinary skill before the effective filing date of the claimed invention to incorporate the specific detecting a gas migration event of Coates as part of the broad anomaly detection and mitigation system of Querales to maintain operational efficiency or prevent potential damage caused by the gas migration (page 2, lines 3-7).
Regarding claim 10, the combination of Querales and Coates teaches the system of claim 9. Querales also teaches the system of claim 9, wherein the computer is further configured to: determine, with the computational model, a gas lift efficiency of the production well based on the process data (page 8 line 15 “GL system performance curves”); and adjust one or more operation parameters in the set of operation parameters to optimize a production rate of the production well based on the gas lift efficiency (page 7 lines 5-17, page 8 lines 1-2).
Regarding claim 12, the combination of Querales and Coates teaches the system of claim 9. Querales also teaches the system, of claim 9, wherein the plurality of sensors (page 3 lines 27-33, page 4 lines 1-2) comprise: at least one downhole sensor, comprising one or more of: a downhole gas flow rate sensor, a downhole gas injection rate sensor, a downhole liquid production rate sensor, a downhole pressure sensor (page 10 line 4, “bottom hole pressure”), a downhole temperature sensor (page 10 line 6, “bottom hole temperature”), and a downhole gas concentration sensor, and at least one surface sensor, comprising one or more of: a surface pressure sensor, a surface temperature sensor, a gas injection pressure sensor (page 10 line 6, “injected gas pressure”), and a injection temperature sensor (page 10 line 6, “injected gas temperature”).
Regarding claim 13, the combination of Querales and Coates teaches the system of claim 10. Querales also teaches the system of claim 10, wherein the process data comprises: downhole data comprising one or more of: a downhole gas flow rate, a downhole gas injection rate, a downhole liquid production rate, a downhole pressure (page 10 line 4, “bottom hole pressure”), a downhole temperature (page 10 line 6, “bottom hole temperature”), a downhole gas concentration, and a subsurface equipment failures or maintenance report, and surface data, comprising one or more of: a surface pressure, a surface temperature, a gas injection pressure (page 10 line 6, “injected gas pressure”), a gas injection temperature (page 10 line 6, “injected gas temperature”), and surface equipment failures or a maintenance report.
Regarding claim 16, Querales teaches a non-transitory computer-readable memory comprising computer-executable instructions stored thereon that, when executed on a processor, cause the processor to perform steps comprising (page 10, lines 21-23): obtaining process data (abstract “collecting measured data”) from a gas-lifted, hydrocarbon production well (page 1, lines 17-25, 29), wherein the production well is controlled by a set of operation parameters (page 7 line 27, “control settings”); detecting, with a computational model, a an anomaly based on the process data (page 7 lines 3-10); adjusting one or more operation parameters in the set of operation parameters to mitigate the anomaly based on the anomaly and process data (page 7 lines 10-12); determining a maintenance action based on the anomaly (page 7 lines 10-12, page 9 lines 8-11); and generating an alert for the detected anomaly (page 8 line 34, page 9 lines 1-3), wherein the maintenance action is performed on the production well (page 9 lines 8-10).
While Querales teaches controlling a well by adjusting operation parameters to mitigate anomalies, Querales does not explicitly teach that these anomalies are specifically a “gas migration event”.
However, Coates teaches detecting abnormalities (anomalies) indicative of a gas influx (gas migration event) (page 1 Line 30 – Page 2, Line 7, wherein a “gas kick” is equivalent to the claimed gas migration event).
Both Querales and Coates are analogous to the claimed invention because both are in the field of hydrocarbon extraction. It would have been obvious to a person of ordinary skill before the effective filing date of the claimed invention to incorporate the specific detecting a gas migration event of Coates as part of the broad anomaly detection and mitigation system of Querales to maintain operational efficiency or prevent potential damage caused by the gas migration (page 2, lines 3-7).
Regarding claim 17, the combination of Querales and Coates teaches the non-transitory computer-readable memory of claim 16. Querales also teaches the non-transitory computer-readable memory of claim 16, the steps further comprising: determining, with the computational model, a gas lift efficiency of the production well based on the process data (page 8 line 15 “GL system performance curves”); and adjusting one or more operation parameters in the set of operation parameters to optimize a production rate of the production well based on the gas lift efficiency (page 7 lines 5-17).
Claim(s) 3, 6-7, 11, 14-15, and 18-20 is/are rejected under 35 U.S.C. 103 as being unpatentable over Querales and Coates, further in light of Bestman (US Patent No. 20230186218), Bazzocchi (US Patent No. 6999829 B2), and Boguslawski (WO 2021155272 A1).
Regarding claim 3, the combination of Querales and Coates teaches the method of claim 2.
Coates also teaches detecting abnormalities (anomalies) indicative of a gas influx (gas migration event) (page 1 Line 30 – Page 2, Line 7, wherein a “gas kick” is equivalent to the claimed gas migration event).
The combination of Querales and Coates does not teach the method of claim 2, further comprising: obtaining environmental data related to the hydrocarbon production well, the environmental data comprising at least one of: weather forecast data, and seismic activity data; predicting a future gas migration event by processing the process data and the environmental data with the computational model; determining a first expected economic cost from the predicted future gas migration event; determining a preventative maintenance action based on the predicted future gas migration event; generating an alert for the predicted gas migration event; making a first determination of whether a second expected economic cost of performing the preventative maintenance action is less than the first expected economic cost; and performing the preventative maintenance action on the production well based on the first determination that the second expected economic cost is less than the first expected economic cost.
However, Bestman teaches obtaining environmental data related to the hydrocarbon production well, the environmental data comprising at least one of: weather forecast data, and seismic activity data (Paragraph 28).
Querales and Bestman are analogous to the claimed invention because both are in the field of hydrocarbon extraction. It would have been obvious to a person of ordinary skill before the effective filing date of the claimed invention to incorporate the obtaining environmental data of Bestman and predicting a future anomaly with the method of Querales and Coates, as adding the seismic data contributes to models which can predict well behavior and raise alarms similar to Querales (Paragraphs 5-6).
However, the combination of Querales, Coates, and Bestman does not teach predicting a future gas migration event by processing the process data and the environmental data with the computational model; determining a first expected economic cost from the predicted future gas migration event; determining a preventative maintenance action based on the predicted future gas migration event; generating an alert for the predicted gas migration event; making a first determination of whether a second expected economic cost of performing the preventative maintenance action is less than the first expected economic cost; and performing the preventative maintenance action on the production well based on the first determination that the second expected economic cost is less than the first expected economic cost.
However, Bazzocchi teaches determining a first expected economic cost from a predicted anomaly (col. 5 line 65-67, col. 6 lines 1-3); determining a preventative maintenance action based on the predicted anomaly (col. 3 line 60 “maintenance triggers”, lines 64-65), making a first determination of whether a second expected economic cost of performing the preventative maintenance action is less than the first expected economic cost (col. 5 line 65-67, col. 6 lines 1-3); and performing the preventative maintenance action on the production well based on the first determination that the second expected economic cost is less than the first expected economic cost (col. 6 lines 38-41, 47-48).
Bazzocchi and Querales are analogous to the claimed invention because both are in the field of operating an industrial plant. It would have been obvious to a person of ordinary skill before the effective filing date of the claimed invention to incorporate the determining a first expected economic cost of Bazzocchi with the method of Querales, Coates, and Bestman since determining an expected economic cost and performing the preventative maintenance action when the cost of maintenance is lower than the cost of failure minimizes the cost in general (col. 6 lines 38-41, 47-48).
The combination of Querales, Coates, Bestman, and Bazzocchi does not specifically teach predicting a future gas migration event by processing the process data and the environmental data with the computational model; and generating an alert for the predicted gas migration event.
However, Boguslawski teaches predicting a anomaly by processing the process data and the environmental data (page 8, paragraph 38 “provide early indication”) and generating an alert for a anomaly (page 7, paragraph 35 line 15).
Boguslawski and Querales are analogous art because both are in the field of hydrocarbon production. It would have been obvious to a person of ordinary skill before the effective filing date of the claimed invention to incorporate the predicting a future anomaly and generating an alert of Boguslawski with the method of Querales, Coates, Bestman, and Bazzocchi, as predicting a future anomaly helps improve production and reduce downtime (page 8, paragraph 38 lines 3-4) and an alert reduces risk to field personnel (page 3, paragraph 6 “reduce health and safety risks”).
Regarding claim 6, the combination of Querales, Coates, Bestman, Bazzocchi, and Boguslawski teaches the method of claim 3.
Coates also teaches detecting abnormalities (anomalies) indicative of a gas influx (gas migration event) (page 1 Line 30 – Page 2, Line 7, wherein a “gas kick” is equivalent to the claimed gas migration event).
Boguslawski also teaches an AI model (page 3, paragraph 6 lines 2-3 “machine learning”) that detects the anomaly (page 9, line 1) and a preventative maintenance model that predicts a future anomaly (page 9, lines 2-4).
Querales and Boguslawski are analogous to the claimed invention because all are in the field of hydrocarbon extraction. It would have been obvious to a person of ordinary skill before the effective filing date of the claimed invention to incorporate the AI model and the preventative maintenance model of Boguslawski with the method of Querales because detecting presently occurring and future anomalies reduces risk of damage (page 3, paragraph 6 lines 8-11).
Bestman also teaches wherein the gas migration event is detected by determining, with the AI model, an anomaly status, where the anomaly status is either positive if an anomaly is detected, or negative if an anomaly is not detected (abstract, “performance deviation”, “threshold”).
Querales and Bestman are analogous to the claimed invention because all are in the field of hydrocarbon extraction. It would have been obvious to a person of ordinary skill before the effective filing date of the claimed invention to incorporate the threshold of Bestman with the method of Querales because a threshold is potentially variable and can be altered by an operator depending on conditions (pages 6-7, paragraph 70 “performance deviation”).
Regarding claim 7, the combination of Querales, Coates, Bestman, Bazzocchi, and Boguslawski teaches the method of claim 6.
Bestman also teaches the method of claim 6, wherein the AI model is an anomaly detection model (“prediction & machine learning module 225”, abstract “performance deviation”).
Regarding claim 11, the combination of Querales and Coates teaches the system of claim 9.
Coates also teaches detecting abnormalities (anomalies) indicative of a gas influx (gas migration event) (page 1 Line 30 – Page 2, Line 7, wherein a “gas kick” is equivalent to the claimed gas migration event).
The combination of Querales and Coates does not teach the system of claim 9, wherein the computer is further configured to: receive environmental data related to the hydrocarbon production well, the environmental data comprising one or more of: weather forecast data, and seismic activity data; predict a future gas migration event by processing the process data and the environmental data with the computational model; determine a preventative maintenance action based on the predicted future gas migration event; and generate an alert for the detected gas migration event, wherein the preventative maintenance action is performed on the production well.
However, Bestman teaches obtaining environmental data related to the hydrocarbon production well, the environmental data comprising at least one of: weather forecast data, and seismic activity data (Paragraph 28).
Querales and Bestman are analogous to the claimed invention because both are in the field of hydrocarbon extraction. It would have been obvious to a person of ordinary skill before the effective filing date of the claimed invention to incorporate the obtaining environmental data of Bestman with the system of Querales, as adding the seismic data contributes to models which can predict well behavior (Paragraphs 5-6).
The combination of Querales, Coates, and Bestman does not teach the system of claim 9, wherein the computer is further configured to: predict a future gas migration event by processing the process data and the environmental data with the computational model; determine a preventative maintenance action based on the predicted future gas migration event; and generate an alert for the predicted gas migration event; wherein the preventative maintenance action is performed on the production well.
However, Bazzocchi teaches determining a preventative maintenance action based on the predicted anomaly (col. 3 line 60 “maintenance triggers”, lines 64-65); and performing the preventative maintenance action on the production (col. 6 lines 38-41, 47-48).
Bazzocchi and Querales are analogous to the claimed invention because both are in the field of operating an industrial plant. It would have been obvious to a person of ordinary skill before the effective filing date of the claimed invention to incorporate the determining a first expected economic cost of Bazzocchi with the system of Querales since determining an expected economic cost and performing the preventative maintenance action when the cost of maintenance is lower than the cost of failure minimizes the cost in general (col. 6 lines 38-41, 47-48).
The combination of Querales, Coates, Bestman, and Bazzocchi does not specifically teach the system of claim 9, wherein the computer is further configured to: predict a future gas migration event by processing the process data and the environmental data with the computational model; and generate an alert for the predicted gas migration event; wherein the preventative maintenance action is performed on the production well.
However, Boguslawski teaches predicting a future anomaly by processing the process data and the environmental data (page 8, paragraph 38 “provide early indication”) and generating an alert for a predicted anomaly (page 7, paragraph 35 line 15).
Boguslawski and Querales are analogous art because both are in the field of hydrocarbon production. It would have been obvious to a person of ordinary skill before the effective filing date of the claimed invention to incorporate the predicting a future anomaly and generating an alert of Boguslawski with the system of Querales, as predicting a future anomaly helps improve production and reduce downtime (page 8, paragraph 38 lines 3-4) and an alert reduces risk to field personnel (page 3, paragraph 6 “reduce health and safety risks”).
Regarding claim 14, the combination of Querales, Coates, Bestman, Bazzocchi, and Boguslawski teaches the system of claim 11.
Coates also teaches detecting abnormalities (anomalies) indicative of a gas influx (gas migration event) (page 1 Line 30 – Page 2, Line 7, wherein a “gas kick” is equivalent to the claimed gas migration event).
Boguslawski also teaches an AI model (page 3, paragraph 6 lines 2-3 “machine learning”) that detects the anomaly (page 9, line 1) and a preventative maintenance model that predicts a future anomaly (page 9, lines 2-4).
Querales and Boguslawski are analogous to the claimed invention because all are in the field of hydrocarbon extraction. It would have been obvious to a person of ordinary skill before the effective filing date of the claimed invention to incorporate the AI model and the preventative maintenance model of Boguslawski with the system of Querales because detecting presently occurring and future anomalies reduces risk of damage (page 3, paragraph 6 lines 8-11).
Bestman also teaches wherein the anomaly is detected by determining, with the AI model, an anomaly status, where the anomaly status is either positive if an anomaly is detected, or negative if an anomaly is not detected (abstract, “performance deviation”, “threshold”).
Querales and Bestman are analogous to the claimed invention because all are in the field of hydrocarbon extraction. It would have been obvious to a person of ordinary skill before the effective filing date of the claimed invention to incorporate the threshold of Bestman with the system of Querales because a threshold is potentially variable and can be altered by an operator depending on conditions (pages 6-7, paragraph 70 “performance deviation”).
Regarding claim 15, the combination of Querales, Coates, Bestman, Bazzocchi, and Boguslawski teaches the system of claim 14.
Bestman also teaches the system of claim 14, wherein the AI model is an anomaly detection model (“prediction & machine learning module 225”, abstract “performance deviation”).
Regarding claim 18, the combination of Querales and Coates teaches the non-transitory computer-readable memory of claim 17.
Coates also teaches detecting abnormalities (anomalies) indicative of a gas influx (gas migration event) (page 1 Line 30 – Page 2, Line 7, wherein a “gas kick” is equivalent to the claimed gas migration event).
The combination of Querales and Coates does not teach the non-transitory computer-readable memory of claim 17, the steps further comprising: obtaining environmental data related to the hydrocarbon production well, the environmental data comprising one or more of: weather forecast data, and seismic activity data; predicting a future gas migration event by processing the process data and the environmental data with the computational model; determining a preventative maintenance action based on the predicted future gas migration event; and generating an alert for the detected gas migration event, wherein the preventative maintenance action is performed on the production well.
However, Bestman teaches obtaining environmental data related to the hydrocarbon production well, the environmental data comprising at least one of: weather forecast data, and seismic activity data (Paragraph 28).
Querales and Bestman are analogous to the claimed invention because both are in the field of hydrocarbon extraction. It would have been obvious to a person of ordinary skill before the effective filing date of the claimed invention to incorporate the obtaining environmental data of Bestman with the non-transitory computer-readable memory of Querales, as adding the seismic data contributes to models which can predict well behavior (Paragraphs 5-6).
The combination of Querales, Coates, and Bestman does not teach the non-transitory computer-readable memory of claim 17, the steps further comprising: predicting a future gas migration event by processing the process data and the environmental data with the computational model; determining a preventative maintenance action based on the predicted future gas migration event; and generating an alert for the detected gas migration event, wherein the preventative maintenance action is performed on the production well.
However, Bazzocchi teaches determining a preventative maintenance action based on the predicted anomaly (col. 3 line 60 “maintenance triggers”, lines 64-65); and performing the preventative maintenance action on the production (col. 6 lines 38-41, 47-48).
Bazzocchi and Querales are analogous to the claimed invention because both are in the field of operating an industrial plant. It would have been obvious to a person of ordinary skill before the effective filing date of the claimed invention to incorporate the determining a first expected economic cost of Bazzocchi with the non-transitory computer-readable memory of Querales since determining an expected economic cost and performing the preventative maintenance action when the cost of maintenance is lower than the cost of failure minimizes the cost in general (col. 6 lines 38-41, 47-48).
The combination of Querales, Coates, Bestman, and Bazzocchi does not specifically teach the system of claim 9, wherein the computer is further configured to: predict a future gas migration event by processing the process data and the environmental data with the computational model; and generate an alert for the predicted gas migration event; wherein the preventative maintenance action is performed on the production well.
However, Boguslawski teaches predicting a anomaly by processing the process data and the environmental data (page 8, paragraph 38 “provide early indication”) and generating an alert for a predicted anomaly (page 7, paragraph 35 line 15).
Boguslawski and Querales are analogous art because both are in the field of hydrocarbon production. It would have been obvious to a person of ordinary skill before the effective filing date of the claimed invention to incorporate the predicting a future anomaly and generating an alert of Boguslawski with the non-transitory computer-readable memory of Querales, as predicting a future anomaly helps improve production and reduce downtime (page 8, paragraph 38 lines 3-4) and an alert reduces risk to field personnel (page 3, paragraph 6 “reduce health and safety risks”).
Regarding claim 19, the combination of Querales, Coates, Bestman, Bazzocchi, and Boguslawski teaches the non-transitory computer-readable memory of claim 18.
Coates also teaches detecting abnormalities (anomalies) indicative of a gas influx (gas migration event) (page 1 Line 30 – Page 2, Line 7, wherein a “gas kick” is equivalent to the claimed gas migration event).
Querales does not teach the non-transitory computer-readable memory of claim 18 wherein the computational model comprises: an artificial intelligence (AI) model that detects the gas migration event; and a preventative maintenance model that predicts the future gas migration event; and wherein the gas migration event is detected by determining, with the AI model, a gas migration status, where the gas migration status is either positive if a gas migration is detected, or negative if a gas migration is not detected.
Boguslawski also teaches an AI model (page 3, paragraph 6 lines 2-3 “machine learning”) that detects the anomaly (page 9, line 1) and a preventative maintenance model that predicts a future anomaly (page 9, lines 2-4).
Querales and Boguslawski are analogous to the claimed invention because all are in the field of hydrocarbon extraction. It would have been obvious to a person of ordinary skill before the effective filing date of the claimed invention to incorporate the AI model and the preventative maintenance model of Boguslawski with the non-transitory computer-readable memory of Querales because detecting presently occurring and future anomalies reduces risk of damage (page 3, paragraph 6 lines 8-11).
Bestman also teaches wherein the anomaly is detected by determining, with the AI model, a an anomaly status, where the anomaly status is either positive if an anomaly is detected, or negative if an anomaly is not detected (abstract, “performance deviation”, “threshold”).
Querales and Bestman are analogous to the claimed invention because all are in the field of hydrocarbon extraction. It would have been obvious to a person of ordinary skill before the effective filing date of the claimed invention to incorporate the threshold of Bestman with the non-transitory computer-readable memory of Querales because a threshold is potentially variable and can be altered by an operator depending on conditions (pages 6-7, paragraph 70 “performance deviation”).
Regarding claim 20, the combination of Querales, Coates, Bestman, Bazzocchi, and Boguslawski teaches the non-transitory computer-readable memory of claim 19.
Bestman also teaches the non-transitory computer-readable memory of claim 19, wherein the AI model is an anomaly detection model (“prediction & machine learning module 225”, abstract “performance deviation”).
Claim(s) 4 is/are rejected under 35 U.S.C. 103 as being unpatentable over Querales and Coates in light of Boguslawski.
Regarding claim 4, the combination of Querales and Coates teaches the method of claim 1.
Coates also teaches detecting abnormalities (anomalies) indicative of a gas influx (gas migration event) (page 1 Line 30 – Page 2, Line 7, wherein a “gas kick” is equivalent to the claimed gas migration event).
The combination of Querales and Coates does not teach the method of claim 1, further comprising adjusting one or more operation parameters in the set of operation parameters, using the computational model, in order to prevent a future gas migration event.
However, Boguslawski teaches adjusting one or more operation parameters in the set of operation parameters, using the computational model, in order to prevent a future anomaly (page 9, lines 2-4 “predict abnormal events”, lines 13-14 “corrective action”).
Querales and Boguslawski are analogous to the claimed invention because they are all in the field of hydrocarbon extraction. It would have been obvious to a person of ordinary skill before the effective filing date of the claimed invention to incorporate the adjusting one or more parameters to prevent a future anomaly of Boguslawski with the generic anomaly detection and mitigation of Querales to reduce damage from a future event (page 3, paragraph 6 lines 8-11).
Claim(s) 8 is/are rejected under 35 U.S.C. 103 as being unpatentable over Querales and Coates further in light of Bestman, further in light of Bazzocchi, further in light of Boguslawski, further in light of McMurry (US 3601191 A).
Regarding claim 8, the combination of Querales, Coates, Bestman, Bazzocchi, and Boguslawski teaches the method of claim 3.
Bazzocchi also teaches the repair or replacement of components (col. 4 lines 39-44).
Querales and Bazzocchi are analogous to the claimed invention because all are in the field of operating an industrial plant. It would have been obvious to a person of ordinary skill before the effective filing date of the claimed invention to incorporate the incorporate the repairing and replacement of components of Bazzocchi with the method of Querales, since gas-lift well (plant) assets need maintenance/replacement (col. 1, lines 20-26)
The combination of Querales, Coates, Bestman, Bazzocchi, and Boguslawski does not specifically teach the method of claim 3, wherein the maintenance action is one of: repairing cement, repairing valves, and replacing valves, wherein the predictive maintenance action is one of: improving cement condition, replacing valves, and updating hydrocarbon production equipment.
However, McMurry teaches the replacing of defective valves (col. 1 lines 17-21).
Querales and McMurry are analogous to the claimed invention because all are in the field of hydrocarbon extraction. It would have been obvious to a person of ordinary skill before the effective filing date of the replacing of defective valves of McMurry with the method of Querales, since gas-lift valves are known to be an important component in gas-lift wells (col 1. lines 9-16) respectively.
Conclusion
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/WILLIAM XIANG ZHANG/Examiner, Art Unit 2117
/ROBERT E FENNEMA/Supervisory Patent Examiner, Art Unit 2117