DETAILED ACTION
Claims 1-25 are presented for examination. Claims 1, 19, 22, and 23 stand currently amended. Claim 25 is new.
The present application, filed on or after March 16, 2013, is being examined under the first inventor to file provisions of the AIA .
Continued Examination Under 37 CFR 1.114
A request for continued examination under 37 CFR 1.114, including the fee set forth in 37 CFR 1.17(e), was filed in this application after final rejection. Since this application is eligible for continued examination under 37 CFR 1.114, and the fee set forth in 37 CFR 1.17(e) has been timely paid, the finality of the previous Office action has been withdrawn pursuant to 37 CFR 1.114. Applicant's submission filed on 9 March 2026 has been entered.
Response to Arguments
Applicant's remarks filed 9 March 2026 have been fully considered and Examiner’s response is as follows:
Applicant remarks page 11 argues:
In the Office Action, the Examiner indicated that Bello "does not explicitly disclose determining a presence of malfunctioning hardware." See Office Action, p. 20. Indeed, Applicant agrees that Bello appears silent at least as to determining a presence of malfunctioning hardware and, therefore, is silent as to "determining, based on the calculated fluid flow, the updated fluid flow, or both, a presence of one or more malfunctioning hardware components at the desired location, wherein the one or more malfunctioning hardware components comprises an inflow control device, a valve, one or more packers, one or more tubulars, or any combination thereof," as generally recited in amended independent claim 1. Thus, Applicant submits that Bello does not anticipate amended independent claim 1 because it does not disclose each and every feature of the this claim.
…. Ba is silent, however, regarding malfunctioning "hardware components at the desired location, wherein the one or more malfunctioning hardware components comprises an inflow control device, a valve, one or more packers, one or more tubulars, or any combination thereof," as recited in amended independent claim 1. Thus, Ba fails to teach at least this claim element.
This argument has been fully considered and is persuasive. Therefore, the rejection has been withdrawn. However, upon further consideration, a new ground(s) of rejection is made over US 2016/0356125 A1 Bello, et al. (cited in IDS dated 11 April 2023) [herein “Bello”] in view of US patent 10,590,752 B2 Al-Gouhi, et al. [herein “Al-Gouhi”].
Applicant remarks page 14 further argues:
For substantially the same reasons as those discussed above regarding independent claims 1 and 23, Applicant submits that Bello, taken either alone or in hypothetical combination, fails to teach or suggest at least these recitations. Further, Applicant submits that Ba, taken alone or in combination, fails to obviate these deficiencies of Bello. Indeed, as discussed above, the Examiner stated in the Non-Final Office Action dated 16 October 2025 that "[n]one of the references taken either alone or in combination with the prior art of record disclose 'after recalibrating the model, comparing the model with a digital twin of the well."' See Non-Final Office Action, p. 21. Thus, the cited references fail to teach at least "comparing, based on at least the calculated fluid flow, the recalibrated model with the digital twin of the well to determine whether one or more properties of the recalibrated model varies from one or more corresponding properties of the digital twin," as recited in amended independent claim 19.
This argument is unpersuasive.
Claim 19 is not similarly situated as either claim 1 or 23 which recite the recalibrated model and the digital twin as separate models. Claim 19 however, recites the recalibrated model is the digital twin. This raises a §112(b) issue and also materially alters the scope of claim 19 in comparison to claims 1 and 23. See Examiner’s detailed rejections below.
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 19-22 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 pre-AIA the applicant regards as the invention.
Claim 19 recites “wherein the model is a digital twin of the well” and “dynamically recalibrating, …, the model.” This defines antecedent basis for “the model” as the digital twin and clearly defines the recalibrating as on the digital twin model. However, claim 19 then recites “comparing, …, the recalibrated model with the digital twin.” How is the recalibrated digital twin compared with itself? There is no separate model recited within claim 19 with which to make a comparison because the recalibrated model is itself the digital twin. Accordingly, a person of ordinary skill in the art would be uncertain of how to perform this comparing.
Dependent claims 20-22 are rejected for depending from a rejected claim.
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 for establishing a background for determining obviousness under 35 U.S.C. 103 are summarized as follows:
1. Determining the scope and contents of the prior art.
2. Ascertaining the differences between the prior art and the claims at issue.
3. Resolving the level of ordinary skill in the pertinent art.
4. Considering objective evidence present in the application indicating obviousness or nonobviousness.
Claims 1-12, 14, 15, 17, and 18
Claims 1-12, 14, 15, 17, and 18 are rejected under 35 U.S.C. 103 as being unpatentable over US 2016/0356125 A1 Bello, et al. (cited in IDS dated 11 April 2023) [herein “Bello”] in view of US patent 10,590,752 B2 Al-Gouhi, et al. [herein “Al-Gouhi”].
Claim 1 recites “1. A computer-implemented method to perform a real-time analysis of fluid flow in a well.” Bello title discloses “Real-Time Monitoring and Estimation of Well system Production Performance.” See further Bello paragraph 17 “to estimate formation, borehole and production properties (e.g., oil, gas and/or water rates from single and multi-zonal well systems) and predict or forecast such properties based on measurement data.”
Claim 1 further recites “comprising: streaming raw data from a plurality of sensors disposed in a well; receiving the raw data from each sensor of the plurality of sensors simultaneously, wherein the raw data is indicative of fluid flow within the well.” Bello paragraph 19 discloses:
to receive real time measurement data from one or more energy industry sources such as single and/or multi-zonal production wells. Measurement data is obtained from downhole sensor devices such as permanent downhole gauges, distributed temperature sensors, distributed acoustic sensors and/or production logging tools
Downhole sensor devices correspond with sensors disposed in a well. Receiving real-time measurement data corresponds with a streaming of respective raw measurement data.
Bello paragraph 19 discloses “The measurement data is automatically filtered and input into a forward model of subsurface thermal multi-phase flow through porous media.” Using the measurement data for modeling multi-phase flow shows the measurement data is indicative of at least the respective fluid flow being modeled. Furthermore, Bello paragraph 31 discloses “measurement devices (e.g., for pressure, temperature and flow rate).” A downhole flow rate measurement is indicative of fluid flow. Furthermore, Bello paragraph 31 discloses “measurement devices (e.g., for pressure, temperature and flow rate).” A downhole flow rate measurement is indicative of fluid flow.
Claim 1 further recites “populating a model of the well using a plurality of input parameters, wherein the plurality of input parameters into the model are based on the raw data from each sensor of the plurality of sensors.” Bello paragraph 69 discloses “Input data includes various types of information that is used to build the model.” The data used to build the model corresponds with populating the model with respective data.
Bello paragraph 19 discloses “The measurement data is automatically filtered and input into a forward model of subsurface thermal multi-phase flow through porous media, which incorporates measurement response functions for numerical simulation using measurement data.” Bello paragraph 71 discloses “The system also employs an inverse model in conjunction with the forward model to perform functions such as calibration of the forward model and real time automatic adjustment of model parameters.”
Claim 1 further recites “identifying a desired location within a portion of the well to calculate a fluid flow of a fluid at the desired location.” Bello paragraph 27 discloses:
Various sensors are placed at suitable locations in the borehole 14 and/or the production string 12 to provide measurements or information relating to downhole parameters of interest. …. Density sensors may be fluid density measurements for fluid from each production zone and that of the combined fluid from two or more production zones. Resistivity sensors may provide measurements relating to the water content or the water cut of the fluid mixture received from each production zones. Other sensors may be used to estimate the oil/water ratio and gas/oil ratio for each production zone and for the combined fluid.
The sensor measurements from “each production zone” corresponds with desired locations within a portion of the well.
Bellow paragraph 32 discloses “estimation of multi-phase flow rates and production allocation from single and multi-zonal wells using a coupled thermal reservoir-borehole model, and automatic and/or online production prediction and model calibration.” The flow rates and production allocation of respective zones of multi-zonal wells is calculating fluid flow at respective desired locations of the respective zones within the well. Bello paragraph 25 teaches an upper and “lower production zone.” Without loss of generality, a lower production zone is a desired location within the well.
Bello paragraph 76 discloses:
forward simulation output is received, such as fluid flow rates, multi-phase flow rates and production allocation. At block 95, additional measurements are taken during production (at a next time step immediately following the current time step), such as downhole pressure, temperature and flow rates.
Downhole flow rates and production allocation correspond with fluid flow from respective desired location zones within the well(s).
Claim 1 further recites “calculating, based on the model, the fluid flow of the fluid flowing through the desired location within the well.” Bello paragraph 44 last sentence discloses “the modeling engine calculates hourly oil, gas and water flow rates for each individual well and/or clusters of wells in real time.” Calculating an oil, gas, and water flow rate for well(s) corresponds with calculating a fluid flow through a well location based on the model.
Bellow paragraph 32 discloses “estimation of multi-phase flow rates and production allocation from single and multi-zonal wells using a coupled thermal reservoir-borehole model, and automatic and/or online production prediction and model calibration.” The flow rates and production allocation of respective zones of multi-zonal wells is calculating fluid flow at respective desired locations of the respective zones within the well. Bello paragraph 25 teaches an upper and “lower production zone.” Without loss of generality, a lower production zone is a desired location within the well.
Claim 1 further recites “in response to receiving new data streamed from one or more sensors of the plurality of sensors: dynamically recalibrating the model in real time based on the new data.” Bello paragraph 82 discloses “The updating or calibration process may be performed in real time, i.e., as measurement data becomes available, performed according to a pre-set schedule or performed in response to a user instruction.” As measurement data becomes available corresponds with receiving new data from sensors.
Claim 1 further recites “and calculating an updated fluid flow of the fluid flowing through the desired location within the well based on the recalibrated model.” Bello paragraph 84 discloses:
However, if the simulated values are outside the tolerance, the system automatically calibrates the model by using the inverse model to adjust the forward model parameters so that acceptable agreement between simulated or calculate values and measured values is reached. When satisfactory agreement is reached, the production and formation properties may be output to a user, and the resulting numerical model may be used to make deterministic and probabilistic production forecasting until the next scheduled update time.
Using the updated model for production forecasting corresponds with calculating updated fluid flow(s).
Claim 1 further recites “and determining, based on the calculated fluid flow, the updated fluid flow, or both, a presence of one or more malfunctioning hardware components at the desired location, wherein the one or more malfunctioning hardware components comprises an inflow control device, a valve, one or more packers, one or more tubulars, or any combination thereof.” From the above list of alternatives the Examiner is selecting “a valve.”
Bello does not explicitly disclose determining a presence of malfunctioning hardware; however, in analogous art of predicting maintenance for downhole valves, Al-Gouhi column 11 lines 3-12 teaches “The FDM 112 can connect to the downhole sensors 102 and detect faults and abnormal conditions. Abnormal conditions can be abnormal hydraulic condition, valve condition, position condition, wellbore conditions and the well performance condition.” Al-Gouhi column 12 lines 14-18 teaches “If the measured flow rate change is significantly different from the expect flow rate change, for example, by an amount more than a predetermined threshold, the APPMDV system can determine that there is a valve abnormal condition.” Detecting faults and abnormal valve conditions correspond with determining a presence of a malfunction in a valve. Determining an abnormal condition by using a flow rate change is determining the presence of the malfunction based on a calculated fluid flow.
It would have been obvious to a person having ordinary skill in the art before the effective filing date of the claimed invention to combine Bello and Al-Gouhi. One having ordinary skill in the art would have found motivation to use predicting faults in valve conditions into the system of real-time monitoring and estimation of well systems for the advantageous purpose of “Manually scheduling maintenance operations by an operator can be inefficient and ineffective, especially if downhole valve conditions are unknown or there are a large number of valves in a well system.” See Al-Gouhi column 1 lines 20-24.
Claim 2 further recites “2. The computer-implemented method of claim 1, wherein populating the model comprises populating the model while the raw data is being simultaneously streamed from the plurality of sensors.” Bello paragraph 69 discloses “Input data includes various types of information that is used to build the model.” The data used to build the model corresponds with populating the model with said data.
Bello paragraph 19 discloses “The measurement data is automatically filtered and input into a forward model of subsurface thermal multi-phase flow through porous media, which incorporates measurement response functions for numerical simulation using measurement data.” Bello paragraph 82 discloses “The updating or calibration process may be performed in real time, i.e., as measurement data becomes available.” Real-time as data becomes available corresponds with simultaneous streaming of data.
Claim 2 further recites “and wherein calculating the fluid flow comprises calculating the fluid flow while the raw data is being simultaneously streamed from the plurality of sensors.” Bello paragraph 44 last sentence discloses “the modeling engine calculates hourly oil, gas and water flow rates for each individual well and/or clusters of wells in real time.” Calculating an oil, gas, and water flow rate for well(s) corresponds with calculating a fluid flow through a well location based on the model. Real-time corresponds with while data is simultaneously streaming.
Claim 3 further recites “3. The computer-implemented method of claim 1, wherein calculating the fluid flow of the fluid flowing through the desired location comprises calculating the fluid flow through the desired location before the raw data is stored on a storage medium, and wherein calculating the updated fluid flow of the fluid flowing through the desired location comprises calculating the fluid flow of the fluid flowing through the desired location before the updated data is stored on the storage medium.” Bello paragraph 96 discloses “The application performs calculations and writes the results in another CSV file (e.g., production rates, forecasts, etc.) that can be stored in a specific folder.” Performing the calculations before writing the results to be stored in a specific folder corresponds with calculating the respective fluid flow before storing in the specific folder.
Claim 4 further recites “4. The computer-implemented method of claim 1, wherein, in response to receiving the new data, the method further comprising: determining a change in a parameter of the plurality of input parameters over a period of time; and dynamically recalibrating the model in real time based on the change in the parameter over the period of time.” Bello paragraph 81 discloses “If a deviation between the simulated data and received measurement data beyond a selected magnitude is detected, certain model parameters are adjusted to compensate for the model drift.” Model drift corresponds with a determined change in parameter over a period of time. The deviation of data corresponds with the change in parameter. Adjusting the model parameters to compensate corresponds with dynamically recalibrating the model in real time based on the change. See further Bello ¶84.
Claim 5 further recites “5. The computer-implemented method of claim 1, wherein, in response to receiving the new data, the method further comprising: determining a change in a characteristic of the fluid flow over a period of time; and dynamically recalibrating the model in real time based on the change in the characteristic of the fluid flow over the period of time.” Bello paragraph 81 discloses “If a deviation between the simulated data and received measurement data beyond a selected magnitude is detected, certain model parameters are adjusted to compensate for the model drift.” Model drift corresponds with a determined change in parameter over a period of time. The deviation of data corresponds with the change in parameter and parameters are a species of characteristics. Adjusting the model parameters to compensate corresponds with dynamically recalibrating the model in real time based on the change. See further Bello ¶84.
Claim 6 further recites “6. The computer-implemented method of claim 1, further comprising: determining if a model is within a threshold range; and in response to a determination that the output is not within a threshold range, dynamically recalibrating the model to fit the model within the threshold range.” Bello paragraph 84 discloses:
However, if the simulated values are outside the tolerance, the system automatically calibrates the model by using the inverse model to adjust the forward model parameters so that acceptable agreement between simulated or calculate values and measured values is reached. When satisfactory agreement is reached, the production and formation properties may be output to a user, and the resulting numerical model may be used to make deterministic and probabilistic production forecasting until the next scheduled update time.
The tolerance corresponds with the threshold range.
Claim 7 further recites “7. The computer-implemented method of claim 1, further comprising: determining if a model is within a threshold range; and in response to a determination that the output is not within a threshold range, requesting a manual recalibration of the model.” Bello paragraph 70 disclose “At block 85, estimated parameters including production and/or injection rates are compared to pre-selected facility constraints (e.g., oil, gas and water processing capacity), and if the estimated parameters exceed such constraints, they are recalculated.” The pre-selected facility constraints correspond with a threshold range. Determining that the parameter(s) exceeds the constraints corresponds with a determination that the output is not within the threshold range. Recalculating corresponds with a recalibration.
Bello paragraph 83 discloses:
The system provides an automatic updating and adjusting capability without requiring the availability of human intervention. The system functions in the background, updating and adjusting the model without intervention by the user so that the model continuously remains accurate as needed.
This partially teaches away from manual recalibration because while this “automatic updating and adjusting capability” is acting, Bello is teaching there is no need for intervention by a user. However, Bello paragraph 97 discloses:
Using the data interpretation from the application, users can have supervisory control of field assets and keep active control of the operations. In addition, results can be used to trigger exceptions using alarm management capabilities embedded in the solutions. … set based on user preference (e.g., email, SMS, sound or visual based alarms).
Users performing any active control of the operation corresponds with respective recalibration of respective parameters and/or settings. The alarm to the user corresponds with a request for the user to perform some corresponding manual action(s).
Claim 8 further recites “8. The computer-implemented method of claim 1, wherein the raw data and the new data comprise data indicative of at least one of a flow rate, a pressure, and a derivative of the pressure over time at the desired location.” From the above list of alternatives the Examiner is selecting “a pressure.”
Bello paragraph 79 discloses:
The received data (referred to as field data) includes measurement data and data relating to well system configurations. Such data includes, for example, current time, choke valve position, oil, gas and water rates, flow meter status, master and wing valve status, flow meter pressures and temperatures, bottomhole pressures and temperatures, and pressures and temperatures taken along a borehole (e.g., before choke pressures and temperatures, and after choke pressures and temperatures).
Claim 9 further recites “9. The computer-implemented method of claim 1, further comprising: determining a change in a coefficient of the model; and in response to a determination that the change is greater than a threshold value, dynamically recalibrating the model in real time.” Bello paragraph 33 discloses “managing sensor data for improved production monitoring and characterization (pressure and temperature transient detection) based on pattern recognition.” Transient detection based on patten recognition corresponds with determining that a coefficient value (i.e. temperature) is in a transient phase. Transient means the value (e.g. temperature) is changing and thus corresponds to when a change exceeds a threshold value.
Bello paragraph 80 disclose “Static data 104 and dynamic or transient (e.g., real-time) data 106 are forwarded via an input module 108 to a forward simulation module 110.” Bello paragraph 82 discloses “The updating or calibration process may be performed in real time, i.e., as measurement data becomes available, performed according to a pre-set schedule or performed in response to a user instruction.”
Claim 10 further recites “10. The computer-implemented method of claim 1, further comprising: projecting a first flow rate at a wellhead based on the model.” Bello paragraph 44 last sentence discloses “the modeling engine calculates hourly oil, gas and water flow rates for each individual well and/or clusters of wells in real time.” Calculating an oil, gas, and water flow rate for well(s) corresponds with calculating a first fluid flow through a well location based on the model.
Claim 10 further recites “determining a second flow rate that is measured at the wellhead.” Bello paragraph 29 disclose “receives data from downhole and surface sensors.” Surface sensors correspond with wellhead measurement. Bello paragraph 79 discloses:
The received data (referred to as field data) includes measurement data and data relating to well system configurations. Such data includes, for example, current time, choke valve position, oil, gas and water rates, flow meter status, master and wing valve status, flow meter pressures and temperatures, bottomhole pressures and temperatures, and pressures and temperatures taken along a borehole (e.g., before choke pressures and temperatures, and after choke pressures and temperatures).
Oil, gas, and water rate measurement data corresponds with measured flow rates.
Claim 10 further recites “comparing the first flow rate and the second flow rate; and in response to a determination that the first flow rate and the second flow rate vary by more than a threshold; dynamically recalibrating the model in real time.” Bello paragraph 84 discloses:
However, if the simulated values are outside the tolerance, the system automatically calibrates the model by using the inverse model to adjust the forward model parameters so that acceptable agreement between simulated or calculate values and measured values is reached. When satisfactory agreement is reached, the production and formation properties may be output to a user, and the resulting numerical model may be used to make deterministic and probabilistic production forecasting until the next scheduled update time.
Determining if the simulated values are outside the tolerance corresponds with comparing the flow rates to determine the difference varies by more than a threshold. The tolerance corresponds with the threshold. Adjusting the forward model parameters so that acceptable agreement is reached corresponds with dynamically recalibrating the model.
Claim 10 further recites “and calculating an updated fluid flow of the fluid flowing through the desired location within the well based on the recalibrated model.” Bello paragraph 84 discloses:
However, if the simulated values are outside the tolerance, the system automatically calibrates the model by using the inverse model to adjust the forward model parameters so that acceptable agreement between simulated or calculate values and measured values is reached. When satisfactory agreement is reached, the production and formation properties may be output to a user, and the resulting numerical model may be used to make deterministic and probabilistic production forecasting until the next scheduled update time.
Using the updated model for production forecasting corresponds with calculating updated fluid flow(s).
Claim 11 further recites “11. The computer-implemented method of claim 1, further comprising: projecting a first pressure at a wellhead based on the model.” Bello paragraph 76 discloses “At block 96, an inversion model is run to calculate values of expected measurements, e.g., pressure, temperature and/or flow rates, compare the expected measurements to the additional measurements and iteratively adjust parameters of the forward model.
Claim 11 further recites “determining a second pressure that is measured at the wellhead.” Bello paragraph 29 disclose “receives data from downhole and surface sensors.” Bello paragraph 28 discloses “Sensors also may be provided at the surface, such as a sensor for measuring the water content in the received fluid, total flow rate for the received fluid, fluid pressure at the wellhead, temperature, etc.”
Claim 11 further recites “and in response to a determination that the first pressure and the second pressure vary by more than a threshold; dynamically recalibrating the model in real time.” Bello paragraph 84 discloses:
However, if the simulated values are outside the tolerance, the system automatically calibrates the model by using the inverse model to adjust the forward model parameters so that acceptable agreement between simulated or calculate values and measured values is reached. When satisfactory agreement is reached, the production and formation properties may be output to a user, and the resulting numerical model may be used to make deterministic and probabilistic production forecasting until the next scheduled update time.
Determining if the simulated values are outside the tolerance corresponds with comparing the flow rates to determine the difference varies by more than a threshold. The tolerance corresponds with the threshold. Adjusting the forward model parameters so that acceptable agreement is reached corresponds with dynamically recalibrating the model.
Claim 11 further recites “and calculating an updated fluid flow of the fluid flowing through the desired location within the well based on the recalibrated model.” Bello paragraph 84 discloses:
However, if the simulated values are outside the tolerance, the system automatically calibrates the model by using the inverse model to adjust the forward model parameters so that acceptable agreement between simulated or calculate values and measured values is reached. When satisfactory agreement is reached, the production and formation properties may be output to a user, and the resulting numerical model may be used to make deterministic and probabilistic production forecasting until the next scheduled update time.
Using the updated model for production forecasting corresponds with calculating updated fluid flow(s).
Claim 12 further recites “12. The computer-implemented method of claim 1, further comprising: analyzing the data and previously received data from the plurality of sensors for a pattern associated with the fluid flow of the fluid; determining a change to the pattern associated with the fluid flow; and in response to a determination that the change to the pattern is greater than a threshold, dynamically recalibrating the model in real time.” Bello paragraph 33 discloses “managing sensor data for improved production monitoring and characterization (pressure and temperature transient detection) based on pattern recognition.” Transient detection based on patten recognition corresponds with determining that a coefficient value (i.e. temperature) is in a transient phase. Transient means the value (e.g. temperature) is changing and thus corresponds to when a change exceeds a threshold value.
Bello paragraph 80 disclose “Static data 104 and dynamic or transient (e.g., real-time) data 106 are forwarded via an input module 108 to a forward simulation module 110.” Bello paragraph 82 discloses “The updating or calibration process may be performed in real time, i.e., as measurement data becomes available, performed according to a pre-set schedule or performed in response to a user instruction.”
Claim 14 further recites “14. The computer-implemented method of claim 1, further comprising: predicting the fluid flow of the fluid within a threshold period of time based on the model; and in response to receiving the new data, predicting the fluid flow of the fluid within the threshold period of time based on the new data.” Bello paragraph 84 discloses:
However, if the simulated values are outside the tolerance, the system automatically calibrates the model by using the inverse model to adjust the forward model parameters so that acceptable agreement between simulated or calculate values and measured values is reached. When satisfactory agreement is reached, the production and formation properties may be output to a user, and the resulting numerical model may be used to make deterministic and probabilistic production forecasting until the next scheduled update time.
Using the updated model for production forecasting corresponds with predicting updated fluid flow(s). The respectively scheduled update times correspond with threshold periods of time.
Claim 15 further recites “15. The computer-implemented method of claim 1, wherein one or more input parameters of the plurality of input parameters comprise a property of one or more hardware components of the well, and wherein populating the model of the well with the data comprises populating the model of the well based on the property of the one or more hardware components.” Bello paragraph 79 discloses:
The received data (referred to as field data) includes measurement data and data relating to well system configurations. Such data includes, for example, current time, choke valve position, oil, gas and water rates, flow meter status, master and wing valve status, flow meter pressures and temperatures, bottomhole pressures and temperatures, and pressures and temperatures taken along a borehole (e.g., before choke pressures and temperatures, and after choke pressures and temperatures).
Flow meter status and pressures correspond with properties of hardware components.
Claim 17 further recites “17. The computer-implemented method of claim 1, wherein calculating the fluid flow comprises calculating a flow rate of a hydrocarbon resource at the desired location, and wherein calculating the updated fluid flow comprises calculating the updated flow rate of the hydrocarbon resource at the desired location.” Bello paragraph 44 last sentence discloses “the modeling engine calculates hourly oil, gas and water flow rates for each individual well and/or clusters of wells in real time.” Calculating oil and gas flow rate(s) for well(s) corresponds with calculating a hydrocarbon fluid flow through a well location based on the model.
Bello paragraph 84 discloses:
However, if the simulated values are outside the tolerance, the system automatically calibrates the model by using the inverse model to adjust the forward model parameters so that acceptable agreement between simulated or calculate values and measured values is reached. When satisfactory agreement is reached, the production and formation properties may be output to a user, and the resulting numerical model may be used to make deterministic and probabilistic production forecasting until the next scheduled update time.
Using the updated model for production forecasting corresponds with calculating updated fluid flow(s).
Claim 18 further recites “18. The computer-implemented method of claim 1, further comprising: creating a fluid flow map of the fluid flow through the desired location within the well.” Bello paragraph 44 last sentence discloses “the modeling engine calculates hourly oil, gas and water flow rates for each individual well and/or clusters of wells in real time.” Calculating an oil, gas, and water flow rate for well(s) corresponds with calculating a fluid flow through a well location based on the model. Bello paragraph 50 discloses:
Calculation of the forward model includes performing a fluid property simulation using available information and assumptions. The fluid property simulation includes inputting PVT (pressure, volume and temperature) properties into a compositional simulator to predict thermodynamic and transport properties of fluids produced from a formation or reservoir. The thermodynamic and transport properties allow for prediction of fluid behavior from the reservoir along the borehole.
Predicted thermodynamic, transport properties, and fluid behavior along the borehole corresponds to a mapping of fluid flow through respective sections of the borehole/well. Along the borehole includes desired locations within the well, i.e. of a lower or upper zone in a multi-zonal well.
Claim 18 further recites “and in response to receiving new data streamed from the one or more sensors of the plurality of sensors: creating an updated fluid flow map of the fluid flow through the desired location within the well.” Bello paragraph 84 discloses:
However, if the simulated values are outside the tolerance, the system automatically calibrates the model by using the inverse model to adjust the forward model parameters so that acceptable agreement between simulated or calculate values and measured values is reached. When satisfactory agreement is reached, the production and formation properties may be output to a user, and the resulting numerical model may be used to make deterministic and probabilistic production forecasting until the next scheduled update time.
Using the updated model for production forecasting corresponds with creating updated fluid flow(s) in response to the automatically calibrated model.
Dependent Claim 13
Claim 13 is rejected under 35 U.S.C. 103 as being unpatentable over US 2016/0356125 A1 Bello, et al. (cited in IDS dated 11 April 2023) [herein “Bello”] and US patent 10,590,752 B2 Al-Gouhi, et al. [herein “Al-Gouhi”] as applied to claim 1 above, and further in view of US patent 10,365,200 B2 Liu, et al. [herein “Liu”].
Claim 13 further recites “13. The computer-implemented method of claim 1, further comprising: determining a flow direction of the fluid flowing through the desired location within the well based on the model.” Bello paragraph 44 last sentence discloses “the modeling engine calculates hourly oil, gas and water flow rates for each individual well and/or clusters of wells in real time.” Calculating an oil, gas, and water flow rate for well(s) corresponds with calculating a fluid flow through a well location based on the model. Bello paragraph 50 discloses:
Calculation of the forward model includes performing a fluid property simulation using available information and assumptions. The fluid property simulation includes inputting PVT (pressure, volume and temperature) properties into a compositional simulator to predict thermodynamic and transport properties of fluids produced from a formation or reservoir. The thermodynamic and transport properties allow for prediction of fluid behavior from the reservoir along the borehole.
Predicted thermodynamic, transport properties, and fluid behavior along the borehole corresponds to a mapping of fluid flow through respective sections of the borehole/well.
Bello paragraph 110 discloses “output parameters such as oil, gas and water flows vs time (for PDGs) and/or depth (for PLTs) are written to a file called 'oil-gas-flux-v-time'.”
Bello does not explicitly disclose a determined flow direction; however, in analogous art of hydrocarbon reservoir analysis, Liu column 9 lines 27-28 teaches “The liquid flux q can be positive or negative to represent its direction.” A representation of flux direction corresponds with a determined flow direction.
It would have been obvious to a person having ordinary skill in the art before the effective filing date of the claimed invention to combine Bello, Al-Gouhi, and Liu. One having ordinary skill in the art would have found motivation to use positive or negative to represent direction into the system of real-time monitoring and estimation of well systems for the advantageous purpose of providing a flux representation consistent with Darcy’s equations. Compare Bello paragraph 55 (“Darcy’s equation is used”) with Liu column 9 lines 25-27.
Claim 13 further recites “and in response to receiving the new data: dynamically recalibrating the model in real time based on the new data.” Bello paragraph 82 discloses “The updating or calibration process may be performed in real time, i.e., as measurement data becomes available, performed according to a pre-set schedule or performed in response to a user instruction.” As measurement data becomes available corresponds with receiving new data from sensors.
Claim 13 further recites “and determining the flow direction of the fluid flowing through the desired location within the well based on the recalibrated model.” Bello paragraph 84 discloses:
However, if the simulated values are outside the tolerance, the system automatically calibrates the model by using the inverse model to adjust the forward model parameters so that acceptable agreement between simulated or calculate values and measured values is reached. When satisfactory agreement is reached, the production and formation properties may be output to a user, and the resulting numerical model may be used to make deterministic and probabilistic production forecasting until the next scheduled update time.
Using the updated model for production forecasting corresponds with calculating updated fluid flow(s).
Claims 19-22
Claims 19-22 are rejected under 35 U.S.C. 103 as being unpatentable over US 2016/0356125 A1 Bello, et al. (cited in IDS dated 11 April 2023) [herein “Bello”] and US patent 10,590,752 B2 Al-Gouhi, et al. [herein “Al-Gouhi”] in view of US patent 12,116,878 B2 Ba, et al. [herein “Ba”].
Claim 19 recites “19. A computer-implemented method to perform a real-time analysis of a well operation.” Bello title discloses “Real-Time Monitoring and Estimation of Well system Production Performance.” See further Bello paragraph 17 “to estimate formation, borehole and production properties (e.g., oil, gas and/or water rates from single and multi-zonal well systems) and predict or forecast such properties based on measurement data.”
Claim 19 further recites “comprising: determining, based on raw data streamed from a plurality of sensors disposed in a well, a presence of a malfunctioning hardware disposed in the well.” Bello paragraph 19 discloses:
to receive real time measurement data from one or more energy industry sources such as single and/or multi-zonal production wells. Measurement data is obtained from downhole sensor devices such as permanent downhole gauges, distributed temperature sensors, distributed acoustic sensors and/or production logging tools
Downhole sensor devices correspond with sensors disposed in a well. Receiving real-time measurement data corresponds with a streaming of respective raw measurement data.
Bello paragraph 84 teaches comparing simulated a measured values with respect to a tolerance to determine whether the model should be automatically calibrated.
But Bello does not explicitly disclose determining a presence of malfunctioning hardware; however, in analogous art of oil and gas field well operations, Ba column 8 lines 20-28 teaches:
a potential sensor malfunction may be determined based on the data quality and the comparison, as at 214. In other words, the combination of the data quality and the discrepancy may determine whether the model should be updated, or the sensor is likely malfunctioning, or both. For example, if the data quality is low, it may be determined that the sensor is malfunctioning. If the data quality is high, it may be more likely that the model should be adjusted, e.g., depending on the size of the discrepancy.
Determining a sensor malfunction corresponds with determining the presence of a malfunctioning hardware disposed in the well.
It would have been obvious to a person having ordinary skill in the art before the effective filing date of the claimed invention to combine Bello, Al-Gouhi, and Ba. One having ordinary skill in the art would have found motivation to use determining sensor malfunctions into the system of real-time monitoring and estimation of well systems for the advantageous purpose to “provide insight into the veracity of the sensor measurement when compared to model predictions.” See Ba column 8 lines 2-3 and see further Ba column 7 line 9 to column 8 line 26.
Claim 19 further recites “wherein the raw data is indicative of fluid flow within the well.” Bello paragraph 19 discloses “The measurement data is automatically filtered and input into a forward model of subsurface thermal multi-phase flow through porous media.” Using the measurement data for modeling multi-phase flow shows the measurement data is indicative of at least the respective fluid flow being modeled. Furthermore, Bello paragraph 31 discloses “measurement devices (e.g., for pressure, temperature and flow rate).” A downhole flow rate measurement is indicative of fluid flow. Furthermore, Bello paragraph 31 discloses “measurement devices (e.g., for pressure, temperature and flow rate).” A downhole flow rate measurement is indicative of fluid flow.
Claim 19 further recites “populating a model of the well based on the raw data from each sensor of the plurality of sensors, wherein the model is a digital twin of the well.” Bello paragraph 69 discloses “Input data includes various types of information that is used to build the model.” The data used to build the model corresponds with populating the model with respective data.
Bello paragraph 19 discloses “The measurement data is automatically filtered and input into a forward model of subsurface thermal multi-phase flow through porous media, which incorporates measurement response functions for numerical simulation using measurement data.” Bello paragraph 71 discloses “The system also employs an inverse model in conjunction with the forward model to perform functions such as calibration of the forward model and real time automatic adjustment of model parameters.” Without loss of generality, the forward model corresponds with a digital twin of the well.
Claim 19 further recites “dynamically recalibrating, in real time, the model to populate a recalibrated model that optimizes a property of the model based on new data streamed from the plurality of sensors.” Bello paragraph 82 discloses “The updating or calibration process may be performed in real time, i.e., as measurement data becomes available, performed according to a pre-set schedule or performed in response to a user instruction.” As measurement data becomes available corresponds with receiving new data from sensors.
Claim 19 further recites “identifying a desired location within the well to calculate, via the recalibrated model, a fluid flow of a fluid flowing through the desired location, wherein the desired location is located within a portion of the well.” Bello paragraph 27 discloses:
Various sensors are placed at suitable locations in the borehole 14 and/or the production string 12 to provide measurements or information relating to downhole parameters of interest. …. Density sensors may be fluid density measurements for fluid from each production zone and that of the combined fluid from two or more production zones. Resistivity sensors may provide measurements relating to the water content or the water cut of the fluid mixture received from each production zones. Other sensors may be used to estimate the oil/water ratio and gas/oil ratio for each production zone and for the combined fluid.
The sensor measurements from “each production zone” corresponds with desired locations within a portion of the well.
Bellow paragraph 32 discloses “estimation of multi-phase flow rates and production allocation from single and multi-zonal wells using a coupled thermal reservoir-borehole model, and automatic and/or online production prediction and model calibration.” The flow rates and production allocation of respective zones of multi-zonal wells is calculating fluid flow at respective desired locations of the respective zones within the well. Bello paragraph 25 teaches an upper and “lower production zone.” Without loss of generality, a lower production zone is a desired location within the well.
Bello paragraph 76 discloses:
forward simulation output is received, such as fluid flow rates, multi-phase flow rates and production allocation. At block 95, additional measurements are taken during production (at a next time step immediately following the current time step), such as downhole pressure, temperature and flow rates.
Downhole flow rates and production allocation correspond with fluid flow from respective desired location zones within the well(s).
Claim 19 further recites “and comparing, based on at least the calculated fluid flow, the recalibrated model with the digital twin of the well to determine whether one or more properties of the recalibrated model varies from one or more corresponding properties of the digital twin.” This claim recitation involves significant indefiniteness issues. See §112(b) section above. For purposes of compact prosecution the Examiner is interpreting the comparison as a comparison between the recalibrated digital twin model and any other model.
Bello paragraph 76 discloses:
At block 96, an inversion model is run to calculate values of expected measurements, e.g., pressure, temperature and/or flow rates, compare the expected measurements to the additional measurements and iteratively adjust parameters of the forward model.
The inversion model is a different model. A next time step of this iterative process involves comparing the recalibrated model of a previous iteration with the expected measurements of the inverse model of the current iteration to determine additional adjustments to parameters of the at least once recalibrated forward model.
Claim 20 further recites “20. The computer-implemented method of claim 19, wherein determining the presence of the malfunctioning hardware comprises determining, based on the raw data streamed from the plurality of sensors over a period of time, the presence of the malfunctioning hardware.” Bello paragraph 84 discloses:
However, if the simulated values are outside the tolerance, the system automatically calibrates the model by using the inverse model to adjust the forward model parameters so that acceptable agreement between simulated or calculate values and measured values is reached. When satisfactory agreement is reached, the production and formation properties may be output to a user, and the resulting numerical model may be used to make deterministic and probabilistic production forecasting until the next scheduled update time.
The scheduled update time corresponds with a threshold period of time and thus corresponds with being over a period of time.
But Bello does not explicitly disclose determining a presence of malfunctioning hardware; however, in analogous art of oil and gas field well operations, Ba column 8 lines 20-28 teaches:
a potential sensor malfunction may be determined based on the data quality and the comparison, as at 214. In other words, the combination of the data quality and the discrepancy may determine whether the model should be updated, or the sensor is likely malfunctioning, or both. For example, if the data quality is low, it may be determined that the sensor is malfunctioning. If the data quality is high, it may be more likely that the model should be adjusted, e.g., depending on the size of the discrepancy.
Determining a sensor malfunction corresponds with determining the presence of a malfunctioning hardware disposed in the well. See further Ba column 7 lines 29-34 (“timeliness data quality dimension”).
It would have been obvious to a person having ordinary skill in the art before the effective filing date of the claimed invention to combine Bello, Al-Gouhi, and Ba. One having ordinary skill in the art would have found motivation to use determining sensor malfunctions into the system of real-time monitoring and estimation of well systems for the advantageous purpose to “provide insight into the veracity of the sensor measurement when compared to model predictions.” See Ba column 8 lines 2-3 and see further Ba column 7 line 9 to column 8 line 26.
Claim 21 further recites “21. The computer-implemented method of claim 19, further comprising predicting, based on the model, the fluid flow of the fluid flowing through the desired location within a threshold period of time.” Bello paragraph 84 discloses:
However, if the simulated values are outside the tolerance, the system automatically calibrates the model by using the inverse model to adjust the forward model parameters so that acceptable agreement between simulated or calculate values and measured values is reached. When satisfactory agreement is reached, the production and formation properties may be output to a user, and the resulting numerical model may be used to make deterministic and probabilistic production forecasting until the next scheduled update time.
The scheduled update time corresponds with a threshold period of time.
Bello paragraph 44 last sentence discloses “the modeling engine calculates hourly oil, gas and water flow rates for each individual well and/or clusters of wells in real time.” Calculating an oil, gas, and water flow rate for well(s) corresponds with calculating a fluid flow through a well location based on the model.
Claim 22 further recites “22. The computer-implemented method of claim 19, further comprising: performing a fluid flow simulation of the fluid flowing through the desired location within the well to assess the fluid flow of the fluid; determining an existing configuration of hardware components associated with the fluid flow of the fluid; and providing a recommendation to reconfigure the hardware components to optimize the fluid flow of the fluid.” Bello paragraph 33 disclose “integrating production data monitoring inflow control devices, predicting and optimizing flow control device settings to improve hydrocarbon recovery.” Improving hydrocarbon recovery corresponds to an optimized fluid flow of the fluid. Optimizing the flow control device settings corresponds with provided recommended reconfigurations of respective hardware. The inflow control devices are hardware. Using the prediction for the improved hydrocarbon recovery corresponds with using the fluid flow simulation to determine said improvements to fluid flow.
Allowable Subject Matter
Claims 23-25 are allowed.
Claim 16 is objected to as being dependent upon a rejected base claim, but would be allowable if rewritten in independent form including all of the limitations of the base claim and any intervening claims.
Examiner previously presented reasons for the indication of allowable subject matter in the office action dated 16 October 2025.
Conclusion
Prior art made of record and not relied upon is considered pertinent to applicant's disclosure.
US 10927663 B2 Williamson; Patrick et al.
Teaches
Fault detection and recovery in a tubing string located in a hydrocarbon well
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/Jay Hann/Primary Examiner, Art Unit 2186 2 April 2026
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