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 .
Response to Amendment
The Amendments to the Claims filed 12/16/2025 have been entered. Claims 1-19 are pending in the application. Claim 20 has been canceled. Due to amendments to the claims new 35 USC103 rejections are presented below.
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) 1-4, 6-14, and 16-19 is/are rejected under 35 U.S.C. 103 as being unpatentable over Lovorn et al. (US 20130062122 A1) in view of Al Kadem et al. (US 10983513 B1) and Xie et al. (US 20210404849 A1).
Regarding Claims 1 and 11. Lovorn teaches:
A method for determining phase flow rates of a multiphase fluid flowing in a pipeline of a well, the method comprising:
receiving flow meter diagnostic inputs from a flow meter disposed on the pipeline (See Fig. 1, Fig. 2, para[0029], para[0047], and para[0074]: Additional sensors include temperature sensors 54, 56, Coriolis flowmeter 58, and flowmeters 62, 64, 66, 88. For example, if a received actual parameter value is outside of an acceptable range, unavailable (e.g., due to a non-functioning sensor) or differs by more than a predetermined maximum amount from a predicted value for that parameter (e.g., due to a malfunctioning sensor).);
receiving a first phase flow rates measurement from the MPFM (See para[0008], para[0029] – para[0031], para[0035], para[0047], and para[0095]: For example, another flowmeter 67 could be used to measure the rate of flow of the fluid 18 exiting the wellhead 24.);
receiving virtual flow meter (VFM) inputs from a plurality of field devices disposed on the pipeline (See para[0066] – para[0070]: The control system 90 also preferably includes a predictive device 148 and a data validator 150. The predictive device 148 preferably comprises one or more neural network models for predicting various well parameters. These parameters could include outputs of any of the sensors 36, 38, 40, 44, 46, 54, 56, 58, 60, 62, 64, 66, 67, 88, 102. The predictive device 148 is preferably "trained" by inputting present and past actual values for the parameters to the predictive device.);
determining, with a first model, a state of the MPFM, wherein the first model processes the MPFM diagnostic inputs (See para[0070] – para[0074]; The predicted parameter values can be supplied to the data validator 150 for use in its data validation processes. For example, if a received actual parameter value is outside of an acceptable range, unavailable (e.g., due to a non-functioning sensor) or differs by more than a predetermined maximum amount from a predicted value for that parameter (e.g., due to a malfunctioning sensor), then the data validator 150 may flag that actual parameter value as being "invalid.");
determining, with a second model, a second phase flow rates measurement, wherein the second model processes the VFM inputs (See para[0066] – para[0070] and para[0078] – para[0079]: Predicted flowmeter. The control system 90 also preferably includes a predictive device 148 and a data validator 150. The predictive device 148 preferably comprises one or more neural network models for predicting various well parameters. These parameters could include outputs of any of the sensors 36, 38, 40, 44, 46, 54, 56, 58, 60, 62, 64, 66, 67, 88, 102. The predictive device 148 is preferably "trained" by inputting present and past actual values for the parameters to the predictive device.);
setting a determined phase flow rates measurement to the first phase flow rates measurement if the state of the MPFM is determined not to be in fault, otherwise, setting the determined phase flow rates measurement to the second phase flow rates measurement (See para[0079] – para[0082]: If, for example, during the drill string connection process described above, one of the flowmeters 62, 64, 66 malfunctions, or its output is otherwise unavailable or invalid, then the data validator 150 can substitute the predicted flowmeter output for the actual (or nonexistent) flowmeter output. It is contemplated that, in actual practice, only one or two of the flowmeters 62, 64, 66 may be used. Thus, if the data validator 150 ceases to receive valid output from one of those flowmeters, determination of the proportions of fluid 18 flowing through the standpipe 26 and bypass line 72 could not be readily accomplished, if not for the predicted parameter values output by the predictive device 148.); and
in response to the state of the MPFM determined not to be in fault (See para[0074] and para[0079]: Valid parameter values would be used for training the predictive device 148, for updating the hydraulics model 92.):
retraining the second model using the VFM inputs and the first phase flow rates measurement (See para[0074], para[0081]: if a received actual parameter value is outside of an acceptable range, unavailable (e.g., due to a non-functioning sensor)… then the data validator 150 may flag that actual parameter value as being "invalid." Invalid parameter values may not be used for training the predictive device 148. Valid parameter values would be used for training the predictive device 148, for updating the hydraulics model 92.); and
adjusting one or more field devices in the plurality of field devices to optimize production of the well based on the determined phase flow rates measurement (See para[0090] – para[0093]: The hydraulics model 92 can be provided with the information as to the fluid 18 flow rate and, during drilling operations, the annulus 20 pressure set point will be adjusted as needed to achieve and maintain a desired well pressure. Thus, it is conceived that a desired temperature could be achieved and maintained at any particular location in a well, by adjusting the fluid 18 density, solids content and flow rate through the drill string 16 and wellbore 12.).
Regarding Claim 11, Lovorn teaches the additional limitations:
an alarm generator (See para[0082]: Additionally, when the data validator 150 determines that a sensor is malfunctioning or its output is unavailable, the data validator can generate an alarm and/or post a warning, identifying the malfunctioning sensor, so that an operator can take corrective action.); and
a maintenance recommender (See para[0082]: Additionally, when the data validator 150 determines that a sensor is malfunctioning or its output is unavailable, the data validator can generate an alarm and/or post a warning, identifying the malfunctioning sensor, so that an operator can take corrective action.).
Lovorn is silent as to the language of:
receiving multiphase flow meter (MPFM) diagnostic inputs from a MPFM disposed on the pipeline;
determining, using a comparison function, a model performance using the first phase flow rates measurement and the second phase flow rates measurement, and
retraining the second model using the VFM inputs and the first phase flow rates measurement in response to the model performance determined to be degrading.
Nevertheless Al Kadem teaches:
receiving multiphase flow meter (MPFM) diagnostic inputs from a MPFM disposed on the pipeline (See Fig. 2, Col. 1 lines 32 – 40, Col. 5 line 40 – Col. 6 line 30, and Col. 8 lines 40 – 47: At 204, for each MPFM, an MPFM health rule that corresponds to the MPFM readings is determined using the MPFM readings and logic of MPFM health rules. For example, a particular row in Table 1 can be identified that includes logic values indicated in the second column of Table 1 (for parameters identified in the third column).).
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to modify Lovorn by receiving multiphase flow meter (MPFM) diagnostic inputs from a MPFM disposed on the pipeline such as that of Al Kadem. Al Kadem teaches, “The present disclosure describes techniques that can be used for maintaining multiphase flow meters (MPFMs). For example, intuitive applications can be used to monitor MPFMs using predefined logic to proactively prevent equipment failure” (See Col. 1 lines 33 - 40). One of ordinary skill would have been motivated to modify Lovorn, because receiving multiphase flow meter (MPFM) diagnostic inputs would have helped to monitor MPFMs and proactively prevent equipment failure, as recognized by Al Kadem.
Al Kadem is silent as to the language of:
determining, using a comparison function, a model performance using the first phase flow rates measurement and the second phase flow rates measurement, and
retraining the second model using the VFM inputs and the first phase flow rates measurement in response to the model performance determined to be degrading.
Nevertheless Xie teaches:
determining, using a comparison function, a model performance using the first phase flow rates measurement and the second phase flow rates measurement (See para[0015], para[0083], and para[0135]: The validating including predicting flow rates using the trained neural network, calculating an error between predicted flow rates and the reference flow rates of the second portion of the input feature dataset, and accepting the trained neural network in response to the error being less than a second threshold.), and
retraining the second model using the VFM inputs and the first phase flow rates measurement in response to the model performance determined to be degrading (See Fig. 18, para[0015], para[0136], and para[0236]: Alternatively, in response to the fluid parameter engine validator 606 determining that the quantity of predictions that are correct does not satisfy the threshold, the process returns to block 1806.).
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to modify Lovorn by determining, using a comparison function, a model performance using the first phase flow rates measurement and the second phase flow rates measurement, and retraining the second model using the VFM inputs and the first phase flow rates measurement in response to the model performance determined to be degrading such as that of Xie. Xie teaches, “Once a desired neural network behavior has been achieved (e.g., a machine has been trained to operate according to a specified threshold, etc.), the machine can be deployed for use (e.g., testing the machine with measurement data from the flowmeter 102, etc.)” (See para[0128]). One of ordinary skill would have been motivated to modify Lovorn, because retraining a model when the model performance degrades would have helped to determine when a model was ready to be deployed as recognized by Xie, as recognized by Xie.
Regarding Claims 2 and 12. Lovorn teaches:
The method of claim 1, or the system of claim 11,
wherein the first model and the second model are machine-learned models (See para[0066] and para[0071] – para[0072]: The predictive device 148 preferably comprises one or more neural network models for predicting various well parameters.).
Regarding Claims 3 and 13. Lovorn teaches:
The method of claim 1, or the system of claim 11,
wherein the multiphase fluid is composed of water, oil, and gas (See para[0008], para[0095]: different fluid types (oil, water, gas, etc.).).
Regarding Claims 4 and 14. Lovorn is silent as to the language of:
The method of claim 1, or the system of claim 11,
wherein the MPFM diagnostic inputs comprise gamma counts, supplied voltage, Venturi differential pressure, and flow computer temperature.
Nevertheless Al Kadem teaches:
wherein the MPFM diagnostic inputs comprise gamma counts (See Col. 5 line 40 – Col. 6 line 30, and Col. 8 lines 40 – 47: Gamma counts.), supplied voltage (See Col. 5 line 40 – Col. 6 line 30, and Col. 8 lines 40 – 47: Input voltage.), Venturi differential pressure (See Col. 5 line 40 – Col. 6 line 30, and Col. 8 lines 40 – 47: Venturi differential pressure.), and flow computer temperature (See Col. 5 line 15 – Col. 6 line 30: MPFM temperature transmitter issues. MPFM temperature.).
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to modify Lovorn wherein the MPFM diagnostic inputs comprise gamma counts, supplied voltage, Venturi differential pressure, and flow computer temperature such as that of Al Kadem. Al Kadem teaches, “The present disclosure describes techniques that can be used for maintaining multiphase flow meters (MPFMs). For example, intuitive applications can be used to monitor MPFMs using predefined logic to proactively prevent equipment failure” (See Col. 1 lines 33 - 40). One of ordinary skill would have been motivated to modify Lovorn, because receiving multiphase flow meter (MPFM) diagnostic inputs would have helped to monitor MPFMs and proactively prevent equipment failure, as recognized by Al Kadem.
Regarding Claims 6 and 16. Lovorn is silent as to the language of:
The method of claim 1, or the system of claim 11,
further comprising recommending maintenance for the MPFM based on, at least in part, the first model.
Nevertheless Al Kadem teaches:
recommending maintenance for the MPFM based on, at least in part, the first model (See Col. 4 line 60 – Col. 5 line 7: The analytics system can rely on applied and automated system algorithms to detect MPFM maintenance issue for different type of meters. The alarms that can be implemented using techniques of the present disclosure can be based on experience by engineers in dealing with different types of meters and maintenance issues.).
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to modify Lovorn with recommending maintenance for the MPFM based on, at least in part, the first model such as that of Al Kadem. Al Kadem teaches, “The present disclosure describes techniques that can be used for maintaining multiphase flow meters (MPFMs). For example, intuitive applications can be used to monitor MPFMs using predefined logic to proactively prevent equipment failure” (See Col. 1 lines 33 - 40). One of ordinary skill would have been motivated to modify Lovorn, because recommending maintenance based on a model would have helped to monitor MPFMs and proactively prevent equipment failure, as recognized by Al Kadem.
Regarding Claims 7 and 17. Lovorn teaches:
The method of claim 1, or the system of claim 11,
further comprising generating an alarm for the MPFM based on, at least in part, the first model (See para[0082]: Additionally, when the data validator 150 determines that a sensor is malfunctioning or its output is unavailable, the data validator can generate an alarm and/or post a warning, identifying the malfunctioning sensor, so that an operator can take corrective action.).
Regarding Claims 8 and 18. Lovorn teaches:
The method of claim 1, or the system of claim 11,
further comprising transmitting the phase flow rates to an external system (See para[0033]: Various forms of wired or wireless telemetry (acoustic, pressure pulse, electromagnetic, etc.) may be used to transmit the downhole sensor measurements to the surface.).
Regarding Claim 9. Lovorn is silent as to the language of:
The method of claim 6,
further comprising performing the recommended maintenance on the MPFM.
Nevertheless Al Kadem teaches:
performing the recommended maintenance on the MPFM (See Col. 1 lines 45 – 54 and Col. 3 lines 25 - 30: A recommendation is determined for each MPFM based on the MPFM health rule. A health status and an alarm priority are determined for each MPFM using the MPFM readings and the logic of MPFM health rules. A dashboard is presented to a user that includes, for each MPFM, the MPFM readings, the recommendation, the health status, and the alarm priority. The triggered alarms can allow an engineer to diagnose and identify the failure type. The engineer can complete this work using a desktop application or a mobile application, for example.).
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to modify Lovorn by performing the recommended maintenance on the MPFM such as that of Al Kadem. Al Kadem teaches, “The present disclosure describes techniques that can be used for maintaining multiphase flow meters (MPFMs). For example, intuitive applications can be used to monitor MPFMs using predefined logic to proactively prevent equipment failure” (See Col. 1 lines 33 - 40). One of ordinary skill would have been motivated to modify Lovorn, because performing maintenance would have helped to monitor MPFMs and proactively prevent equipment failure, as recognized by Al Kadem.
Regarding Claims 10 and 19. Lovorn is silent as to the language of:
The method of claim 7, or the system of claim 17,
further comprising determining a priority for the alarm.
Nevertheless Al Kadem teaches:
determining a priority for the alarm (See Col. 1 lines 45 – 54 and Col. 6 line 50 – Col. 7 line 30: A health status and an alarm priority are determined for each MPFM using the MPFM readings and the logic of MPFM health rules. A dashboard is presented to a user that includes, for each MPFM, the MPFM readings, the recommendation, the health status, and the alarm priority. The following alarm table can define alarms that are prioritized based on the alarm severity (for example, major/minor) contributing to major maintenance work with proposed recommendations.).
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to modify Lovorn by determining a priority for the alarm such as that of Al Kadem. Al Kadem teaches, “Handling these issues can help MPFM maintenance engineers to monitor, plan, schedule, and optimize MPFM maintenance visits utilizing real-time information and automated logic” (See Col. 5 lines 20 - 25). One of ordinary skill would have been motivated to modify Lovorn, because determining an alarm priority would have helped to organize and plan maintenance visits, as recognized by Al Kadem.
Claim(s) 5 and 15 is/are rejected under 35 U.S.C. 103 as being unpatentable over Lovorn et al. (US 20130062122 A1) in view of Al Kadem et al. (US 10983513 B1) and Xie et al. (US 20210404849 A1) as applied to claims 1 and 11 above, and further in view of Drew et al. (US 20240118118 A1) and Bikmukhametov et al. (Bikmukhametov, Timur, and Johannes Jäschke. "First principles and machine learning virtual flow metering: a literature review." Journal of Petroleum Science and Engineering 184 (2020): 106487.).
Regarding Claims 5 and 15. Lovorn is silent as to the language of:
The method of claim 1, or the system of claim 11,
wherein the VFM inputs comprise wellhead pressure, upstream wellhead temperature, downstream wellhead pressure, Venturi differential pressure, choke valve position, electrical submersible pump (ESP) frequency, and ESP motor current.
Nevertheless Drew teaches:
wherein the VFM inputs comprise downstream wellhead pressure (See para[0043]: For instance, sensors, such as pressure sensors 162 and temperature sensors 160 of FIG. 1, may be available to obtain measurements such as pressure and temperature.),
choke valve position (See para[0043]; the surface production equipment of a well may include a choke manifold with a flow control valve. The processor of computer 170 may obtain the position of the flow control valve as a production measurement.),
electrical submersible pump (ESP) frequency (See para[0043]: For example, an electric submersible pump (ESP) may be disposed in a wellbore. Measurements from the ESP such as inlet pressure, outlet pressure, motor amperage, operating frequency, etc. may be obtained as production measurements.), and
ESP motor current (See para[0043]: For example, an electric submersible pump (ESP) may be disposed in a wellbore. Measurements from the ESP such as inlet pressure, outlet pressure, motor amperage, operating frequency, etc. may be obtained as production measurements.).
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to modify Lovorn wherein the VFM inputs comprise downstream wellhead pressure, choke valve position, electrical submersible pump (ESP) frequency, and ESP motor current such as that of Drew. Drew teaches, “Another approach may include training a machine learning model based entirely on collected data, including collected calibration data (e.g., operating a test separator or a multi-phase flow meter) or occasionally shutting in multiple/select wells. More generally, a hybrid approach may be taken that incorporates both a physics-based model in addition to data from actual measurements to construct and train a machine learning model” (See para[0007]). One of ordinary skill would have been motivated to modify Lovorn because inputting data into a virtual flow meter would have helped to train a virtual flow meter, as recognized by Drew.
Drew is silent as to the language of:
wherein the VFM inputs comprise wellhead pressure, upstream wellhead temperature, and Venturi differential pressure.
Nevertheless Bikmukhametov teaches:
wherein the VFM inputs comprise wellhead pressure (See Page 2: wellhead pressure.), upstream wellhead temperature (See Page 2: Wellhead pressure and temperature upstream of the choke (PWHCU and TWHCU).), and Venturi differential pressure (See Page 10: Additional devices such as Venturi, densitometer or partly working MPFM may help to improve the VFM system accuracy.).
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to modify Lovorn wherein the VFM inputs comprise wellhead pressure, upstream wellhead temperature, and Venturi differential pressure such as that of Bikmukhametov. Bikmukhametov teaches, “Some methods are currently emerging and aiming to improve the accuracy of the flowrate predictions, while yet other methods are currently not used in the industry but have a good potential to move the VFM development forward in the future” (See Page 3). One of ordinary skill would have been motivated to modify Lovorn, because inputting data into a virtual flow meter would have helped to improve the accuracy of the virtual flow meter, as recognized by Bikmukhametov.
Response to Arguments
Applicant's arguments filed 12/16/2025 have been fully considered but they are not persuasive.
Applicant argues that: In other words, Lovorn teaches a predictive device that predicts a value for a parameter given the values of other observed parameters. Lovorn does not directly determine the validity (or, in comparison, the state of an instrument such as the MPFM) given observed parameter values.
In response to applicant's argument that the references fail to show certain features of the invention, during patent examination, the pending claims must be "given their broadest reasonable interpretation consistent with the specification." The Federal Circuit’s en banc decision in Phillips v. AWH Corp., 415 F.3d 1303, 1316, 75 USPQ2d 1321, 1329 (Fed. Cir. 2005).
As shown in further detail in the 35 USC 103 rejection above, Lovorn teaches “For example, if a received actual parameter value is outside of an acceptable range, unavailable (e.g., due to a non-functioning sensor) or differs by more than a predetermined maximum amount from a predicted value for that parameter (e.g., due to a malfunctioning sensor), then the data validator 150 may flag that actual parameter value as being "invalid."” (See para[0074]).
Claims 1 and 11 recite the limitation “in response to the state of the MPFM determined not to be in fault”. In view of the state of the art, the examiner understands a broadest reasonable interpretation of “determined not to be in fault” to includes determining if a parameter is not outside of an acceptable range or not unavailable. Applicant’s specification does not rebut the presumption that the term “determined not to be in fault” is to be given its broadest reasonable interpretation by clearly setting forth a different definition of the term. Lovorn discloses a broadest reasonable interpretation of the recited limitation, because Lovorn discloses determining if a parameter is outside of outside of an acceptable range or unavailable (e.g., due to a non-functioning sensor).
In response to applicant's argument that the examiner's conclusion of obviousness is based upon improper hindsight reasoning, it must be recognized that any judgment on obviousness is in a sense necessarily a reconstruction based upon hindsight reasoning. But so long as it takes into account only knowledge which was within the level of ordinary skill at the time the claimed invention was made, and does not include knowledge gleaned only from the applicant's disclosure, such a reconstruction is proper. See In re McLaughlin, 443 F.2d 1392, 170 USPQ 209 (CCPA 1971). In this case because Lovorn teaches methods other than “differs by more than a predetermined maximum amount from a predicted value for that parameter” to determine if a parameter is valid the combination of Lovorn and Xie is interpreted as not precluded by the teachings of Lovorn. Accordingly, applicant’s arguments regarding the recited limitation are not persuasive and the rejection is maintained.
Applicant argues that: Further, limitations (iii) and (v) of amended independent claim 1 indicate that a different set of inputs, namely VFM inputs, are used by the second model to determine a second phase flow rates measurement, where the VFM inputs are different from the diagnostic inputs.
In response to applicant's argument that the references fail to show certain features of the invention, it is noted that the features upon which applicant relies (i.e., where the VFM inputs are different from the diagnostic inputs) are not recited in the rejected claim(s). Although the claims are interpreted in light of the specification, limitations from the specification are not read into the claims. See In re Van Geuns, 988 F.2d 1181, 26 USPQ2d 1057 (Fed. Cir. 1993).
Applicant argues that: Again, this is possible because the present invention enables determination of model performance separate from the state of the MPFM. Lovorn cannot determine model performance as it does not envision a method for determining the validity of observed parameter values other than a divergence between these values and those predicted by its predictive device.
Applicant’s arguments with respect to claim(s) 1 and 11 have been considered but are moot because the new ground of rejection does not rely on any reference applied in the prior rejection of record for any teaching or matter specifically challenged in the argument.
Conclusion
Applicant's amendment necessitated the new ground(s) of rejection presented in this Office action. Accordingly, THIS ACTION IS MADE FINAL. See MPEP § 706.07(a). Applicant is reminded of the extension of time policy as set forth in 37 CFR 1.136(a).
A shortened statutory period for reply to this final action is set to expire THREE MONTHS from the mailing date of this action. In the event a first reply is filed within TWO MONTHS of the mailing date of this final action and the advisory action is not mailed until after the end of the THREE-MONTH shortened statutory period, then the shortened statutory period will expire on the date the advisory action is mailed, and any nonprovisional extension fee (37 CFR 1.17(a)) pursuant to 37 CFR 1.136(a) will be calculated from the mailing date of the advisory action. In no event, however, will the statutory period for reply expire later than SIX MONTHS from the mailing date of this final action.
Any inquiry concerning this communication or earlier communications from the examiner should be directed to CARTER W FERRELL whose telephone number is (571)272-0551. The examiner can normally be reached Monday - Friday 10 am - 8 pm.
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If attempts to reach the examiner by telephone are unsuccessful, the examiner’s supervisor, Catherine T. Rastovski can be reached at (571) 270-0349. The fax phone number for the organization where this application or proceeding is assigned is 571-273-8300.
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/CARTER W FERRELL/Examiner, Art Unit 2857
/Catherine T. Rastovski/Supervisory Primary Examiner, Art Unit 2857