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 .
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 a judicial exception (i.e., a law of nature, a natural phenomenon, or an abstract idea) without significantly more.
Specifically, representative Claim 1 recites:
“A system, comprising: an acoustic detection subsystem to generate flowline pressure data from a detected reflection of a pressure wave caused by a target feature residing within the flowline at a known distance from a pressure wave generating device; and a computing device communicatively coupled to the acoustic detection subsystem and comprising: a processor; and a non-transitory computer-readable medium having instructions stored thereon that are executable by the processor for causing the processor to perform operations comprising: accessing a known temperature model including a plurality of predicted temperature values that form a predicted temperature profile of the flowline along a length thereof; determining a predicted pressure wave velocity within a section of the flowline between the pressure generation device and the target feature, the predicted pressure wave velocity determined based on a temperature value from the known temperature model that corresponds to the section of the flowline between the pressure generation device and the target feature and fluid/gas characteristics at the temperature value from the known temperature model; calculating an expected time of flight of the pressure wave to the target feature based on the predicted pressure wave velocity and the known distance between the pressure wave generating device and the target feature; interrogating flowline pressure data of a sensor of the acoustic detection subsystem to observe an actual time of flight of the pressure wave to the target feature; detecting a difference between the actual time of flight and the expected time of flight; in response to detecting the difference between the actual time of flight and the expected time of flight, calculating an actual pressure wave velocity based on the actual time of flight and the known distance between the pressure wave generating device and the target feature; calculating a corrected temperature value that corresponds to the actual pressure wave velocity; and outputting a command to revise the known temperature model by substituting the corrected temperature value for the predicted temperature value.”
The claim limitations in the abstract idea have been highlighted in bold above; the remaining limitations are “additional elements”.
Under the Step 1 of the eligibility analysis, we determine whether the claims are to a statutory category by considering whether the claimed subject matter falls within the four statutory categories of patentable subject matter identified by 35 U.S.C. 101: Process, machine, manufacture, or composition of matter. The above claim is considered to be in a statutory category (process).
Under the Step 2A, Prong One, we consider whether the claim recites a judicial exception (abstract idea). In the above claim, the highlighted portion constitutes an abstract idea because, under a broadest reasonable interpretation, it recites limitations that fall into/recite an abstract idea exceptions. Specifically, under the 2019 Revised Patent Subject matter Eligibility Guidance, it falls into the groupings of subject matter when recited as such in a claim limitation that falls into the grouping of subject matter when recited as such in a claim limitation, that covers mathematical concepts - mathematical relationships, mathematical formulas or equations, mathematical calculations.
The step of “accessing a known temperature model including a plurality of predicted temperature values that form a predicted temperature profile of the flowline along a length thereof; determining a predicted pressure wave velocity within a section of the flowline between the pressure generation device and the target feature, the predicted pressure wave velocity determined based on a temperature value from the known temperature model that corresponds to the section of the flowline between the pressure generation device and the target feature and fluid/gas characteristics at the temperature value from the known temperature model; calculating an expected time of flight of the pressure wave to the target feature based on the predicted pressure wave velocity and the known distance between the pressure wave generating device and the target feature, interrogating flowline pressure data of a sensor of the acoustic detection subsystem to observe an actual time of flight of the pressure wave to the target feature; detecting a difference between the actual time of flight and the expected time of flight; in response to detecting the difference between the actual time of flight and the expected time of flight, calculating an actual pressure wave velocity based on the actual time of flight and the known distance between the pressure wave generating device and the target feature; calculating a corrected temperature value that corresponds to the actual pressure wave velocity” is treated as belonging to the mathematical concepts grouping.
Similar limitations comprise the abstract ideas of Claims 8 and 16.
Next, under the Step 2A, Prong Two, we consider whether the claim that recites a judicial exception is integrated into a practical application.
In this step, we evaluate whether the claim recites additional elements that integrate the exception into a practical application of that exception.
The above claims comprise the following additional elements:
Claim 1: A system, comprising: an acoustic detection subsystem to generate flowline pressure data from a detected reflection of a pressure wave caused by a target feature residing within the flowline at a known distance from a pressure wave generating device; and a computing device communicatively coupled to the acoustic detection subsystem and comprising: a processor; and a non-transitory computer-readable medium having instructions stored thereon that are executable by the processor for causing the processor to perform operations comprising and outputting a command to revise the known temperature model by substituting the corrected temperature value for the predicted temperature value.
Claim 8: A computer-implemented method, comprising: receiving, by a processor of a computing device, from an acoustic detection subsystem, flowline pressure data from a detected reflection of a pressure wave caused by a target feature residing within the flowline at a known distance from a pressure wave generating device; and performing, by the processor, each of the following additional operations: and outputting a command to revise the known temperature model by substituting the corrected temperature value for the predicted temperature value.
Claim 16: A non-transitory computer-readable medium comprising instructions that are executable by a processor of a computing device, for causing the processor to: receive, from an acoustic detection subsystem, flowline pressure data from a detected reflection of a pressure wave caused by a target feature residing within the flowline at a known distance from a pressure wave generating device; and perform each of the following operations: and output a command to revise the known temperature model by substituting the corrected temperature value for the predicted temperature value.
The generically recited a system, comprising: an acoustic detection subsystem to generate flowline pressure data from a detected reflection of a pressure wave caused by a target feature residing within the flowline at a known distance from a pressure wave generating device represents a field-of-use limitation.
The claim limitations of a computing device communicatively coupled to the acoustic detection subsystem and comprising: a processor; and a non-transitory computer-readable medium having instructions stored thereon that are executable by the processor for causing the processor to perform operations comprising is an example of generic computer equipment (components) that is generally recited and therefore is not qualified as a particular machine and outputting a command to revise the known temperature model by substituting the corrected temperature value for the predicted temperature value represents an extra-solution activity.
Therefore, the claims are directed to a judicial exception and require further analysis under the Step 2B.
However, the above claims do not include additional elements that are
sufficient to amount to significantly more than the judicial exception (Step 2B
analysis) because these additional elements/steps are well-understood and
conventional in the relevant art based on the prior art of record including
references (Barfoed and Quintero).The independent claims, therefore, are not patent eligible.
With regards to the dependent claims, claims 2-7, 9-15, and 17-20, either recite additional elements that are not meaningful as recited in generality and represent insignificant extra-solution activity or provide additional features/steps which are part of an expanded abstract idea of the independent claims (additionally comprising mathematical relationship process steps) and, therefore, these dependent claims are not eligible without additional elements that reflect a practical application or qualified for significantly more for substantially similar reasons as discussed with regards to independent Claim 1.
Examiner Note with regards to Prior art of record
Claims 1-20 are distinguished over the prior art of record because Barfoed, Quintero, Hauge, and Rossi do not explicitly disclose calculating an actual pressure wave velocity based on the actual time of flight and the known distance between the pressure wave generating device and the target feature; calculating a corrected temperature value that corresponds to the actual pressure wave velocity.
Regarding Claim 1, Barfoed (US20170089179) discloses a system, comprising: an acoustic detection subsystem to generate flowline pressure data (Said well data may comprise survey data obtained during designing the well, and/or survey data obtained during drilling of the well, and/or completion data, and/or intervention data obtained during well operation, and/or wellbore characteristics including temperature and/or pressure and/or flow [0017]); and a computing device communicatively coupled to the acoustic detection subsystem and comprising: a processor; and a non-transitory computer-readable medium having instructions stored thereon that are executable by the processor for causing the processor to perform operations comprising (Now turning to FIG. 4, an apparatus 300 configured to verify a well model is shown. The apparatus 300 comprises suitable computer hardware, such as processor(s), memory, display, radio communication means, etc. as well as computer software for generating the well model and for allowing a user, or well operator, to navigate through the model. [0066]): accessing a known temperature model including a plurality of predicted temperature values that form a predicted temperature profile of the flowline along a length thereof (The present invention also relates to a well model verifying apparatus, wherein said apparatus is configured to [0031]; Said well data may comprise survey data obtained during designing the well, and/or survey data obtained during drilling of the well, and/or completion data, and/or intervention data obtained during well operation, and/or wellbore characteristics including temperature and/or pressure and/or flow [0017]); determining a predicted pressure wave within a section of the flowline between the pressure generation device and the target feature, the predicted pressure wave determined based on a temperature value from the known temperature model and the target feature and fluid/gas characteristics at the temperature value from the known temperature model (Said well data may comprise survey data obtained during designing the well, and/or survey data obtained during drilling of the well, and/or completion data, and/or intervention data obtained during well operation, and/or wellbore characteristics including temperature and/or pressure and/or flow [0017]); calculating an expected time of flight of the pressure wave to the target feature and the known distance between the pressure wave generating device and the target feature (For this purpose, the apparatus 300 comprises a memory 302 storing a priori well data used to generate the model. A prior data may e.g. be survey data 302a from the design phase or the drilling phase, and/or completion data 302b, and/or measurement data 302c from intervention processes, and/or calculations 302d either from survey data or from measurement data, and/or notes 302e, and/or logged diagnosis 302f [0074]); and outputting a command to revise the known temperature model by substituting the corrected temperature value for the predicted temperature value (The verification unit 312 thus receives well data as well as tool data and is configured to perform a verification of the well model by comparing the well data of the model with the tool data. The verification unit 312 is preferably also configured to transmit an output to the model generator 304 for displaying the result of the verification to an operator [0079]).
Regarding Claim 1, Quintero (US20180058189) discloses a system, comprising: an acoustic detection subsystem to generate flowline pressure data from a detected reflection of a pressure wave caused by a target feature residing within the flowline at a known distance from a pressure wave generating device (The backscattered signal may be used to provide information regarding the time varying state of strain along the optical fiber, which may be equated to locations where fluctuations in acoustic (vibration) is occurring [0023]); and a computing device communicatively coupled to the acoustic detection subsystem and comprising (The data acquisition system 142 may further include a signal processor or signal analysis equipment associated with the detector 144, which may include a standard optical spectral analyzer having a processor for processing, storing in a computer-readable storage medium for storing a program code executed by the processor [0034]): a processor; and a non-transitory computer-readable medium having instructions stored thereon that are executable by the processor for causing the processor to perform operations comprising (In some embodiments, the processor may be provided with a user interface for input and control, such as by generating reports and performing fast Fourier transform analyses. In at least one embodiment, the data acquisition system 142 may be configured to provide acoustic, Doppler, and temperature logs of the entire length of the wellbore 102 so that a well operator can analyze the presence, location, and flow rate of water between the casing 108 and the formation 106 and between overlapping sections of the casing 108 [0034]): accessing a known model including a plurality of predicted values that form a predicted temperature profile of the flowline along a length thereof (The travel time and energy spectrum ratios are provided to a model that determines (or predicts) the velocity of the water flow in each flow path in the wellbore 102 and the radial distance of each flow path from center of the wellbore 102 (or, alternatively, the radial distance from the tool string 114) [0040]); determining a predicted pressure wave velocity within a section of the flowline between the pressure generation device and the target feature (The travel time and energy spectrum ratios are provided to a model that determines (or predicts) the velocity of the water flow in each flow path in the wellbore 102 and the radial distance of each flow path from center of the wellbore 102 (or, alternatively, the radial distance from the tool string 114 [0040]), the predicted pressure wave velocity determined based on a temperature value from the known temperature model that corresponds to the section of the flowline from the known temperature model (The backscattered electromagnetic radiation measured by the detector 144 may be correlated to strain (dynamic and static) and temperature profiles sensed by the cable 126, which may be indicative of fluid flow between the surrounding formation 106 and the wellbore 102 and/or between overlapping sections of the casing 108 [0035]); calculating a corrected temperature value that corresponds to the actual pressure wave velocity (The temperature data from the data acquisition system 142 may be provided to a DTS borehole model that determines (or predicts) the velocity of the water flow in each flow path determined to be present in the wellbore 102 and the radial distance of each flow path from the center of the wellbore 102 (or, alternatively, the radial distance from the tool string 114) [0056]); and outputting to the known temperature model (The data acquisition system 142 may further include a signal processor or signal analysis equipment associated with the detector 144, which may include a standard optical spectral analyzer having a processor for processing, storing in a computer-readable storage medium for storing a program code executed by the processor, and displaying to a user the detected results [0034]).
Regarding Claim 1, Hauge (US20190169982) discloses a system, comprising: an acoustic detection subsystem to generate flowline pressure data from a detected of a pressure wave caused by a target feature residing within the flowline at a known distance (A flow simulation model may also include equations that account for flowline and surface facility performance, for example, to perform a comprehensive production system analysis [0026]; As an example, an analysis using a flow simulation model may be a network analysis to: identify production bottlenecks and constraints; assess benefits of new wells, additional pipelines, compression systems, etc.; calculate deliverability from field gathering systems; predict pressure and temperature profiles through flow paths; or plan full-field development [0032]); and a computing device communicatively coupled to the acoustic detection subsystem and comprising: a processor; and a non-transitory computer-readable medium having instructions stored thereon that are executable by the processor for causing the processor to perform operations comprising (The method 150 is shown in FIG. 1 in association with various computer-readable media (CRM) blocks 153, 155, 157, 159 and 161. Such blocks generally include instructions suitable for execution by one or more processors (or processing cores) 172 to instruct the computing device or system 170 to perform one or more actions [0036]): accessing a known temperature model including a plurality of predicted temperature values that form a predicted temperature profile of the flowline along a length thereof (As to the method 150 of FIG. 1, it can include a build block 152 for building a network model that represents a production system for fluid; an assignment block 154 for assigning equations to sub-networks in the network model (e.g., where at least one of the sub-networks is optionally assigned equations formulated for solving for pressure and momentum implicitly and simultaneously, conservation mass and energy/temperature in separate stages), a provision block 156 for providing data; a transfer block 158 for transferring the data to the network model [0035]); determining a predicted pressure wave velocity within a section of the flowline between the pressure generation device and the target feature (An oilfield network may be solved by identifying pressure drop (e.g., pressure differential), for example, through use of momentum equations. As an example, an equation for pressure differential may account for factors such as fluid potential energy (e.g., hydrostatic pressure), friction (e.g., shear stress between conduit wall and fluid), and acceleration (e.g., change in fluid velocity) [0076]), the pressure generation device (As an example, a piece of equipment may include an electric motor operatively coupled to a mechanism to move fluid (e.g., a pump, compressor, etc.). As an example, a piece of equipment may include a heater coupled to a power source, a fuel source, etc. (e.g., consider a steam generator) [0037]); calculating an expected time of flight of the pressure wave to the target feature (As an example, a method can include determining a location of a fluid leak by, at least in part, detecting a change in slope of a fluid pressure profile of fluid pressure (e.g., and/or detecting a change in difference in slope of field and model fluid pressure profiles) with respect to a distance metric of a hydrocarbon fluid production network [0170]); detecting a difference between the actual time of flight and the expected time of flight; in response to detecting the difference between the actual time of flight and the expected time of flight (As an example, a method such as the method 700 of FIG. 7 can include determining a location of a fluid leak based at least in part on detecting a difference in slope between at least a portion of a measurement information-based pressure profile and at least a portion of a simulated information-based pressure profile with respect to a distance metric of a hydrocarbon fluid production network (see, e.g., the plots 1100 and 1200 of FIGS. 11 and 12) [0171]), calculating an actual pressure wave velocity based on the actual time of flight and the known distance between the pressure wave generating device and the target feature (As an example, a method such as the method 700 of FIG. 7 can include determining a location of a fluid leak based at least in part on detecting a difference in slope between at least a portion of a measurement information-based pressure profile and at least a portion of a simulated information-based pressure profile with respect to a distance metric of a hydrocarbon fluid production network (see, e.g., the plots 1100 and 1200 of FIGS. 11 and 12) [0171]); and outputting the known temperature model (As an example, a method can include outputting a location of a fluid leak in a hydrocarbon fluid production network via a network interface of a computing system. In such an example, the method can include receiving the location via a network interface of a device where the device may be, for example, a mobile device (e.g., a smartphone, a GPS device, a drone, a vehicle, etc.) [0174]).
Regarding Claim 1, Rossi (US20200133251) discloses a system, comprising: an acoustic detection subsystem to generate flowline pressure data (According to various embodiments, a method of detecting and correcting for discrepancy events in a fluid pipeline is presented. The method includes obtaining a plurality of empirical temperature and pressure measurements at a plurality of locations within the pipeline; simulating, using a pipeline model, a plurality of simulated temperature and pressure measurements for the plurality of locations within the pipeline [0003]); and a computing device communicatively coupled to the acoustic detection subsystem and comprising: a processor; and a non-transitory computer-readable medium having instructions stored thereon that are executable by the processor for causing the processor to perform operations comprising (The system includes at least one electronic processor that executes persistently stored instructions to perform operations including: obtaining a plurality of empirical temperature and pressure measurements at a plurality of locations within the pipeline; simulating, using a pipeline model, a plurality of simulated temperature and pressure measurements for the plurality of locations within the pipeline; detecting, by a discrepancy event detector, at least one discrepancy event representing a discrepancy between the empirical temperature and pressure measurements and the simulated temperature and pressure measurements [0005]): accessing a known temperature model including a plurality of predicted temperature values that form a predicted temperature profile of the flowline along a length thereof (According to various embodiments, a method of detecting and correcting for discrepancy events in a fluid pipeline is presented. The method includes obtaining a plurality of empirical temperature and pressure measurements at a plurality of locations within the pipeline; simulating, using a pipeline model, a plurality of simulated temperature and pressure measurements for the plurality of locations within the pipeline [0003]); calculating a corrected temperature value that corresponds to the actual pressure wave velocity (The system includes at least one electronic processor that executes persistently stored instructions to perform operations including: obtaining a plurality of empirical temperature and pressure measurements at a plurality of locations within the pipeline; simulating, using a pipeline model, a plurality of simulated temperature and pressure measurements for the plurality of locations within the pipeline; detecting, by a discrepancy event detector, at least one discrepancy event representing a discrepancy between the empirical temperature and pressure measurements and the simulated temperature and pressure measurements [0005]); and outputting a command to revise the known temperature model by substituting the corrected temperature value for the predicted temperature value (At block 2714, method 2700 outputs the corrected branch flow rate determined per block 2712. The output may be accomplished an any of a variety of ways. According to some embodiments, the output comprises displaying the corrected branch flow rate to a user, e.g., via a computer monitor [0184]).
Conclusion
The prior art made of record and not relied upon is considered pertinent to applicant's disclosure.
Jy-An Wang (US20150275639) discloses an apparatus for simulating a pulsed pressure induced cavitation technique (PPCT) from a pressurized working fluid (F) provides laboratory research and development for enhanced geothermal systems (EGS), oil, and gas wells.
Arthur Kozak (US20160168978) discloses a method of determining true vertical depth within a well penetrating a ground surface while positioning a pressure sensor at a downhole position within the well at the base of a fluid column extended from a ground surface to the pressure sensor.
Jian Hou (US10815778) discloses a method for obtaining formation parameters of a gas hydrate reservoir through well testing interpretation while establishing a physical model for well testing interpretation of the gas hydrate reservoir according to multiphase flow, and establishing a mathematical model for well testing interpretation of the gas hydrate reservoir.
Any inquiry concerning this communication or earlier communications from the examiner should be directed to SHARAH ZAAB whose telephone number is (571)272-4973. The examiner can normally be reached Monday - Friday 7:00 am - 4:30 pm.
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If attempts to reach the examiner by telephone are unsuccessful, the examiner’s supervisor, Catherine Rastovski can be reached on 571-272-0349. The fax phone number for the organization where this application or proceeding is assigned is 571-273-8300.
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/SHARAH ZAAB/Examiner, Art Unit 2863
/Catherine T. Rastovski/Supervisory Primary Examiner, Art Unit 2863 63 amended recites, “calculating a value of an acoustic property of one or more media/