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 .
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.
Claims 1-3, 5, 8, 11, 14-16, and 18 are rejected under 35 U.S.C. 103 as being unpatentable Granville et al. (US20210032979), hereinafter referred to as ‘Granville’ and in further view of Kabbanik et al.( US20210032984), hereinafter referred to as ‘Kabbanik’ and Bennett et al. (US20220317091), hereinafter referred to as ‘Bennett’.
Regarding Claim 1, Granville discloses a system, comprising: a conduit monitoring subsystem including a pressure wave generating mechanism positionable to introduce a pressure wave into fluid flowing within a conduit of interest and at least one sensor positionable on the conduit of interest and in communication with the fluid flowing therein to measure transient pressure changes resulting from reflections of the pressure wave caused by anomalies in the conduit of interest (Retrievable (temporarily deployed) fiber optic cable 104 (Referring to FIG. 1) may be deployed either via a cable or multi-cable pack that may also include conductors (for instance a Wireline cable), or deployed via coiled tubing (not illustrated). Although fiber optic data may be quite valuable for obtaining acoustic, temperature, and other information about oil wells and pipelines, it has been a challenge for the industry to develop accurate methods for correlating fiber optic data to depth or distance along production tubing 106, casing 108, tubular structures, and/or the like. Non-destructive methods based on pressure pulse technology may allow for accurate depth measurements within production tubing 106, casing 108, tubular structures, and/or the like [0021]); a computing device communicatively coupled to the conduit monitoring subsystem to receive measured pressure data generated by the conduit monitoring subsystem from measured transient pressure changes in the conduit of interest (Information handling system 120 may be disposed on fiber optic cable 104 or otherwise positioned on surface 112. Information handling system 120 may act as a data acquisition system and possibly a data processing system that analyzes information from fiber optic cable 104 [0018]), the computing device including a processor and a memory including instructions that are executable by the processor for causing the processor to: (Non-transitory computer-readable media 128 may include, for example, storage media such as a direct access storage device (e.g., a hard disk drive or floppy disk drive), a sequential access storage device [0018]) input at least some of the measured pressure data generated by the conduit monitoring subsystem (The pressure pulse travels at the speed of sound forming a wave through the annular fluid in the pipe, which generates reflections back when it encounters changes to the inner diameter of the pipe. These changes to the inner diameter may result from features such as mineral deposits, residue buildup, or a change in annular fluid properties (density, viscosity, velocity). Data may be recorded via one or more pressure transducers (not illustrated) deployed inside the production pipeline or oil well tubular of interest. It should be noted that the features and/or their location may be known during operations [0022]).
However, Granville does not explicitly disclose input at least some of the measured pressure data generated by the conduit monitoring subsystem to a predictive model trained on a training dataset comprising a multitude of previously measured pressure data samples for each of a plurality of different hydrocarbon well conduits and a plurality of key attributes in each of the pressure data samples, where at least one key attribute is a point of largest measured acoustic energy in each of the conduits and the pressure data samples have been filtered by applying a low-pass filter followed by a second filter; cause the predictive model to output a prediction including a location and a size of a deposition or a leak in the conduit of interest by analyzing an identified difference between an observed pressure profile defined by the measured pressure data and an expected pressure profile determined by the predictive model for a conduit devoid of anomalies; and in response to the prediction, automatically execute an action directed to remediating the deposition or leak.
Nevertheless, Kabannik discloses input at least some of the measured pressure data generated by the conduit monitoring subsystem to a predictive model trained on a training dataset comprising a multitude of previously measured pressure data samples for each of a plurality of different hydrocarbon well conduits and a plurality of key attributes in each of the pressure data samples (The pressure pulse travels at the speed of sound forming a wave through the annular fluid in the pipe, which generates reflections back when it encounters changes to the inner diameter of the pipe. These changes to the inner diameter may result from features such as mineral deposits, residue buildup, or a change in annular fluid properties (density, viscosity, velocity). Data may be recorded via one or more pressure transducers (not illustrated) deployed inside the production pipeline or oil well tubular of interest. It should be noted that the features and/or their location may be known during operations [0022]; Both processing algorithms take the raw wellhead pressure signal comprising a useful signal and a pump noise signal as an input; then performed is preprocessing the obtained wellbore pressure signal to localize the at least one useful signal in frequency domain. The preprocessing of the obtained wellbore pressure signal is performed by applying a bandpass filter implemented as one of Gaussian derivative bandpass filter, zero frequency notch filter, or Butterworth lowpass filter or their combination. The Gaussian derivative bandpass filter and Butterworth lowpass filter having a bandwidth 10-20 Hz [0070]), where at least one key attribute is a point of largest measured acoustic energy in each of the conduits and the pressure data samples have been filtered by applying a low-pass filter followed by a second filter (Both processing algorithms take the raw wellhead pressure signal comprising a useful signal and a pump noise signal as an input; then performed is preprocessing the obtained wellbore pressure signal to localize the at least one useful signal in frequency domain. The preprocessing of the obtained wellbore pressure signal is performed by applying a bandpass filter implemented as one of Gaussian derivative bandpass filter, zero frequency notch filter, or Butterworth lowpass filter or their combination. The Gaussian derivative bandpass filter and Butterworth lowpass filter having a bandwidth 10-20 Hz [0070]).
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to modify the invention of Granville with the teachings of Kabbanik to determine a flow obstruction and improve accuracy of the prediction model.
However, Granville and Kabbanik does not explicitly disclose cause the predictive model to output a prediction including a location and a size of a deposition or a leak in the conduit of interest by analyzing an identified difference between an observed pressure profile defined by the measured pressure data and an expected pressure profile determined by the predictive model for a conduit devoid of anomalies; and in response to the prediction, automatically execute an action directed to remediating the deposition or leak.
Nevertheless, Bennett discloses cause the predictive model to output a prediction including a location and a size of a deposition or a leak in the conduit of interest by analyzing an identified difference between an observed pressure profile defined by the measured pressure data and an expected pressure profile determined by the predictive model for a conduit devoid of anomalies (At block 508, the processor 202 can determine, based on another reflection signal 214b and the adjusted model 210, a presence of the deposition 218…The processor 202 can use the adjusted model 210 to generate an expected reflection signal and then compare the expected reflection signal to the observed reflection signal 214b received from the flowline to determine the presence of the deposition. The processor 202 may additionally determine an amount of the deposition and a position of the deposition based on the comparison. The presence of the deposition and characteristics of the deposition can be stored as deposition data 218 [0036]); and in response to the prediction, automatically execute an action directed to remediating the deposition or leak (The computing device 200 can use the adjusted model to determine whether a deposition is present in a flowline and use an action module 222 to implement remediation operations for detected depositions. The process of adjusting the model 210 is further described with respect to FIG. 3 below [0026]).
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to modify the invention of Granville and Kabbanik with the teachings of Bennett to determine whether a deposition is present in a flowline (Bennett [0026]) and improve accuracy of the prediction model.
Regarding Claim 2, Granville, Kabbanik, and Bennett disclose the claimed invention discussed in claim 1.
Granville discloses the conduit monitoring subsystem further comprises a data acquisition device that is communicatively coupled to the at least one sensor to record or store pressure wave data generated by the at least one sensor (During operations, and discussed further below, a pressure pulse may be generated by opening and closing a valve in a fluid-filled annulus inside a tubular structure where a fiber optic cable may be deployed internal and/or external to the tubular structure. The pressure pulse may be recorded along the fiber optic cable and its arrival may be correlated to distance along the fiber optic cable, as well as distance along the tubular structure [0014]); and the instructions are further executable by the processor for causing the computing device to receive the pressure data samples from the conduit monitoring system data acquisition device (Information handling system 120 may act as a data acquisition system and possibly a data processing system that analyzes information from fiber optic cable 104 [0018]).
Regarding Claim 3, Granville, Kabbanik, and Bennett disclose the claimed invention discussed in claim 2.
Granville discloses the plurality of key attributes further includes a pressure data sample characteristic selected from the group consisting of a point at which the pressure wave generating mechanism of the conduit monitoring is turned off a point at which the pressure in the conduit begins to recover from introduction of the pressure wave into the conduit, a time period of interest of a total time period for which the pressure data was produced by the conduit monitoring subsystem, and combinations thereof (A method for determining a deployment profile of a fiber optic cable may comprise disposing the fiber optic cable into a tubular structure, opening and closing a valve to form a pressure pulse, wherein the pressure puke travels through the tubular structure, reflecting the pressure pulse off at least one feature to form a reflected pressure pulse, sensing the reflected pressure pulse, recording data on time elapsed from opening and closing the valve until sensing the reflected pressure pulse, sending the data to an information handling system, and computing the data to determine the deployment profile of the fiber optic cable [0057]).
Regarding Claim 5, Granville, Kabbanik, and Bennett disclose the claimed invention discussed in claim 1.
Granville discloses the plurality of key attributes in each of the pressure data samples of the training dataset (as discussed above).
However, Granville and Bennett do not explicitly disclose the plurality of key attributes in each of the pressure data samples of the training dataset are identified from a first derivative of each of the pressure data samples.
Nevertheless, Kabbanik discloses the training dataset are identified from a first derivative of each of the pressure data samples (The preprocessing of the obtained wellbore pressure signal is performed by applying a bandpass filter implemented as one of Gaussian derivative bandpass filter, zero frequency notch filter, or Butterworth lowpass filter or their combination. The Gaussian derivative bandpass filter and Butterworth lowpass filter having a bandwidth 10-20 Hz [0070]).
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to modify the invention of Granville and Kabbanik with the teachings of Bennett to determine whether a deposition is present in a flowline (Bennett [0026]) and improve accuracy of the prediction model.
Regarding Claim 8, Granville discloses a computer-implemented method comprising: comprising: introducing a pressure wave into fluid flowing within a conduit of interest using a pressure wave generating mechanism of a conduit monitoring subsystem communicatively coupled to a computing device and measuring, using at least one sensor of the conduit monitoring subsystem, transient pressure changes resulting from reflections of the pressure wave caused by anomalies in the conduit of interest (Retrievable (temporarily deployed) fiber optic cable 104 (Referring to FIG. 1) may be deployed either via a cable or multi-cable pack that may also include conductors (for instance a Wireline cable), or deployed via coiled tubing (not illustrated). Although fiber optic data may be quite valuable for obtaining acoustic, temperature, and other information about oil wells and pipelines, it has been a challenge for the industry to develop accurate methods for correlating fiber optic data to depth or distance along production tubing 106, casing 108, tubular structures, and/or the like. Non-destructive methods based on pressure pulse technology may allow for accurate depth measurements within production tubing 106, casing 108, tubular structures, and/or the like [0021]); receiving, by the computing device, measured pressure data generated by the conduit monitoring subsystem from the measured transient pressure changes within the conduit of interest (Information handling system 120 may be disposed on fiber optic cable 104 or otherwise positioned on surface 112. Information handling system 120 may act as a data acquisition system and possibly a data processing system that analyzes information from fiber optic cable 104 [0018]); causing, by a processor of the computing device, an input of at least some of the measured pressure data generated by the conduit monitoring subsystem (The pressure pulse travels at the speed of sound forming a wave through the annular fluid in the pipe, which generates reflections back when it encounters changes to the inner diameter of the pipe. These changes to the inner diameter may result from features such as mineral deposits, residue buildup, or a change in annular fluid properties (density, viscosity, velocity). Data may be recorded via one or more pressure transducers (not illustrated) deployed inside the production pipeline or oil well tubular of interest. It should be noted that the features and/or their location may be known during operations [0022]).
However, Granville does not explicitly disclose causing, by a processor of the computing device, an input of at least some of the measured pressure data generated by the conduit monitoring subsystem to a predictive model trained on a training dataset comprising a multitude of previously measured pressure data samples for each of a plurality of different hydrocarbon well conduits and a plurality of key attributes in each of the pressure data samples, where at least one key attribute is a point of largest measured acoustic energy in each of the conduits and the pressure data samples have been filtered by applying a low-pass filter followed by a second filter; causing, by the processor of the computing device, the predictive model to output a prediction including a location and a size of a deposition or a leak in the conduit of interest by analyzing an identified difference between an observed pressure profile defined by the measured pressure data and an expected pressure profile determined by the predictive model for a conduit devoid of anomalies; and in response to the prediction, automatically executing an action directed to remediating the deposition or leak.
Nevertheless, Kabbanik discloses causing, by a processor of the computing device, an input of at least some of the measured pressure data generated by the conduit monitoring subsystem to a predictive model trained on a training dataset comprising a multitude of previously measured pressure data samples for each of a plurality of different hydrocarbon well conduits and a plurality of key attributes in each of the pressure data samples (The pressure pulse travels at the speed of sound forming a wave through the annular fluid in the pipe, which generates reflections back when it encounters changes to the inner diameter of the pipe. These changes to the inner diameter may result from features such as mineral deposits, residue buildup, or a change in annular fluid properties (density, viscosity, velocity). Data may be recorded via one or more pressure transducers (not illustrated) deployed inside the production pipeline or oil well tubular of interest. It should be noted that the features and/or their location may be known during operations [0022]; Both processing algorithms take the raw wellhead pressure signal comprising a useful signal and a pump noise signal as an input; then performed is preprocessing the obtained wellbore pressure signal to localize the at least one useful signal in frequency domain. The preprocessing of the obtained wellbore pressure signal is performed by applying a bandpass filter implemented as one of Gaussian derivative bandpass filter, zero frequency notch filter, or Butterworth lowpass filter or their combination. The Gaussian derivative bandpass filter and Butterworth lowpass filter having a bandwidth 10-20 Hz [0070]), where at least one key attribute is a point of largest measured acoustic energy in each of the conduits and the pressure data samples have been filtered by applying a low-pass filter followed by a second filter (Both processing algorithms take the raw wellhead pressure signal comprising a useful signal and a pump noise signal as an input; then performed is preprocessing the obtained wellbore pressure signal to localize the at least one useful signal in frequency domain. The preprocessing of the obtained wellbore pressure signal is performed by applying a bandpass filter implemented as one of Gaussian derivative bandpass filter, zero frequency notch filter, or Butterworth lowpass filter or their combination. The Gaussian derivative bandpass filter and Butterworth lowpass filter having a bandwidth 10-20 Hz [0070]).
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to modify the invention of Granville with the teachings of Kabbanik to determine a flow obstruction and improve accuracy of the prediction model.
However, Granville and Kabbanik do not explicitly disclose causing, by the processor of the computing device, the predictive model to output a prediction including a location and a size of a deposition or a leak in the conduit of interest by analyzing an identified difference between an observed pressure profile defined by the measured pressure data and an expected pressure profile determined by the predictive model for a conduit devoid of anomalies; and in response to the prediction, automatically executing an action directed to remediating the deposition or leak.
Nevertheless, Bennett discloses causing, by the processor of the computing device, the predictive model to output a prediction including a location and a size of a deposition or a leak in the conduit of interest by analyzing an identified difference between an observed pressure profile defined by the measured pressure data and an expected pressure profile determined by the predictive model for a conduit devoid of anomalies (At block 508, the processor 202 can determine, based on another reflection signal 214b and the adjusted model 210, a presence of the deposition 218…The processor 202 can use the adjusted model 210 to generate an expected reflection signal and then compare the expected reflection signal to the observed reflection signal 214b received from the flowline to determine the presence of the deposition. The processor 202 may additionally determine an amount of the deposition and a position of the deposition based on the comparison. The presence of the deposition and characteristics of the deposition can be stored as deposition data 218 [0036]); and in response to the prediction, automatically executing an action directed to remediating the deposition or leak (The computing device 200 can use the adjusted model to determine whether a deposition is present in a flowline and use an action module 222 to implement remediation operations for detected depositions. The process of adjusting the model 210 is further described with respect to FIG. 3 below [0026]).
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to modify the invention of Granville and Kabbanik with the teachings of Bennett to determine whether a deposition is present in a flowline (Bennett [0026]) and improve accuracy of the prediction model.
Regarding Claim 11, Granville, Kabbanik, and Bennett disclose the claimed invention discussed in claim 8.
Granville discloses the plurality of key attributes in each of the pressure data samples of the training dataset (as discussed above).
However, Granville and Bennett do not explicitly disclose the plurality of key attributes in each of the pressure data samples of the training dataset are identified from a first derivative of each of the pressure data samples.
Nevertheless, Kabbanik discloses the training dataset are identified from a first derivative of each of the pressure data samples (The preprocessing of the obtained wellbore pressure signal is performed by applying a bandpass filter implemented as one of Gaussian derivative bandpass filter, zero frequency notch filter, or Butterworth lowpass filter or their combination. The Gaussian derivative bandpass filter and Butterworth lowpass filter having a bandwidth 10-20 Hz [0070]).
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to modify the invention of Granville and Kabbanik with the teachings of Bennett to determine whether a deposition is present in a flowline (Bennett [0026]) and improve accuracy of the prediction model.
Regarding Claim 14, Granville discloses a non-transitory computer-readable medium comprising instructions that are executable by a processor of a computing device for causing the processor to: output a command to cause a pressure wave generating mechanism of a conduit monitoring subsystem communicatively coupled to the computing device to introduce a pressure wave into fluid flowing within a conduit of interest and at least one sensor of the conduit monitoring subsystem to measure transient pressure changes resulting from reflections of the pressure wave caused by anomalies in the conduit of interest (This disclosure may generally relate to systems and methods for generating a deployment profile of a fiber optic cable as a function of depth and/or distance along a tubular structure. Tubular structures may include an oil well, gas well, completion tubing, casing, pipeline, and/or the like. It should be noted that the tubular structures may be fluid filled during operations to determine the deployment profile of a fiber optic cable as a function of depth and/or distance [0013]; The pressure pulse travels at the speed of sound forming a wave through the annular fluid in the pipe, which generates reflections back when it encounters changes to the inner diameter of the pipe. These changes to the inner diameter may result from features such as mineral deposits, residue buildup, or a change in annular fluid properties (density, viscosity, velocity) [0022]); receive measured pressure data generated by the conduit monitoring subsystem from the measured transient pressure changes within the conduit of interest (Information handling system 120 may be disposed on fiber optic cable 104 or otherwise positioned on surface 112. Information handling system 120 may act as a data acquisition system and possibly a data processing system that analyzes information from fiber optic cable 104 [0018]); output a command to cause an input of at least some of the measured pressure data generated by the conduit monitoring subsystem (The pressure pulse travels at the speed of sound forming a wave through the annular fluid in the pipe, which generates reflections back when it encounters changes to the inner diameter of the pipe. These changes to the inner diameter may result from features such as mineral deposits, residue buildup, or a change in annular fluid properties (density, viscosity, velocity). Data may be recorded via one or more pressure transducers (not illustrated) deployed inside the production pipeline or oil well tubular of interest. It should be noted that the features and/or their location may be known during operations [0022]).
However, Granville does not explicitly disclose output a command to cause an input of at least some of the measured pressure data generated by the conduit monitoring subsystem to a predictive model trained on a training dataset comprising a multitude of previously measured pressure data samples for each of a plurality of different hydrocarbon well conduits and a plurality of key attributes in each of the pressure data samples, where at least one key attribute is a point of largest measured acoustic energy in each of the conduits and the pressure data samples have been filtered by applying a low-pass filter followed by a second filter; output a command to cause the predictive model to output a prediction including a location and a size of a deposition or a leak in the conduit of interest by analyzing an identified difference between an observed pressure profile defined by the measured pressure data and an expected pressure profile determined by the predictive model for a conduit devoid of anomalies; and in response to the prediction, automatically execute an action directed to remediating the deposition or leak.
Nevertheless, Kabbanik discloses output a command to cause an input of at least some of the measured pressure data generated by the conduit monitoring subsystem to a predictive model trained on a training dataset comprising a multitude of previously measured pressure data samples for each of a plurality of different hydrocarbon well conduits and a plurality of key attributes in each of the pressure data samples (The pressure pulse travels at the speed of sound forming a wave through the annular fluid in the pipe, which generates reflections back when it encounters changes to the inner diameter of the pipe. These changes to the inner diameter may result from features such as mineral deposits, residue buildup, or a change in annular fluid properties (density, viscosity, velocity). Data may be recorded via one or more pressure transducers (not illustrated) deployed inside the production pipeline or oil well tubular of interest. It should be noted that the features and/or their location may be known during operations [0022]; Both processing algorithms take the raw wellhead pressure signal comprising a useful signal and a pump noise signal as an input; then performed is preprocessing the obtained wellbore pressure signal to localize the at least one useful signal in frequency domain. The preprocessing of the obtained wellbore pressure signal is performed by applying a bandpass filter implemented as one of Gaussian derivative bandpass filter, zero frequency notch filter, or Butterworth lowpass filter or their combination. The Gaussian derivative bandpass filter and Butterworth lowpass filter having a bandwidth 10-20 Hz [0070]), where at least one key attribute is a point of largest measured acoustic energy in each of the conduits and the pressure data samples have been filtered by applying a low-pass filter followed by a second filter (Both processing algorithms take the raw wellhead pressure signal comprising a useful signal and a pump noise signal as an input; then performed is preprocessing the obtained wellbore pressure signal to localize the at least one useful signal in frequency domain. The preprocessing of the obtained wellbore pressure signal is performed by applying a bandpass filter implemented as one of Gaussian derivative bandpass filter, zero frequency notch filter, or Butterworth lowpass filter or their combination. The Gaussian derivative bandpass filter and Butterworth lowpass filter having a bandwidth 10-20 Hz [0070]).
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to modify the invention of Granville with the teachings of Kabbanik to determine a flow obstruction and improve accuracy of the prediction model.
However, Granville and Kabbanik do not explicitly disclose output a command to cause the predictive model to output a prediction including a location and a size of a deposition or a leak in the conduit of interest by analyzing an identified difference between an observed pressure profile defined by the measured pressure data and an expected pressure profile determined by the predictive model for a conduit devoid of anomalies; and in response to the prediction, automatically execute an action directed to remediating the deposition or leak.
Nevertheless, Bennett discloses output a command to cause the predictive model to output a prediction including a location and a size of a deposition or a leak in the conduit of interest by analyzing an identified difference between an observed pressure profile defined by the measured pressure data and an expected pressure profile determined by the predictive model for a conduit devoid of anomalies (At block 508, the processor 202 can determine, based on another reflection signal 214b and the adjusted model 210, a presence of the deposition 218…The processor 202 can use the adjusted model 210 to generate an expected reflection signal and then compare the expected reflection signal to the observed reflection signal 214b received from the flowline to determine the presence of the deposition. The processor 202 may additionally determine an amount of the deposition and a position of the deposition based on the comparison. The presence of the deposition and characteristics of the deposition can be stored as deposition data 218 [0036]); and in response to the prediction, automatically execute an action directed to remediating the deposition or leak (The computing device 200 can use the adjusted model to determine whether a deposition is present in a flowline and use an action module 222 to implement remediation operations for detected depositions. The process of adjusting the model 210 is further described with respect to FIG. 3 below [0026]).
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to modify the invention of Granville and Kabbanik with the teachings of Bennett to determine whether a deposition is present in a flowline (Bennett [0026]) and improve accuracy of the prediction model.
Regarding Claim 15, Granville, Kabbanik, and Bennett disclose the claimed invention discussed in claim 14.
Granville discloses the conduit monitoring subsystem further comprises a data acquisition device that is communicatively coupled to the at least one sensor to record or store pressure wave data generated by the at least one sensor (During operations, and discussed further below, a pressure pulse may be generated by opening and closing a valve in a fluid-filled annulus inside a tubular structure where a fiber optic cable may be deployed internal and/or external to the tubular structure. The pressure pulse may be recorded along the fiber optic cable and its arrival may be correlated to distance along the fiber optic cable, as well as distance along the tubular structure [0014]); and the instructions are further executable by the processor for causing the computing device to receive the pressure data samples from the conduit monitoring system data acquisition device (Information handling system 120 may act as a data acquisition system and possibly a data processing system that analyzes information from fiber optic cable 104 [0018]).
Regarding Claim 16, Granville, Kabbanik, and Bennett disclose the claimed invention discussed in claim 15.
Granville discloses the plurality of key attributes further includes a pressure data sample characteristic selected from the group consisting of a point at which the pressure wave generating mechanism of the conduit monitoring is turned off a point at which the pressure in the conduit begins to recover from introduction of the pressure wave into the conduit, a time period of interest of a total time period for which the pressure data was produced by the conduit monitoring subsystem, and combinations thereof (A method for determining a deployment profile of a fiber optic cable may comprise disposing the fiber optic cable into a tubular structure, opening and closing a valve to form a pressure pulse, wherein the pressure puke travels through the tubular structure, reflecting the pressure pulse off at least one feature to form a reflected pressure pulse, sensing the reflected pressure pulse, recording data on time elapsed from opening and closing the valve until sensing the reflected pressure pulse, sending the data to an information handling system, and computing the data to determine the deployment profile of the fiber optic cable [0057]).
Regarding Claim 18, Granville, Kabbanik, and Bennett disclose the claimed invention discussed in claim 14.
Granville discloses the plurality of key attributes in each of the pressure data samples of the training dataset (as discussed above).
However, Granville and Bennett do not explicitly disclose the plurality of key attributes in each of the pressure data samples of the training dataset are identified from a first derivative of each of the pressure data samples.
Nevertheless, Kabbanik discloses the training dataset are identified from a first derivative of each of the pressure data samples (The preprocessing of the obtained wellbore pressure signal is performed by applying a bandpass filter implemented as one of Gaussian derivative bandpass filter, zero frequency notch filter, or Butterworth lowpass filter or their combination. The Gaussian derivative bandpass filter and Butterworth lowpass filter having a bandwidth 10-20 Hz [0070]).
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to modify the invention of Granville and Kabbanik with the teachings of Bennett to determine whether a deposition is present in a flowline (Bennett [0026]) and improve accuracy of the prediction model.
Claims 4, 10,13, and 17 are rejected under 35 U.S.C. 103 as being unpatentable over Granville, Kabbanik, and Bennett, and further in view of Thiruvenkatanathan et al. (WO2021148141) hereinafter referred to as ‘Thiruvenkatanathan’.
Regarding Claim 4, Granville, Kabbanik, and Bennett disclose the claimed invention discussed in claim 1.
However, Granville and Bennett do not explicitly disclose the low-pass filter and the second filter remove noise from the pressure data samples and focus the training dataset on a frequency range of interest of between 6 Hz to 7 Hz; and the low-pass filter is a Butterworth filter and the second filter is a Gaussian filter or a notch filter.
Nevertheless, Kabannik discloses the low-pass filter and the second filter remove noise from the pressure data samples and focus the training dataset on a frequency range of interest of between 6 Hz to 7 Hz; and the low-pass filter is a Butterworth filter and the second filter is a Gaussian filter or a notch filter (…The preprocessing of the obtained wellbore pressure signal is performed by applying a bandpass filter implemented as one of Gaussian derivative bandpass filter, zero frequency notch filter, or Butterworth lowpass filter or their combination. The Gaussian derivative bandpass filter and Butterworth lowpass filter having a bandwidth 10-20 Hz [0070]).
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to modify the invention of Granville and Kabbanik with the teachings of Bennett to determine whether a deposition is present in a flowline (Bennett [0026]) and improve accuracy of the prediction model.
However, Granville, Kabbanik, and Bennett do not explicitly disclose the low-pass filter and the second filter remove noise from the pressure data samples and focus the training dataset on a frequency range of interest of between 6 Hz to 7 Hz.
Nevertheless, Thiruvenkatanathan discloses focus the training dataset on a frequency range of interest of between 6 Hz to 7 Hz (The intensity of the acoustic signal may be proportional to the concentration of particulates 302 generating the excitations such that an increased broad band power intensity can be expected at increasing particulates 302 concentrations. In some embodiments, the resulting broadband acoustic signals that can be identified can include frequencies in the range of about 5 Hz to about 10 kHz [0057]).
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to modify the invention of Granville, Kabbanik, and Bennett with the teachings of Thiruvenkatanathan to determine one or more frequency domain features of the acoustic signal while comparing the resulting frequency domain feature values to the acoustic signatures, and determine whether or not an event is occurring at the selected location based on the analysis and comparison (Thiruvenkatanathan [0066]).
Regarding Claim 10, Granville, Kabbanik, and Bennett disclose the claimed invention discussed in claim 8.
However, Granville and Bennett do not explicitly disclose the low-pass filter and the second filter remove noise from the pressure data samples and focus the training dataset on a frequency range of interest of between 6 Hz to 7 Hz; and the low-pass filter is a Butterworth filter and the second filter is a Gaussian filter or a notch filter.
Nevertheless, Kabannik discloses the low-pass filter and the second filter remove noise from the pressure data samples and focus the training dataset on a frequency range of interest of between 6 Hz to 7 Hz; and the low-pass filter is a Butterworth filter and the second filter is a Gaussian filter or a notch filter (…The preprocessing of the obtained wellbore pressure signal is performed by applying a bandpass filter implemented as one of Gaussian derivative bandpass filter, zero frequency notch filter, or Butterworth lowpass filter or their combination. The Gaussian derivative bandpass filter and Butterworth lowpass filter having a bandwidth 10-20 Hz [0070]).
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to modify the invention of Granville and Kabbanik with the teachings of Bennett to determine whether a deposition is present in a flowline (Bennett [0026]) and improve accuracy of the prediction model.
However, Granville, Kabbanik, and Bennett do not explicitly disclose the low-pass filter and the second filter remove noise from the pressure data samples and focus the training dataset on a frequency range of interest of between 6 Hz to 7 Hz.
Nevertheless, Thiruvenkatanathan discloses focus the training dataset on a frequency range of interest of between 6 Hz to 7 Hz (The intensity of the acoustic signal may be proportional to the concentration of particulates 302 generating the excitations such that an increased broad band power intensity can be expected at increasing particulates 302 concentrations. In some embodiments, the resulting broadband acoustic signals that can be identified can include frequencies in the range of about 5 Hz to about 10 kHz [0057]).
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to modify the invention of Granville, Kabbanik, and Bennett with the teachings of Thiruvenkatanathan to determine one or more frequency domain features of the acoustic signal while comparing the resulting frequency domain feature values to the acoustic signatures, and determine whether or not an event is occurring at the selected location based on the analysis and comparison (Thiruvenkatanathan [0066]).
Regarding Claim 13, Granville, Kabbanik, and Bennett disclose the claimed invention discussed in claim 12.
However, Granville, Kabbnik, and Bennett do not explicitly disclose the remediation action is launching a cleaning pig or a robotic conduit leak repair device.
Nevertheless, Thiruvenkatanathan discloses the remediation action is launching a cleaning pig or a robotic conduit leak repair device (For instance, in various scenarios, a flow line pig may be utilized to performing cleaning, clearing, inspection, maintenance or other operations and functions within a flow line [0018]).
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to modify the invention of Granville, Kabbanik, and Bennett with the teachings of Thiruvenkatanathan to minimize obstructions/blockage within a flow line and improving accuracy of data collection.
Regarding Claim 17, Granville, Kabbanik, and Bennett disclose the claimed invention discussed in claim 14.
However, Granville and Bennett do not explicitly disclose the low-pass filter and the second filter remove noise from the pressure data samples and focus the training dataset on a frequency range of interest of between 6 Hz to 7 Hz; and the low-pass filter is a Butterworth filter and the second filter is a Gaussian filter or a notch filter.
Nevertheless, Kabannik discloses the low-pass filter and the second filter remove noise from the pressure data samples and focus the training dataset on a frequency range of interest of between 6 Hz to 7 Hz; and the low-pass filter is a Butterworth filter and the second filter is a Gaussian filter or a notch filter (…The preprocessing of the obtained wellbore pressure signal is performed by applying a bandpass filter implemented as one of Gaussian derivative bandpass filter, zero frequency notch filter, or Butterworth lowpass filter or their combination. The Gaussian derivative bandpass filter and Butterworth lowpass filter having a bandwidth 10-20 Hz [0070]).
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to modify the invention of Granville and Kabbanik with the teachings of Bennett to determine whether a deposition is present in a flowline (Bennett [0026]) and improve accuracy of the prediction model.
However, Granville, Kabbanik, and Bennett do not explicitly disclose the low-pass filter and the second filter remove noise from the pressure data samples and focus the training dataset on a frequency range of interest of between 6 Hz to 7 Hz.
Nevertheless, Thiruvenkatanathan discloses focus the training dataset on a frequency range of interest of between 6 Hz to 7 Hz (The intensity of the acoustic signal may be proportional to the concentration of particulates 302 generating the excitations such that an increased broad band power intensity can be expected at increasing particulates 302 concentrations. In some embodiments, the resulting broadband acoustic signals that can be identified can include frequencies in the range of about 5 Hz to about 10 kHz [0057]).
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to modify the invention of Granville, Kabbanik, and Bennett with the teachings of Thiruvenkatanathan to determine one or more frequency domain features of the acoustic signal while comparing the resulting frequency domain feature values to the acoustic signatures, and determine whether or not an event is occurring at the selected location based on the analysis and comparison (Thiruvenkatanathan [0066]).
Claims 6, 9, and 19 are rejected under 35 U.S.C. 103 as being unpatentable over Granville, Kabbanik, and Bennett, and further in view of Pop et al. (US20090165548) hereinafter referred to as ‘Pop’.
Regarding Claim 6, Granville, Kabbanik, and Bennett disclose the claimed invention discussed in claim 1.
Granville discloses a first set of pressure data associated with a conduit known to have a deposition (The pressure pulse travels at the speed of sound forming a wave through the annular fluid in the pipe, which generates reflections back when it encounters changes to the inner diameter of the pipe. These changes to the inner diameter may result from features such as mineral deposits, residue buildup, or a change in annular fluid properties (density, viscosity, velocity). Data may be recorded via one or more pressure transducers (not illustrated) deployed inside the production pipeline or oil well tubular of interest [0022]).
However, Granville does not explicitly disclose the training dataset includes a first set of pressure data associated with a conduit known to have a deposition, a second set of pressure data associated with a conduit known to have a leak, and a third set of pressure data associated with an ideal conduit.
Nevertheless, Bennett discloses the training dataset (as discussed above). includes a first set of pressure data associated with a conduit known to have a deposition, a second set of pressure data associated with a conduit known to have a leak, and a third set of pressure data associated with an ideal conduit.
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to modify the invention of Granville and Kabbanik with the teachings of Bennett to determine whether a deposition is present in a flowline (Bennett [0026]) and improve accuracy of the prediction model.
However, Granville, Kabbanik, and Bennett do not explicitly disclose the training dataset includes a first set of pressure data associated with a conduit known to have a deposition, a second set of pressure data associated with a conduit known to have a leak, and a third set of pressure data associated with an ideal conduit.
Nevertheless, Pop discloses a second set of pressure data associated with a conduit known to have a leak (FIG. 44A is a graphical representation of a pressure measurements versus time plot generated by a formation tester depicting a leak during a buildup [0080]), and a third set of pressure data associated with an ideal conduit (Typically, confidence tokens are used to identify the resemblance between the pressure response measured during an actual pretest and the corresponding expected response in ideal conditions, or prototypical pretest [0293]).
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to modify the invention of Granville, Kabbanik, and Bennett with the teachings of Pop to determine whether a deposition is present in a flowline (Bennett [0026]) and improve accuracy of the prediction model.
Regarding Claim 9, Granville, Kabbanik, and Bennett disclose the claimed invention discussed in claim 8.
Granville discloses a first set of pressure data associated with a conduit known to have a deposition (The pressure pulse travels at the speed of sound forming a wave through the annular fluid in the pipe, which generates reflections back when it encounters changes to the inner diameter of the pipe. These changes to the inner diameter may result from features such as mineral deposits, residue buildup, or a change in annular fluid properties (density, viscosity, velocity). Data may be recorded via one or more pressure transducers (not illustrated) deployed inside the production pipeline or oil well tubular of interest [0022]).
However, Granville does not explicitly disclose the training dataset includes a first set of pressure data associated with a conduit known to have a deposition, a second set of pressure data associated with a conduit known to have a leak, and a third set of pressure data associated with an ideal conduit.
Nevertheless, Bennett discloses the training dataset (as discussed above). includes a first set of pressure data associated with a conduit known to have a deposition, a second set of pressure data associated with a conduit known to have a leak, and a third set of pressure data associated with an ideal conduit.
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to modify the invention of Granville and Kabbanik with the teachings of Bennett to determine whether a deposition is present in a flowline (Bennett [0026]) and improve accuracy of the prediction model.
However, Granville, Kabbanik, and Bennett do not explicitly disclose the training dataset includes a first set of pressure data associated with a conduit known to have a deposition, a second set of pressure data associated with a conduit known to have a leak, and a third set of pressure data associated with an ideal conduit.
Nevertheless, Pop discloses a second set of pressure data associated with a conduit known to have a leak (FIG. 44A is a graphical representation of a pressure measurements versus time plot generated by a formation tester depicting a leak during a buildup [0080]), and a third set of pressure data associated with an ideal conduit (Typically, confidence tokens are used to identify the resemblance between the pressure response measured during an actual pretest and the corresponding expected response in ideal conditions, or prototypical pretest [0293]).
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to modify the invention of Granville, Kabbanik, and Bennett with the teachings of Pop to determine whether a deposition is present in a flowline (Bennett [0026]) and improve accuracy of the prediction model.
Regarding Claim 19, Granville, Kabbanik, and Bennett disclose the claimed invention discussed in claim 14.
Granville discloses a first set of pressure data associated with a conduit known to have a deposition (The pressure pulse travels at the speed of sound forming a wave through the annular fluid in the pipe, which generates reflections back when it encounters changes to the inner diameter of the pipe. These changes to the inner diameter may result from features such as mineral deposits, residue buildup, or a change in annular fluid properties (density, viscosity, velocity). Data may be recorded via one or more pressure transducers (not illustrated) deployed inside the production pipeline or oil well tubular of interest [0022]).
However, Granville does not explicitly disclose the training dataset includes a first set of pressure data associated with a conduit known to have a deposition, a second set of pressure data associated with a conduit known to have a leak, and a third set of pressure data associated with an ideal conduit.
Nevertheless, Bennett discloses the training dataset (as discussed above). includes a first set of pressure data associated with a conduit known to have a deposition, a second set of pressure data associated with a conduit known to have a leak, and a third set of pressure data associated with an ideal conduit.
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to modify the invention of Granville and Kabbanik with the teachings of Bennett to determine whether a deposition is present in a flowline (Bennett [0026]) and improve accuracy of the prediction model.
However, Granville, Kabbanik, and Bennett do not explicitly disclose the training dataset includes a first set of pressure data associated with a conduit known to have a deposition, a second set of pressure data associated with a conduit known to have a leak, and a third set of pressure data associated with an ideal conduit.
Nevertheless, Pop discloses a second set of pressure data associated with a conduit known to have a leak (FIG. 44A is a graphical representation of a pressure measurements versus time plot generated by a formation tester depicting a leak during a buildup [0080]), and a third set of pressure data associated with an ideal conduit (Typically, confidence tokens are used to identify the resemblance between the pressure response measured during an actual pretest and the corresponding expected response in ideal conditions, or prototypical pretest [0293]).
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to modify the invention of Granville, Kabbanik, and Bennett with the teachings of Pop to determine whether a deposition is present in a flowline (Bennett [0026]) and improve accuracy of the prediction model.
Claims 7, 12, and 20 are rejected under 35 U.S.C. 103 as being unpatentable over Granville, Kabbanik, and Bennett, and further in view of Revheim et al. (US20210293130) hereinafter referred to as ‘Revheim’.
Regarding Claim 7, Granville, Kabbanik, and Bennett disclose the claimed invention discussed in claim 1.
Granville discloses the action is generating a notification indicating-a location and the size of the deposition or the leak (as discussed above).
However, Granville, Kabbanik, and Bennett do not explicitly disclose the action is generating a notification indicating-a location and the size of the deposition or the leak, scheduling a maintenance procedure, initiating a deposition or leak remediation operation, and combinations thereof.
Nevertheless, Revheim discloses the action is generating a notification (As described in further detail herein, the disclosure provides a method and system for capturing sensor data from a real time, or historical time and/or depth based data stream from an oil rig or similar unit related to drilling, completion and intervention activities in the oil and gas field. The method and system will filter and normalize these data and feed them to one or more predictive machine learning models to provide predicted time and/or depth data series. The predicted data series is then compared to a predefined rule based or modelled success/failure criteria. In case the predefined criteria are met the alarms are generated. Both the predicted data and the alarms are converted to a time and/or depth based data series which are stored and displayed on a computer system, thus enabling qualified personnel to intervene in drilling and well operations to secure a successful drilling, completion or intervention operations [0039]).
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to modify the invention of Granville, Kabbanik, and Bennett with the teachings of Revheim to determine whether a deposition is present in a flowline (Bennett [0026]) and improve accuracy of the prediction model.
Regarding Claim 12, Granville, Kabbanik, and Bennett disclose the claimed invention discussed in claim 8.
Granville discloses the action is generating a notification indicating-a location and the size of the deposition or the leak (as discussed above).
However, Granville, Kabbanik, and Bennett do not explicitly disclose the action is generating a notification indicating-a location and the size of the deposition or the leak, scheduling a maintenance procedure, initiating a deposition or leak remediation operation, and combinations thereof.
Nevertheless, Revheim discloses the action is generating a notification (As described in further detail herein, the disclosure provides a method and system for capturing sensor data from a real time, or historical time and/or depth based data stream from an oil rig or similar unit related to drilling, completion and intervention activities in the oil and gas field. The method and system will filter and normalize these data and feed them to one or more predictive machine learning models to provide predicted time and/or depth data series. The predicted data series is then compared to a predefined rule based or modelled success/failure criteria. In case the predefined criteria are met the alarms are generated. Both the predicted data and the alarms are converted to a time and/or depth based data series which are stored and displayed on a computer system, thus enabling qualified personnel to intervene in drilling and well operations to secure a successful drilling, completion or intervention operations [0039]).
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to modify the invention of Granville, Kabbanik, and Bennett with the teachings of Revheim to determine whether a deposition is present in a flowline (Bennett [0026]) and improve accuracy of the prediction model.
Regarding Claim 20, Granville, Kabbanik, and Bennett disclose the claimed invention discussed in claim 14.
Granville discloses the action is generating a notification indicating-a location and the size of the deposition or the leak (as discussed above).
However, Granville, Kabbanik, and Bennett do not explicitly disclose the action is generating a notification indicating-a location and the size of the deposition or the leak, scheduling a maintenance procedure, initiating a deposition or leak remediation operation, and combinations thereof.
Nevertheless, Revheim discloses the action is generating a notification (As described in further detail herein, the disclosure provides a method and system for capturing sensor data from a real time, or historical time and/or depth based data stream from an oil rig or similar unit related to drilling, completion and intervention activities in the oil and gas field. The method and system will filter and normalize these data and feed them to one or more predictive machine learning models to provide predicted time and/or depth data series. The predicted data series is then compared to a predefined rule based or modelled success/failure criteria. In case the predefined criteria are met the alarms are generated. Both the predicted data and the alarms are converted to a time and/or depth based data series which are stored and displayed on a computer system, thus enabling qualified personnel to intervene in drilling and well operations to secure a successful drilling, completion or intervention operations [0039]).
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to modify the invention of Granville, Kabbanik, and Bennett with the teachings of Revheim to determine whether a deposition is present in a flowline (Bennett [0026]) and improve accuracy of the prediction model.
Response to Arguments
USC § 101
Applicant’s arguments filed, 05/04/2026, with respect to claims 1-20 have been fully considered and are persuasive. The rejection of claims 1-20 has been withdrawn.
35 USC § 103
Applicant's arguments filed 05/04/2026 have been fully considered but they are not persuasive.
Applicant' s arguments with respect to claims 1-20 have been considered but are moot in view of new grounds of rejection.
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 extension fee 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 date of this final action.
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.
/SHARAH ZAAB/Examiner, Art Unit 2857
/Catherine T. Rastovski/Supervisory Primary Examiner, Art Unit 2857
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