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 § 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: a processor; and a memory including instructions that are executable by the processor for causing the processor to: access a training dataset comprising a multitude of measured pressure data samples for a conduit of a hydrocarbon well operation; filter the pressure data samples of the training dataset by applying a low- pass filter to the pressure data samples and by applying a second filter to the pressure data samples subsequent to applying the low-pass filter to the pressure data samples; identify a plurality of key attributes in each of the pressure data samples, at least one key attribute of the plurality of key attributes being a point of largest measured acoustic energy in the conduit; and train a machine learning model using the training dataset and the plurality of key attributes to minimize standard deviation and mean average between distance to blockage or distance to leak predictions calculated from at least some of the pressure data samples in the training dataset, to generate a predictive model.”
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 (machine).
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 and mental processes — concepts performed in the human mind including an observation, evaluation, judgement, and/or opinion.
For example, the steps of “the plurality of key attributes to minimize standard deviation and mean average between distance to blockage or distance to leak predictions calculated from at least some of the pressure data samples in the training dataset, to generate a predictive model” are treated as belonging to mathematical process grouping.
For example, the steps of “filter the pressure data samples of the training dataset by applying a low- pass filter to the pressure data samples and by applying a second filter to the pressure data samples subsequent to applying the low-pass filter to the pressure data samples and identifying a plurality of key attributes in each of the pressure data samples, at least one key attribute of the plurality of key attributes being a point of largest measured acoustic energy in the conduit” are treated as belonging to mental process grouping.
With regards to the steps of “filter the pressure data samples of the training dataset by applying a low- pass filter to the pressure data samples and by applying a second filter to the pressure data samples subsequent to applying the low-pass filter to the pressure data samples and identifying a plurality of key attributes in each of the pressure data samples, at least one key attribute of the plurality of key attributes being a point of largest measured acoustic energy in the conduit”, this mental step represents a process that, under its broadest reasonable interpretation, cover performance of the limitations in the mind. That is, nothing in the claim element precludes the step from practically being performed in the mind. In the context of this claim, it encompasses the user making mental decisions (evaluation/judgement) with regards to determining a point of largest measured acoustic energy in the conduit.
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: a processor; and a memory including instructions that are executable by the processor for causing the processor to: access a training dataset comprising a multitude of measured pressure data samples for a conduit of a hydrocarbon well operation; and train a machine learning model using the training dataset
Claim 8: A computer-implemented method comprising: accessing, by a processor, a training dataset comprising a multitude of measured pressure data samples for a conduit of a hydrocarbon well operation and training, by the processor, a machine learning model using the training dataset
Claim 14: A non-transitory computer-readable medium comprising instructions that are executable by a processor for causing the processor to: access a training dataset comprising a multitude of measured pressure data samples for a conduit of a hydrocarbon well operation; and train a machine learning model using the training dataset
The above additional elements in Claim 1 such as a system, comprising: a processor; and a memory including instructions that are executable by the processor for causing the processor to: access a training dataset comprising a multitude of measured pressure data samples for a conduit of a hydrocarbon well operation are examples of data gathering and are generically recited and are not meaningful. The additional elements in Claims 8 and 14 such a computer, a processor, and a non-transitory computer-readable medium comprising instructions that are executable by a processor for causing the processor is an example of generic computer equipment (components) that is generally recited and, therefore, is not qualified as a particular machine.
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 in the submitted IDS (12/09/2025) by the Applicant (Revheim and Thiruvenkatanathan).
The independent claims, therefore, are not patent eligible.
With regards to the dependent claims, claims 2-7, 9-13, and 15-20 provide additional features/steps which are either part of an expanded abstract idea of the independent claims (additionally comprising mathematical (Claims 2-7, 9-13, and 15-20) or adding additional elements/steps that are not meaningful as they are recited in generality and/or not qualified as particular machine/ and/or eligible transformation and, therefore, do not reflect a practical application as well as not qualified for “significantly more” based on prior art of record.
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 1-20 are rejected under 35 U.S.C. 103 as being unpatentable Revheim et al. (US20210293130), hereinafter referred to as ‘Revheim’ and in further view of Thiruvenkatanathan et al.(WO2021148141), hereinafter referred to as ‘Thiruvenkatanathan’ and Kabannik et al. (US20210032984), hereinafter referred to as ‘Kabannik’.
Regarding Claim 1, Revheim discloses system, comprising: a processor; and a memory including instructions that are executable by the processor for causing the processor to (an electronic processor, and a memory. The memory storing instructions that when executed by the electronic processor configure the electronic processor to receive data from the real time data system, [0005]): access a training dataset comprising a multitude of measured pressure data samples for a conduit of a hydrocarbon well operation (The system 400 includes sensors 404 that are coupled to rig equipment or downhole tools. The sensors 404 are coupled to the drilling rig 100 and provide time-based data that is captured by a real-time data system 408 including storage and distribution. In some embodiments, the real-time data system 408 includes wellbore trajectory data from real time sensors or planned data. The sensor 404 describes operation of the drill rig 100, including but not limited to, hook load (i.e., the weight of the string 112), position (e.g., the position of the string 112), torque (i.e., the force used to rotate the string 112), rpm (i.e., the number of rotations per minute applied to the drill string 112), pump pressure and flow rate (i.e., the output values from the pump 108C). The sensor 404 may also be connected to the downhole string 112, either providing measurements on the rig operations, such as load, torque, rpm, flowrate, pump pressure or sensors measuring the properties of the downhole formations including GR, Neutron Density data, Sonic response data and others). [0048]); filter the pressure data samples of the training dataset (The memory storing instructions that when executed by the electronic processor configure the electronic processor to receive data from the real time data system, filter the data received from the real time data system, generate a time prediction and a value prediction using a machine learning model based on the filtered data [0005]).
However, Revheim does not explicitly disclose filter the pressure data samples of the training dataset by applying a low- pass filter to the pressure data samples and by applying a second filter to the pressure data samples subsequent to applying the low-pass filter to the pressure data samples; identify a plurality of key attributes in each of the pressure data samples, at least one key attribute of the plurality of key attributes being a point of largest measured acoustic energy in the conduit; and train a machine learning model using the training dataset and the plurality of key attributes to minimize standard deviation and mean average between distance to blockage or distance to leak predictions calculated from at least some of the pressure data samples in the training dataset, to generate a predictive model.
Nevertheless, Thiruvenkatanathan discloses identify a plurality of key attributes in each of the pressure data samples (When the presence of a flow obstruction 310, 312 is detected, the results can be compared to the data from other sensors in the system to verify the presence of the flow obstruction event such as flow or pressure sensors to detect an increased pressure drop, i.e., key attributes, across a flow obstruction [0075]), at least one key attribute of the plurality of key attributes being a point of largest measured acoustic energy in the conduit (When the presence of a flow obstruction 310, 312 is detected, the results can be compared to the data from other sensors in the system to verify the presence of the flow obstruction event such as flow or pressure sensors to detect an increased pressure drop across a flow obstruction [0075]); train a machine learning model using the training dataset and (As an example for training of a model used to determine the presence or absence of a flow obstruction within the flow line 114, the training of the model can begin with providing the one or more frequency domain features to the logistic regression model corresponding to one or more reference data sets in which flow obstructions are present [0077]) the plurality of key attributes to … standard deviation and mean average between distance to blockage or distance to leak predictions calculated from at least some of the data samples in the training dataset, to generate a predictive model (Within the processing unit 404, the acoustic data can be analyzed, for example, by being compared to one or more acoustic signatures to determine if an event of interest is present (e.g., such as a flow obstruction event as previously described above), and/or used with one or more models (e.g., one or more machine learning models, multivariate models, etc.)[0068]; The resulting spectral signatures can then be used along with processed acoustic signal data to determine if an event (e.g., such as a flow obstruction event, previously described above) is occurring at a distance range of interest along the optical fiber, which can correspond to a particular section of the flow line 114 [0055]; As an example, the spectral centroid denotes the “brightness” of the sound captured by the optical fiber 116 and indicates the center of gravity of the frequency spectrum in the acoustic sample. The spectral centroid can be calculated as the weighted mean of the frequencies present in the signal [0089]).
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 Revheim with the teachings of Thiruvenkatanathan to determine a flow obstruction and improve accuracy of the prediction model.
However, the combination does not explicitly disclose filter the pressure data samples of the training dataset by applying a low- pass filter to the pressure data samples and by applying a second filter to the pressure data samples subsequent to applying the low-pass filter to the pressure data samples.
Nevertheless, Kabannik filter the pressure data samples of the training dataset by applying a low- pass filter to the pressure data samples and by applying a second filter to the pressure data samples subsequent to applying the low-pass filter to the pressure data samples (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 Revheim with the teachings of Thiruvenkatanathan and Kabannik to process/extract data while suppressing noise and improving statistical accuracy.
Regarding Claim 2, Revheim, Thiruvenkatanathan, and Kabannik disclose the claimed invention discussed in claim 1.
Revheim discloses the pressure data samples are produced by a conduit monitoring system configured to introduce a pressure wave into the conduit and to measure a magnitude of reflected pressure waves using at least one sensor; the conduit monitoring system is communicatively coupled to a computing device of the system, the computing device including the processor, the memory, and the instructions; 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 (The output 124 from the sensor 120 (i.e., sensor output data) is captured as part of a real-time data system 128 that then stores the output 124 in a drill site computer 154. The drill site computer 154 is typically located on the premises of the drilling rig 100. In the illustrated embodiment, the drill site computer 154 includes a memory storage 158 and a display 162. In some embodiments, the output 124 from the sensor 120 is shown on the display 162 and can be monitored by qualified personnel P1 to verify the quality of operations and to identify deviations or early warnings for undesired events [0032]).
However, Revheim does not explicitly disclose the pressure data samples are produced by a conduit monitoring system configured to introduce a pressure wave into the conduit and to measure a magnitude of reflected pressure waves using at least one sensor.
Nevertheless, Thiruvenkatanathan discloses the data samples are produced by a conduit monitoring system configured to introduce a wave into the conduit and to measure a magnitude of reflected waves using at least one sensor (generating an acoustic signal within a flow line, detecting the acoustic signal using an optical fiber coupled to the flow line; determining a plurality of frequency domain features from the acoustic signal [0004]; The light reflected back up the optical fiber 116 as a result of the backscatter can travel back to the source 166, where the signal can be collected by a sensor 164 and processed (e.g., using a processor 168) [0052]).
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 Revheim with the teachings of Thiruvenkatanathan and Kabannik to obtain a signal at the sensor and process for statistical analysis.
Regarding Claim 3, Revheim, Thiruvenkatanathan, and Kabannik disclose the claimed invention discussed in claim 2.
Revheim discloses the plurality of key attributes further includes a pressure data sample characteristic selected from the group consisting of a point at which a mechanism used by the conduit monitoring system to introduce the pressure wave into the conduit is turned off (as discussed above); 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 system, and combinations thereof (as discussed above).
However, Revheim does not explicitly disclose the plurality of key attributes further includes a pressure data sample characteristic selected from the group consisting of a point at which a mechanism used by the conduit monitoring system to introduce the pressure wave into the conduit 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 system, and combinations thereof.
Nevertheless, Thiruvenkatanathan discloses the plurality of key attributes further includes a data sample characteristic selected from the group consisting of a point at which a mechanism used by the conduit monitoring system to introduce the wave into the conduit is turned off (As used herein, the term “real time” refers to a time that takes into account various communication and latency delays within a system, and can include actions taken within about ten seconds, within about thirty seconds, within about a minute, within about five minutes, or within about ten minutes of the action occurring. Various sensors (e.g., distributed fiber optic acoustic sensors, etc.) can be used to obtain an acoustic sampling at various points along the flow line. The acoustic sample can then be processed using signal processing architecture with various feature extraction techniques (e.g., spectral feature extraction techniques) to obtain a measure of one or more frequency domain features that enable selectively extracting the acoustic signals of interest from background noise and consequently aiding in improving the accuracy of the identification of the movement of fluids and/or solids (e.g., particulates passing through the bend of a flow line, etc.) in real time [0019]); a point at which in the conduit begins to recover from introduction of the wave into the conduit, a time period of interest of a total time period for which the data was produced by the conduit monitoring system, and combinations thereof (As used herein, the term “real time” refers to a time that takes into account various communication and latency delays within a system, and can include actions taken within about ten seconds, within about thirty seconds, within about a minute, within about five minutes, or within about ten minutes of the action occurring. Various sensors (e.g., distributed fiber optic acoustic sensors, etc.) can be used to obtain an acoustic sampling at various points along the flow line. The acoustic sample can then be processed using signal processing architecture with various feature extraction techniques (e.g., spectral feature extraction techniques) to obtain a measure of one or more frequency domain features that enable selectively extracting the acoustic signals of interest from background noise and consequently aiding in improving the accuracy of the identification of the movement of fluids and/or solids (e.g., particulates passing through the bend of a flow line, etc.) in real time [0019]).
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 Revheim with the teachings of Thiruvenkatanathan and Kabannik to obtain a measure of one or more pressure features that enable selectively extracting the signals and improve the accuracy.
Regarding Claim 4, Revheim, Thiruvenkatanathan, and Kabannik disclose the claimed invention discussed in claim 1.
Revheim discloses the filter are usable in combination to remove noise from the pressure data samples and to focus the training dataset (The method includes receiving time-based data from a real-time data system including a sensor, filtering the time-based data from the sensor, and generating, using a machine learning model, a prediction based on the filtered time-based data from the sensor [0004]).
However, Revheim does not explicitly disclose the low-pass filter and the second filter are usable in combination to remove noise from the pressure data samples and to 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, Thiruvenkatanathan discloses the low-pass filter and the second filter are usable in combination to remove noise from the pressure data samples and to 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 (As a result, it is expected that the sounds from the mechanical instrumentation and geophysical sources can be filtered out based on various filtering techniques (e.g., a low-pass frequency filter, etc.) [0063]; 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, frequencies in the range of about 5 Hz to about 5 kHz or about 50 Hz to about 5 kHz, or frequencies in the range of about 500 Hz to about 5 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 Revheim 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]).
However, the combination does not explicitly disclose the low-pass filter and the second filter are usable in combination to remove noise from the pressure data samples and to 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 are usable in combination to remove noise from the pressure data samples and to 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 (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 Revheim with the teachings of Thiruvenkatanathan and Kabannik 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 5, Revheim, Thiruvenkatanathan, and Kabannik disclose the claimed invention discussed in claim 1.
Revheim discloses the instructions are further executable by the processor for causing the processor to (as discussed above):after filtering of the pressure data samples (filtering the time-based data from the sensor, and generating, using a machine learning model, a prediction based on the filtered time-based data from the sensor [0004]), calculate the pressure data samples (filtering the time-based data from the sensor, and generating, using a machine learning model, a prediction based on the filtered time-based data from the sensor [0004]); and identify the plurality of key attributes in each of the pressure data samples (as discussed above).
However, Revheim does not explicitly disclose calculate at least a first derivative of each of the pressure data samples; and identify the plurality of key attributes in each of the pressure data samples from the first derivative of each of the pressure data samples.
Nevertheless, Kabannik discloses calculate at least a first derivative of each of the data samples (as discussed above); and the first derivative of each of the data samples (as discussed above).
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 Revheim with the teachings of Thiruvenkatanathan and Kabannik 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 6, Revheim, Thiruvenkatanathan, and Kabannik disclose the claimed invention discussed in claim 1.
Revheim discloses 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 (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, … The predicted data series is then compared to a predefined rule based or modelled success/failure criteria [0039];Any deviation from the trendline 1204 signifies a change in conditions (e.g., more pressure is required to maintain the same flow in the case of poor hole cleaning) [0061]).
However, Revheim 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 (as discussed above).
Nevertheless, Thiruvenkatanathan discloses 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 (…Since the multivariate models define thresholds for the determination and/or identification of specific conditions, the multivariate models and fluid flow model using such multivariate models can be considered to be event signatures for each type of fluid flow, flow obstruction type, composition, location, and/or size, and the like [0079]).
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 Revheim with the teachings of Thiruvenkatanathan and Kabannik to determine and/or identification of specific conditions and improve accuracy.
Regarding Claim 7, Revheim, Thiruvenkatanathan, and Kabannik disclose the claimed invention discussed in claim 1.
Revheim discloses the instructions are further executable by the processor for causing the processor to (as discussed above): predict a location and a size of a deposition or a leak in a conduit of interest by applying the predictive model to measured pressure data associated with the conduit of interest and analyzing a difference between an observed pressure profile defined by the measured pressure data and an expected pressure profile for an ideal conduit (the present disclosure provides a method and a system for using machine learning systems to predict values and compare the predicted values with trigger thresholds (i.e., success or failure criteria) [0038]; 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]).
However, Revheim does not explicitly disclose output a command to execute an action selected from the group consisting of generating a notification indicating a location and a magnitude of the deposition or the leak, scheduling a maintenance procedure, initiating a remediation action relative to the deposition or the leak, and combinations thereof.
Nevertheless, Thiruvenkatanathan discloses output a command to execute an action selected from the group consisting of generating a notification indicating a location and a magnitude of the deposition or the leak, scheduling a maintenance procedure, initiating a remediation action relative to the deposition or the leak, and combinations thereof (In addition, in some embodiments, the systems and methods disclosed herein may be utilized to track a changing location of a flow obstruction within a flow line (e.g., such as in the case when a flow obstruction comprises a flow line pig). As used herein, a “flow line pig” refers to a device or tool that is flowed or otherwise progressed through a flow line in order to perform one or more functions therein. 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 Revheim with the teachings of Thiruvenkatanathan to perform cleaning, clearing, inspection, maintenance or other operations and functions within a flow line (Thiruvenkatanathan [0018]).
Regarding Claim 8, Revheim discloses a computer-implemented method comprising: accessing, by a processor, (an electronic processor, and a memory. The memory storing instructions that when executed by the electronic processor configure the electronic processor to receive data from the real time data system, [0005]): access a training dataset comprising a multitude of measured pressure data samples for a conduit of a hydrocarbon well operation (The system 400 includes sensors 404 that are coupled to rig equipment or downhole tools. The sensors 404 are coupled to the drilling rig 100 and provide time-based data that is captured by a real-time data system 408 including storage and distribution. In some embodiments, the real-time data system 408 includes wellbore trajectory data from real time sensors or planned data. The sensor 404 describes operation of the drill rig 100, including but not limited to, hook load (i.e., the weight of the string 112), position (e.g., the position of the string 112), torque (i.e., the force used to rotate the string 112), rpm (i.e., the number of rotations per minute applied to the drill string 112), pump pressure and flow rate (i.e., the output values from the pump 108C). The sensor 404 may also be connected to the downhole string 112, either providing measurements on the rig operations, such as load, torque, rpm, flowrate, pump pressure or sensors measuring the properties of the downhole formations including GR, Neutron Density data, Sonic response data and others). [0048]); filtering, by the processor, the pressure data samples of the training dataset (The memory storing instructions that when executed by the electronic processor configure the electronic processor to receive data from the real time data system, filter the data received from the real time data system, generate a time prediction and a value prediction using a machine learning model based on the filtered data [0005]); identifying, by the processor, a plurality of key attributes in each of the pressure data samples, at least one key attribute of the plurality of key attributes being a point of largest measured acoustic energy in the conduit (With continued reference to FIG. 4, the system 400 includes a success or failure model (i.e., a trigger threshold) 456. The trigger threshold 456 is a rule-based success failure model, where the threshold values are configurable and can be dependent upon the activity being performed. Generally, both hook load and torque values change as a function of pipe length. Longer pipe require more force to move the pipe, resulting in higher values. FIG. 10 illustrates an example hook load curve 1010 where the long-term trend 1014 is decreasing, thus representative of removing pipe from the wellbore [0054]); and training, by the processor, a machine learning model using the training dataset and the plurality of key attributes to minimize standard deviation and mean average between distance to blockage or distance to leak predictions calculated from at least some of the pressure data samples in the training dataset, to generate a predictive model (The system 600 implements filtering and normalizing mechanism 616 where only sensor data corresponding to time stamps with flow rates having values exceeding a minimum. An additional filter mechanism, in some embodiments, discards crossplot SPP/flow values that deviate more than a configurable threshold value, from the previous non-discarded values [0061]).
However, Revheim does not explicitly disclose filtering, by the processor, the pressure data samples of the training dataset by applying a low-pass filter to the pressure data samples and applying a second filter to the pressure data samples subsequent to applying the low-pass filter to the pressure data samples; identifying, by the processor, a plurality of key attributes in each of the pressure data samples, at least one key attribute of the plurality of key attributes being a point of largest measured acoustic energy in the conduit; and training, by the processor, a machine learning model using the training dataset and the plurality of key attributes to minimize standard deviation and mean average between distance to blockage or distance to leak predictions calculated from at least some of the pressure data samples in the training dataset, to generate a predictive model.
Nevertheless, Thiruvenkatanathan discloses training, by the processor, a machine learning model using the training dataset (As an example for training of a model used to determine the presence or absence of a flow obstruction within the flow line 114, the training of the model can begin with providing the one or more frequency domain features to the logistic regression model corresponding to one or more reference data sets in which flow obstructions are present [0077]) and the plurality of key attributes to minimize standard deviation and mean average between distance to blockage or distance to leak predictions calculated from at least some of the pressure data samples in the training dataset, to generate a predictive model (Within the processing unit 404, the acoustic data can be analyzed, for example, by being compared to one or more acoustic signatures to determine if an event of interest is present (e.g., such as a flow obstruction event as previously described above), and/or used with one or more models (e.g., one or more machine learning models, multivariate models, etc.)[0068]; The resulting spectral signatures can then be used along with processed acoustic signal data to determine if an event (e.g., such as a flow obstruction event, previously described above) is occurring at a distance range of interest along the optical fiber, which can correspond to a particular section of the flow line 114 [0055]; As an example, the spectral centroid denotes the “brightness” of the sound captured by the optical fiber 116 and indicates the center of gravity of the frequency spectrum in the acoustic sample. The spectral centroid can be calculated as the weighted mean of the frequencies present in the signal [0089]).
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 Revheim with the teachings of Thiruvenkatanathan to determine a flow obstruction and improve accuracy of the prediction model.
However, the combination does not explicitly disclose filtering, by the processor, the pressure data samples of the training dataset by applying a low-pass filter to the pressure data samples and applying a second filter to the pressure data samples subsequent to applying the low-pass filter to the pressure data samples.
Nevertheless, Kabannik filtering, by the processor, the pressure data samples of the training dataset by applying a low-pass filter to the pressure data samples and applying a second filter to the pressure data samples subsequent to applying the low-pass filter to the pressure data samples (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 Revheim with the teachings of Thiruvenkatanathan and Kabannik to process/extract data while suppressing noise and improving statistical accuracy.
Regarding Claim 9, Revheim, Thiruvenkatanathan, and Kabannik disclose the claimed invention discussed in claim 8.
Revheim discloses the training dataset includes a first set of pressure data associated with a conduit, a second set of pressure data associated with a conduit, and a third set of pressure data associated (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, … The predicted data series is then compared to a predefined rule based or modelled success/failure criteria [0039];Any deviation from the trendline 1204 signifies a change in conditions (e.g., more pressure is required to maintain the same flow in the case of poor hole cleaning) [0061]).
However, Revheim 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, Thiruvenkatanathan discloses the training dataset includes a first set of data associated with a conduit known to have a deposition, a second set of data associated with a conduit known to have a leak, and a third set of data associated with an ideal conduit (…Since the multivariate models define thresholds for the determination and/or identification of specific conditions, the multivariate models and fluid flow model using such multivariate models can be considered to be event signatures for each type of fluid flow, flow obstruction type, composition, location, and/or size, and the like [0079]).
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 Revheim with the teachings of Thiruvenkatanathan and Kabannik to determine and/or identification of specific conditions of the flow line and improve accuracy.
Regarding Claim 10, Revheim, Thiruvenkatanathan, and Kabannik disclose the claimed invention discussed in claim 8.
Revheim discloses the filter are usable in combination to remove noise from the pressure data samples and to focus the training dataset (The method includes receiving time-based data from a real-time data system including a sensor, filtering the time-based data from the sensor, and generating, using a machine learning model, a prediction based on the filtered time-based data from the sensor [0004]).
However, Revheim does not explicitly disclose the low-pass filter and the second filter are usable in combination to remove noise from the pressure data samples and to 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, Thiruvenkatanathan discloses the low-pass filter and the second filter are usable in combination to remove noise from the pressure data samples and to focus the training dataset on a frequency range of interest of between 6 Hz to 7 Hz (As a result, it is expected that the sounds from the mechanical instrumentation and geophysical sources can be filtered out based on various filtering techniques (e.g., a low-pass frequency filter, etc.) [0063]; 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, frequencies in the range of about 5 Hz to about 5 kHz or about 50 Hz to about 5 kHz, or frequencies in the range of about 500 Hz to about 5 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 Revheim 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]).
However, the combination does not explicitly disclose the low-pass filter and the second filter are usable in combination to remove noise from the pressure data samples and to 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 is a Butterworth filter and the second filter is a Gaussian filter or a notch 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 Revheim with the teachings of Thiruvenkatanathan and Kabannik 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 11, Revheim, Thiruvenkatanathan, and Kabannik disclose the claimed invention discussed in claim 8.
Revheim discloses after filtering of the pressure data samples (filtering the time-based data from the sensor, and generating, using a machine learning model, a prediction based on the filtered time-based data from the sensor [0004]), calculating , by the processor (filtering the time-based data from the sensor, and generating, using a machine learning model, a prediction based on the filtered time-based data from the sensor [0004]); and identifying, by the processor the plurality of key attributes in each of the pressure data samples (as discussed above).
However, Revheim does not explicitly disclose calculate at least a first derivative of each of the pressure data samples; and identify the plurality of key attributes in each of the pressure data samples from the first derivative of each of the pressure data samples.
Nevertheless, Kabannik discloses calculate at least a first derivative of each of the data samples (as discussed above); and the first derivative of each of the data samples (as discussed above).
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 Revheim with the teachings of Thiruvenkatanathan and Kabannik to identify changes in pressure over time, flow regimes, and observe how quickly pressure changes to improve accuracy.
Regarding Claim 12, Revheim, Thiruvenkatanathan, and Kabannik disclose the claimed invention discussed in claim 8.
Revheim discloses predicting, by the processor, a location and a size of a deposition or a leak in a conduit of interest by applying the predictive model to measured pressure data associated with the conduit of interest and analyzing a difference between an observed pressure profile defined by the measured pressure data and an expected pressure profile for an ideal conduit (the present disclosure provides a method and a system for using machine learning systems to predict values and compare the predicted values with trigger thresholds (i.e., success or failure criteria) [0038]; 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]).
However, Revheim does not explicitly disclose and in response to predicting a deposition or a leak in the conduit of interest, outputting by the processor, a command selected from the group consisting of generating a notification indicating a location and a magnitude of the deposition or the leak, scheduling a maintenance procedure, initiating a remediation action relative to the deposition or the leak, and combinations thereof.
Nevertheless, Thiruvenkatanathan discloses in response to predicting a deposition or a leak in the conduit of interest, outputting by the processor, a command selected from the group consisting of generating a notification indicating a location and a magnitude of the deposition or the leak, scheduling a maintenance procedure, initiating a remediation action relative to the deposition or the leak, and combinations thereof (In addition, in some embodiments, the systems and methods disclosed herein may be utilized to track a changing location of a flow obstruction within a flow line (e.g., such as in the case when a flow obstruction comprises a flow line pig). As used herein, a “flow line pig” refers to a device or tool that is flowed or otherwise progressed through a flow line in order to perform one or more functions therein. 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 Revheim with the teachings of Thiruvenkatanathan and Kabannik to perform cleaning, clearing, inspection, maintenance or other operations and functions within a flow line (Thiruvenkatanathan [0018]).
Regarding Claim 13, Revheim, Thiruvenkatanathan, and Kabannik disclose the claimed invention discussed in claim 8.
However, Revheim does 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 Revheim with the teachings of Thiruvenkatanathan and Kabannik to minimize obstructions/blockage within a flow line and improving accuracy of data collection.
Regarding Claim 14, Revheim discloses a non-transitory computer-readable medium comprising instructions that are executable by a processor for causing the processor to (an electronic processor, and a memory. The memory storing instructions that when executed by the electronic processor configure the electronic processor to receive data from the real time data system, [0005]): access a training dataset comprising a multitude of measured pressure data samples for a conduit of a hydrocarbon well operation (The system 400 includes sensors 404 that are coupled to rig equipment or downhole tools. The sensors 404 are coupled to the drilling rig 100 and provide time-based data that is captured by a real-time data system 408 including storage and distribution. In some embodiments, the real-time data system 408 includes wellbore trajectory data from real time sensors or planned data. The sensor 404 describes operation of the drill rig 100, including but not limited to, hook load (i.e., the weight of the string 112), position (e.g., the position of the string 112), torque (i.e., the force used to rotate the string 112), rpm (i.e., the number of rotations per minute applied to the drill string 112), pump pressure and flow rate (i.e., the output values from the pump 108C). The sensor 404 may also be connected to the downhole string 112, either providing measurements on the rig operations, such as load, torque, rpm, flowrate, pump pressure or sensors measuring the properties of the downhole formations including GR, Neutron Density data, Sonic response data and others). [0048]); filter the pressure data samples of the training dataset (The memory storing instructions that when executed by the electronic processor configure the electronic processor to receive data from the real time data system, filter the data received from the real time data system, generate a time prediction and a value prediction using a machine learning model based on the filtered data [0005]).
However, Revheim does not explicitly disclose filter the pressure data samples of the training dataset by applying a low-pass filter to the pressure data samples and by applying a second filter to the pressure data samples subsequent to applying the low-pass filter to the pressure data samples; identify a plurality of key attributes in each of the pressure data samples, at least one key attribute of the plurality of key attributes being a point of largest measured acoustic energy in the conduit; and train a machine learning model using the training dataset and the plurality of key attributes to minimize standard deviation and mean average between distance to blockage or distance to leak predictions calculated from at least some of the pressure data samples in the training dataset, to generate a predictive model.
Nevertheless, Thiruvenkatanathan discloses train a machine learning model using the training dataset (As an example for training of a model used to determine the presence or absence of a flow obstruction within the flow line 114, the training of the model can begin with providing the one or more frequency domain features to the logistic regression model corresponding to one or more reference data sets in which flow obstructions are present [0077]) and the plurality of key attributes to … standard deviation and mean average between distance to blockage or distance to leak predictions calculated from at least some of the pressure data samples in the training dataset, to generate a predictive model (Within the processing unit 404, the acoustic data can be analyzed, for example, by being compared to one or more acoustic signatures to determine if an event of interest is present (e.g., such as a flow obstruction event as previously described above), and/or used with one or more models (e.g., one or more machine learning models, multivariate models, etc.)[0068]; The resulting spectral signatures can then be used along with processed acoustic signal data to determine if an event (e.g., such as a flow obstruction event, previously described above) is occurring at a distance range of interest along the optical fiber, which can correspond to a particular section of the flow line 114 [0055]; As an example, the spectral centroid denotes the “brightness” of the sound captured by the optical fiber 116 and indicates the center of gravity of the frequency spectrum in the acoustic sample. The spectral centroid can be calculated as the weighted mean of the frequencies present in the signal [0089]).
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 Revheim with the teachings of Thiruvenkatanathan to determine a flow obstruction and improve accuracy of the prediction model.
However, the combination does not explicitly disclose filter the pressure data samples of the training dataset by applying a low- pass filter to the pressure data samples and by applying a second filter to the pressure data samples subsequent to applying the low-pass filter to the pressure data samples.
Nevertheless, Kabannik filter the pressure data samples of the training dataset by applying a low- pass filter to the pressure data samples and by applying a second filter to the pressure data samples subsequent to applying the low-pass filter to the pressure data samples (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 Revheim with the teachings of Thiruvenkatanathan and Kabannik to process/extract data while suppressing noise and improving statistical accuracy.
Regarding Claim 15, Revheim, Thiruvenkatanathan, and Kabannik disclose the claimed invention discussed in claim 14.
Revheim discloses the pressure data samples are produced by a conduit monitoring system configured to (as discussed above); the conduit monitoring system is communicatively coupled to a computing device of the system, the computing device including the processor, the memory, and the instructions; 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 (The output 124 from the sensor 120 (i.e., sensor output data) is captured as part of a real-time data system 128 that then stores the output 124 in a drill site computer 154. The drill site computer 154 is typically located on the premises of the drilling rig 100. In the illustrated embodiment, the drill site computer 154 includes a memory storage 158 and a display 162. In some embodiments, the output 124 from the sensor 120 is shown on the display 162 and can be monitored by qualified personnel P1 to verify the quality of operations and to identify deviations or early warnings for undesired events [0032]).
However, Revheim does not explicitly disclose the pressure data samples are produced by a conduit monitoring system configured to introduce a pressure wave into the conduit and to measure a magnitude of reflected pressure waves using at least one sensor.
Nevertheless, Thiruvenkatanathan discloses the data samples are produced by a conduit monitoring system configured to introduce a wave into the conduit and to measure a magnitude of reflected waves using at least one sensor (generating an acoustic signal within a flow line, detecting the acoustic signal using an optical fiber coupled to the flow line; determining a plurality of frequency domain features from the acoustic signal [0004]; The light reflected back up the optical fiber 116 as a result of the backscatter can travel back to the source 166, where the signal can be collected by a sensor 164 and processed (e.g., using a processor 168) [0052]).
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 Revheim with the teachings of Thiruvenkatanathan and Kabannik to obtain a signal at the sensor and process for statistical analysis.
Regarding Claim 16, Revheim, Thiruvenkatanathan, and Kabannik disclose the claimed invention discussed in claim 15.
Revheim discloses the plurality of key attributes further includes a pressure data sample characteristic selected from the group consisting of a point at which a mechanism used by the conduit monitoring system to introduce the pressure wave into the conduit (as discussed above); a time period of interest of a total time period for which the pressure data was produced by the conduit monitoring system, and combinations thereof (as discussed above).
However, Revheim does not explicitly disclose the plurality of key attributes further includes a pressure data sample characteristic selected from the group consisting of a point at which a mechanism used by the conduit monitoring system to introduce the pressure wave into the conduit 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 system, and combinations thereof.
Nevertheless, Thiruvenkatanathan discloses the plurality of key attributes further includes a data sample characteristic selected from the group consisting of a point at which a mechanism used by the conduit monitoring system to introduce the wave into the conduit is turned off (As used herein, the term “real time” refers to a time that takes into account various communication and latency delays within a system, and can include actions taken within about ten seconds, within about thirty seconds, within about a minute, within about five minutes, or within about ten minutes of the action occurring. Various sensors (e.g., distributed fiber optic acoustic sensors, etc.) can be used to obtain an acoustic sampling at various points along the flow line. The acoustic sample can then be processed using signal processing architecture with various feature extraction techniques (e.g., spectral feature extraction techniques) to obtain a measure of one or more frequency domain features that enable selectively extracting the acoustic signals of interest from background noise and consequently aiding in improving the accuracy of the identification of the movement of fluids and/or solids (e.g., particulates passing through the bend of a flow line, etc.) in real time [0019]); a point at which in the conduit begins to recover from introduction of the wave into the conduit, a time period of interest of a total time period for which the data was produced by the conduit monitoring system, and combinations thereof (As used herein, the term “real time” refers to a time that takes into account various communication and latency delays within a system, and can include actions taken within about ten seconds, within about thirty seconds, within about a minute, within about five minutes, or within about ten minutes of the action occurring. Various sensors (e.g., distributed fiber optic acoustic sensors, etc.) can be used to obtain an acoustic sampling at various points along the flow line. The acoustic sample can then be processed using signal processing architecture with various feature extraction techniques (e.g., spectral feature extraction techniques) to obtain a measure of one or more frequency domain features that enable selectively extracting the acoustic signals of interest from background noise and consequently aiding in improving the accuracy of the identification of the movement of fluids and/or solids (e.g., particulates passing through the bend of a flow line, etc.) in real time [0019]).
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 Revheim with the teachings of Thiruvenkatanathan and Kabannik to obtain a measure of one or more pressure features that enable selectively extracting the signals and improve the accuracy.
Regarding Claim 17, Revheim, Thiruvenkatanathan, and Kabannik disclose the claimed invention discussed in claim 14.
Revheim discloses the filter are usable in combination to remove noise from the pressure data samples and to focus the training dataset (The method includes receiving time-based data from a real-time data system including a sensor, filtering the time-based data from the sensor, and generating, using a machine learning model, a prediction based on the filtered time-based data from the sensor [0004]).
However, Revheim does not explicitly disclose the low-pass filter and the second filter are usable in combination to remove noise from the pressure data samples and to 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, Thiruvenkatanathan discloses the low-pass filter and the second filter are usable in combination to remove noise from the pressure data samples and to 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 (As a result, it is expected that the sounds from the mechanical instrumentation and geophysical sources can be filtered out based on various filtering techniques (e.g., a low-pass frequency filter, etc.) [0063]; 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, frequencies in the range of about 5 Hz to about 5 kHz or about 50 Hz to about 5 kHz, or frequencies in the range of about 500 Hz to about 5 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 Revheim 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]).
However, the combination does not explicitly disclose 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 is a Butterworth filter and the second filter is a Gaussian filter or a notch 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 Revheim with the teachings of Thiruvenkatanathan and Kabannik 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 18, Revheim, Thiruvenkatanathan, and Kabannik disclose the claimed invention discussed in claim 14.
Revheim discloses the instructions are further executable by the processor for causing the processor to (as discussed above):after filtering of the pressure data samples (filtering the time-based data from the sensor, and generating, using a machine learning model, a prediction based on the filtered time-based data from the sensor [0004]), calculate the pressure data samples (filtering the time-based data from the sensor, and generating, using a machine learning model, a prediction based on the filtered time-based data from the sensor [0004]); and identify the plurality of key attributes in each of the pressure data samples (as discussed above).
However, Revheim does not explicitly disclose calculate at least a first derivative of each of the pressure data samples; and identify the plurality of key attributes in each of the pressure data samples from the first derivative of each of the pressure data samples.
Nevertheless, Kabannik discloses calculate at least a first derivative of each of the data samples (as discussed above); and the first derivative of each of the data samples (as discussed above).
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 Revheim with the teachings of Thiruvenkatanathan and Kabannik 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 19, Revheim, Thiruvenkatanathan, and Kabannik disclose the claimed invention discussed in claim 14.
Revheim discloses the training dataset includes a first set of pressure data associated with a conduit, a second set of pressure data associated with a conduit, and a third set of pressure data (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, … The predicted data series is then compared to a predefined rule based or modelled success/failure criteria [0039];Any deviation from the trendline 1204 signifies a change in conditions (e.g., more pressure is required to maintain the same flow in the case of poor hole cleaning) [0061]).
However, Revheim 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, Thiruvenkatanathan discloses the training dataset includes a first set of data associated with a conduit known to have a deposition, a second set of data associated with a conduit known to have a leak, and a third set of data associated with an ideal conduit (…Since the multivariate models define thresholds for the determination and/or identification of specific conditions, the multivariate models and fluid flow model using such multivariate models can be considered to be event signatures for each type of fluid flow, flow obstruction type, composition, location, and/or size, and the like [0079]).
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 Revheim with the teachings of Thiruvenkatanathan and Kabannik to determine and/or identification of specific conditions and improve accuracy.
Regarding Claim 20, Revheim, Thiruvenkatanathan, and Kabannik disclose the claimed invention discussed in claim 14.
Revheim discloses the instructions are further executable by the processor for causing the processor to (as discussed above): predict a location and a size of a deposition or a leak in a conduit of interest by applying the predictive model to measured pressure data associated with the conduit of interest and analyzing a difference between an observed pressure profile defined by the measured pressure data and an expected pressure profile for an ideal conduit (the present disclosure provides a method and a system for using machine learning systems to predict values and compare the predicted values with trigger thresholds (i.e., success or failure criteria) [0038]; 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]).
However, Revheim does not explicitly disclose output a command to execute an action selected from the group consisting of generating a notification indicating a location and a magnitude of the deposition or the leak, scheduling a maintenance procedure, initiating a remediation action relative to the deposition or the leak, and combinations thereof.
Nevertheless, Thiruvenkatanathan discloses output a command to execute an action selected from the group consisting of generating a notification indicating a location and a magnitude of the deposition or the leak, scheduling a maintenance procedure, initiating a remediation action relative to the deposition or the leak, and combinations thereof (In addition, in some embodiments, the systems and methods disclosed herein may be utilized to track a changing location of a flow obstruction within a flow line (e.g., such as in the case when a flow obstruction comprises a flow line pig). As used herein, a “flow line pig” refers to a device or tool that is flowed or otherwise progressed through a flow line in order to perform one or more functions therein. 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 Revheim with the teachings of Thiruvenkatanathan and Kabannik to perform cleaning, clearing, inspection, maintenance or other operations and functions within a flow line (Thiruvenkatanathan [0018]).
Conclusion
The prior art made of record and not relied upon is considered pertinent to applicant's disclosure.
Pedro Mujica (US20180298738) discloses techniques for managing a hydraulic fluid pipeline pressure include measuring a fluid pressure of a hydrocarbon fluid circulating, from a wellbore by a pump positioned in the wellbore.
Michael Fripp (US20140352981) discloses a wellbore servicing system comprising one or more stationary receiving well tools disposed within a wellbore, wherein the stationary receiving well tools are configured to selectively transition from an inactive state to an active state in response to a triggering signal.
Julian Pop (US20090165548) discloses techniques for analyzing well data and tests that may exhibit an indication of anomalous behavior, defects, errors or events that may have occurred during testing.
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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/SHARAH ZAAB/Examiner, Art Unit 2863
/Catherine T. Rastovski/Supervisory Primary Examiner, Art Unit 2863