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 .
DETAILED ACTION
The following NON-FINAL Office Action is in response to application 18/627,092 filed on 04/04/2024. This communication is the first action on the merits.
Drawings
The drawings were received on 04/04/2024. These drawings are acceptable.
Claim Rejections - 35 USC § 112
The following is a quotation of 35 U.S.C. 112(b):
(b) CONCLUSION.—The specification shall conclude with one or more claims particularly pointing out and distinctly claiming the subject matter which the inventor or a joint inventor regards as the invention.
The following is a quotation of 35 U.S.C. 112 (pre-AIA ), second paragraph:
The specification shall conclude with one or more claims particularly pointing out and distinctly claiming the subject matter which the applicant regards as his invention.
Claim 20 rejected under 35 U.S.C. 112(b) or 35 U.S.C. 112 (pre-AIA ), second paragraph, as being indefinite for failing to particularly point out and distinctly claim the subject matter which the inventor or a joint inventor (or for applications subject to pre-AIA 35 U.S.C. 112, the applicant), regards as the invention.
Claim 20 recites the limitation "in response to classifying another engineering feature as not being a slug capable of affecting performance of the ESP". There is insufficient antecedent basis for this limitation in the claim. Claim 16, from which Claim 20 depends, recites outputting an anomaly estimation factor and does not recite any step of classifying an engineering feature. It is therefore unclear whether claim 20 requires an additional classifying step not otherwise recited, or whether the recited limitation refers to the anomaly estimation factor associated with another engineering feature not exceeding the threshold.
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 1-21 are rejected under 35 U.S.C. 101 because the claimed invention is directed to a judicial exception without significantly more. A subject matter eligibility analysis is set forth below. See MPEP 2106.
Specifically, representative Claim 1 recites:
A method of training a model to classify slugs, comprising:
generating a synthetic distributed acoustic sensor (DAS) signature by modeling slugs in a well;
combining field-recorded DAS flow noise and the synthetic DAS signature to generate data;
feature engineering the data; and
training a machine learning model to classify slugs as being capable of affecting performance of an electric submersible pump (ESP), based on the feature engineered data.
The claim limitations in the abstract idea have been highlighted in bold above; the remaining limitations are “additional elements.”
Similar limitations comprise the abstract idea of Claim 6 which performs claim 1 and comprises:
receiving data from a distributed acoustic sensor (DAS) in the well;
feature engineering the data to generate an engineering feature;
classifying the engineering feature using a machine learning model; and
in response to classifying the engineering feature as a slug capable of affecting performance of an electric submersible pump (ESP) in the well, changing an operation of the ESP in the well.
Similar limitations comprise the abstract idea of Claim 11 which performs claim 1 and comprises:
collecting data from a distributed acoustic sensor (DAS) in a well at a time period in which no slug affects performance of an electric submersible pump (ESP) in the well;
Similar limitations comprise the abstract idea of Claim 16 which performs claim 6 and comprises:
outputting an anomaly estimation factor, using a machine learning model, based on the engineering feature
Under Step 1 of the analysis, claim 1 belongs to a statutory category, namely it is a method claim. Likewise, claim 6, 11, and 16 is also method claim.
Under Step 2A, prong 1: This part of the eligibility analysis evaluates whether the claim recites a judicial exception. As explained in MPEP 2106.04, subsection II, a claim “recites” a judicial exception when the judicial exception is “set forth” or “described” in the claim.
In the instant case, claim 1 is found to recite at least one judicial exception (i.e. abstract idea), that being a Mental Process and/or a Mathematical Concept. Specifically, “generating a synthetic distributed acoustic sensor (DAS) signature by modeling slugs in a well” involves forward modeling of well conditions (see Spec. [0052]); “combining field-recorded DAS flow noise and the synthetic DAS signature to generate data” involves adding noise data to modeled data (see. Spec. [0053]); and “feature engineering the data” involves selecting and transforming data, including mathematical manipulation of the data (see Spec. [0054]). These operations constitute mathematical calculations and evaluations of information. Further, “training a machine learning model to classify slugs as being capable of affecting performance of an electric submersible pump (ESP), based on the feature engineered data” recites analyzing the engineering data to identify relationships and classify the data. These limitations further recite a mental process because the underlying steps involve observations, evaluations, and/or judgements in order to model, combine, and evaluate DAS-related data and determine whether a slug is capable of affecting performance of an electric submersible pump (ESP) and is capable of being performed mentally and/or with the aid of pen and paper. Additionally, the aforementioned limitations recite mathematical calculations, e.g. see Spec. [0179]-[0182] describing training a model to detect potentially damaging slugs in the well, using the trained model to evaluate DAS sensor data relating to well conditions in real time, and iteratively recalibrating and applying the model based on collected DAS sensor data.
Similar limitations comprise the abstract ideas of Claim 11
Claim 6 is found to recite at least one judicial exception (i.e. abstract idea), that being a Mental Process and a Mathematical Concept. This can be seen in the claim limitations of “feature engineering the data to generate an engineering feature”, “classifying the engineering feature using a machine learning model”, and which is the judicial exception of a mental process because these limitations are merely data observations, evaluations, and/or judgements in order to evaluate DAS-related data and determine, based on an engineering feature, whether a slug is capable of affecting performance of an electric submersible pump (ESP) and is capable of being performed mentally and/or with the aid of pen and paper.
Claim 16 comprise the abstract ideas of Claim 6 and recites “outputting an anomaly estimation factor, using a machine learning model, based on the engineering feature” which is the judicial exception of a mental process because these limitations are merely data observations, evaluations, and/or judgements in order to evaluate DAS-related data, generate an anomaly estimation factor based on an engineering feature, and determine whether the anomaly estimation factor exceeds a threshold and is capable of being performed mentally and/or with the aid of pen and paper.
Step 2A, prong 2 of the eligibility analysis evaluates whether the claim as a whole integrates the recited judicial exception(s) into a practical application of the exception. This evaluation is performed by (a) identifying whether there are any additional elements recited in the claim beyond the judicial exception, and (b) evaluating those additional elements individually and in combination to determine whether the claim as a whole integrates the exception into a practical application.
In addition to the abstract ideas recited in claim 1 and 11, the claimed method of claim 11 recites additional elements including “collecting data from a distributed acoustic sensor (DAS) in a well at a time period in which no slug affects performance of an electric submersible pump (ESP) in the well”. However, limiting the data used in the recited analysis to DAS data obtained from a well merely limits the judicial exception to a particular technological environment or field of use. The claim does not recite that the collection of the DAS data itself improves the operation of the DAS, the ESP, or another technological process. Rather, the collected DAS data is used as input for the subsequent analysis. Accordingly, this additional element does not integrate the judicial exception into a practical application.
Moreover, Claims 6 and 16 recites additional elements including “receiving data from a distributed acoustic sensor (DAS) in the well” and “in response to classifying the engineering feature as a slug capable of affecting performance of an electric submersible pump (ESP) in the well, changing an operation of the ESP in the well” Although the claims recite changing an operation of the ESP in response to the classification, the claims do not recite a particular manner in which the classification result is applied to improve operation of the ESP. For example, the claims do not recite changing the parameter by a particular amount or using a particular control technique to implement the change. Further, although the system may change operation of an ESP in response to slugging, claims 6 and 16 do not require a specific change that prevents damage or improves ESP operation. The claims only broadly recite changing an operation of the ESP based on the analysis. Likewise, the DAS only provides the data used in the analysis and limiting the data to DAS data from a well merely places the abstract idea in a particular technological environment. Therefore, when the additional elements are considered individually and together, they do not integrate the judicial exception into a practical application.
The generic data gathering, processing, and output steps, are recited at such a high level of generality that it represents no more than mere instructions to apply the judicial exceptions on a computer. It can also be viewed as nothing more than an attempt to generally link the use of the judicial exceptions to the technological environment of a computer. Noting MPEP 2106.04(d)(I): “It is notable that mere physicality or tangibility of an additional element or elements is not a relevant consideration in Step 2A Prong Two. As the Supreme Court explained in Alice Corp., mere physical or tangible implementation of an exception does not guarantee eligibility. Alice Corp. Pty. Ltd. v. CLS Bank Int’l, 573 U.S. 208, 224, 110 USPQ2d 1976, 1983-84 (2014) ("The fact that a computer ‘necessarily exist[s] in the physical, rather than purely conceptual, realm,’ is beside the point")”.
Thus, under Step 2A, prong 2 of the analysis, even when viewed in combination, these additional elements do not integrate the recited judicial exception into a practical application and the claim is directed to the judicial exception. No specific practical application is associated with the claimed system. For instance, nothing is done with the result of the analysis other than broadly changing an operation of the electric submersible pump (ESP). For example, the claim does not recite changing a particular operating parameter, changing the parameter by a particular amount, or using a specific control technique to implement the change.
Under Step 2B, the claims do not include additional elements that are sufficient to amount to significantly more than the judicial exception because the additional elements, as described above with respect to Step 2A Prong 2, merely amount to a general purpose computer system that attempts to apply the abstract idea in a technological environment, limiting the abstract idea to a particular field of use, and/or merely performs insignificant extra-solution activit(ies) (claims 1, 6, 11 and 16). Such insignificant extra-solution activity, e.g. data gathering and output, when re-evaluated under Step 2B is further found to be well-understood, routine, and conventional as evidenced by MPEP 2106.05(d)(II) (describing conventional activities that include transmitting and receiving data over a network, electronic recordkeeping, storing and retrieving information from memory, and electronically scanning or extracting data from a physical document).
With respect to claim 1 and 11, the recited collecting of data from a distributed acoustic sensor (DAS) in a well at a time period in which no slug affects performance of an electric submersible pump (ESP) is a data gathering step performed at a high level of generality. The DAS merely serves as the source of data used in the subsequent analysis. Accordingly, the recited collection of DAS data constitutes insignificant extra solution activity. Similarly, with respect to claims 6 and 16, the distributed acoustic sensor (DAS) merely serves as the source of the data used in the subsequent analysis. The recited receiving of DAS data is therefore a data-gathering step performed at a high level of generality and constitutes insignificant extra solution activity.
Therefore, similarly the combination and arrangement of the above identified additional elements when analyzed under Step 2B also fails to necessitate a conclusion that claim 1, as well as claim 6, 11, and 16, amount to significantly more than the abstract idea.
With regards to the dependent claims, claims 2-5, 7-8, 10, 12-15, 17-18, and 20-21, merely further expand upon the algorithm/abstract idea and do not set forth further additional elements that integrate the recited abstract idea into a practical application or amount to significantly more. Therefore, these claims are found ineligible for the reasons described for claims 1, 6, 11, and 16. Specifically:
With respect to dependent claims 2, 7, 8, 12, 17, and 18 specifically, the claims further recite additional data processing including low-pass filtering, integrating the signal, removing noise, standardizing and truncating the strain data, collapsing the data to a single time series, extracting amplitude and measured depth length, and sliding a truncated analysis window in time. These limitations merely further define how the DAS data is processed and analyzed and therefore merely expand upon the abstract idea. The limitations do not improve the DAS, computer functionality, or another technology, but instead merely perform additional analysis of the data. Accordingly, these limitations fail to integrate the abstract idea into a practical application or amount to significantly more. See MPEP 2106.05(f)(g).
With respect to dependent claims 3 and 13 specifically, the claims further recite using the machine learning model to classify slugs in a physical well or another physical well. These limitations merely limit the abstract idea to the technological environment of a well and do not recite any specific improvement to well technology, DAS operation, ESP operation, or computer functionality. Accordingly, these limitations fail to integrate the abstract idea into a practical application or amount to significantly more. See MPEP 2106.05(h).
With respect to dependent claims 4, 5, 14, and 15 specifically, the claims further recite the claims further recite changing an operation of the ESP based on the results of the analysis. However, these limitations merely use the result of the abstract analysis to broadly change ESP operation and do not recite any specific improvement to ESP technology. For example, the claims do not recite any particular control algorithm, operating parameter, amount to adjust, or specific manner of controlling the ESP. Accordingly, these limitations fail to integrate the abstract idea into a practical application or amount to significantly more. See MPEP 2106.05(f)(h).
With respect to dependent claim 10, 20, and 21 specifically, the claims further recite maintaining a current speed of the ESP based on the result of the analysis and recalibrating the model when the anomaly estimation factor does not exceed the threshold despite a slug affecting performance of the ESP. These limitations merely further use the result of abstract analysis to maintain an operating condition or perform additional model processing. The claims do not recite any specific manner of maintaining ESP speed or any particular recalibration technique that improves the machine learning model or another technology. Accordingly, these limitations fail to integrate the abstract idea into a practical application or amount to significantly more. See MPEP 2106.05(f)(h).
Accordingly, for the reasons above and those discussed in relation to independent claim 1, 6, 11, and 16, the dependent claims are insufficient to integrate the claimed abstract ideas into a practical application or significant more.
Claim Rejections - 35 USC § 102
The following is a quotation of the appropriate paragraphs of 35 U.S.C. 102 that form the basis for the rejections under this section made in this Office action:
A person shall be entitled to a patent unless –
(a)(1) the claimed invention was patented, described in a printed publication, or in public use, on sale, or otherwise available to the public before the effective filing date of the claimed invention.
(a)(2) the claimed invention was described in a patent issued under section 151, or in an application for patent published or deemed published under section 122(b), in which the patent or application, as the case may be, names another inventor and was effectively filed before the effective filing date of the claimed invention.
Claims 6-7, 9-10, 16-17, and 19-21 are rejected under 35 U.S.C. 102(a)(1)/(a)(2) as being anticipated by US 20200174149 A1, Thiruvenkatanathan (hereinafter Thiruvenkatanathan).
Regarding Claim 6 and 16, Thiruvenkatanathan discloses a method of pumping a fluid from a well (Thiruvenkatanathan, [0037] facilities monitoring (e.g., monitoring equipment such as electric submersible pumps, wind turbines, compressors, etc.) [0039] a pump can be monitored to determine if the pump is active or inactive, if fluid is flowing through the pump, if a bearing is bad, and the like all through the use of an acoustic sample and the spectral characteristics and/or models), comprising:
receiving data from a distributed acoustic sensor (DAS) in the well (Thiruvenkatanathan, [0060] In FIG. 2, the DAS system comprises an optical fiber 162 based acoustic sensing system that uses the optical backscatter component of light injected into the optical fiber for detecting acoustic perturbations (e.g., dynamic strain) along the length of the fiber 162 [0062] An acquisition device 160 can be coupled to one end of the optical fiber 162);
feature engineering the data to generate an engineering feature (Thiruvenkatanathan, [0087] The processor 168 can be further configured to transform the filtered data from the time domain into the frequency domain using a transform [0090] a method of this disclosure comprises determining one or more frequency domain features or spectral features at step 300. The use of frequency domain features to identify events can provide a number of advantages. First, the use of frequency domain features results in significant data reduction relative to the raw DAS data stream. Thus, a number of frequency domain features can be calculated and used to allow for event identification while the remaining data can be discarded or otherwise stored, and the remaining analysis can be performed using the frequency domain features [0112] A method of identifying one or more events can optionally comprise normalizing the one or the plurality of frequency domain features at 400 prior to identifying the one or more event locations at 500 and/or prior to identifying the one or more events at 600);
classifying the engineering feature using a machine learning model (Thiruvenkatanathan, [0118] In some embodiments, identifying one or more events comprises providing the plurality of frequency domain features to an event detection model at 600′ for each type of event; and determining that at least one event is present based on the event detection model. In some embodiments, the event detection model can be developed using and/or include machine learning such as a neural network, a Bayesian network, a decision tree, a logistical regression model, or a normalized logistical regression, or other supervised learning models [0082] calculating one or more frequency domain features and utilizing a model or models obtained from a machine learning approach (e.g., a supervised learning approach, etc.) on the one or more frequency domain features as described further hereinbelow to determine if one or more events are present, and, if present, determining information for the event(s)); and
in response to classifying the engineering feature as a slug capable of affecting performance of an electric submersible pump (ESP) in the well, changing an operation of the ESP in the well (Thiruvenkatanathan, [0164] The output of the plurality of event detection models can then be passed to a supervisory application at step 966. The supervisory application can be a control application, monitoring application or the like. The supervisory application can serve to process various event identifications and/or information associated with the events and make a supervisory determination such as a control decision, alarm decision, reporting decision, or the like. In some embodiments, an output of the supervisory application can be used to automatically control one or more systems or items of equipment within a process [0166] a control signal to a pump to control a flow rate output from the pump, a control signal to a security system to indicate an alarm is needed, a control signal to a pipeline monitoring application to report on the operation of the pipeline)
Regarding Claim 7 and 17, Thiruvenkatanathan discloses the method of claim 6, wherein the feature engineering comprises sliding a truncated analysis
window in time (Thiruvenkatanathan, [0087] Discrete Fourier transformations (DFT) or a short time Fourier transform (STFT) of the acoustic variant time domain data measured at each depth section along the fiber or a section thereof may be performed [0095] The value of the spectral centroid, C.sub.i, of the i.sup.th frame of the acoustic signal captured at a spatial location on the fiber [0105] a measure of the frame-to-frame squared difference of the spectral magnitude vector summed across all frequencies or a selected portion of the spectrum [0085] A spatial sample point filter can be applied that uses a filter to obtain a portion of the acoustic signal corresponding to a desired depth in the wellbore or length or location along a path of the fiber).
Regarding Claim 9 and 19, Thiruvenkatanathan discloses the method of claim 6, wherein the classifying of the engineering feature comprises classifying the engineering feature by a controller using the machine learning model (Thiruvenkatanathan, [0164] The supervisory application can serve to process various event identifications and/or information associated with the events and make a supervisory determination such as a control decision [0165] The supervisory application can comprise a model similar to any of the event detection models described herein. In some embodiments, the supervisory application can comprise one or more regression models, multivariate models, neural networks, Bayesian networks, and the like, and the supervisory application can be trained using data from known events [0080] Referring again to FIG. 2, the processor 168 within the acquisition device 160 can be configured to perform various data processing to detect the presence of an event within the wellbore 114), and wherein the changing of the operation of the ESP comprises slowing down, idling, or stopping the ESP by the controller (Thiruvenkatanathan, [0166] a control signal to a pump to control a flow rate output from the pump, a control signal to a security system to indicate an alarm is needed, a control signal to a pipeline monitoring application to report on the operation of the pipeline. The output of the supervisory application can comprise a simple yes/no response in some embodiments. This can provide an indication of the presence or absence of a control status, a control signal an item of equipment to turn the equipment on or off, or the like. In some embodiments, the output of the supervisory application can comprise control setting(s). For example, the output may set a flow control device to a position between fully open and fully closed in order to control a flow rate within the system [0034] a well can be shut in, production can be increased or decreased, and/or remedial measures can be taken in the wellbore [0157] the production levels can be adjusted based on the event locations and amounts), in response to the controller classifying the engineering feature as the slug capable of affecting performance of the ESP (Thiruvenkatanathan, [0159] the use of one or more models for wellbore events can allow for a wellbore control program to be implemented to take the event identifications into account. Events such as fluid inflow identification and quantification can be combined with sand detection to control a production rate or draw down within a wellbore [0166] a control signal to a pump to control a flow rate output from the pump)
Regarding Claim 10 and 20, Thiruvenkatanathan discloses the method of claim 6, further comprising maintaining a current speed of the ESP, in response to classifying another engineering feature as not being a slug capable of affecting performance of the ESP (Thiruvenkatanathan, [0163] The presence of an event can be important and inform various actions to be taken, and the absence of an event can also be used to inform various supervisory applications [0144] the absence of the event can be indicated when the calculated value is on the other side of the threshold [0166] the output of the supervisory application can comprise a simple yes/no response in some embodiments. This can provide an indication of the presence or absence of a control status, a control signal an item of equipment to turn the equipment on or off, or the like. In some embodiments, the output of the supervisory application can comprise control settings).
Regarding Claim 21, Thiruvenkatanathan discloses the method of claim 16, further comprising recalibrating the model (Thiruvenkatanathan, [0089] Preprocessing at 200 can further comprise calibrating the acoustic signal. Calibrating the acoustic signal can comprise removing a background signal from the acoustic signal, and/or correcting the acoustic signal for signal variations in the measured data. In some embodiments, calibrating the acoustic signal comprises identifying one or more anomalies within the acoustic signal and removing one or more portions of the acoustic signal outside the one or more anomalies [0137] the inflow tests can be calibrated to a certain reservoir depth...The resulting acoustic signals can then represent or be approximations of acoustic signals received under wellbore conditions. During the flow tests, acoustic data can be obtained under known flow conditions. The resulting acoustic data can then be used as training and/or test data for purposes of preparing the fluid flow model), in response to an anomaly estimation factor associated with another engineering feature not exceeding the threshold and a slug associated with the engineering feature affecting performance of the ESP (Thiruvenkatanathan, [0168] a pressure setting can be automatically controlled by the supervisory application to control the drawdown in a wellbore. As an example, the supervisory application can receive an indication of fluid inflows, fluid inflow rates, phase information, leak information, and/or sand ingress information. The information could be used to automatically control the drawdown pressure in the wellbore to increase the fluid inflow rates while avoiding or keeping sand ingress below a threshold).
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, 11, and 13-15 are rejected under 35 U.S.C. 103 as being unpatentable over US 20200174149 A1, Thiruvenkatanathan (hereinafter Thiruvenkatanathan) in view of US 20230324577 A1, Wu et al. (hereinafter Wu).
Regarding Claim 1 and 11, Thiruvenkatanathan discloses a method of training a model to classify slugs (Thiruvenkatanathan, [0129] FIG. 7 illustrates a flow diagram of a method II of developing an event detection model according to some embodiments [0072] An event comprising in-well sand transport and or the movement of a sand slug can be characterized by an acoustic signature including a spectral centroid within a low frequency range in a range of 0 Hz to about 20 Hz), comprising:
generating a synthetic distributed acoustic sensor (DAS) signature by modeling slugs in a well (Thiruvenkatanathan, [0127] sand inflow and/or flow in a fluid phase within a conduit can be modeled. The sand flow can be modeled in different fluid phases, at different sand amounts, in different orientations, and through different types of production assemblies, pipes, annuli, and the like [0137] The resulting acoustic signals can then represent or be approximations of acoustic signals received under wellbore conditions);
feature engineering the data (Thiruvenkatanathan, [0139] one or more frequency domain features can be obtained across the portion of the conduit including the predetermined locations at 910); and
training a machine learning model to classify slugs as being capable of affecting performance of an electric submersible pump (ESP), based on the feature engineered data (Thiruvenkatanathan, [0139] training the event detection model can use the one or more frequency domain features for a plurality of the tests and the predetermined locations at 920. The training of the event detection model can use machine learning, including any supervised or unsupervised learning approach. For example, the event detection model can be a neural network, a Bayesian network, a decision tree, a logistical regression model, a normalized logistical regression model, k-means clustering or the like);
Thiruvenkatanathan does not disclose combining field-recorded DAS flow noise and the synthetic DAS signature to generate data;
However, Wu teaches combining field-recorded DAS flow noise and the synthetic DAS signature to generate data (Wu, [0024] the single initial microseismic data is generated by: [0025] setting acquisition parameters according to the real microseismic monitoring system, comprising setting source parameters which comprises source mechanism parameters, source mechanism range and source location parameters; [0026] using a analytical Green’s function under a uniform velocity model to obtain synthetic DAS microseismic strain data, with a size of Nt*Ns; [0027] collecting monitored background noise data, adding the background noise data to the above-mentioned synthetic DAS microseismic strain data, simulating a signal-to-noise ratio and characteristics of collected filed data, to obtain the synthetic microseismic data)
Before the effective filing date of the claimed invention, It would have been obvious to one of ordinary skill in the art to combine Thiruvenkatanathan and Wu teaching because Thiruvenkatanathan teaches modeling sand flow/slug events and obtaining resulting acoustic signal representative of wellbore conditions, while Wu teaches generating synthetic DAS data and adding monitored field background noise data to the synthetic DAS data to simulate the signal to noise ratio and characteristics of collected field data. A person of ordinary skill in the art would have been motivated to combine these teachings to provide modeled DAS data that more closely represents actual field recorded DAS data that more closely represents actual field recorded DAS data.
Regarding Claim 3 and 13, Thiruvenkatanathan in view of Wu discloses the method of claim 1, wherein the machine learning model is used to classify slugs in a physical well (Thiruvenkatanathan, [0146] the event detection model can be used to detect and/or identify events within a wellbore [0137] a first portion of the data can be used with machine learning techniques to train the fluid flow model).
Regarding Claim 4 and 14, Thiruvenkatanathan in view of Wu discloses the method of claim 3, wherein an operation of the ESP (Thiruvenkatanathan, [0166] the supervisory application can comprise a control signal to a valve to control a pressure or flowrate within a system, a control signal to a pump to control a flow rate output from the pump, a control signal to a security system to indicate an alarm is needed, a control signal to a pipeline monitoring application to report on the operation of the pipeline), which is in the physical well, is changed, in response to classifying a slug in the physical well as being capable of affecting performance of the ESP (Thiruvenkatanathan, [0159] the use of one or more models for wellbore events can allow for a wellbore control program to be implemented to take the event identifications into account. Events such as fluid inflow identification and quantification can be combined with sand detection to control a production rate or draw down within a wellbore).
Regarding Claim 5 and 15, Thiruvenkatanathan in view of Wu discloses the method of claim 4, wherein the operation of the ESP is changed by a controller (Thiruvenkatanathan, [0164] The output of the plurality of event detection models can then be passed to a supervisory application at step 966), in response to the controller classifying the slug in the physical well as capable of affecting performance of the ESP, using the machine learning model (Thiruvenkatanathan, [0165] The supervisory application can comprise a model similar to any of the event detection models described herein. In some embodiments, the supervisory application can comprise one or more regression models, multivariate models, neural networks, Bayesian networks, and the like, and the supervisory application can be trained using data from known events, [0166] Using the outputs of the plurality of event detection models as inputs, the supervisory application can determine an output signal at step 968).
Claims 8 and 18 are rejected under 35 U.S.C. 103 as being unpatentable over US 20200174149 A1, Thiruvenkatanathan (hereinafter Thiruvenkatanathan) in view of US 20200309982 A1, Jin et al (hereinafter Jin).
Regarding Claim 8 and 18, Thiruvenkatanathan discloses the method of claim 6, wherein the feature engineering comprises:
receiving a differential phase time domain signal of the combined DAS flow noise and DAS signature (Thiruvenkatanathan, [0135] The inflow tests can include various combinations of injected fluid and background flowing fluid. For example, a single phase (e.g., water, gas, or hydrocarbon liquid) can be injected into a background fluid comprising one or multiple phases (e.g., water, gas, and/or hydrocarbon liquid) flowing in a particular flow regime [0131] In some embodiments, the conduit comprises a flow loop, and various types of events can be recreated in the presence or absence of a fluid flowing within the loop, though a fluid may be present in the conduit without flowing for some event tests [0136] In order to understand the variability in the measured signal for testing purposes, the flow for each type of flow can be incremented over time);
low-pass filtering the signal (Thiruvenkatanathan, [0100] a lower frequency threshold between 0 and 5 Hz, between 0 and 10 Hz, or between 0 and 15 Hz can be used, which can result in the first bin including a frequency range between 5 Hz and 20 Hz, between 10 Hz and 20 Hz, or between 15 Hz and 20 Hz depending on the lower frequency threshold used [0101] The total RMS energy of the acoustic waveform calculated in the time domain can indicate the loudness of the acoustic signal. In some embodiments, the total RMS energy can also be extracted from the temporal domain after filtering the signal for noise [0084] the optical data may or may not be phase coherent and may be pre-processed to improve the signal quality [0087] Discrete Fourier transformations (DFT) or a short time Fourier transform (STFT) of the acoustic variant time domain data measured at each depth section along the fiber or a section thereof may be performed to provide the data from which the plurality of frequency domain features can be determined);
removing noise from the strain data, and standardizing the strain data (Thiruvenkatanathan, [0084] denoised for opto-electronic noise normalization/de-trending single point-reflection noise removal through the use of median filtering techniques or even through the use of spatial moving average computations with averaging windows set to the spatial resolution of the acquisition unit [0088] Preprocessing at 200 can optionally include a noise normalization routine to improve the signal quality [0143] a normalized variant of the spectral spread (NVSS) and/or a normalized variant of the spectral centroid (NVSC) [0060] In FIG. 2, the DAS system comprises an optical fiber 162 based acoustic sensing system that uses the optical backscatter component of light injected into the optical fiber for detecting acoustic perturbations (e.g., dynamic strain) along the length of the fiber 162);
truncating the strain data at a depth interval and a time interval (Thiruvenkatanathan, [0085] A spatial sample point filter can be applied that uses a filter to obtain a portion of the acoustic signal corresponding to a desired depth in the wellbore or length or location along a path of the fiber…This may allow a specific location to be isolated for further analysis [0158] the sample data set may represent a portion of the acoustic signal for a defined depth range or point. In some embodiments, the acoustic signal can be obtained in the time domain); and
extracting amplitude (Thiruvenkatanathan, [0061] any acoustic or dynamic strain disturbances along the length of the optical fiber 162 can result in a change in the properties of the backscattered light, allowing for a distributed measurement of both the acoustic magnitude (e.g., amplitude), frequency and, in some cases, of the relative phase of the disturbance [0148] determining an amplitude of each of the determined at least one of a gas phase inflow, an aqueous phase inflow, or a hydrocarbon liquid phase inflow over a time period at the identified one or more fluid inflow locations [0100] The RMS band energy provides a measure of the signal energy within defined frequency bins that may then be used for signal amplitude population)
Thiruvenkatanathan does not disclose integrating the signal over a time length to convert strain rate of the signal to strain data in a time domain;
collapsing the truncated strain data to a single time series, and depth length of a tensional signature from the truncated strain data.
However, Jin teaches integrating the signal over a time length to convert strain rate of the signal to strain data in a time domain (Jin, [0083] which may be defined as the summation of strain rate during pumping period (For ε.sub.d, channels with fracture hits show strong positive value, and channels in the stress shadow show negative values));
collapsing the truncated strain data to a single time series (Jin, [0083] the 2-D LFDAS data are transformed into eight one-dimensional features in the channel axis. The features are designed to characterize a corresponding fracture-hit signal from different perspectives [0053] Low Frequency Distributed Acoustic Sensing (LFDAS) signals (e.g., less than 0.05 Hz) have been used to detect fracture hits at offset monitoring wells during hydraulic fracturing operations that can provide critical information to constrain hydraulic fracture geometry [0065] LFDAS data may be compiled in a 2-dimensiolan (channel and time) matrix), and depth length of a tensional signature from the truncated strain data (Jin, [0089] In one example, for each stage in the training and validation datasets, only channels within the range of 10 channels below the toe-most fracture-hit channel are selected to avoid the interference from the fractures generated by previous stages, and 100 channels above the heel-most one are selected to exclude low-amplitude negative samples. This channel selection may result 6426 samples in the training set, within which 794 samples are channels labelled as fracture hits. The positive samples are duplicated (e.g., 4 times) to increase their percentage in the training dataset).
Before the effective filing date of the claimed invention, It would have been obvious to one of ordinary skill in the art to combine Thiruvenkatanathan and Jin teachings because both references are directed to processing DAS data obtained from fiber optic sensing in wells and generating features for machine learning based event detection. Also, Jin teaches processing low frequency DAS strain rate data by integrating the strain rate data over a time period to obtain strain data and transforming two-dimensional DAS data into one-dimensional features for characterizing a detected signal. A person of ordinary skill in the art would have been motivated to integrate the signal processing techniques of Jin into the DAS event detection system of Thiruvenkatanathan in order to further process and reduce the DAS data into features that characterize the detected wellbore event.
Claims 2 and 12 are rejected under 35 U.S.C. 103 as being unpatentable over US 20200174149 A1, Thiruvenkatanathan (hereinafter Thiruvenkatanathan) in view of US 20230324577 A1, Wu et al. (hereinafter Wu), in further view of US 20200309982 A1, Jin et al (hereinafter Jin).
Regarding Claim 2 and 12, Thiruvenkatanathan in view of Wu discloses the method of claim 1, wherein the feature engineering comprises:
receiving a differential phase time domain signal of the combined DAS flow noise and DAS signature (Thiruvenkatanathan, [0135] The inflow tests can include various combinations of injected fluid and background flowing fluid. For example, a single phase (e.g., water, gas, or hydrocarbon liquid) can be injected into a background fluid comprising one or multiple phases (e.g., water, gas, and/or hydrocarbon liquid) flowing in a particular flow regime [0131] In some embodiments, the conduit comprises a flow loop, and various types of events can be recreated in the presence or absence of a fluid flowing within the loop, though a fluid may be present in the conduit without flowing for some event tests [0136] In order to understand the variability in the measured signal for testing purposes, the flow for each type of flow can be incremented over time);
low-pass filtering the signal (Thiruvenkatanathan, [0100] a lower frequency threshold between 0 and 5 Hz, between 0 and 10 Hz, or between 0 and 15 Hz can be used, which can result in the first bin including a frequency range between 5 Hz and 20 Hz, between 10 Hz and 20 Hz, or between 15 Hz and 20 Hz depending on the lower frequency threshold used [0101] The total RMS energy of the acoustic waveform calculated in the time domain can indicate the loudness of the acoustic signal. In some embodiments, the total RMS energy can also be extracted from the temporal domain after filtering the signal for noise [0084] the optical data may or may not be phase coherent and may be pre-processed to improve the signal quality [0087] Discrete Fourier transformations (DFT) or a short time Fourier transform (STFT) of the acoustic variant time domain data measured at each depth section along the fiber or a section thereof may be performed to provide the data from which the plurality of frequency domain features can be determined);
removing noise from the strain data, and standardizing the strain data (Thiruvenkatanathan, [0084] denoised for opto-electronic noise normalization/de-trending single point-reflection noise removal through the use of median filtering techniques or even through the use of spatial moving average computations with averaging windows set to the spatial resolution of the acquisition unit [0088] Preprocessing at 200 can optionally include a noise normalization routine to improve the signal quality [0143] a normalized variant of the spectral spread (NVSS) and/or a normalized variant of the spectral centroid (NVSC) [0060] In FIG. 2, the DAS system comprises an optical fiber 162 based acoustic sensing system that uses the optical backscatter component of light injected into the optical fiber for detecting acoustic perturbations (e.g., dynamic strain) along the length of the fiber 162);
truncating the strain data at a depth interval and a time interval (Thiruvenkatanathan, [0085] A spatial sample point filter can be applied that uses a filter to obtain a portion of the acoustic signal corresponding to a desired depth in the wellbore or length or location along a path of the fiber…This may allow a specific location to be isolated for further analysis [0158] the sample data set may represent a portion of the acoustic signal for a defined depth range or point. In some embodiments, the acoustic signal can be obtained in the time domain); and
extracting amplitude (Thiruvenkatanathan, [0061] any acoustic or dynamic strain disturbances along the length of the optical fiber 162 can result in a change in the properties of the backscattered light, allowing for a distributed measurement of both the acoustic magnitude (e.g., amplitude), frequency and, in some cases, of the relative phase of the disturbance [0148] determining an amplitude of each of the determined at least one of a gas phase inflow, an aqueous phase inflow, or a hydrocarbon liquid phase inflow over a time period at the identified one or more fluid inflow locations [0100] The RMS band energy provides a measure of the signal energy within defined frequency bins that may then be used for signal amplitude population)
Thiruvenkatanathan in view of Wu does not disclose integrating the signal over a time length to convert strain rate of the signal to strain data in a time domain;
collapsing the truncated strain data to a single time series, and depth length of a tensional signature from the truncated strain data.
However, Jin teaches integrating the signal over a time length to convert strain rate of the signal to strain data in a time domain (Jin, [0083] which may be defined as the summation of strain rate during pumping period (For ε.sub.d, channels with fracture hits show strong positive value, and channels in the stress shadow show negative values));
collapsing the truncated strain data to a single time series (Jin, [0083] the 2-D LFDAS data are transformed into eight one-dimensional features in the channel axis. The features are designed to characterize a corresponding fracture-hit signal from different perspectives [0053] Low Frequency Distributed Acoustic Sensing (LFDAS) signals (e.g., less than 0.05 Hz) have been used to detect fracture hits at offset monitoring wells during hydraulic fracturing operations that can provide critical information to constrain hydraulic fracture geometry [0065] LFDAS data may be compiled in a 2-dimensiolan (channel and time) matrix), and depth length of a tensional signature from the truncated strain data (Jin, [0089] In one example, for each stage in the training and validation datasets, only channels within the range of 10 channels below the toe-most fracture-hit channel are selected to avoid the interference from the fractures generated by previous stages, and 100 channels above the heel-most one are selected to exclude low-amplitude negative samples. This channel selection may result 6426 samples in the training set, within which 794 samples are channels labelled as fracture hits. The positive samples are duplicated (e.g., 4 times) to increase their percentage in the training dataset).
Before the effective filing date of the claimed invention, It would have been obvious to one of ordinary skill in the art to combine Thiruvenkatanathan in view of Wu and Jin teachings because both references are directed to processing DAS data obtained from fiber optic sensing in wells and generating features for machine learning based event detection. Also, Jin teaches processing low frequency DAS strain rate data by integrating the strain rate data over a time period to obtain strain data and transforming two-dimensional DAS data into one-dimensional features for characterizing a detected signal. A person of ordinary skill in the art would have been motivated to integrate the signal processing techniques of Jin into the DAS event detection system of Thiruvenkatanathan in order to further process and reduce the DAS data into features that characterize the detected wellbore event.
Pertinent Prior Art
The prior art made of record and not relied upon is considered pertinent to applicant’s disclose:
-US 20200032639 A1, describing methods for detecting downhole sand ingress using acoustic data obtained from a wellbore including filtering the acoustic data, evaluating frequency-domain characteristics, and determining the presence of a downhole event based on the processed signal.
-WO 2016007161 A1, describing distributed fiber optic sensing in a wellbore to monitor fluid flow, including generating noise logs from distributed vibration data, evaluating vibration energy at different frequencies, depth, and determining characteristics of fluid flow in the well.
-WO 2015117051 A1, describing systems for monitoring equipment associated with a wellbore equipment associated with a wellbore, including an electric submersible pump, using vibration sensing data and controlling operation of the equipment based on the determined operating condition, including slowing down or shutting down the equipment.
Conclusion
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/IBRAHIM NAGI SHOHATEE/Examiner, Art Unit 2857
/KYLE R QUIGLEY/Primary Examiner, Art Unit 2857
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