WO2021151504A1 - Apprentissage machine à l'aide de données de série chronologique - Google Patents

Apprentissage machine à l'aide de données de série chronologique Download PDF

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Publication number
WO2021151504A1
WO2021151504A1 PCT/EP2020/052445 EP2020052445W WO2021151504A1 WO 2021151504 A1 WO2021151504 A1 WO 2021151504A1 EP 2020052445 W EP2020052445 W EP 2020052445W WO 2021151504 A1 WO2021151504 A1 WO 2021151504A1
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Prior art keywords
features
machine learning
learning model
feature
application interface
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PCT/EP2020/052445
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English (en)
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Pradyumna Thiruvenkatanathan
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Lytt Limited
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Priority to PCT/EP2020/052445 priority Critical patent/WO2021151504A1/fr
Priority to EP20733976.3A priority patent/EP4097660A1/fr
Priority to US17/792,092 priority patent/US20230052691A1/en
Priority to PCT/EP2020/067043 priority patent/WO2021151521A1/fr
Publication of WO2021151504A1 publication Critical patent/WO2021151504A1/fr

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    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06N—COMPUTING ARRANGEMENTS BASED ON SPECIFIC COMPUTATIONAL MODELS
    • G06N5/00—Computing arrangements using knowledge-based models
    • G06N5/02—Knowledge representation; Symbolic representation
    • G06N5/022—Knowledge engineering; Knowledge acquisition
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06N—COMPUTING ARRANGEMENTS BASED ON SPECIFIC COMPUTATIONAL MODELS
    • G06N3/00—Computing arrangements based on biological models
    • G06N3/02—Neural networks
    • G06N3/04—Architecture, e.g. interconnection topology
    • G06N3/045—Combinations of networks
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06N—COMPUTING ARRANGEMENTS BASED ON SPECIFIC COMPUTATIONAL MODELS
    • G06N3/00—Computing arrangements based on biological models
    • G06N3/02—Neural networks
    • G06N3/08—Learning methods

Definitions

  • Data is generated by instrumentation and sensors, for example, in chemical plants and wellbore environments.
  • the data can generally be monitored by computers and personnel for any fluctuations and abnormalities in order to control the operation, for example, to react to alarms that are set off due to readings that exceed thresholds in plant or wellbore operation.
  • the data can also be stored for analysis.
  • a method includes determining a plurality of features in a data signal, correlating the plurality of features to determine similarity scores between two or more features of the plurality of features, presenting information related to at least a first feature of the plurality of features, receiving feedback on the information, and determining, using a first machine learning model, information related to at least a second feature. The determination is made using the similarity scores and the feedback in the first machine learning model.
  • a system comprises: a processor and a memory.
  • the memory stores a program, that when executed on the processor, configures the processor to: generate an application interface, wherein the application interface displays one or more features, receive a plurality of selections of the plurality of features, train, using at least the plurality of selections, a machine learning model to determine one or more workflows, and present at least one of the one or more workflows on the application interface.
  • the selections comprise one or more feedback signals associated with selections of one or more features of the plurality of features, and the one or more workflows defines a set of features of the plurality of features.
  • a system comprises: an insight engine executing on a processor, and a learning engine.
  • the insight engine is configured to receive a sensor data signal from one or more sensors, and the insight engine is configured to: execute a first machine learning model, identify, using the first machine learning model, one or more features in the sensor data signal, and generate an indication of the one or more features on an application interface.
  • the learning engine is configured to: receive a plurality of selections on the application interface, train, using at least the plurality of selections, a second machine learning model to determine a one or more sub-features associated with the one or more features, and present the one or more sub-features on the application interface.
  • a method comprises: performing, using one or more computing devices: identifying, using a first machine learning model, one or more features in a data signal, receiving a plurality of selections from an application interface based on presenting the one or more features on the application interface, identifying, using a second machine learning model, a corresponding feature based on the plurality of selections, identifying, using the one or more features and the corresponding feature, a solution associated with the one or more features and the corresponding feature, and presenting the solution on the application interface in association with the one or more features.
  • the plurality of selections provides an indication of an identification of the one or more features.
  • a method comprises: identifying, using a first machine learning model, one or more features in a data signal, receiving a selection from an application interface based on presenting the one or more features on the application interface, updating, using at least the selection, the first machine learning model, and re-identifying, using the first machine learning model, the one or more features in the sensor data signal.
  • the selection provides an indication of an identification of the one or more features.
  • a method comprises: determining a plurality of features in a data signal, correlating the plurality of features to determine similarity scores between two or more features of the plurality of features, presenting information related to at least a first feature of the plurality of features, and determining, using a first machine learning model, information related to at least a second feature, wherein the determination is made using the similarity scores in the first machine learning model.
  • Embodiments described herein comprise a combination of features and characteristics intended to address various shortcomings associated with certain prior devices, systems, and methods.
  • the foregoing has outlined rather broadly the features and technical characteristics of the disclosed embodiments in order that the detailed description that follows may be better understood.
  • the various characteristics and features described above, as well as others, will be readily apparent to those skilled in the art upon reading the following detailed description, and by referring to the accompanying drawings. It should be appreciated that the conception and the specific embodiments disclosed may be readily utilized as a basis for modifying or designing other structures for carrying out the same purposes as the disclosed embodiments. It should also be realized that such equivalent constructions do not depart from the spirit and scope of the principles disclosed herein.
  • FIG. 1 is a schematic diagram of embodiments of the disclosed computer system that utilizes machine learning models to determine workflow from time series data using feedback from an application interface.
  • FIG. 2 is a schematic diagram of embodiments of the disclosed computer system that utilizes machine learning models to present sub-features in time series data or to present a solution that is associated with sub-features.
  • FIG. 3 is a schematic diagram of embodiments of the disclosed computer system that utilizes machine learning models to present a solution that is associated with features.
  • FIG. 4 is a schematic diagram of embodiments of the disclosed computer system that utilizes machine learning models to identify features in time series data and train the machine learning models using feedback from an application interface.
  • FIG. 5 is a schematic diagram of embodiments of the disclosed computer system that utilizes machine learning models to determine features are related to one another.
  • FIGS. 6A and 6B are schematic diagrams illustrating how time series data can be obtained for input to the disclosed computer systems.
  • FIG. 7 illustrates a schematic diagram of a computer system that can implement any of the components of the systems in FIGS. 1-5.
  • any use of any form of the terms “connect,” “engage,” “couple,” “attach,” or any other term describing an interaction between elements is not meant to limit the interaction to direct interaction between the elements and may also include indirect interaction between the elements described.
  • the terms “including” and “comprising” are used in an open-ended fashion, and thus should be interpreted to mean “including, but not limited to . . . “.
  • references to up or down will be made for purposes of description with “up,” “upper,” “upward,” “upstream,” or “above” meaning toward the surface of the wellbore and with “down,” “lower,” “downward,” “downstream,” or “below” meaning toward the terminal end of the well, regardless of the wellbore orientation.
  • Reference to inner or outer will be made for purposes of description with “in,” “inner,” or “inward” meaning towards the central longitudinal axis of the wellbore and/or wellbore tubular, and “out,” “outer,” or “outward” meaning towards the wellbore wall.
  • the term “longitudinal” or “longitudinally” refers to an axis substantially aligned with the central axis of the wellbore tubular, and “radial” or “radially” refer to a direction perpendicular to the longitudinal axis.
  • environment e.g., industrial plant, processing facilities, production facilities, wellbores, etc.
  • machine learning models in data analysis, especially in the context of chemical plants and wellbore environments, can provide a better understanding of the operation of plants and wellbores.
  • time series data refers to data that is collected over time and can be labeled (e.g., timestamped) such that the particular time which the data value is collected is associated with the data value.
  • Time series data can be displayed to a user and updated periodically to show new time series data along with historical time series data over a corresponding time period.
  • time series data generated in an industrial setting can include data generated by a multitude of sensors.
  • most industrial plants contain many temperature sensors, pressure sensors, flow sensors, position sensors (e.g., to indicate the positioning of a valve, hatch, etc.), fluid level sensors, and the like.
  • the resulting data can be used in various systems to determine features of the system such as a state of a unit (operating, filling, emptying, etc.), a type and flow rate of a fluid, fluid stream compositions, and the like.
  • various sensor data can be used to determine the presence of one or more features such as anomalies or events.
  • acoustic sensor data can be used to detect a wellbore event within a wellbore.
  • the event detection process can comprise using the time series data to determine the presence of one or more features.
  • the features and information related to the features can be presented to a user on an application interface. Users can then choose to view certain data on the application interface. As a user selects various information to display, the selections can be used to train a machine learning model on both a user’s workflow as well as features or events that are related. Thus, the system can learn by recording the user interactions to identify and develop workflows.
  • the models can review all of the available features to determine which ones may be related.
  • the related features can be correlated and presented to a user either as a related feature or a recommendation for a related feature. Based on the continued user feedback, the system can learn which features are properly related and which features, even if appearing to be related, are not related in certain situations.
  • the system can also be used to identify certain sets of features that can be used to identify specific problems. Once a problem is identified, historical data on the actions taken by users can be identified and used to present common solutions to the problems. The problems can be identified based on a machine learning model using the identified common features as input to thereby identify the specific problems and scenarios associated with the recommended solutions. In some embodiments, a range of solutions can be provided, and feedback provided based on the selected parameters can be used to narrow down the solutions based on the feedback. This can allow the system to learn and adapt over time to provide feedback to the users.
  • the methods and systems described herein can be used with a wide variety of industrial processes. For example, hydrocarbon production facilities, pipelines, security settings, transportation systems, industrial processing facilities, chemical facilities, and the like can all use a variety of sensors or other devices that can produce timer series data. The resulting data can be used in various processing systems, and the systems and methods as described herein can be used with those systems to provide additional insights on the workflows of the users and related features that may not be intuitively related to most, if any, users of the systems.
  • FIG. 1 is a schematic diagram of embodiments of a computer system 100 that can use one or more machine learning models to determine workflow from time series data using feedback from an application interface.
  • the components of the computer system 100 can be implemented on a computer or other device comprising a processor as described in FIG. 7.
  • the components include one or more of an application interface 110, a machine learning label encoder 115, a first machine learning model 120, a second machine learning model 130, and a similarity engine 140.
  • the system 100 can be configured to receive time series data, determine one or more features based on the time series data using various functions or applications, present the one or more features and/or time series data, and learn a workflow of a user processing the one or more features and/or time series data.
  • the system 100 can correlate information that is related based on user feedback and update the presentation of information over time to provide insights to the users operating the system.
  • the system can then learn the workflows associated with specific events across many users, providing insights to the existing and future users of the system.
  • the system 100 can comprise an application interface 110.
  • the application interface 110 can be configured to receive time series data (e.g., via a sensor signal received from one or more sensors shown in FIGS. 6A and 6B) and/or one or more features based on the received time series data.
  • the application interface 110 (and any application interface described in the embodiments herein) can be further configured to display a user interface on a display device (e.g., a phone, tablet, AR/VR device, laptop, etc.) for interpretation of the feature(s) by a user, such as an operations engineer of a chemical plant or wellbore environment.
  • the user interface can be interactive with the user such that the user can make one or more selections regarding the functions displayed on the user interface.
  • the user interface can display plant or wellbore data and inform the user via the application interface 110 of the time series data and/or features.
  • the selections can be received by the application interface 110 as feedback, and the application interface 110 can then use the selections as input to the first machine learning model 120 and/or the second machine learning module 130.
  • the triggering of an alarm by a user can also be considered feedback.
  • the selections or feedback can be weighted based on one or more factors such as an identity of the user, type of features, technology area, ratings per use, or the like. For example, a higher weighting can be given to a more experienced user, and a lower weighting can be given to a less experienced user. For example, senior engineers may be given higher weightings than junior engineers using the system. As another example, certain technology areas may be more highly weighted than others. As still another example, the solutions provided by certain users may have better results than other uses. The users with the better results may be provided an identification associated with a higher weighting based on an overall results assessment than other users that have lower result assessments.
  • the weightings can be applied to the feedback when the feedback is provided to the first machine learning model 120 and/or the second machine learning model 130.
  • the weightings can affect the training of the models to provide a more accurate output.
  • the selections before being received by the first machine learning model 120 and/or the second machine learning model 130, can first be encoded in the machine learning encoder 115.
  • the selections can be associated with one or more features, or labeled as associated to one or more functions of the system, via any technique for labeling in the context of machine learning. In this context, the selections can be considered to be part of feedback received from the user through the application interface 110.
  • the user may select various information based on the presentation of the features. For example, an alarm or alert may be triggered by the time series data.
  • a user may select various time series data streams from certain sensors to try to diagnose the cause of the alarm or alert.
  • the selections of the specific data streams can be considered feedback from the user. Further, the streams that are displayed together can also be correlated and considered as feedback for use by the system. Further, the specific order in which the time series data and/or features are displayed can be representative of a workflow when looking at the data. This workflow can be captured by using the selections and interactions of the user with the system as feedback for further analysis by the system.
  • the set of features, the order of presentation of the information, and/or the layout of information can be captured as a feature set that can define the workflow(s).
  • the first machine learning 120 can accept inputs from the application interface 110 including information on the time series data and/or features, the feedback, and any workflow information available.
  • the inputs can be obtained directly from the application interface 110 and/or the machine learning encoder 115, which can provide the inputs in a form more easily usable by the first machine learning model 120.
  • the first machine learning model 120 can process the inputs and determine an output including an identification of one or more features and/or time series data components that are related or correlated. This information can include an order of presentation of the features or time series data, which can be used to define a workflow for the user using the system 100.
  • the output of the first machine learning model 120 can then be used to recreate the workflows of the system and present the workflows upon the occurrence of specific events represented by one or more features.
  • the workflow can be named or identified to provide one or more workflows available to a user on the application interface.
  • a list of available workflows learned within the system can be provided as a selection option, and the selection of a workflow can serve to present the information in the feature set defining the workflow can be presented on the application interface.
  • the computer system 100 can be configured to train the first machine learning model 120 to determine a workflow that can be recommended in response to an occurrence of one or more of the features.
  • the first machine learning model 120 can be trained using supervised or unsupervised learning techniques using the information obtained through the feedback in the system.
  • the stream or sequence of functions in a workflow can be modified as the user provides more selections over time (e.g., feedback signals) regarding the functions that they have viewed on the display device and made selections therefor.
  • the workflows as determined by the first machine learning model 120 can be based on outcomes or solutions associated with the workflows.
  • Historical data can be used to train the first machine learning model, and the historical data can comprise at least some information on outcomes or actions associated with the feedback, features, and time series data.
  • the training data can be weighted based on the outcomes or solutions associated with the data.
  • the solutions may be defined by a series of steps or actions taken in response to the presentation of the features and/or time series data, including any recommendations or feature sets provided by the system. For example, outcome or solutions indicated as being successful can be weighted more heavily than those in which the solution is only partially successful or not successful at all (which may have a zero or small weighting factor).
  • the outcomes or solutions can also be weighted at each step or action within the solution.
  • a step forming part of the solution that is determined to be incorrect e.g., based on feedback or by the model
  • can be de- weighted e.g., penalized
  • This can help to reduce the likelihood that such a step in the solution is recommended by the system upon the occurrence of a similar feature set.
  • the first machine learning model 120 can include a deep neural network (DNN) model, a clustering model, a principal component analysis (PCA) model, a canonical correlation analysis (CCA) model, a supervised matrix factorization model, or a combination thereof.
  • DNN deep neural network
  • PCA principal component analysis
  • CCA canonical correlation analysis
  • a supervised matrix factorization model or a combination thereof.
  • more than one type of machine learning model may be employed as the first machine learning model 120. For example, a high-dimensional feature vector may be generated using a DNN model, and then the dimensionality of the vector may be lowered using another model.
  • workflows may be generated using a single machine learning model as the first machine learning model 120.
  • the first machine learning model 120 can have one or more inputs (time series data, features, selections, and optionally similarity scores explained below) and use a single ML model to obtain the output workflow.
  • multiple machine learning (ML) models can collectively define the first machine learning model 120.
  • one ML model of the first machine learning model 120 may be used to generate a first workflow vector based on selections received for a function, and a second ML model of the first machine learning model 120 may be used to generate a second workflow vector based on a similarity score received from the similarity engine 140.
  • the workflow vectors obtained from the two ML models in the first machine learning model 120 may be aggregated (e.g., via concatenation, or using another machine learning model) and used for sending a output (e.g., a recommended workflow) to the application interface 110, which presents the output to a user via a user interface.
  • a output e.g., a recommended workflow
  • the second machine learning model 130 can receive one or more selections from the application interface 110 as input, for example, via the machine learning encoder 115.
  • the selections received as input by the first machine learning model 120 and the selections received as input by the second machine learning model 130 are the same selections; alternatively, the application interface 110 and the machine learning encoder 115 can be configured to send a first set of selections as input to the first machine learning model 120 and a second set of selections as input to the second machine learning model 130, where the first and second sets do not include any of the same selections; alternatively, the application interface 110 and the machine learning encoder 115 can be configured to send a first set of selections as input to the first machine learning model 120 and a second set of selections as input to the second machine learning model 130, where the first and second sets have at least one selection in common.
  • the second machine learning model 130 can be configured to generate one or more recommendations as an output for a function or feature based on the one or more selections that are received as input to the second machine learning model 130.
  • the features can be generated by functions within the system.
  • the recommendations can be for features generated by the functions that are correlated to the current workflow obtained through feedback in the application interface. This can include features that correlate to those features and/or time series data components being displayed, even if the feedback has not requested the features and/or time series data components.
  • the recommendations can represent insights into additional features or data that may be related but may not be apparent to a user as being related or part of a problem within the setting in which the time series data is being provided. Any of the recommendations generated as output by the second machine learning model 130 can be sent to the application interface 110.
  • the second machine learning model 130 can include a deep neural network (DNN) model, a clustering model, a principal component analysis (PCA) model, a canonical correlation analysis (CCA) model, a supervised matrix factorization model, or a combination thereof.
  • DNN deep neural network
  • PCA principal component analysis
  • CCA canonical correlation analysis
  • a supervised matrix factorization model or a combination thereof.
  • more than one type of machine learning model may be employed as the second machine learning model 130. For example, a high-dimensional feature vector may be generated using a DNN model, and then the dimensionality of the vector may be lowered using another model.
  • recommendations may be generated using a single machine learning model as the second machine learning model 130.
  • the second machine learning model 130 can have one or more inputs (selections, and optionally similarity scores explained below) and use a single ML model to obtain the output recommendation.
  • recommendations may be generated using multiple machine learning (ML) models as the second machine learning model 130.
  • one ML model of the second machine learning model 130 may be used to generate a first recommendation vector based on selections received for a function, and a second ML model of the second machine learning model 130 may be used to generate a second recommendation vector based on a similarity score received from the similarity engine 140.
  • the recommendation vectors obtained from the two ML models in the second machine learning model 130 may be aggregated (e.g., via concatenation, using another machine learning model, etc.) and used for sending the one or more recommendations to the application interface 110, which presents the one or more recommendations to a user via a user interface.
  • the computer system 100 can be configured to train the second machine learning model 130 using selections received from the application interface 110.
  • the second machine learning model 130 can be trained using supervised or unsupervised learning techniques.
  • the computer system 100 can be further configured to identify, using the second machine learning model 130, one or more additional features, time series data components, and/or functions to be included in the any of the recommendations generated by the second machine learning model 130.
  • the similarity engine 140 can be configured to provide information to the first machine learning model 140 regarding similarity of time series data 101 and/or features based on the time series data 101 that is received by the similarity engine 140.
  • the similarity engine 140 can be configured to identify, using one or more functions, one or more features (e.g., an event, an anomaly, etc. in the time series data) derived from the time series data (e.g., time series data received by the computer system from a sensor signal).
  • the similarity engine 140 can additionally be configured to determine a similarity score between multiple features in the time series data.
  • the similarity score can be a measure of any correlation between the features.
  • a correlation metric, autocorrelation feature, or other comparison can be performed with respect to the features and/or time series data components to determine which features and/or time series data components are related.
  • the similarity engine 140 can include a simple binary classifier, a machine learning model, or the like, and the similarly score can be a binary score (e.g., related or not related), or a rating of the degree of relation between identified features and/or time series data components.
  • the similarly engine 140 can then output the similarly score to the first machine learning model 120 for use as an input.
  • the first machine learning model 120 and/or the second machine learning model 130 can additionally use one or more similarity scores that are optionally associated with one or more of the features based on the time series data (e.g., by machine learning encoder 145).
  • the similarity scores can be associated with one or more features, or labeled as associated to one or more features via any technique for labeling in the context of machine learning.
  • the similarity engine 140 can include a logistic regression model and/or a support vector machine (SVM) model, for example. Any of a number of different approaches may be taken with respect to logistic regression. For example, in at least one embodiment, a Bayesian analysis may be performed, with pairwise item preferences derived from the time series data; alternatively, a frequentist rather than a Bayesian analysis may be used.
  • SVM support vector machine
  • FIG. 2 is a schematic diagram of an embodiment of a computer system 200 that uses machine learning models that can present or recommend anomaly features in time series data.
  • the components of the computer system 200 can be implemented on a computer or other device comprising a processor as described in FIG. 7.
  • the components include one or more of a first machine learning model 210, an application interface 220, a machine learning label encoder 225, and a second machine learning model 230.
  • the computer system 200 can be configured to receive time series data (e.g., via a sensor signal received from one or more sensors shown in FIGS. 6A and 6B), execute a first machine learning model 210, and identify, using the first machine learning model 210, one or more features (e.g., events, anomalies, process states, etc.) in the time series data.
  • the first machine learning model 210 can be configured to send an identification of the one or more features in the time series data to the application interface 220.
  • one or more models or functions can operate to determine the features from the time series data.
  • the functions can comprise machine learning models, signature based event identification models, threshold indications, correlations, or the like.
  • the functions in the first machine learning model 210 can be trained using historical data and/or test data.
  • first principles models can be used to identify one or more features within the time series data as part of the first machine learning model 210.
  • various sensors can be associated with a wellbore to allow for monitoring of the wellbore during production of hydrocarbon fluids to the surface.
  • Sensors can include temperature sensors, pressure sensors, vibration sensors, and the like.
  • the temperature sensor can comprise a distributed temperature sensor (DTS) that uses a fiber optic cable to detect a distributed temperature signal along the length of the wellbore.
  • DTS distributed temperature sensor
  • DAS distributed acoustic sensor
  • Additional sensors can also be present in the wellbore and at the surface (e.g., flow sensors, fluid phase sensors, etc.).
  • the output of the sensors can be provided to the first machine learning model 210 as a time series data stream.
  • one or more functions or models can be performed to derive features such as statistical features from the time series data.
  • the time series data can be pre-processed using various techniques such as denoising, filtering, and/or transformed to provide data that can be processed to provide the features.
  • one or more frequency domain features can be obtained from the DAS acoustic data
  • one or more temperature features e.g., statistical features through time and/or depth
  • the features can be used in various models to determine one or more features within the wellbore such as one or more event identifications.
  • the DAS and/or DTS data can be used to determine the presence of fluid flowing into the wellbore, determine fluid phase discrimination within the wellbore, detect fluid leaks, detect the presence of sand ingress, and the like.
  • the features produced within the first machine learning model 210 can comprise an identification of the one or more events within the wellbore as an example.
  • the application interface 220 can be configured to generate an indication and present the indication of the one or more features to a user interface for viewing by a user.
  • the application interface 220 can also present one or more components of the time series data along with the indication of the features. The presentation of the features and/or time series data components can be used by a user to monitor the process, identify and diagnose problems within the process, and/or identify if solutions are producing the desired effects.
  • the application interface 220 can be configured to present information and accept feedback by the user. When viewed by a user, the application interface 220 can receive feedback in the form of one or more selections from the user interface and send the selections to the second machine learning model 230. As noted above, the feedback can be weighted in some embodiments based on an identification of a user (e.g., a user role, seniority, etc.) such that certain feedback is weighted differently than others. In some embodiments, the selections can first be encoded in the machine learning encoder 225.
  • the selections can be associated with one or more features (e.g., received from the application interface 220 along with the selections) and/or one or more functions (e.g., received from the application interface 220 along with selections), or labeled as associated to one or more features and/or to one or more functions via any technique for labeling in the context of machine learning.
  • the application interface 220 can present an indication of one or more events occurring within a wellbore based on the time series data obtained and used by the first machine learning model.
  • various events can include fluid inflow events (e.g., including fluid inflow detection, fluid inflow location determination, fluid inflow quantification, fluid inflow discrimination, etc.), fluid outflow detection (e.g., fluid outflow detection, fluid outflow quantification), fluid phase segregation, fluid flow discrimination within a conduit, well integrity monitoring, including in-well leak detection (e.g., downhole casing and tubing leak detection, leaking fluid phase identification, etc.), flow assurance (e.g., wax deposition), annular fluid flow diagnosis, overburden monitoring, fluid flow detection behind a casing, fluid induced hydraulic fracture detection in the overburden (e.g., micro-seismic events, etc.), sand detection (e.g., sand ingress, sand flows, etc.).
  • fluid inflow events e.g., including fluid inflow detection,
  • One or more components of the time series data can also be presented along with the features. For example, pressure readings within the wellbore can be displaced along with an indication of sand ingress at one or more locations along the wellbore on a wellbore schematic.
  • a user can view the features and select additional information to be added to the application interface, remove some features and/or components of the time series data, and/or request entirely different features or time series data to be viewed. Each selection of the data can be recorded as feedback by the application interface 220.
  • the feedback can include the selection of the temperature feature as well as an indication that the selected temperature feature can be related or correlated with the sand ingress event identifications and the pressure readings.
  • the machine learning encode 225 can then optionally encode the information for use with the second machine learning model 230.
  • the second machine learning model 230 can be configured to receive the one or more selections or feedback from the application interface 220.
  • the computer system 200 can train, using the received selections, the second machine learning model 230 to determine one or more additional features, additional time series data components, and/or sub-features (e.g., anomaly features) associated with the one or more features (e.g., anomalies) and/or time series data components provided by the application interface.
  • the second machine learning model 230 can send the one or more sub-features associated with one or more features to the application interface 220, and the application interface 220 can be configured to present the sub-features of the features to a user interface for view by a user.
  • the additional features can also be presented as suggestions or recommendations for display on the application interface 220.
  • a recommendation can be provided to the application interface 220 to indicate to a user that an identified feature may be related to the features and/or time series data components being viewed.
  • the additional feedback obtained based on the recommendation can be used as further input into the second machine learning model 230.
  • the second machine learning model 230 can also determine feature sets, which can represent features and/or time series data components that are related.
  • the feature sets can be determined using similarity scores and/or using first principles models.
  • the second machine learning model 230 can initially base feature sets using the similarity scores and/or the first principle models and identify the features as being related.
  • the features within the feature sets can be used in presenting or recommending additional features as part of the output of the second machine learning model 230.
  • the feedback can then be used to verify that the features within the feature sets are related.
  • the second machine learning model 230 can determine that the feature is not part of the feature set. Additional features can also be identified as being part of a feature set based on user feedback even if the initial similarity scores and/or first principles models do not identify the feature as part of a feature set. Depending on the amount of data in the time series data, a plurality of feature sets can be identified within the time series data and/or the features obtained based on the time series data. Any given feature can be part of one or more feature sets identified by the system.
  • features including events and measurements within the wellbore can be determined from the time series data provided by the sensors such as the DAS and DTS sensors within or associated with the wellbore.
  • the features can include a set of features, some of which can represent anomalies or events and some which may not.
  • the features can be determined for a range of possible events, and those features that are related to an event can be grouped as being related to each other, thereby forming a feature set. When one or more features of the feature set are being displayed, the remaining features or information about the event can also be displayed.
  • one or more frequency domain features obtained from the acoustic signal are used to determine the presence of sand ingress at a location within the wellbore
  • one or more additional features such as other frequency domain features, a pressure signal, and/or a temperature feature can also be determined to be part of the feature set and displayed or recommended for display on the application interface 220. If a feature such as a temperature feature is displayed and feedback from the user closes the display, this can be seen as an indication to the second machine learning model 230 that the identified temperature feature may not be properly part of the feature set.
  • the second machine learning model 230 can be configured to receive the information from the application interface 220 (e.g., via encoder 225). For example, the second machine learning model 230 can receive an indication of the features and/or time series data components being displayed, the feedback, an order in which the data is requested, specific data being viewed, and the like. The second machine learning model 230 can additionally determine a workflow, where the workflow defines a set of features and/or time series data components being viewed and/or instructions being selected or provided through the system. The second machine learning model 230 can provide an output to the application interface to learn the workflows and update the information provided to the application interface to match the workflows.
  • the first machine learning model 210 can be configured to receive selections from the application interface 220, optionally associated with one or more features and/or one or more time series data components by the machine learning encoder 225.
  • the first machine learning model 210 can be configured to update itself using the received selections and identify, using the updated first machine learning model 210 a second set of features of the time series data (e.g., a second anomaly).
  • Embodiments of the first machine learning model 210 and/or the second machine learning model 230 can independently include a deep neural network (DNN) model, a principal component analysis (PCA) model, a canonical correlation analysis (CCA) model, a supervised matrix factorization model, or a combination thereof.
  • the first machine learning model 210 and/or the second machine learning model 230 can be trained using supervised or unsupervised learning techniques.
  • features based on the time series data may be generated using a single machine learning model as the first machine learning model 210.
  • the first machine learning model 210 can have one or more input (time series data, features, and optionally selections from the application interface 220) and use a single ML model to obtain the output features.
  • multiple machine learning (ML) models can collectively define the first machine learning model 210.
  • one ML model of the first machine learning model 210 may be used to generate a first feature vector based on time series data that is received, and a second ML model of the first machine learning model 210 may be used to generate a second feature vector based on selections received from the application interface 220.
  • the feature vectors obtained from the two ML models in the first machine learning model 210 may be aggregated (e.g., via concatenation, or using another machine learning model) and used for sending the output (e.g., the one or more features) to the application interface 220, which presents the output to a user via a user interface.
  • sub-features or workflows may be generated using a single machine learning model as the second machine learning model 230.
  • the second machine learning model 230 can have one input (selections) and use a single ML model to obtain the output workflow or output sub-features that are sent to the application interface 220.
  • the ability of the system to provide indications of additional features, time series data components, and/or workflows can allow insights into the occurrence of features or events within the wellbore.
  • additional events or the cause of events can be identified.
  • the additional features can be provided as a display or recommendation to help additional users recognize common problems within the wellbore. For example, features that may not intuitively be linked to an event in the wellbore can be identified as being correlated and presented to a user.
  • the system can learn which features are related and provide recommendations for various features related to certain events identified from the time series data.
  • FIG. 3 is a schematic diagram of embodiments of a computer system 300 that utilizes machine learning models to present a solution that is associated with features (e.g., events, anomalies, etc.).
  • the components of the computer system 300 can be implemented on a computer or other device comprising a processor as described in FIG. 7.
  • the components include one or more of a first machine learning model 310, an application interface 320, a machine learning label encoder 325, and a second machine learning model 330.
  • the computer system 300 can be configured to receive time series data (e.g., via a sensor signal received from one or more sensors shown in FIGS. 6 A and 6B), and the computer system 300 can be further configured to use the first machine learning model 310 to identify one or more features (e.g., events, anomalies, etc.) in the time series data and send/present/recommend the one or more features on the application interface 320.
  • the only input to the first machine learning model 310 may be the time series data or a representation thereof.
  • the application interface 320 can be configured to present the one or more features to a user via the user interface and to receive selections from the user via the user interface (e.g., feedback, etc.).
  • the computer system 300 can be configured to receive the selections from the application interface 320 based on the first machine learning model 310 presenting the one or more features on the application interface 320, where each selection provides an indication of an identification of one or more of the features.
  • the second machine learning model 330 can be configured to identify a corresponding feature that corresponds to the one or more features identified by the first machine learning model 310.
  • the second machine learning model 330 can then identify a solution that is associated with the corresponding feature and present the solution to the application interface 320.
  • the first machine learning model 310 may only receive time series data as input (and does not receive selections from the application interface 320 as inputs).
  • the application interface 320 can send the selections to the second machine learning model 330.
  • the selections can first be encoded in the machine learning encoder 325.
  • the selections can be associated with one or more features (e.g., received from the application interface 320 along with the selections) and/or one or more solutions (e.g., generated by the second machine learning model 330), or labeled as associated to one or more features and/or to one or more solutions via any technique for labeling in the context of machine learning.
  • Embodiments of the first machine learning model 310 and the second machine learning model 330 can independently include a deep neural network (DNN) model, a principal component analysis (PCA) model, a canonical correlation analysis (CCA) model, a supervised matrix factorization model, or a combination thereof.
  • DNN deep neural network
  • PCA principal component analysis
  • CCA canonical correlation analysis
  • supervised matrix factorization model or a combination thereof.
  • features may be generated using a single machine learning model as the first machine learning model 310.
  • the first machine learning model 310 can have one input (time series data) and use a single ML model to obtain the output features.
  • the solution may be generated using a single machine learning model as the second machine learning model 330.
  • the second machine learning model 320 can have one input (selections) and use a single ML model to obtain the output workflow or output sub-features that are sent to the application interface 320.
  • the time series data can comprise data from one or more sensors within a wellbore, which can include DAS acoustic data and/or DTS based temperature data.
  • the time series data can be provided to the first machine learning model 310 to determine the presence of one or more events or anomalies within the wellbore.
  • the resulting event identifications can be provided to the application interface along with one or more time series data components. Based on the feedback from a user through the application interface 320, the presence of the event can be confirmed as well as any associated features within the time series data.
  • the resulting feedback can be passed to the second machine learning model 330.
  • an identification of sand ingress along with associated time series data such as pressure readings, flow rates, and the like can be provided as inputs to the second machine learning model.
  • the second machine learning model can then use the set of features and events to identify similar occurrences in historical data. For example, a feature set can be identified along with past occurrences involving the feature set.
  • the historical data can then be examined to identify actions taken based on the same or similar set of features.
  • the resulting actions can then be recommended or presented on the application interface. For example, a cause of the sand ingress can be provided to the application interface. Multiple solutions may be possible simply based on one of the features or events, and the remaining features can be used to identify the closest solution.
  • an identified sand ingress at a given location may be caused by a first cause when a correlated pressure reading is within a first range, and correlated to a second cause when the pressure reading is within a second range or rate of change.
  • the system and the second machine learning model may consider all of the related features in finding the solution to the problem, thereby improving diagnostic workflows as well as providing improved resolutions or work plans for correcting any issues with the wellbore.
  • FIG. 4 is a schematic diagram of embodiments of the disclosed computer system 400 that utilizes machine learning models to identify features in time series data and train the machine learning models using feedback from an application interface.
  • the components of the computer system 400 can be implemented on a computer or other device comprising a processor as described in FIG. 7.
  • the components include one or more of a machine learning model 410, an application interface 420, and a machine learning label encoder 425.
  • the machine learning model 410 can be configured to receive time series data (e.g., via a sensor signal received from one or more sensors shown in FIGS. 6A and 6B) as input and determine one or more features (e.g., events, anomalies, etc.) in the time series data as the output.
  • the machine learning model 410 can send one or more of the determined features to the application interface 420, which is configured to present one or more of the features and/or time series data components to a user via a user interface.
  • the application interface 420 can be configured to receive selections from the user interface, and can send/present the selections to the machine learning model 410 as a second input for the first machine learning model 410.
  • the machine learning model 410 can be configured to receive the selection(s) from the application interface 420, wherein each selection provides an indication of an identification of one or more of the features.
  • the first machine learning model 410 can be trained using training data.
  • the training data can comprise a set of time series data that is used for training the model.
  • historical data on features obtained from the time series data can be used to train the first machine learning model 410.
  • the machine learning model 410 can be re-trained or updated using the received selection(s), and the re-trained machine learning model 410 can then re-identify one or more features in subsequent time series data that is received by the machine learning model 410.
  • the historical data set can be updated over time based on the newly received features, time series data, and selections.
  • the updated historical data can then be used to update (e.g., re-train, adjust, etc.) the first machine learning model to take into account the new information.
  • the updating of the first machine learning model can take place after each set of feedback occurs, periodically at defined intervals, or upon any other suitable trigger.
  • the updated historical data can be labeled data and include both the features, any identified feature sets, one or more time series data components, and potential outcomes, results, or solutions associated with the features and time series data.
  • the application interface 420 can receive one or more selections from the user interface and send the selections to the machine learning model 410.
  • the selections can first be encoded in the machine learning encoder 415.
  • the selections can be associated with one or more features (e.g., received from the application interface 410 along with the selections), or labeled as associated to one or more features via any technique for labeling in the context of machine learning.
  • Embodiments of the machine learning model 410 can include a deep neural network (DNN) model, a clustering model a principal component analysis (PCA) model, a canonical correlation analysis (CCA) model, a supervised matrix factorization model, or a combination thereof.
  • DNN deep neural network
  • PCA principal component analysis
  • CCA canonical correlation analysis
  • features may be generated using a single machine learning model as the machine learning model 410.
  • the machine learning model 410 can use a single ML model to obtain the output features.
  • FIG. 5 is a schematic diagram of embodiments of the disclosed computer system 500 that utilizes machine learning models to determine features are related to one another.
  • the components of the computer system 500 can be implemented on a computer or other device comprising a processor as described in FIG. 7.
  • the components include one or more of a first machine learning model 510, a similarity engine 520, an application interface 530, a machine learning label encoder 535, and a second machine learning model 540.
  • the first machine learning model 510 received the time series data (e.g., any of the sensor signals described herein) as input and can be configured to determine one or more features in the time series data.
  • the first machine learning model 510 can be configured to send the features to a similarity engine 520, which is configured to determine similarity scores between two or more of the features received from the first machine learning model 510.
  • the similarity engine 520 can be configured to send the similarity scores to the application interface 530, which is configured to present information related to at least a first feature of the features to a user interface for view by a user of the computer system 500.
  • the similarity engine 520 can include a logistic regression model and/or a support vector machine (SVM) model, for example. Any of a number of different approaches may be taken with respect to logistic regression. For example, in at least one embodiment, a Bayesian analysis may be performed, with pairwise item preferences derived from the time series data; alternatively, a frequentist rather than a Bayesian analysis may be used.
  • the application interface 530 can be configured to receive feedback on the information via the application interface 530 from the user.
  • the application interface 530 can be configured to send the feedback to the second machine learning model 540, and the similarity engine 520 is configured to send similarity scores to the second machine learning model 540.
  • the second machine learning model 540 is configured to determine information related to at least a second feature of the features using the feedback and the similarity scores.
  • the second machine learning model 540 can then be configured to send information related to the first feature and information related to the second feature to the application interface 530.
  • the second machine learning model 540 can use reinforcement learning to update the information related to the features to provide the outputs from the model.
  • the application interface 530 can be configured to present the information to a user via the application interface, and the feedback loop (iterations of the described process) can be repeated where feedback is received from the user at the application interface 530 and sent to the second machine learning model 540.
  • the selections or feedback can be optionally weighted based on any available identity of the user. For example, a higher weighting can be given to a more experienced user, and a lower weighting can be given to a less experienced user. For example, senior engineers may be given higher weightings than junior engineers using the system.
  • the initial set of feedback may or may not include information related to the first feature or second feature for which the second machine learning model 540 generates. Thus, unless one or more criteria for terminating feedback have been met, the next feedback iteration may be begin upon receipt of each feedback from the application interface.
  • the termination criteria may, for example, include input from the user that no further information is to be presented and/or the use of the system is terminated.
  • a set of one or more feedback signals may be collected and interpreted by the application interface 530. Depending on the size of the set of feedback signals, the feedback signals may be collected and/or interpreted even before the features have been identified as the first and second features.
  • the application interface 530 can receive feedback from the user interface and send the feedback to the second machine learning model 540.
  • the feedback can first be encoded in the machine learning encoder 535.
  • the feedback can be associated with one or more similarity scores (e.g., received from the application interface 530 along with the feedback), or labeled as associated to one or more similarity scores via any technique for labeling in the context of machine learning.
  • Embodiments of the first machine learning model 510 can include a deep neural network (DNN) model, a clustering model, a principal component analysis (PCA) model, a canonical correlation analysis (CCA) model, a supervised matrix factorization model, or a combination thereof.
  • DNN deep neural network
  • PCA principal component analysis
  • CCA canonical correlation analysis
  • features may be generated using a single machine learning model as the first machine learning model 510.
  • the first machine learning model 510 can have one input (time series data) and use a single ML model to obtain the output features.
  • Embodiments of the second machine learning model 510 can include a deep neural network (DNN) model, a clustering model a principal component analysis (PCA) model, a canonical correlation analysis (CCA) model, a supervised matrix factorization model, or a combination thereof.
  • information related to the first and second features may be generated using a single machine learning model as the second machine learning model 540.
  • the second machine learning model 540 can have one input (time series data) and use a single ML model to obtain the output features.
  • multiple machine learning (ML) models can collectively define the second machine learning model 540.
  • one ML model of the second machine learning model 540 may be used to generate a first feature information vector based on one of i) feedback, ii) similarity scores, or iii) features that is received, and a second ML model of the second machine learning model 540 may be used to generate a second feature information vector based on one of i) feedback, ii) similarity scores, or iii) features.
  • one ML model of the second machine learning model 540 may be used to generate a first feature information vector based on feedback
  • a second ML model of the second machine learning model 540 may be used to generate a second feature information vector based on similarity scores
  • a third LM model of the second machine learning model 540 can be used to generated a third feature information vector based on features.
  • the multiple feature information vectors obtained from the two or three ML models in the second machine learning model 540 may be aggregated (e.g., via concatenation, or using another machine learning model) and used for sending the output (e.g., the information related to the first and second features) to the application interface 540, which presents the output to a user via a user interface.
  • the second machine learning model 540 can be configured to cluster the information related to the first feature and information related to the second feature for form clustered information.
  • the second machine learning model 540 can send the clustered information, in addition to the unclustered information or in lieu of the unclustered information, to the application interface 530.
  • the application interface 530 can present the clustered information to a user via the user interface.
  • the clustered information is presented when the first feature or the second feature are determined in the time series data by the first machine learning model 510.
  • the feedback comprises a selection of information related to the second feature.
  • determining the features in the time series data comprises using the first machine learning model 510 to detect one or more downhole events in the time series data.
  • the application interfaces 110/220/320/420/530 can include an interactive interface configured to receive one or more inputs, wherein the one or more inputs comprise at least one of: a selection of an item, a gesture, or a deselection of an item.
  • sensors 601a-n can be any sensor that measures a parameter with respect to time, such as pressure transducers, temperature sensors (e.g., thermocouples, DTS based temperature sensors, etc.), gas analyzers, acoustic sensors (e.g., DAS based sensors), optical sensors, downhole sensors, flow sensors, etc.
  • the sensors can provide the time series data directly to any of the systems provided herein as shown in FIG. 6A.
  • an edge based computing system 610 can be used at or near the location of the sensors.
  • the edge computing device can be configured to process the time series data to provide a format that can be sent to the computing systems as described herein.
  • one or more features can be determined in the edge computing device 610. For example, a machine learning model used to identify one or more events can be executed in the edge computing device 610, and an identification of the events can then be sent to the systems as described herein.
  • the edge computing device 610 can help to control the data load being transferred from the sensors to the systems, which can be helpful when the systems are executing remotely from the sensors themselves.
  • FIG. 7 illustrates a computer system 700 suitable for implementing one or more embodiments disclosed herein such as the acquisition device or any portion thereof.
  • the computer system 700 includes a processor 782 (which may be referred to as a central processor unit or CPU) that is in communication with memory devices including secondary storage 784, read only memory (ROM) 786, random access memory (RAM) 788, input/output (I/O) devices 790, and network connectivity devices 792.
  • the processor 782 may be implemented as one or more CPU chips.
  • a design that is still subject to frequent change may be preferred to be implemented in software, because re-spinning a hardware implementation is more expensive than re-spinning a software design.
  • a design that is stable that will be produced in large volume may be preferred to be implemented in hardware, for example in an application specific integrated circuit (ASIC), because for large production runs the hardware implementation may be less expensive than the software implementation.
  • ASIC application specific integrated circuit
  • a design may be developed and tested in a software form and later transformed, by well-known design rules, to an equivalent hardware implementation in an application specific integrated circuit that hardwires the instructions of the software.
  • the CPU 782 may execute a computer program or application.
  • the CPU 782 may execute software or firmware stored in the ROM 786 or stored in the RAM 788.
  • the CPU 782 may copy the application or portions of the application from the secondary storage 784 to the RAM 788 or to memory space within the CPU 782 itself, and the CPU 782 may then execute instructions that the application is comprised of.
  • the CPU 782 may copy the application or portions of the application from memory accessed via the network connectivity devices 792 or via the I/O devices 790 to the RAM 788 or to memory space within the CPU 782, and the CPU 782 may then execute instructions that the application is comprised of.
  • an application may load instructions into the CPU 782, for example load some of the instructions of the application into a cache of the CPU 782.
  • an application that is executed may be said to configure the CPU 782 to do something, e.g., to configure the CPU 782 to perform the function or functions promoted by the subject application.
  • the CPU 782 becomes a specific purpose computer or a specific purpose machine.
  • the secondary storage 784 is typically comprised of one or more disk drives or tape drives and is used for non-volatile storage of data and as an over-flow data storage device if RAM 788 is not large enough to hold all working data. Secondary storage 784 may be used to store programs which are loaded into RAM 788 when such programs are selected for execution.
  • the ROM 786 is used to store instructions and perhaps data which are read during program execution. ROM 786 is a non-volatile memory device which typically has a small memory capacity relative to the larger memory capacity of secondary storage 784.
  • the RAM 788 is used to store volatile data and perhaps to store instructions. Access to both ROM 786 and RAM 788 is typically faster than to secondary storage 784.
  • the secondary storage 784, the RAM 788, and/or the ROM 786 may be referred to in some contexts as computer readable storage media and/or non-transitory computer readable media.
  • I/O devices 790 may include printers, video monitors, liquid crystal displays (LCDs), touch screen displays, keyboards, keypads, switches, dials, mice, track balls, voice recognizers, card readers, paper tape readers, or other well-known input devices.
  • LCDs liquid crystal displays
  • touch screen displays keyboards, keypads, switches, dials, mice, track balls, voice recognizers, card readers, paper tape readers, or other well-known input devices.
  • the network connectivity devices 792 may take the form of modems, modem banks, Ethernet cards, universal serial bus (USB) interface cards, serial interfaces, token ring cards, fiber distributed data interface (FDDI) cards, wireless local area network (WLAN) cards, radio transceiver cards that promote radio communications using protocols such as code division multiple access (CDMA), global system for mobile communications (GSM), long-term evolution (LTE), worldwide interoperability for microwave access (WiMAX), near field communications (NFC), radio frequency identity (RFID), and/or other air interface protocol radio transceiver cards, and other well-known network devices.
  • CDMA code division multiple access
  • GSM global system for mobile communications
  • LTE long-term evolution
  • WiMAX worldwide interoperability for microwave access
  • NFC near field communications
  • RFID radio frequency identity
  • These network connectivity devices 792 may enable the processor 782 to communicate with the Internet or one or more intranets.
  • the processor 782 might receive information from the network, or might output information to the network (e.g., to an event database) in the course of performing the above-described method steps.
  • information which is often represented as a sequence of instructions to be executed using processor 782, may be received from and outputted to the network, for example, in the form of a computer data signal embodied in a carrier wave.
  • Such information which may include data or instructions to be executed using processor 782 for example, may be received from and outputted to the network, for example, in the form of a computer data baseband signal or signal embodied in a carrier wave.
  • the baseband signal or signal embedded in the carrier wave may be generated according to several methods well-known to one skilled in the art.
  • the baseband signal and/or signal embedded in the carrier wave may be referred to in some contexts as a transitory signal.
  • the processor 782 executes instructions, codes, computer programs, scripts which it accesses from hard disk, floppy disk, optical disk (these various disk based systems may all be considered secondary storage 784), flash drive, ROM 786, RAM 788, or the network connectivity devices 792. While only one processor 782 is shown, multiple processors may be present. Thus, while instructions may be discussed as executed by a processor, the instructions may be executed simultaneously, serially, or otherwise executed by one or multiple processors.
  • the computer system 700 may comprise two or more computers in communication with each other that collaborate to perform a task.
  • an application may be partitioned in such a way as to permit concurrent and/or parallel processing of the instructions of the application.
  • the data processed by the application may be partitioned in such a way as to permit concurrent and/or parallel processing of different portions of a data set by the two or more computers.
  • virtualization software may be employed by the computer system 700 to provide the functionality of a number of servers that is not directly bound to the number of computers in the computer system 700. For example, virtualization software may provide twenty virtual servers on four physical computers.
  • Cloud computing may comprise providing computing services via a network connection using dynamically scalable computing resources.
  • Cloud computing may be supported, at least in part, by virtualization software.
  • a cloud computing environment may be established by an enterprise and/or may be hired on an as-needed basis from a third party provider.
  • Some cloud computing environments may comprise cloud computing resources owned and operated by the enterprise as well as cloud computing resources hired and/or leased from a third party provider.
  • the computer program product may comprise one or more computer readable storage medium having computer usable program code embodied therein to implement the functionality disclosed above.
  • the computer program product may comprise data structures, executable instructions, and other computer usable program code.
  • the computer program product may be embodied in removable computer storage media and/or non-removable computer storage media.
  • the removable computer readable storage medium may comprise, without limitation, a paper tape, a magnetic tape, magnetic disk, an optical disk, a solid state memory chip, for example analog magnetic tape, compact disk read only memory (CD-ROM) disks, floppy disks, jump drives, digital cards, multimedia cards, and others.
  • the computer program product may be suitable for loading, by the computer system 700, at least portions of the contents of the computer program product to the secondary storage 784, to the ROM 786, to the RAM 788, and/or to other non-volatile memory and volatile memory of the computer system 700.
  • the processor 782 may process the executable instructions and/or data structures in part by directly accessing the computer program product, for example by reading from a CD-ROM disk inserted into a disk drive peripheral of the computer system 700.
  • the processor 782 may process the executable instructions and/or data structures by remotely accessing the computer program product, for example by downloading the executable instructions and/or data structures from a remote server through the network connectivity devices 792.
  • the computer program product may comprise instructions that promote the loading and/or copying of data, data structures, files, and/or executable instructions to the secondary storage 784, to the ROM 786, to the RAM 788, and/or to other non-volatile memory and volatile memory of the computer system 700.
  • the secondary storage 784, the ROM 786, and the RAM 788 may be referred to as a non-transitory computer readable medium or a computer readable storage media.
  • a dynamic RAM embodiment of the RAM 788 likewise, may be referred to as a non-transitory computer readable medium in that while the dynamic RAM receives electrical power and is operated in accordance with its design, for example during a period of time during which the computer system 700 is turned on and operational, the dynamic RAM stores information that is written to it.
  • the processor 782 may comprise an internal RAM, an internal ROM, a cache memory, and/or other internal non-transitory storage blocks, sections, or components that may be referred to in some contexts as non-transitory computer readable media or computer readable storage media.
  • a method comprises: determining a plurality of features in a data signal; correlating the plurality of features to determine similarity scores between two or more features of the plurality of features; presenting information related to at least a first feature of the plurality of features; receiving feedback on the information; and determining, using a first machine learning model, information related to at least a second feature, wherein the determination is made using the similarity scores and the feedback in the first machine learning model.
  • a second aspect can include the method of the first aspect, further comprising: presenting information related to the at least second feature with the information related to at least the first feature.
  • a third aspect can include the method of the first aspect, wherein the feedback comprises a selection of information related to the second feature.
  • a fourth aspect can include the method of the first aspect, wherein the one or more sensors comprises one or more downhole sensors.
  • a fifth aspect can include the method of the fourth aspect, wherein the one or more downhole sensors comprise a distributed acoustic sensor, a distributed temperature sensor, or both.
  • a sixth aspect can include the method of any one of the first to fifth aspects, wherein the plurality of features comprise one or more downhole events.
  • a seventh aspect can include the method of any one of the first to sixth aspects, wherein determining the plurality of features in the data signal comprises using at least a second machine learning model configured to detect one or more downhole events in the data signal.
  • An eighth aspect can include the method of any one of the first to seventh aspects, further comprising: clustering the information related to at least the first feature and the information related to the second feature to form a feature set of information; and presenting the feature set when the first feature or the second feature are detected in the data signal.
  • a ninth aspect can include the method of any one of the first to eighth aspects, wherein the data signal comprises one or more sensor signals from one or more sensors.
  • a tenth aspect can include the method of any one of the first to ninth aspects, wherein the data signal comprises multidimensional data.
  • An eleventh aspect can include the method of any one of the first to tenth aspects, further comprising: presenting or more solutions based on the correlating of the plurality of features.
  • a system comprises: a processor, a memory, wherein the memory stores a program, that when executed on the processor, configures the processor to: generate an application interface, wherein the application interface displays one or more features; receive a plurality of selections of the plurality of features, where the selections comprise one or more feedback signals associated with selections of one or more features of the plurality of features; train, using at least the plurality of selections, a machine learning model to determine one or more workflows, wherein the one or more workflows defines a set of features of the plurality of features; present at least one of the one or more workflows on the application interface.
  • a thirteenth aspect can include the system of the twelfth aspect, wherein the one or more workflows further define an order of presentation of the set of features.
  • a fourteenth aspect can include the system of the twelfth aspect, wherein the processor is further configured to: receive a second plurality of selections from the application interface; generate, using a second machine learning model, one or more recommendations for a feature of the plurality of feature, wherein the one or more recommendations are based on the second plurality of selections received through the application interface.
  • a fifteenth aspect can include the system of the fourteenth aspect, wherein the processor is further configured to: receive a second plurality of selections from the application interface; train the second machine learning model using the second plurality of selections; and identify, using the trained second machine learning model, one or more additional features of the plurality of features to be included in the one or more recommendations.
  • a sixteenth aspect can include the system of the fourteenth aspect, wherein the second machine learning model uses reinforcement learning with the plurality of selections to identify the one or more additional features to be included in the one or more recommendations.
  • a seventeenth aspect can include the system of any one of the twelfth to sixteenth aspects, wherein the processor is further configured to: identify, using the plurality of features, a plurality of features from a sensor signal; determine a similarity score between the plurality of features, wherein the machine learning model is trained using the plurality of selections and the similarity scores.
  • An eighteenth aspect can include the system of any one of the twelfth to seventeenth aspects, wherein the plurality of features comprise an identification of one or more events within a wellbore.
  • a nineteenth aspect can include the system of the eighteenth aspect, wherein the one or more events comprise a fluid inflow event, a fluid outflow detection event, a fluid phase segregation event, fluid flow discrimination within a conduit, well integrity monitoring, a flow assurance event, annular fluid flow diagnosis, overburden monitoring, fluid flow detection behind a casing, fluid induced hydraulic fracture detection in the overburden, sand detection, and combinations thereof.
  • the one or more events comprise a fluid inflow event, a fluid outflow detection event, a fluid phase segregation event, fluid flow discrimination within a conduit, well integrity monitoring, a flow assurance event, annular fluid flow diagnosis, overburden monitoring, fluid flow detection behind a casing, fluid induced hydraulic fracture detection in the overburden, sand detection, and combinations thereof.
  • a twentieth aspect can include the system of any one of the twelfth to nineteenth aspects, wherein the features are determined based on one or more sensor inputs.
  • a system comprises: an insight engine executing on a processor, wherein the insight engine is configured to receive a sensor data signal from one or more sensors, wherein the insight engine is configured to: execute a first machine learning model, identify, using the first machine learning model, one or more features in the sensor data signal, and generate an indication of the one or more features on an application interface; a learning engine, wherein the learning engine is configured to: receive a plurality of selections on the application interface; train, using at least the plurality of selections, a second machine learning model to determine a one or more sub-features associated with the one or more features, and presenting the one or more sub-features on the application interface.
  • a twenty second aspect can include the system of the twenty first aspect, wherein the learning engine is further configured to: determine, using the second machine learning model, one or more workflows, wherein the one or more workflows define a set of features of the plurality of features; and present at least one of the one or more workflows on the application interface.
  • a twenty third aspect can include the system of the twenty second aspect, wherein the insight engine is further configured to: receive the plurality of selections from the application interface; update the first machine learning model using the plurality of selections; and identify, using the updated first machine learning model, a second set of one or more features.
  • a twenty fourth aspect can include the system of any one of the twenty first to twenty third aspect, wherein the application interface comprises an interactive interface configured to receive one or more inputs, wherein the one or more inputs comprise at least one of: a selection of an item, a gesture, or a deselection of an item.
  • a method comprises: performing, using one or more computing devices: identifying, using a first machine learning model, one or more features in a data signal; receiving a plurality of selections from an application interface based on presenting the one or more features on the application interface, wherein the plurality of selections provides an indication of an identification of the one or more features; identifying, using a second machine learning model, a corresponding feature based on the plurality of selections; identifying, using the one or more features and the corresponding feature, a solution associated with the one or more features and the corresponding feature; and presenting the solution on the application interface in association with the one or more features.
  • a twenty sixth aspect can include the method of the twenty fifth aspect, wherein the data signal is a sensor data signal provided by one or more sensors.
  • a twenty seventh aspect can include the method of the twenty fifth or twenty sixth aspect, wherein the plurality of features comprise an identification of one or more events within a wellbore.
  • a twenty eighth aspect can include the method of the twenty seventh aspect, wherein the one or more events comprise a fluid inflow event, a fluid outflow detection event, a fluid phase segregation event, fluid flow discrimination within a conduit, well integrity monitoring, a flow assurance event, annular fluid flow diagnosis, overburden monitoring, fluid flow detection behind a casing, fluid induced hydraulic fracture detection in the overburden, sand detection, and combinations thereof.
  • the one or more events comprise a fluid inflow event, a fluid outflow detection event, a fluid phase segregation event, fluid flow discrimination within a conduit, well integrity monitoring, a flow assurance event, annular fluid flow diagnosis, overburden monitoring, fluid flow detection behind a casing, fluid induced hydraulic fracture detection in the overburden, sand detection, and combinations thereof.
  • a twenty ninth aspect can include the method of any one of the twenty fifth to twenty eighth aspects, wherein the features are determined based on one or more sensor inputs.
  • a method comprises: performing, using one or more computing devices: identifying, using a first machine learning model, one or more features in a data signal; receiving a selection from an application interface based on presenting the one or more features on the application interface, wherein the selection provides an indication of an identification of the one or more features; updating, using at least the selection, the first machine learning model; and re-identifying, using the first machine learning model, the one or more features in the sensor data signal.
  • a thirty first aspect can include the method of the thirtieth aspect, wherein the data signal comprises a sensor data signal from one or more sensors.
  • a thirty second aspect can include the method of the thirty first aspect, wherein the one or more sensors comprises one or more downhole sensors.
  • a thirty third aspect can include the method of the thirty second aspect, wherein the one or more downhole sensors comprise a distributed acoustic sensor, a distributed temperature sensor, or both.
  • a thirty fourth aspect can include the method of any one of the thirtieth to thirty third aspects, wherein the one or more features comprise one or more downhole events.
  • a thirty fifth aspect can include the method of any one of the thirtieth to thirty fourth aspects, wherein identifying the one or more features in the data signal comprises using at least a second machine learning model configured to detect one or more downhole events in the data signal.
  • a thirty sixth aspect can include the method of any one of the thirtieth to thirty fifth aspects, wherein the data signal is; 1) received from one or more sensors, 2) a time series data, 3) a depth series data, or 4) any combination thereof.
  • a method comprises: determining a plurality of features in a data signal; correlating the plurality of features to determine similarity scores between two or more features of the plurality of features; presenting information related to at least a first feature of the plurality of features; and determining, using a first machine learning model, information related to at least a second feature, wherein the determination is made using the similarity scores in the first machine learning model.
  • a thirty eighth aspect can include the method of the thirty seventh aspect, further comprising: presenting information related to the at least second feature with the information related to at least the first feature.
  • a thirty ninth aspect can include the method of the thirty seventh or thirty eighth aspect, further comprising: clustering the information related to at least the first feature and the information related to the second feature to form a feature set of information; and presenting the feature set when the first feature or the second feature are detected in the data signal.
  • a fortieth aspect can include the method of any one of the thirty seventh to thirty ninth aspects, wherein the data signal comprises one or more sensor signals from one or more sensors.
  • a forty first aspect can include the method of any one of the thirty seventh to fortieth aspects, wherein the data signal comprises multidimensional data.
  • a forty second aspect can include the method of any one of the thirty seventh to forty first aspects, further comprising: presenting or more solutions based on the correlating of the plurality of features.

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Abstract

L'invention concerne un procédé comprenant les étapes suivantes : la détermination d'une pluralité de caractéristiques dans un signal de données, la corrélation de la pluralité de caractéristiques pour déterminer des scores de similarité entre au moins deux caractéristiques de la pluralité de caractéristiques, la présentation d'informations relatives à au moins une première caractéristique de la pluralité de caractéristiques, la réception d'une rétroaction sur les informations, et la détermination, à l'aide d'un premier modèle d'apprentissage machine, d'informations relatives à au moins une seconde caractéristique. La détermination est effectuée à l'aide des scores de similarité et de la rétroaction dans le premier modèle d'apprentissage machine.
PCT/EP2020/052445 2020-01-31 2020-01-31 Apprentissage machine à l'aide de données de série chronologique WO2021151504A1 (fr)

Priority Applications (4)

Application Number Priority Date Filing Date Title
PCT/EP2020/052445 WO2021151504A1 (fr) 2020-01-31 2020-01-31 Apprentissage machine à l'aide de données de série chronologique
EP20733976.3A EP4097660A1 (fr) 2020-01-31 2020-06-18 Apprentissage machine à l'aide de données de série chronologique
US17/792,092 US20230052691A1 (en) 2020-01-31 2020-06-18 Maching learning using time series data
PCT/EP2020/067043 WO2021151521A1 (fr) 2020-01-31 2020-06-18 Apprentissage machine à l'aide de données de série chronologique

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Cited By (5)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
US11530606B2 (en) 2016-04-07 2022-12-20 Bp Exploration Operating Company Limited Detecting downhole sand ingress locations
US11643923B2 (en) 2018-12-13 2023-05-09 Bp Exploration Operating Company Limited Distributed acoustic sensing autocalibration
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US11593683B2 (en) 2020-06-18 2023-02-28 Lytt Limited Event model training using in situ data

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