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公开(公告)号：US20240401586A1
公开(公告)日：2024-12-05
申请号：US18694581
申请日：2022-10-26
Applicant: Schlumberger Technology Corporation
Inventor： Amey Ambade , Piyush Umate , Supriya Gupta , Abhishek Sharma
IPC: F04B51/00 , E21B47/12 , F04B47/02 , G06N3/0464 , G06N20/10
Abstract: Methods and systems are provided for monitoring the operation of a sucker rod pump (SRP), which involves a workflow that processes surface operational data and downhole operational data related to the operation of the SRP. The surface operational data is derived from real-time measurements performed by surface-located sensors, while the downhole operational data is derived from real-time measurements performed by downhole sensors. The surface operational data is processed to generate input data for supply to a first machine learning model (e.g., Surface Data Classifier) and the downhole operational data is processed to generate input data for supply to a second machine learning model (e.g., Downhole Data Classifier). The output of at least one of the first and second machine learning models is used to characterize an operational condition or status of the SRP.
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公开(公告)号：US20210233008A1
公开(公告)日：2021-07-29
申请号：US16890208
申请日：2020-06-02
Applicant: Schlumberger Technology Corporation
Inventor： Supriya Gupta , Jason Baihly , Saniya Karnik , David Rossi , Andrew Acock , Asim Malik
Abstract: A method for analyzing data includes obtaining data objects from a data repository. The data objects include observational data related to one or more oil wells, oil fields, or a combination thereof. The method also includes classifying the data objects based on data contained therein using a machine-learning algorithm, and determining output data from the data objects after classifying the data objects. The output data represents one or more historical data analytics for the one or more oil wells, oil fields, or a combination thereof. The method further includes visualizing the output data including the one or more historical data analytics.
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公开(公告)号：US20240169273A1
公开(公告)日：2024-05-23
申请号：US18552241
申请日：2022-03-25
Applicant: Schlumberger Technology Corporation
Inventor： Garud Sridhar, Sr. , Surej Kumar Subbiah , Muhammad Ibrahim , Adrian Enrique Rodriguez Herrera , Nasser Alhamad , Supriya Gupta , Assef Mohamad Hussein , Vigneshwaran Santhalingam , Rajeev Ranjan Sinha
IPC: G06N20/00
CPC classification number: G06N20/00
Abstract: A method can include receiving real-time, time series data from equipment at a wellsite that includes a wellbore in contact with a fluid reservoir; processing the time series data as input to a trained machine learning model to predict a future solids event related to influx of solids into the wellbore from the fluid reservoir; and outputting a time of the future solids event.
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Patent Agency Ranking
公开(公告)号：US20240018863A1
公开(公告)日：2024-01-18
申请号：US18353198
申请日：2023-07-17
Applicant: Schlumberger Technology Corporation
Inventor： Amey Ambade , Sreekrishnan Ramachandran , Piyush Umate , Supriya Gupta
IPC: E21B44/00 , E21B47/002
CPC classification number: E21B44/00 , E21B47/0025 , E21B2200/22
Abstract: A method can include receiving results from a field device at a field site, where the results are generated using a trained machine learning model at the field site and real-time field equipment data from field equipment at the field site; issuing a signal responsive to an assessment of the results, where the signal indicates a performance-related issue of the trained machine learning model; updating training data with at least a portion of the real-time field equipment data to address the performance-related issue; and generating a new trained machine learning model for deployment to the field site using at least a portion of the updated training data.
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公开(公告)号：US20200257932A1
公开(公告)日：2020-08-13
申请号：US16646603
申请日：2018-09-13
Applicant: Schlumberger Technology Corporation
Inventor： Timothy J. Roberts , Supriya Gupta , Aria Abubakar
Abstract: A method includes receiving historical well-production data, and determining curve parameters based on the historical well production data. Determining the curve parameters includes accounting for uncertainty. The method also includes determining curve fits for the historical data based on the curve parameters using a plurality of models, calculating accuracy for each of the models based on a comparison of the curve fits to the well-production data, comparing the accuracy for each of the models using an information criteria that accounts for uncertainty, selecting one of the models based on the comparison, and determining an estimated ultimate recovery (EUR) for the well using at least the selected one of the models.
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公开(公告)号：US11238379B2
公开(公告)日：2022-02-01
申请号：US16615956
申请日：2018-05-22
Applicant: Schlumberger Technology Corporation
Inventor： Supriya Gupta , Satya Deepthi Gopisetti
Abstract: A method for optimizing oil production includes receiving historical production data for one or more wells. A first model is generated based at least partially upon the historical production data. The production in the one or more wells is predicted based at least partially upon the first model. A second model is generated based at least partially on the predicted production. A production allocation is determined in the one or more wells to maximize production based at least partially upon the second model. The production allocation is then implemented.
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公开(公告)号：US20210230981A1
公开(公告)日：2021-07-29
申请号：US16890189
申请日：2020-06-02
Applicant: Schlumberger Technology Corporation
Inventor： Supriya Gupta , Saniya Karnik , David Saier
Abstract: A method includes generating a structured data object from a plurality of data files in a data repository, preprocessing the structured data object based on one or more features from the structured data object, executing an unsupervised machine-learning technique to identify one or more clusters of data files from the plurality of data files in the data repository, presenting at least one set of text from the one or more clusters to a user along with a word cloud for each of the one or more clusters, and receiving one or more labels for respective clusters of the one or more clusters.
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公开(公告)号：US20200097864A1
公开(公告)日：2020-03-26
申请号：US16615956
申请日：2018-05-22
Applicant: Schlumberger Technology Corporation
Inventor： Supriya Gupta , Satya Deepthi Gopisetti
Abstract: A method for optimizing oil production includes receiving historical production data for one or more wells. A first model is generated based at least partially upon the historical production data. The production in the one or more wells is predicted based at least partially upon the first model. A second model is generated based at least partially on the predicted production. A production allocation is determined in the one or more wells to maximize production based at least partially upon the second model. The production allocation is then implemented.
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