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公开(公告)号：US20220157167A1
公开(公告)日：2022-05-19
申请号：US17535236
申请日：2021-11-24
Applicant: Schlumberger Technology Corporation
Inventor： Soumya Gupta , John Z. Pang , Andrey Konchenko , Erik Burton , Mugdha Bhusari , Jose R. Celaya , Ivan Joel Alaniz , Crispin Chatar
IPC: G08G1/0968 , G08G1/0967 , G08G1/01
Abstract: A method including generating, by a navigation service, a route for navigating from a route origin to a route destination using a private roads repository. The method includes identifying a ghost origin and a ghost destination of a ghost road along the route. The method includes sending, using an application programming interface of a base roads engine, a first request for a route from the ghost origin to the ghost destination. The method includes receiving, from the base roads engine in response to the first request, a replacement section from the ghost origin to the ghost destination. The method includes replacing, in the route, the ghost road with the replacement section to create an updated route including segments. The method includes generating an estimated travel time from the route origin to the route destination over the segments of the updated route. The method includes presenting the estimated travel time.
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公开(公告)号：US20250118073A1
公开(公告)日：2025-04-10
申请号：US18936194
申请日：2024-11-04
Applicant: Schlumberger Technology Corporation
Inventor： Laeticia Shao , Suhas Suresha , Indranil Roychoudhury , Crispin Chatar , Soumya Gupta , Jose Celaya Galvan
IPC: G06V10/764 , G06T7/20 , G06V10/25 , G06V10/44
Abstract: A method includes receiving training images representing a portion of a drilling rig over a first period of time, associating individual training images of the training images with times at which the individual training images were captured, determining a rig state at each of the times, classifying the individual training images based on the rig state at each of the times, training a machine learning model to identify rig state based on the classified training images, receiving additional images representing the portion of the drilling rig over a second period of time, and determining one or more rig states of the drilling rig during the second period of time using the machine learning model based on the additional images.
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公开(公告)号：US20250052589A1
公开(公告)日：2025-02-13
申请号：US18929110
申请日：2024-10-28
Applicant: Schlumberger Technology Corporation
Inventor： Soumya Gupta , John Z. Pang , Andrey Konchenko , Erik Burton , Mugdha Bhusari , Jose R. Celaya Galvan , Ivan Joel Alaniz , Crispin Chatar
Abstract: A method including generating, by a navigation service, a route for navigating from a route origin to a route destination using a private roads repository. The method includes identifying a ghost origin and a ghost destination of a ghost road along the route. The method includes sending, using an application programming interface of a base roads engine, a first request for a route from the ghost origin to the ghost destination. The method includes receiving, from the base roads engine in response to the first request, a replacement section from the ghost origin to the ghost destination. The method includes replacing, in the route, the ghost road with the replacement section to create an updated route including segments. The method includes generating an estimated travel time from the route origin to the route destination over the segments of the updated route. The method includes presenting the estimated travel time.
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公开(公告)号：US20240175344A1
公开(公告)日：2024-05-30
申请号：US18432202
申请日：2024-02-05
Applicant: Schlumberger Technology Corporation
Inventor： Crispin Chatar , Soumya Gupta , Jose R. Celaya Galvan
CPC classification number: E21B44/00 , E21B47/00 , E21B49/00 , E21B2200/20
Abstract: A method for predicting a stick-slip event includes measuring one or more surface properties using a sensor at the surface. The method also includes measuring one or more downhole properties using a downhole tool in a wellbore. The method also includes determining that the one or more surface properties and the one or more downhole properties match a distribution. The distribution occurs before two or more previously-detected stick-slip events. The method also includes determining a likelihood that a stick-slip event will occur based at least partially upon the distribution that the one or more surface properties and the one or more downhole properties match.
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公开(公告)号：US20230349281A1
公开(公告)日：2023-11-02
申请号：US17756822
申请日：2019-12-05
Applicant: Schlumberger Technology Corporation
Inventor： Crispin Chatar , Soumya Gupta , Jose R. Celaya Galvan
CPC classification number: E21B44/00 , E21B47/00 , E21B49/00 , E21B2200/20
Abstract: A method for predicting a stick-slip event includes measuring one or more surface properties using a sensor at the surface. The method also includes measuring one or more downhole properties using a downhole tool in a wellbore. The method also includes determining that the one or more surface properties and the one or more downhole properties match a distribution. The distribution occurs before two or more previously-detected stick-slip events. The method also includes determining a likelihood that a stick-slip event will occur based at least partially upon the distribution that the one or more surface properties and the one or more downhole properties match.
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Patent Agency Ranking
公开(公告)号：US12136267B2
公开(公告)日：2024-11-05
申请号：US17995324
申请日：2021-04-05
Applicant: Schlumberger Technology Corporation
Inventor： Laetitia Shao , Suhas Suresha , Indranil Roychoudhury , Crispin Chatar , Soumya Gupta , Jose Celaya Galvan
Abstract: A method includes receiving training images representing a portion of a drilling rig over a first period of time, associating individual training images of the training images with times at which the individual training images were captured, determining a rig state at each of the times, classifying the individual training images based on the rig state at each of the times, training a machine learning model to identify rig state based on the classified training images, receiving additional images representing the portion of the drilling rig over a second period of time, and determining one or more rig states of the drilling rig during the second period of time using the machine learning model based on the additional images.
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公开(公告)号：US12123294B2
公开(公告)日：2024-10-22
申请号：US18432202
申请日：2024-02-05
Applicant: Schlumberger Technology Corporation
Inventor： Crispin Chatar , Soumya Gupta , Jose R. Celaya Galvan
CPC classification number: E21B44/00 , E21B47/00 , E21B49/00 , E21B2200/20
Abstract: A method for predicting a stick-slip event includes measuring one or more surface properties using a sensor at the surface. The method also includes measuring one or more downhole properties using a downhole tool in a wellbore. The method also includes determining that the one or more surface properties and the one or more downhole properties match a distribution. The distribution occurs before two or more previously-detected stick-slip events. The method also includes determining a likelihood that a stick-slip event will occur based at least partially upon the distribution that the one or more surface properties and the one or more downhole properties match.
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公开(公告)号：US11920454B2
公开(公告)日：2024-03-05
申请号：US17756822
申请日：2019-12-05
Applicant: Schlumberger Technology Corporation
Inventor： Crispin Chatar , Soumya Gupta , Jose R. Celaya Galvan
CPC classification number: E21B44/00 , E21B47/00 , E21B49/00 , E21B2200/20
Abstract: A method for predicting a stick-slip event includes measuring one or more surface properties using a sensor at the surface. The method also includes measuring one or more downhole properties using a downhole tool in a wellbore. The method also includes determining that the one or more surface properties and the one or more downhole properties match a distribution. The distribution occurs before two or more previously-detected stick-slip events. The method also includes determining a likelihood that a stick-slip event will occur based at least partially upon the distribution that the one or more surface properties and the one or more downhole properties match.
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公开(公告)号：US20230186627A1
公开(公告)日：2023-06-15
申请号：US17995324
申请日：2021-04-05
Applicant: Schlumberger Technology Corporation
Inventor： Laeticia Shao , Suhas Suresha , Indranil Roychoudhury , Crispin Chatar , Soumya Gupta , Jose Celaya Galvan
CPC classification number: G06V20/41 , E21B19/00 , E21B21/065 , E21B44/00 , G06T7/248 , G06V10/82 , G06V10/774 , G06V20/50 , E21B2200/20 , E21B2200/22 , G06T2207/10016 , G06T2207/10024 , G06T2207/20081 , G06T2207/20084
Abstract: A method includes receiving training images representing a portion of a drilling rig over a first period of time, associating individual training images of the training images with times at which the individual training images were captured, determining a rig state at each of the times, classifying the individual training images based on the rig state at each of the times, training a machine learning model to identify rig state based on the classified training images, receiving additional images representing the portion of the drilling rig over a second period of time, and determining one or more rig states of the drilling rig during the second period of time using the machine learning model based on the additional images.
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公开(公告)号：US20240401460A1
公开(公告)日：2024-12-05
申请号：US18697789
申请日：2022-10-26
Applicant: Schlumberger Technology Corporation
Inventor： Soumya Gupta , Indranil Roychoudhury , Crispin Chatar , Alfredo De La Fuente , Jose R. Celaya Galvan , Prasham Sheth
Abstract: A method includes generating one or more hybrid physics models each configured to predict a value for a drilling condition based on training data, training a machine learning model to predict a drilling condition severity based on the training data and the value of the drilling condition predicted by the one or more hybrid physics models, receiving sensor data representing present drilling data, predicting the drilling condition, based at least in part on the sensor data, using the hybrid physics model, and predicting the drilling condition severity, based at least in part on the drilling condition that was predicted and the sensor data, using machine learning model that was trained.
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