公开(公告)号：US20240219602A1

公开(公告)日：2024-07-04

申请号：US18537910

申请日：2023-12-13

Applicant: Schlumberger Technology Corporation

Inventor： Indranil Roychoudhury ,  Crispin Chatar ,  Jose R. Celaya Galvan ,  Prasham Sheth ,  Mengdi Gao ,  Sai Shravani Sistla ,  Priya Mishra

IPC: G01V20/00 ,  G06F30/27

CPC classification number: G01V20/00 ,  G06F30/27

Abstract: Systems and methods for generating digital gamma-ray logs for target wells based on combined physics and machine learning model using real-time information (e.g., drilling parameters, survey data, gamma-ray logs, and so forth) obtained from offset wells analogous to the subject well in terms of gamma-ray readings. The systems and methods may provide solutions that may lower the cost of Measuring While Drilling (MWD) and/or Logging While Drilling (LWD) process and facilitate the users (e.g., drillers, geoscientists, and so forth) to make enhanced data driven decisions.

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

IPC: E21B44/00 ,  G01V20/00

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.

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.

公开(公告)号：US20240219255A1

公开(公告)日：2024-07-04

申请号：US18537895

申请日：2023-12-13

Applicant: Schlumberger Technology Corporation

Inventor： Anatoly Aseev ,  Andrey Sergeevich Konchenko ,  Jose R. Celaya Galvan ,  Indranil Roychoudhury ,  Prasham Sheth

IPC: G01M3/04 ,  G05B15/02 ,  G06V10/44 ,  G06V10/764 ,  G06V10/77 ,  G06V10/774 ,  G06V10/82 ,  G06V20/52

CPC classification number: G01M3/04 ,  G05B15/02 ,  G06V10/44 ,  G06V10/764 ,  G06V10/7715 ,  G06V10/774 ,  G06V10/82 ,  G06V20/52

Abstract: A method may include receiving, via one or more processors, a set of image data representative of equipment configured to distribute a gas. The method may then involve determining a type of equipment depicted in the first set of image data, retrieving a leak detection model corresponding to the type of equipment depicted in the first set of image data, and determining that a gas leak is present on the equipment based on the set of image data and the leak detection model. After determining that the gas leak is present, the method may include sending a notification to a computing device in response to detecting the gas leak.

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

IPC: E21B19/00 ,  E21B21/06 ,  E21B44/00 ,  G06T7/246 ,  G06V10/774 ,  G06V10/82 ,  G06V20/40 ,  G06V20/50

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.

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

IPC: G06V20/40 ,  G06V20/50 ,  G06V10/82 ,  G06T7/246 ,  G06V10/774 ,  E21B44/00 ,  E21B19/00 ,  E21B21/06

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.