The patent badge is an abbreviated version of the USPTO patent document. The patent badge does contain a link to the full patent document.

The patent badge is an abbreviated version of the USPTO patent document. The patent badge covers the following: Patent number, Date patent was issued, Date patent was filed, Title of the patent, Applicant, Inventor, Assignee, Attorney firm, Primary examiner, Assistant examiner, CPCs, and Abstract. The patent badge does contain a link to the full patent document (in Adobe Acrobat format, aka pdf). To download or print any patent click here.

Date of Patent:
Nov. 05, 2024

Filed:

Apr. 05, 2021
Applicant:

Schlumberger Technology Corporation, Sugar Land, TX (US);

Inventors:

Laetitia Shao, Menlo Park, CA (US);

Suhas Suresha, Menlo Park, CA (US);

Indranil Roychoudhury, Menlo Park, CA (US);

Crispin Chatar, Menlo Park, CA (US);

Soumya Gupta, Menlo Park, CA (US);

Jose Celaya Galvan, Menlo Park, CA (US);

Assignee:
Attorney:
Primary Examiner:
Int. Cl.
CPC ...
E21B 19/00 (2006.01); E21B 21/06 (2006.01); E21B 44/00 (2006.01); G06T 7/246 (2017.01); G06V 10/774 (2022.01); G06V 10/82 (2022.01); G06V 20/40 (2022.01); G06V 20/50 (2022.01);
U.S. Cl.
CPC ...
G06V 20/41 (2022.01); E21B 19/00 (2013.01); E21B 21/065 (2013.01); E21B 44/00 (2013.01); G06T 7/248 (2017.01); G06V 10/774 (2022.01); G06V 10/82 (2022.01); G06V 20/50 (2022.01); E21B 2200/20 (2020.05); E21B 2200/22 (2020.05); G06T 2207/10016 (2013.01); G06T 2207/10024 (2013.01); G06T 2207/20081 (2013.01); G06T 2207/20084 (2013.01);
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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