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. 22, 2022

Filed:

Jul. 23, 2020
Applicant:

Saudi Arabian Oil Company, Dhahran, SA;

Inventors:

Arturo Magana Mora, Dhahran, SA;

Michael Affleck, Aberdeenshire, GB;

Chinthaka Pasan Gooneratne, Dhahran, SA;

William Contreras Otalvora, Dhahran, SA;

Krzysztof Karol Machocki, Aberdeen, GB;

Assignee:
Attorney:
Primary Examiner:
Int. Cl.
CPC ...
E21B 47/002 (2012.01); G06N 20/00 (2019.01); E21B 44/08 (2006.01); G06T 7/00 (2017.01);
U.S. Cl.
CPC ...
E21B 47/002 (2020.05); E21B 44/08 (2013.01); G06N 20/00 (2019.01); G06T 7/0004 (2013.01); G06T 2207/20081 (2013.01);
Abstract

Disclosed are methods, systems, and computer-readable medium to perform operations including: capturing, using an image sensor directed at a drilling system, an image feed of movement of a component of the drilling system with respect to a vertical reference line; converting the image feed into a digital representation of the movement of the component, the digital representation including a number of offset pixels from the component to the vertical reference line in the image feed; converting the digital representation into a machine learning (ML), the ML representation including a plurality of vectors each including the number of offset pixels from the component to the vertical reference line at a respective time; training a ML model using the ML representation to characterize the movement of the component as normal or abnormal; and using the trained ML model to characterize the movement of the component in real-time as normal or abnormal.


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