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:
Jun. 13, 2023

Filed:

Nov. 15, 2018
Applicant:

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

Inventors:

Yingwei Yu, Katy, TX (US);

Qiuhua Liu, Sugar Land, TX (US);

Richard John Meehan, Houston, TX (US);

Sylvain Chambon, Katy, TX (US);

Mohammad Khairi Hamzah, Katy, TX (US);

Assignee:
Attorney:
Primary Examiner:
Int. Cl.
CPC ...
E21B 44/00 (2006.01); E21B 21/08 (2006.01); G06N 7/00 (2023.01); G01V 1/50 (2006.01); E21B 49/00 (2006.01); G01V 11/00 (2006.01); G06F 30/20 (2020.01); G06F 17/15 (2006.01); G06F 17/16 (2006.01); G06F 30/27 (2020.01); G06N 3/044 (2023.01); G06N 3/047 (2023.01); G06N 3/08 (2023.01); G06T 13/80 (2011.01);
U.S. Cl.
CPC ...
E21B 44/00 (2013.01); E21B 21/08 (2013.01); E21B 49/003 (2013.01); G01V 1/50 (2013.01); G01V 11/00 (2013.01); G06F 17/15 (2013.01); G06F 17/16 (2013.01); G06F 30/20 (2020.01); G06F 30/27 (2020.01); G06N 3/044 (2023.01); G06N 3/047 (2023.01); G06N 3/08 (2013.01); G06N 7/00 (2013.01); G01V 2200/14 (2013.01); G01V 2200/16 (2013.01); G06T 13/80 (2013.01);
Abstract

A method can include training a deep neural network to generate a trained deep neural network where the trained deep neural network represents functions of a nonlinear Kalman filter that represents a dynamic system of equipment and environment via an internal state vector of the dynamic system; generating a base internal state vector, that corresponds to a pre-defined operational procedure, using the trained deep neural network; receiving operation data from the equipment responsive to operation in the environment; generating an internal state vector using the operation data and the trained deep neural network; and comparing at least the internal state vector to at least the base internal state vector.


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