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:
Sep. 16, 2025

Filed:

Mar. 09, 2021
Applicants:

Chevron U.s.a. Inc., San Ramon, CA (US);

Triad National Security, Llc, Los Alamos, NM (US);

Inventors:

Rajesh S. Nair, Houston, TX (US);

Karim Shafik Zaki, Houston, TX (US);

Yan Li, Houston, TX (US);

Margaretha Catharina Maria Rijken, Houston, TX (US);

Velimir Valentinov Vesselinov, Los Alamos, NM (US);

Assignees:

Chevron U.S.A. Inc., San Ramon, CA (US);

TRIAD National Security, LLC, Los Alamos, NM (US);

Attorney:
Primary Examiner:
Int. Cl.
CPC ...
G06F 30/27 (2020.01); G06F 30/23 (2020.01); G06N 3/04 (2023.01); G06N 3/08 (2023.01);
U.S. Cl.
CPC ...
G06N 3/08 (2013.01); G06F 30/23 (2020.01); G06F 30/27 (2020.01); G06N 3/04 (2013.01);
Abstract

A computer implemented method for prediction of geomechanical performance including productivity index decline and completion integrity for a well or a hydrocarbon reservoir using a geomechanics informed machine intelligence (GIMI) algorithm. The method includes running a geomechanical reservoir simulator to generate training datasets for the hydrocarbon reservoir and incorporating physical models and identified variables into the GIMI algorithm. The method further includes training a neural network of the GIMI algorithm by using correlated training datasets that correlate to the physical models to produce a resulting prediction model and performing sensitivity analysis on the resulting prediction model. Additionally, the method includes identifying dominant variables for damage mechanisms through design of experiment statistics and performing history matching and blind test on the resulting prediction model. Lastly, the method includes updating the identified variables and models incorporated into the GIMI algorithm.


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