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:
Mar. 14, 2023

Filed:

Dec. 06, 2019
Applicant:

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

Inventors:

Tao Sun, Houston, TX (US);

Sebastien B. Strebelle, Houston, TX (US);

Ashley D. Harris, Houston, TX (US);

Maisha Lara Amaru, Houston, TX (US);

Lewis Li, Houston, TX (US);

Assignee:

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

Attorney:
Primary Examiner:
Assistant Examiner:
Int. Cl.
CPC ...
G01V 99/00 (2009.01); G06F 30/27 (2020.01); G06N 20/00 (2019.01); G06N 5/04 (2023.01);
U.S. Cl.
CPC ...
G06F 30/27 (2020.01); G01V 99/005 (2013.01); G06N 5/04 (2013.01); G06N 20/00 (2019.01); G01V 2210/661 (2013.01);
Abstract

A computational stratigraphy model may be run for M mini-steps to simulate changes in a subsurface representation across M mini-steps (from 0-th subsurface representation to M-th subsurface representation), with a mini-step corresponding to a mini-time duration. The subsurface representation after individual steps may be characterized by a set of computational stratigraphy model variables. Some or all of the computational stratigraphy model variables from running of the computational stratigraphy model may be provided as input to a machine learning model. The machine learning model may predict changes to the subsurface representation over a step corresponding to a time duration longer than the mini-time duration and output a predicted subsurface representation. The subsurface representation may be updated based on the predicted subsurface representation outputted by the machine learning model. Running of the computational stratigraphy model and usage of the machine learning model may be iterated until the end of the simulation.


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