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:
Oct. 31, 2023

Filed:

Oct. 26, 2021
Applicant:

Shell Oil Company, Houston, TX (US);

Inventors:

Satyakee Sen, Houston, TX (US);

Russell David Potter, Houston, TX (US);

Donald Paul Griffith, Houston, TX (US);

Sam Ahmad Zamanian, Houston, TX (US);

Sergey Frolov, Houston, TX (US);

Assignee:

SHELL USA, INC., Houston, TX (US);

Attorney:
Primary Examiner:
Int. Cl.
CPC ...
G01V 1/30 (2006.01); G06N 3/084 (2023.01); G06F 18/214 (2023.01); G06N 3/08 (2023.01); G06N 20/00 (2019.01); G06N 3/0895 (2023.01); G06N 3/09 (2023.01);
U.S. Cl.
CPC ...
G01V 1/30 (2013.01); G06F 18/2155 (2023.01); G06N 3/08 (2013.01); G06N 3/084 (2013.01); G06N 20/00 (2019.01); G01V 2210/6161 (2013.01); G06N 3/0895 (2023.01); G06N 3/09 (2023.01);
Abstract

A method for improving a backpropagation-enabled process for identifying subsurface features from seismic data involves a model that has been trained with an initial set of training data. A target data set is used to compute a set of initial inferences on the target data set that are combined with the initial training data to define updated training data. The model is trained with the updated training data. Updated inferences on the target data set are then computed. A set of further-updated training data is defined by combining at least a portion of the initial set of training data and at least a portion of the target data and associated updated inferences. The set of further-updated training data is used to train the model. Further-updated inferences on the target data set are then computed and used to identify the occurrence of a user-selected subsurface feature in the target data set.


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