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:

Apr. 02, 2020
Applicant:

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

Inventors:

Yansong Huang, Beijing, CN;

Paul Bolchover, Beijing, CN;

Assignee:
Attorney:
Primary Examiner:
Int. Cl.
CPC ...
G06N 3/08 (2006.01); G05B 17/02 (2006.01); G05B 23/02 (2006.01); G05B 13/02 (2006.01); G06F 16/9038 (2019.01); G06F 16/9035 (2019.01); G05B 13/04 (2006.01); G06F 18/214 (2023.01); G06N 3/045 (2023.01); G06V 10/82 (2022.01); G06V 20/13 (2022.01);
U.S. Cl.
CPC ...
G05B 17/02 (2013.01); G05B 13/027 (2013.01); G05B 13/04 (2013.01); G05B 23/0283 (2013.01); G06F 16/9035 (2019.01); G06F 16/9038 (2019.01); G06F 18/214 (2023.01); G06N 3/045 (2023.01); G06N 3/08 (2013.01); G06V 10/82 (2022.01); G06V 20/13 (2022.01);
Abstract

A computer-implemented method may include receiving test data representing a cutter/rock interaction for a cutter/rock pair; calibrating an analytical model to represent the cutter/rock interaction mechanism for a cutter/rock pair; applying the calibrated analytical model to expand the test data to form one of a plurality of expanded test datasets; generating a first neural network model, of a plurality of first neural network models, representing cutter/rock interaction between a plurality of cutters of different cutter sizes and a particular rock type, wherein the first neural network is generated using the plurality of expanded test datasets as training input; generating a second neural network model using the plurality of first neural network models as training input, wherein the second neural network model represents non-tested cutter/rock interactions between a plurality of cutters of different cutter sizes and a plurality of rock types.


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