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:
Dec. 02, 2025

Filed:

Apr. 28, 2021
Applicant:

Landmark Graphics Corporation, Houston, TX (US);

Inventors:

Robello Samuel, Cypress, TX (US);

Manish Kumar Mittal, Cypress, TX (US);

Rishi Adari, Houston, TX (US);

Nanda Kumar Nookala, Houston, TX (US);

Assignee:

Landmark Graphics Corporation, Houston, TX (US);

Attorneys:
Primary Examiner:
Int. Cl.
CPC ...
E21B 44/04 (2006.01); E21B 37/00 (2006.01); G01V 20/00 (2024.01); G08B 21/18 (2006.01);
U.S. Cl.
CPC ...
E21B 44/04 (2013.01); E21B 37/00 (2013.01); G08B 21/182 (2013.01); E21B 2200/20 (2020.05); G01V 20/00 (2024.01); G01V 2210/62 (2013.01); G01V 2210/66 (2013.01);
Abstract

The disclosure presents processes and methods for determining a packoff event at a location in a borehole undergoing a drilling operation. The packoff event can be represented by a packoff risk indicator (PRI) that presents, for example, a percentage risk of the packoff event occurring. The PRI can be utilized to initiate a remediation operation prior to the packoff event becoming more severe, such as a stuck drill string. In some aspects, the generation of the PRI can utilize an uncertainty model to provide a range of input parameters and an uncertainty parameter used by other systems to evaluate the risk of the potential packoff event has on borehole operations. In some aspects, the generation of the PRI can utilize machine learning algorithms or deep neural network algorithms to pre-process the input parameters to improve the accuracy of the PRI and of the models used to generate the PRI.


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