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:
Aug. 03, 2021

Filed:

Mar. 16, 2017
Applicants:

University of Houston System, Houston, TX (US);

Frontender Corporation, Houston, TX (US);

Halliburton, Houston, TX (US);

Inventors:

Supriya Gupta, Houston, TX (US);

Michael Nikolaou, Houston, TX (US);

Luigi Saputelli, Houston, TX (US);

Cesar Bravo, Richmond, TX (US);

Assignees:

University of Houston System, Houston, TX (US);

Frontender Corporation, Houston, TX (US);

Halliburton, Houston, TX (US);

Attorneys:
Primary Examiner:
Int. Cl.
CPC ...
E21B 47/008 (2012.01); G01R 31/34 (2020.01);
U.S. Cl.
CPC ...
E21B 47/008 (2020.05); G01R 31/34 (2013.01);
Abstract

The electrical submersible pump (ESP) is currently the fastest growing artificial-lift pumping technology. Deployed across 15 to 20 percent of oil-wells worldwide, ESPs are an efficient and reliable option at high production volumes and greater depths. However, ESP performance is often observed to decline gradually and reach the point of service interruption due to factors like high gas volumes, high temperature, and corrosion. The financial impact of ESP failure is substantial, from both lost production and replacement costs. Therefore, ESP performance in extensively monitored, and numerous workflows exist to suggest actions in case of break-downs. However, such workflows are reactive in nature, i.e., action is taken after tripping or failure. Therefore, a data-driven analytical framework is proposed to advance towards a proactive approach to ESP health monitoring based on predictive analytics to detect impending problems, diagnose their cause, and prescribe preventive action.


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