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Proceedings Paper

Machine learning application to hydraulic fracturing
Author(s): Giovanni Tamez; Danytza Castillo; Aaron Colmenero; Jorge A. Ayala; Colleen P. Bailey
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Paper Abstract

Hydraulic fracturing has greatly impacted the oil and gas industry and is a large component of future oil production. Proper operation is dependent on supervisors monitoring data for signs of dangerous pressure spikes. Human error becomes a large factor in processing such a large amount of data in real time. The system proposed in this paper is able to read and interpret the data from a well to make accurate predictions on when potential pressure spikes will occur within the well, saving time and money on projects that are pushed to the limit.

Paper Details

Date Published: 13 May 2019
PDF: 6 pages
Proc. SPIE 10989, Big Data: Learning, Analytics, and Applications, 109890A (13 May 2019); doi: 10.1117/12.2518996
Show Author Affiliations
Giovanni Tamez, Univ. of North Texas (United States)
Danytza Castillo, Univ. of North Texas (United States)
Aaron Colmenero, Univ. of North Texas (United States)
Jorge A. Ayala, Univ. of North Texas (United States)
Colleen P. Bailey, Univ. of North Texas (United States)

Published in SPIE Proceedings Vol. 10989:
Big Data: Learning, Analytics, and Applications
Fauzia Ahmad, Editor(s)

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