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13 May 2019 Machine learning application to hydraulic fracturing
Giovanni Tamez, Danytza Castillo, Aaron Colmenero, Jorge A. Ayala, Colleen P. Bailey
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Proceedings Volume 10989, Big Data: Learning, Analytics, and Applications; 109890A (2019) https://doi.org/10.1117/12.2518996
Event: SPIE Defense + Commercial Sensing, 2019, Baltimore, MD, United States
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.
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Giovanni Tamez, Danytza Castillo, Aaron Colmenero, Jorge A. Ayala, and Colleen P. Bailey "Machine learning application to hydraulic fracturing", Proc. SPIE 10989, Big Data: Learning, Analytics, and Applications, 109890A (13 May 2019); https://doi.org/10.1117/12.2518996
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KEYWORDS
Machine learning
Neural networks
Data processing
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