Predictive Maintenance for Rod Pumps
- Patrick Bangert (algorithmica technologies Inc.) | Sayed Sharaf (Tatweer Petroleum)
- Document ID
- Society of Petroleum Engineers
- SPE Western Regional Meeting, 23-26 April, San Jose, California, USA
- Publication Date
- Document Type
- Conference Paper
- 2019. Society of Petroleum Engineers
- Machine Learning, Predictive Maintenance, Rod Pump, Preventative Maintenance, Dynamometer Card
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- 308 since 2007
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Approximately 20% of all oilwells in the world use a beam pump to raise crude oil to the surface. The proper maintenance of these pumps is thus an important issue in oilfield operations. We wish to know, preferably before the failure, what is wrong with the pump. Maintenance issues on the downhole part of a beam pump can be reliably diagnosed from a plot of the displacement and load on the traveling valve; a diagram known as a dynamometer card. We demonstrate in this paper that this analysis can be fully automated using machine learning techniques that teach themselves to recognize various classes of damage in advance of the failure. We use a dataset of of 35292 sample cards drawn from 299 beam pumps in the Bahrain oilfield. We can detect 11 different damage classes from each other and from the normal class with an accuracy of 99.9%. This high accuracy makes it possible to automatically diagnose beam pumps in real-time and for the maintenance crew to focus on fixing pumps instead of monitoring them, which increases overall oil yield and decreases environmental impact.
|File Size||976 KB||Number of Pages||12|
Bezerra, Marco A.D.; Schnitman, Leizer; Barreto, M. de A.Filho; de Souza, J.A.M. Felippe (2009): Pattern Recognition for Downhole Dynamometer Card in Oil Rod Pump System using Artificial Neural Networks. ICEIS 2009 - Proceedings of the 11th International Conference on Enterprise Information Systems, Volume AIDSS, Milan, Italy, May 6-10, 2009.
de Souza, J.A.M. Felippe; Bezerra, Marco A.D.; Schnitman, Leizer; Barreto, M. de A.Filho (2009): Artificial Neural Networks for Pattern Recognition in Oil Rod Pump System Anomalies. Proceedings of the 2009 International Conference on Artificial Intelligence, ICAI 2009, July 13-16, 2009, Las Vegas Nevada, USA.