Video: Converting Time Series Data into Images: An Innovative Approach to Detect Abnormal Behavior of Progressive Cavity Pumps Deployed in Coal Seam Gas Wells
- Fahd Saghir (University of Adelaide) | M. E. Gonzalez Perdomo (University of Adelaide) | Peter Behrenbruch (University of Adelaide)
- Document ID
- Society of Petroleum Engineers
- Publication Date
- Document Type
- 2019. Copyright is retained by the author. This presentation is distributed by SPE with the permission of the author. Contact the author for permission to use material from this video.
- Machine Learning, Coal Seam Gas, Progressive Cavity Pump, Analytics, Artificial Lift
- 1 in the last 30 days
- 3 since 2007
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Progressive Cavity Pumps (PCPs) are the predominant form of artificial lift method deployed by Australian operators in Coal Seam Gas (CSG) wells. With over five thousand CSG wells  operating in Queensland's Bowen and Surat Basins, managing and maintaining PCP supported production becomes a significant challenge for operators. Especially when these pumps face regular failures due to the production of coal fines.
It is possible to gauge the holistic production performance of PCPs with the aid of real-time data, as this allows for pro-active and informed management of artificially lifted CSG wells. Based on data obtained from two (2) CSG operators, this paper will discuss in detail how features extracted from time series data can be converted to images, which can then aid in autonomously detecting abnormal PCP behavior.