Limited research work and publications are available to examine the performance of Progressive Cavity Pumps (PCP) based on machine learning methods, especially in Coal Seam Gas (CSG) operations. Previous work done in this space either focuses on exception-based surveillance on time-series data [1], or the use of machine learning to optimize completion design [2] and production [3].

This paper will discuss how data approximation and unsupervised machine learning methods can be applied to time-series data-sets, using data gathered from automation systems, to help analyze PCP performance and detect anomalous pump behavior.

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