Application of Exploratory Data Analytics EDA in Coal Seam Gas wells with Progressive Cavity Pumps PCPs

Abstract

Artificial lift methods typically drive Coal Seam Gas (CSG) wells, and Progressive Cavity Pump (PCP) isthe preferred method of lift with Australian CSG operators. CSG wells in Australia are typically equippedwith necessary instrumentation and automation systems to provide real-time data gathering for monitoringand control purposes. Real-time data gathered from CSG wells presents an opportunity to better understandPCP performance by identifying anomalous pump behavior. However, before undertaking any real-time analytics exercise, it is pertinent to carry out Exploratory DataAnalytics (EDA) to understand time series data behavior and extract relevant features; and this exerciseis particularly important with multi-variate data sets. Obtaining significant data features from multivariatetime series data can help define which analytics and machine learning methods could be exploited to analyzePCP performance in near real time. This paper will discuss EDA methodologies that can help streamline time-series data normalization andfeature extraction techniques. A three (3) year time-series dataset, gathered from forty-two (42) CSG wells,will be used to showcase EDA methodologies utilized as part of this research. All EDA activities coveredin this paper are based on the Python programming language and its supporting libraries.