Performance analysis of artificial lift systems deployed in natural gas wells: A time-series analytics approach

Abstract

In Coal Seam Gas (CSG) production, Artificial Lift (AL) systems comprising various downhole pumps produce coal-fine-laden water. Over time, the accumulation of coal fines results in mechanical failures of such pumps, which leads to a loss in natural gas production. This problem is exacerbated by CSG operators having to manage thousands of wells in their day-to-day operations. The reason for having such an exuberant number of natural gas wells is due to the fact that CSG reservoirs have a short production cycle when compared to conventional reservoirs. Therefore, thousands of CSG wells must operate simultaneously to sustain natural gas volumes that satisfy domestic energy demand, and help meet contractual export obligations.To manage a large fleet of CSG wells, operators rely on Petroleum and Surveillance Engineers to mitigate production issues and advise timely corrective actions. Current methods of monitoring CSG wells involve observing real-time trends and alarms from Supervisory Control and Data Acquisition (SCADA) systems. However, managing a large fleet of wells with a monitoring-only approach can be detrimental to AL operations. Methods such as Exception Based Surveillance have been used to improve engineers' workload, but such methods are informative at best. In addition, they do not provide early indications of changed downhole pump performance. To improve how AL systems are monitored in CSG operation, we propose a novel well surveillance method that can help mitigate pump failure and provide engineers with insightful information to take corrective actions.In this work, we will present an innovative time-series analytics approach that examines the performance of AL systems in near real-time and helps Well Surveillance and Production Engineers make timely decisions that aid in mitigating pump failures. We used time-series data from 448 CSG wells to build the streaming analytics methodology that autonomously detects various performance states of downhole pumps during CSG production. Furthermore, we successfully detected the onset of AL systems failures, such as solids build-up, gas intake, high torque and pump degradation. We validated our solution on a separate set of 428 wells and were able to show reliable detection of detrimental events on live data from producing natural gas wells. This solution is currently deployed by 2 CSG operators where real-time notifications aid Petroleum and Well Surveillance Engineers with proactively managing AL systems across multiple CSG assets.