The coal seam gas (CSG) industry is challenged to maximise gas recoverability while minimising the operational and maintenance costs of subsurface artificial lift technologies. Data acquisition is critical in both optimising assets and characterising early failure. Large data flows must be analysed for variations from normal operation to identify problems when and before they occur; automated processes are essential.Exception based surveillance (EBS) utilises a systematic approach for automatically monitoring asset data (particularly continuous time-series data) for violations of acceptable operating conditions, or for trends that show deviations from expectations. EBS also provides diagnostics that assist in identifying the root cause of exceptions and providing suggestions for remediation.Root cause analysis (RCA)-derived failure mechanisms aid in accurately identifying the source of repeatable failures in wells. Key performance indicators, measured or calculated from continuous data sources allow operators to observe operational trends, notice variations to long-term averages and begin investigatory procedures before failures occur. The information can be used to avoid failure or develop prevention strategies.Progressive Cavity Pumps (PCP) are one of the preferred artificial lift methods for CSG production in both the Bowen and Surat basins in Australia. This paper discusses the innovative use of algorithms and software, in an automated exception based system to diagnose and detect PCP failure in a CSG environment. Using only four different parameters, this paper aims to maximise the value of information to monitor the operating conditions in each well and minimise operational costs by deciphering an in-situ volumetric efficiency variable and a frictional coefficient in parallel to the exisiting parameters without the need for additional monitoring sensors.

Four major operators with significant coal seam gas (CSG) holdings in Queensland, Australia identified Progressing Cavity Pumps (PCPs) as one of the preferred artificial lift technologies to deliquify coal seams and allow the adsorbed coal seam gas to be produced to surface. Each operator also acknowledged the need to accelerate their understanding of PCP technology in order to effectively overcome the unique challenges of this application. To address this need, since early 2012, these operators have met semi-annually to share reliability data on their PCP installations and the experience gained in their field operations. It was found that, through these semi-annual meetings, learnings were accelerated. Possible solutions to key challenges were identified during the meetings, trialled in the field and evaluated for success. The results were then shared with the other operators. This paper describes the process that was used to highlight challenges and identify potential solutions. It also presents examples of solutions that proved to be effective. This collaborative effort shows that challenges can be overcome in a shortened time period, if experience and operational data is shared among operators in similar applications.