Flowing bottom-hole pressure (FBHP) is a key metric for optimising coal seam gas well performance and enhancement of production. Downhole pressure gauges are increasingly being used to measure the FBHP. However, they are impractical, expensive, and complex to install and maintain. Consequently, reliable measurement and prediction of the FBHP, required to forecast well production, remains a challenge. This paper aims to predict the flowing bottom-hole pressure in coal seam gas wells by taking advantage of the temporal data and advanced analytics. Data-driven models have been developed to predict the FBHP by leveraging the temporal data gathered at the surface in order to control the performance of the wells. The data used in the study was obtained from five coal seam gas wells containing seven sensor measurements gathered over 15 -18 months production period. For the prediction of FBHP, we applied linear regression and neural network-based approaches. Overall, neural networks resulted in the best predictions with the root mean squared error (RMSE) within 198 - 450 kPa for the five wells.

Keywords:
production monitoring, Reservoir Surveillance, production control, coalbed methane, neural network, coal seam gas well, production logging, complex reservoir, regression, coal bed methane
Subjects:
Well & Reservoir Surveillance and Monitoring, Formation Evaluation & Management, Unconventional and Complex Reservoirs, Information Management and Systems, Production logging, Drillstem/well testing, Coal seam gas, Neural networks
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2019, Unconventional Resources Technology Conference (URTeC)
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