Reservoir Simulation and Uncertainty Analysis of Enhanced CBM Production Using Artificial Neural Networks

Abstract

Abstract Coalbed methane is becoming one of the major natural gas resources. CO2 injection into CBM reservoirs is used as an effective method for CBM production enhancement (ECBM) and for long term sequestration of CO2 (CO2Seq). Reservoir simulation is used regularly for building representative ECBM and CO2Seq models. Given the wide range of uncertainties that are associated with the geological models (that forms the foundation of any reservoir simulation), comprehensive analysis and uncertainty quantification of ECBM and CO2Seq models become very time consuming if not impossible. This paper addresses the uncertainty quantification of a complex ECBM reservoir model. We use a new technique by developing a Surrogate Reservoir Model (SRM) that can accurately mimic the behavior of the commercial reservoir model. Upon validation of SRM, we perform Monte Carlo Simulation (MCS) in order to quantify the uncertainties associated with the geological (CBM) model. Performing MCS requires thousands of simulation runs that can be performed easily once the SRM is developed. Key Performance Indicators (KPI) of the simulation model are identified to help reservoir engineers concentrate on the most influential parameters on the model’s output when studying the reservoir and performing uncertainty analysis. Unlike conventional geo-statistical techniques that require hundreds of runs to build a response surface or a proxy model, building an SRM only requires a few simulation runs.