Coalbed Methane Reservoir Simulation and Uncertainty Analysis with Artificial Neural Networks

This paper presents the utilization of a newly developed technique for development of a proxy model in reservoir simulation studies to be used in uncertainty analysis on a Coalbed Methane (CBM) reservoir. This technique uses Artificial Neural Networks (ANN) in order to build a Surrogate Reservoir Model (SRM). An SRM is a replica of the full-field reservoir model that mimics the behavior of the reservoir. A small number of realizations of the reservoir are required to develop the SRM. This is a key difference between the SRM technique and other techniques in the literature, such as developing a Response Surface Model using Experimental Design technique or using Reduced Models. Once trained, SRMs can make thousands of simulation runs in a matter of seconds. The high speed of the SRM enables the engineer to exhaustively explore the solution space and perform uncertainty analysis. During the development process of SRM, Key Performance Indicators (KPIs) are identified. KPIs are the reservoir parameters that have the most inuence on the desired objective of the simulation study.