Abstract
Two new, non-intrusive reduced order frameworks for the faster modelling of gas reservoirs with time-varying production are presented and compared. The first method is an extension of a method using proper orthogonal decomposition (POD) in conjunction with radial basis functions (RBFs) that has previously been applied to predicting the performance of oil reservoirs undergoing a constant rate waterflood. The second method uses an autoencoder rather than RBFs to estimate the flow dynamics (pressure distributions) in hyperspace for unseen cases. Both frameworks are 'trained' using sample outputs from off-line, commercial reservoir simulations of a realistic heterogeneous gas reservoir with time-varying production controls typical of gas field operation. These controls include time-varying rate and switching between bottom hole pressure and rate control as well as cases where wells get shut-in. Both POD-based models produce reasonable forecasts of the reservoir performance for new unseen/prediction cases and are between 0.22 and 300 times faster than conventional simulation, including the time spent performing training simulations with conventional simulation solutions. The POD-RBF models are more accurate and consistent with reference commercial simulation outputs than the POD-AE models. In addition, the POD-AE models required more trial and error to set up as the number of hidden layers needed, depends on the particular scenario being modelled. There is no ab initio way of predicting the best number of layers for a given type of scenario. This makes them less suitable for practical application by reservoir engineers. Overall the POD-RBF framework is the most robust and accurate of the two methods.
NSTL
Elsevier