Non-Intrusive Reduced Order Modelling for the fast simulation of gas reservoirs

Abstract

Non-Intrusive Reduced Order Modelling (NIROM) is evaluated for the prediction of gas reservoir performance, including the time evolution of the pressure distribution within the reservoir. The NIROM projects simulation solutions into hyperspace using proper orthogonal decomposition (POD), then radial basis function (RBF) interpolation is used to describe the dynamics of the fluid flow in hyperspace. POD-RBF NIROMs have been used previously for the estimation of saturation distributions over time in oil reservoirs during pressure maintenance (e.g. by gas flooding) but have not been tested for gas reservoir depletion problems. In this work, the accuracy and efficiency of the NIROM were evaluated by comparing its outputs to those obtained from conventional simulations for two test cases: a homogeneous gas reservoir model with gas rate or bottom hole pressure production controls, and a realistic heterogeneous reservoir model with a similar geological description to the Norne field. We show that the NIROM can provide accurate estimates of pressure and saturation distributions as well as production data and it runs between 4 and 40 times faster. This is despite having been implemented using an interpreted language. We show that a 99.9% energy criterion can be used for determining the number of POD basis functions for pressure and saturation distributions, but more basis functions are needed for estimating production data. In all cases, the linear radial basis function gives the best estimation.