Localized model order reduction in porous media flow simulation

Abstract

This paper introduces a new approach to construct an efficient reduced order model for fluid flow simulation and optimization in porous media. For nonlinear systems, one of the most common methodology used is the proper orthogonal decomposition (POD) combined with discrete empirical interpolation method (DEIM) due to its computational efficiency and good approximation. Whereas regular POD-DEIM approximates the fine scale model with just one single reduced subspace, the localized POD (LPOD) and localized DEIM (LDEIM) are constructed by computing several local subspaces. Each subspace characterize a period of solutions in time and all together they not only can approximate the high fidelity model better, but also can reduce the computational cost of simulation. LPOD and LDEIM use classification approach to find these regions in the offline computational phase. After obtaining each class, POD and DEIM is applied to construct the basis vectors of the reduced subspace. In the online phase, at each time step, the reduced states and nonlinear functions will be used to find the most representative basis vectors for POD and DEIM without requiring fine scale information. The advantages of LPOD and LDEIM are shown in a numerical example of two phase flow in porous media.