Complexity Reduction of Multi-Phase Flows in Heterogeneous Porous Media

Abstract

Abstract In this paper, we apply mode decomposition and interpolatory projection methods to speed up simulations of two-phase flows in highly heterogeneous porous media. We propose intrusive and non-intrusive model reduction approaches that enable a significant reduction in the dimension of the flow problem size while capturing the behavior of the fully-resolved solutions. In one approach, we employ the dynamic mode decomposition (DMD) and the discrete empirical interpolation method (DEIM). This approach does not require any modification of the reservoir simulation code but rather postprocesses a set of global snapshots to identify the dynamically-relevant structures associated with the flow behavior. In a second approach, we project the governing equations of the velocity and the pressure fields on the subspace spanned by their proper orthogonal decomposition (POD) modes. Furthermore, we use DEIM to approximate the mobility related term in the global system assembly and then reduce the online computational cost and make it independent of the fine grid. To show the effectiveness and usefulness of the aforementioned approaches, we consider the SPE 10 benchmark permeability field and present a variety of numerical examples of two-phase flow and transport. The proposed model reduction methods can be efficiently used when performing uncertainty quantification or optimization studies and history matching.