Multiscale Methods for Model Order Reduction of Non Linear Multiphase Flow Problems

TL;DR

This paper compares two multiscale model order reduction techniques, adaptive numerical homogenization and generalized multiscale basis functions, for non-linear multiphase flow problems in porous media, demonstrating their effectiveness in capturing fine-scale features.

Contribution

First practical comparison of these two multiscale reduction methods applied to non-linear, multiphase flow in porous media using a realistic benchmark dataset.

Findings

Both methods accurately capture fine-scale flow features.

Adaptive enrichment improves local solution accuracy.

Methods are computationally efficient compared to full fine-scale simulations.

Abstract

Numerical simulations for flow and transport in subsurface porous media often prove computationally prohibitive due to property data availability at multiple spatial scales that can vary by orders of magnitude. A number of model order reduction approaches are available in the existing literature that alleviate this issue by approximating the solution at a coarse scale. We attempt to present a comparison between two such model order reduction techniques, namely: (1) adaptive numerical homogenization and (2) generalized multiscale basis functions. We rely upon a non-linear, multi-phase, black-oil model formulation, commonly encountered in the oil and gas industry, as the basis for comparing the aforementioned two approaches. An expanded mixed finite element formulation is used to separate the spatial scales between non-linear, flow and transport problems. To the author's knowledge this is the first time these approaches have been described for a practical non-linear, multiphase flow problem of interest. A numerical benchmark is setup using fine scale property information from the 10$^{th}$ SPE comparative project dataset for the purpose of comparing accuracies of these two schemes. An adaptive criterion is employed in by both the schemes for local enrichment that allows us to preserve solution accuracy compared to the fine scale benchmark problem. The numerical results indicate that both schemes are able to adequately capture the fine scale features of the model problem at hand.