A conservative multiscale method for stochastic highly heterogeneous flow

TL;DR

This paper introduces a local multiscale model reduction method for stochastic, highly heterogeneous subsurface flow problems, ensuring mass conservation and enabling efficient simulations across different permeability samples.

Contribution

It presents a parameter-independent multiscale basis generation approach that reduces computational complexity in stochastic flow simulations with rigorous convergence analysis.

Findings

High accuracy in 2D and 3D flow models

Efficient online simulation across multiple permeability samples

Robustness demonstrated in single-phase and two-phase flow problems

Abstract

In this paper, we propose a local model reduction approach for subsurface flow problems in stochastic and highly heterogeneous media. To guarantee the mass conservation, we consider the mixed formulation of the flow problem and aim to solve the problem in a coarse grid to reduce the complexity of a large-scale system. We decompose the entire problem into a training and a testing stage, namely the offline coarse-grid multiscale basis generation stage and online simulation stage with different parameters. In the training stage, a parameter-independent and small-dimensional multiscale basis function space is constructed, which includes the media, source and boundary information. The key part of the basis generation stage is to solve some local problems defined specially. With the parameter-independent basis space, one can efficiently solve the concerned problems corresponding to different samples of permeability field in a coarse grid without repeatedly constructing a multiscale space for each new sample. A rigorous analysis on convergence of the proposed method is proposed. In particular, we consider a generalization error, where bases constructed with one source will be used to a different source. In the numerical experiments, we apply the proposed method for both single-phase and twophase flow problems. Simulation results for both 2D and 3D representative models demonstrate the high accuracy and impressive performance of the proposed model reduction techniques.