An efficient distribution method for nonlinear two-phase flow in highly heterogeneous multidimensional stochastic porous media

TL;DR

This paper presents FROST, a novel, efficient method for estimating the probability distribution of wetting saturation in highly heterogeneous porous media, significantly reducing computational effort compared to full Monte Carlo simulations.

Contribution

The paper introduces FROST, a new physical understanding-based method for stochastic two-phase flow, with an accelerated variant and quantile computation capabilities, improving efficiency and robustness.

Findings

FROST accurately estimates saturation distributions compared to Monte Carlo.

The accelerated FROST variant simplifies injection time statistics with minimal loss of accuracy.

Quantile computation within FROST enables robust uncertainty assessment.

Abstract

In the context of stochastic two-phase flow in porous media, we introduce a novel and efficient method to estimate the probability distribution of the wetting saturation field under uncertain rock properties in highly heterogeneous porous systems, where streamline patterns are dominated by permeability heterogeneity, and for slow displacement processes (viscosity ratio close to unity). Our method, referred to as the frozen streamline distribution method (FROST), is based on a physical understanding of the stochastic problem. Indeed, we identify key random fields that guide the wetting saturation variability, namely fluid particle times of flight and injection times. By comparing saturation statistics against full-physics Monte Carlo simulations, we illustrate how this simple, yet accurate FROST method performs under the preliminary approximation of frozen streamlines. Further, we inspect the performance of an accelerated FROST variant that relies on a simplification about injection time statistics. Finally, we introduce how quantiles of saturation can be efficiently computed within the FROST framework, hence leading to robust uncertainty assessment.