Predictive pore‐scale modeling of two‐phase flow in mixed wet media

TLDR

The study aims to predict flow properties of diverse porous media using pore‑scale modeling with realistic networks, leveraging easily acquired data rather than merely fitting experiments. They construct a geologically realistic pore‑network model of Berea sandstone, adjust its pore‑size distribution to match capillary pressure curves while preserving topology, then use it to predict single‑ and multiphase flow properties—including relative permeability and oil recovery—without further tuning, and assign contact angles to reproduce measured wettability indices. The model accurately predicts relative permeability and oil recovery for water‑wet, oil‑wet, and mixed‑wet systems, and reliably captures how these properties vary with wettability and pore‑structure differences in the field.

Abstract

We show how to predict flow properties for a variety of porous media using pore‐scale modeling with geologically realistic networks. Starting with a network representation of Berea sandstone, the pore size distribution is adjusted to match capillary pressure for different media, keeping the rank order of pore sizes and the network topology fixed. Then predictions of single and multiphase properties are made with no further adjustment of the model. We successfully predict relative permeability and oil recovery for water wet, oil wet, and mixed wet data sets. For water flooding we introduce a method for assigning contact angles to match measured wettability indices. The aim of this work is not simply to match experiments but to use easily acquired data to predict difficult to measure properties. Furthermore, the variation of these properties in the field, due to wettability trends and different pore structures, can now be predicted reliably.