Comprehensive comparison of pore-scale models for multiphase flow in porous media

Journal Article (2019)
Author(s)

Benzhong Zhao (McMaster University)

Christopher W. MacMinn (University of Oxford)

Bauyrzhan K. Primkulov (Massachusetts Institute of Technology)

Albert J. Valocchi (University of Illinois at Urbana Champaign)

Jianlin Zhao (ETH Zürich)

Qinjun Kang (Los Alamos National Laboratory)

Kelsey Bruning (University of North Carolina)

Sebastian Geiger (Heriot-Watt University)

Ruben Juanes (Massachusetts Institute of Technology)

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DOI related publication
https://doi.org/10.1073/pnas.1901619116 Final published version
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Publication Year
2019
Language
English
Affiliation
External organisation
Journal title
Proceedings of the National Academy of Sciences of the United States of America
Issue number
28
Volume number
116
Pages (from-to)
13799-13806
Downloads counter
241

Abstract

Multiphase flows in porous media are important in many natural and industrial processes. Pore-scale models for multiphase flows have seen rapid development in recent years and are becoming increasingly useful as predictive tools in both academic and industrial applications. However, quantitative comparisons between different pore-scale models, and between these models and experimental data, are lacking. Here, we perform an objective comparison of a variety of state-of-the-art pore-scale models, including lattice Boltzmann, stochastic rotation dynamics, volume-of-fluid, level-set, phase-field, and pore-network models. As the basis for this comparison, we use a dataset from recent microfluidic experiments with precisely controlled pore geometry and wettability conditions, which offers an unprecedented benchmarking opportunity. We compare the results of the 14 participating teams both qualitatively and quantitatively using several standard metrics, such as fractal dimension, finger width, and displacement efficiency. We find that no single method excels across all conditions and that thin films and corner flow present substantial modeling and computational challenges.