Data-driven methods for flow and transport in porous media: a review

TL;DR

This review discusses recent progress and challenges in applying data-driven methods to analyze flow and transport in porous media, highlighting their potential to overcome computational and modeling limitations.

Contribution

It provides a comprehensive analysis of current data-driven approaches, their limitations, and future directions for integrating these methods with domain expertise in porous media research.

Findings

Data-driven methods can reduce computational costs.

These methods improve modeling of complex heterogeneous structures.

Synergistic integration enhances accuracy and efficiency.

Abstract

This review examined the current advancements in data-driven methods for analyzing flow and transport in porous media, which has various applications in energy, chemical engineering, environmental science, and beyond. Although there has been progress in recent years, the challenges of current experimental and high-fidelity numerical simulations, such as high computational costs and difficulties in accurately representing complex, heterogeneous structures, can still potentially be addressed by state-of-the-art data-driven methods. We analyzed the synergistic potential of these methods, addressed their limitations, and suggested how they can be effectively integrated to improve both the fidelity and efficiency of current research. A discussion on future research directions in this field was conducted, emphasizing the need for collaborative efforts that combine domain expertise in physics and advanced computationald and data-driven methodologies.