Topological Data Analysis combined with Machine Learning for Predicting Permeability of Porous Media

2026-05-17

AI Summary

Predicting permeability in porous media remains challenging due to their complex microstructures, which are difficult to characterize effectively using conventional methods. This study presents a novel machine learning framework that systematically integrates topological data analysis (TDA), pore network modeling, and geometric and connectivity features to predict the permeability of synthetic porous media. By quantitatively assessing the contribution of each feature type to predictive performance, the work demonstrates that topological features extracted via TDA significantly enhance prediction accuracy. These findings establish a new paradigm for permeability modeling grounded in structural information, highlighting the value of topological descriptors in capturing essential pore-scale characteristics that govern fluid transport.

Abstract

Flow in porous media is difficult to address using standard analytical or numerical methods due to its complexity. However, since synthetic representations of porous media are easy to produce and data from physical experiments are becoming more widely available, the problem is well-suited to studies that include machine learning (ML) techniques. We discuss a number of features that can be extracted from such data, and their utility as input variables into a standard ML algorithm. These features include structural measures describing the geometry of the porous media, topological measures describing the connectivity, and network measures obtained by modeling the porous media as simplified pore networks. These features enable the prediction of the permeability of the considered (synthetic) porous materials using ML techniques that also leverage the separately computed exact permeability (ground truth). Comparing results obtained using different input variables helps develop a better understanding of the utility of various measures for predicting permeability based on the porous media structure. We show, in particular, that topological data analysis (TDA) provides a useful set of features that can be easily combined with ML to yield meaningful results.

Problem

• permeability prediction
• porous media
• topological data analysis
• machine learning
• structure-property relationship

Innovation

• Topological Data Analysis
• Machine Learning
• Permeability Prediction
• Porous Media
• Feature Extraction