Machine Learning to Predict Effective Reaction Rates in 3D Porous Media From Pore Structural Features

Abstract

Large discrepancies between well-mixed reaction rates and effective reactions rates estimated under fluid flow conditions have been a major issue for predicting reactive transport in porous media systems. In this study, we introduce a framework that accurately predicts effective reaction rates directly from pore structural features by combining 3D pore-scale numerical simulations with machine learning (ML). We first perform pore-scale reactive transport simulations with fluid-solid reactions in hundreds of porous media and calculate effective reaction rates from pore-scale concentration fields. We then train a Random Forests model with 11 pore structural features and effective reaction rates to quantify the importance of structural features in determining effective reaction rates. Based on the importance information, we train artificial neural networks with varying number of features and demonstrate that effective reaction rates can be accurately predicted with only three pore structural features, which are specific surface, pore sphericity, and coordination number. Finally, global sensitivity analyses using the ML model elucidates how the three structural features affect effective reaction rates. The proposed framework enables accurate predictions of effective reaction rates directly from a few measurable pore structural features, and the framework is readily applicable to a wide range of applications involving porous media flows.

Improved Volume-Of-Solid Formulations for Micro-Continuum Simulation of Mineral Dissolution at the Pore-Scale

“…This database will then be used to identify precisely the boundaries between the regimes and decipher the impact of pore-scale heterogeneities. Machine-learning algorithms will also be used to estimate macro-scale coefficients in a similar way that for singlephase flow and reactive transport (Menke et al, 2021;Liu et al, 2022) but extended to predict their evolution with the porosity change. Because the micro-continuum approach can also be used to simulate dissolution at the Darcy-scale (Liu et al, 1997;Ormond and Ortoleva, 2000;Golfier et al, 2002), our method can be extended to simulate flow, transport and dissolution in multi-scale porous media (Patsoukis-Dimou et al, 2020).…”

Improved Volume-of-Solid formulations for micro-continuum simulation of mineral dissolution at the pore-scale

“…This database will then be used to identify precisely the boundaries between the regimes and decipher the impact of pore-scale heterogeneities. Machine-learning algorithms will also be used to estimate macro-scale coefficients in a similar way that for single-phase flow and reactive transport [50,51] but extended to predict their evolution with the porosity change. Because the micro-continuum approach can also be used to simulate dissolution at the Darcy-scale [52,53,54], our method can be extended to simulate flow, transport and dissolution in multi-scale porous media [55].…”