Modeling of permeability impairment dynamics in porous media: A machine learning approach

Abstract

The prediction of clogging and permeability impairment dynamics in porous media is crucial for the optimization of various industrial and natural processes. This paper presents a novel machine learning-based approach for predicting the dynamics of throat clogging and permeability impairment due to fine migration within realistic porous media under varying hydro-physical conditions. A Computational Fluid Dynamics-Discrete Element Method (CFD-DEM) numerical framework, employing a four-way coupling scheme, was used to generate the data for training and validation of the Machine Learning Model (MLM). One hundred and twenty distinct CFD-DEM simulations were performed to generate over 190,000 data points, at throat level, for the training of the MLM. Simulation cases encompassing ranges of porous media geometry, fine particle size, flow velocity, fine particle concentration, grains surface roughness, and fines and grains zeta potential. Geometries of porous media were extracted from high-resolution 3D images of natural sand obtained using micro-computed tomography imaging. The developed MLM predicts the temporal evolution of clogged throats and permeability impairment. The MLM was established by connecting three Machine Learning Sub-Models (MLSMs). The first is a throat-classification MLSM; which classifies the throats based on their location and size to identify clogged throats. Subsequently, a pore volume regression MLSM is implemented to identify the pore volume at which each clogged throat becomes clogged. Finally, the permeability impairment regression MLSM predicts the permeability reduction based on the clogged throat's information and pore volumes associated with clogging. The throats classification in the final MLM showed an accuracy of 95% in predicting clogged throats when compared to direct CFD-DEM simulations whereas the prediction of the permeability impairment had an R-squared value of 0.99. The MLM developed in this study stands as a robust framework for precisely quantifying key microscale parameters; where its predictions were used to quantify the significance of altering the hydro-physical parameters on the microscale parameters of the clogging dynamics. The proposed MLM provides an accurate and fast prediction of porous media clogging and permeability impairment dynamics, with potential applications in various industries, including oil and gas, environmental engineering, and material science.