| description abstract | Permeability of subsurface porous media is one of the primary factors that affect fluid transport in porous rock. However, accurate prediction of rock permeability is a challenging task due to its intricate pore network. Development of digital rocks provides an effective approach to reveal and characterize the pore network. In this paper, a combination of digital rock petrophysics and ensemble machine learning (ML) models is proposed to improve the permeability prediction of subsurface porous media. The permeability of the numerically generated porous samples as outputs was determined by the lattice Boltzmann method (LBM). The five most important parameters (porosity, tortuosity, fractal dimension, average pore diameter, and coordination number) were selected as inputs for the permeability prediction. To improve the accuracy, feature selection and ML methods comparisons were conducted. Three feature selection methods based on expert knowledge, correlation coefficient, and importance score were compared. Moreover, a comparison was performed on six ML methods (support vector machine, artificial neural network, decision tree, random forest, gradient-boosting machine, and Bayesian ridge regression) that were optimized by particle swarm optimization (PSO). The results indicated that (1) the feature selection based on the expert knowledge obtained a higher performance than the groups based on the correlation coefficient and importance score, implying the importance of expert knowledge on feature selection, and thus on ML performance; (2) artificial neural network with hyperparameter tuning achieved the best performance in predicting permeability; and (3) the optimized ML method outperformed the empirical equations in predicting permeability. In conclusion, this study provides a fast and reliable approach predicting permeability of subsurface porous media based on numerically generated porous images. Moreover, the proposed framework can be further extended to determine other petrophysical properties, for example, the relative permeability and thermal conductivity. | |
