Predicting porosity, permeability, and tortuosity of porous media from images by deep learning

#Abstract: Convolutional neural networks (CNN) are utilized to encode the relation between initial configurations of obstacles and three fundamental quantities #in porous media: porosity (𝜑), permeability (k), and tortuosity (T). The two-dimensional systems with obstacles are considered. The fluid flow through a porous #medium is simulated with the lattice Boltzmann method. The analysis has been performed for the systems with 𝜑∈(0.37,0.99) which covers five orders of magnitude a #span for permeability 𝑘∈(0.78,2.1×105) and tortuosity 𝑇∈(1.03,2.74). It is shown that the CNNs can be used to predict the porosity, permeability, and tortuosity #with good accuracy. With the usage of the CNN models, the relation between T and 𝜑 has been obtained and compared with the empirical estimate.

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Recommended citation: Krzysztof M. Graczyk, Maciej Matyka, Sci Rep 10, 21488 (2020).