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The permeability of porous rock is a crucial indicator evaluating its ability to allow liquids to pass through it. However, challenges are always encountered in measurement and prediction of permeability, including the complicated process of laboratory experiments and the refined 3D numerical reconstruction. In order to improve the efficiency and accuracy of permeability prediction, the digital twin modeling technology is proposed to generate homologous and heterogeneous digital sandstone models based on a single two-dimensional image in this paper. Comparison of the CT reconstruction with the digital twin of sandstone indicates that they are very similar in complementary solid-phase morphology of porous media, global parameters (total porosity, fractal dimension, tortuosity), and local parameters (porosity of sequence layer, pore and throat characteristics, coordination number). In addition, the permeability of CT reconstruction and ten digital twins are analyzed by using the Lattice Boltzmann Method. The results show that the permeability of digital twins is very stable and close to the CT reconstruction at three orthogonal directions. The findings in this study may provide an accurate, stable, rapid and economic method for predicting the permeability of porous media. [Display omitted] [ABSTRACT FROM AUTHOR]

The complexity and diversity of information on rock mass surfaces leads to disparities in many image properties among different rock mass surface images, including image brightness, color, contrast and pixel distribution. As a result, there is currently no unified automatic approach for extracting the rock fracture skeleton from the images. In this paper, according to common features of rock surface images and curvilinearity of fractures which are set as the extraction targets, a dark region curvilinear structure enhancement (DRCSE) algorithm for automatic rock fracture skeleton extraction is proposed. This algorithm mainly relies on the curvilinear structure enhancement filter Frangi in image pre-processing stage. Through multiple image processing, dark region contrast of rock surface images is enhanced while noise is reduced. After this process, the effect of Frangi filtering can be significantly improved. Particularly, by using Homomorphic filtering coupled Gamma transforming (HCG) to enhance dark region contrast, a new edge-entropy (EE) index is proposed to describe the contrast-enhanced image quality. Particle swarm optimization (PSO) is utilized to calculate the value of Gamma transforming parameters that maximize the EE index. The test results show that it has great advantages compared to traditional methods for rock fracture skeleton extraction. [ABSTRACT FROM AUTHOR]