{'fraction_non_alphanumeric': 0.014847161572052401, 'fraction_numerical': 0.0017467248908296944, 'mean_word_length': 5.821428571428571, 'pattern_counts': {'":': 0, '<': 0, '<?xml version=': 0, '>': 0, 'https://': 0, 'lorem ipsum': 0, 'www.': 0, 'xml': 0}, 'pii_count': 0, 'substrings_counts': 0, 'word_list_counts': {'cursed_substrings.json': 0, 'profanity_word_list.json': 0, 'sexual_word_list.json': 0, 'zh_pornsignals.json': 0}}
Multiscale Fusion of Digital Rock Images Based on Deep Generative Adversarial Networks

Computation of petrophysical properties on digital rock images is becoming important in geoscience. However, it is usually complicated for natural heterogeneous porous media due to the presence of multiscale pore structures. To capture the heterogeneity of rocks, we develop a method based on deep generative adversarial networks to assimilate multiscale imaging data for the generation of synthetic high‐resolution digital rocks having a large field of view. The reconstructed images not only honor the geometric structures of 3‐D micro‐CT images but also recover fine details existing at the scale of 2‐D scanning electron microscopy images. Furthermore, the consistency between the real and synthetically generated images in terms of porosity, specific perimeter, two‐point correlation and effective permeability reveals the validity of our proposed method. It provides an effective way to fuse multiscale digital rock images for better characterization of heterogeneous porous media and better prediction of pore‐scale flow and petrophysical properties.