Predicting permeability from 3D rock images based on CNN with physical information

Permeability is one of the most important properties in subsurface flow problems, which measures the ability of rocks to transmit fluid. Normally, permeability is determined through experiments and numerical simulations, both of which are time-consuming. In this paper, we propose a new effective method based on convolutional neural networks with physical information (CNNphys ) to rapidly evaluate rock permeability from its three-dimensional (3D) image. In order to obtain sufficient reliable labeled data, rock image reconstruction is utilized to generate sufficient samples based on the Joshi-Quiblier-Adler method. Next, the corresponding permeability is calculated using the Lattice Boltzmann method. We compare the prediction performance of CNNphys and convolutional neural networks (CNNs). The results demonstrate that CNNphys achieves superior performance, especially in the case of a small dataset and an out-of-range problem. Moreover, the performance of both CNN and CNNphys is greatly improved combined with transfer learning in the case of an out-of-range problem. This opens novel pathways for rapidly predicting permeability in subsurface applications.