Image-based 3D reconstruction and permeability modelling of rock using enhanced interpretable deep residual learning

Abstract

The study presents a novel deep residual learning framework with interpretability for the prediction of rock permeability. The proposed framework, termed ResNet-NiN integrates a 3D deep residual network (ResNet) with network in network (NiN) connections. A total of 1331 datasets were generated using voxel segmentation of DRP samples obtained from actual Berea Sandstone and subsequently, a percolation simulation was performed. The learning rate decay strategy of Cosine Annealing with warmup restarts is used to train the predictive model and prevent the predicted permeability from being trapped in the local optima. To improve the trust and comprehensibility of the model and visualize the ResNet-NiN architecture, we established a compound approach that leverages the internal gradient change of the neural network into the deep learning framework. This integrated approach encompasses the analysis of Integrated Gradients (IG) from the input perspective and Gradient-weighted Class Activation Mapping (Grad-CAM) from the output perspective. The experimental findings demonstrate that the developed model exhibits superiority over the typically employed 3D convolutional neural network architecture in terms of both accuracy and stability. The unified interpretability technique offers a coherent explanation for the provided sample, validating the model's capability and elucidating the factors and relationships underlying the model's prediction.