VP and VS prediction from digital rock images using a combination of U-Net and convolutional neural networks

Predicting elastic parameters based on digital rock images is an interesting application of a convolutional neural network (CNN), which can improve the efficiency of prediction. Predicting elastic parameters by a conventional CNN. which is used for image classification such as LeNet and AlexNet, lacks geophysical constraints, and its accuracy in predicting elastic parameters is poor, with limited training data available. The combination of a U-Net and a convolutional neural network (CUCNN) is proposed to predict the elastic parameters from digital rock images with limited training data. In CUCNN, the rock matrix and pore types segmented from gray-scale images are treated as constraints that induce the convolutional kernels to extract the global as well as the local-scale rock features. The loss function, designed in a composite form to accelerate the convergence speed, contains the segmentation error and elastic parameters predicted from the gray-scale images. By adding geophysical constraints to the CNN. an implicit representation from the gray-scale image to the elastic parameters can be gained, which can improve the accuracy and efficiency of parameter prediction. Our method was tested using training and verification data derived from 1800 2D image slices of Berea sandstone samples, and the results were compared against the CNN model. The V-P and V-S were calculated by the finite-element method as the control to test the performance of both models. Our results show that CUCNN's R-2 score is 0.84, which increased by as much as 0.21 compared to the conventional CNN.