A Two-Branch Neural Network for Gas-Bearing Prediction Using Latent Space Adaptation for Data Augmentation—An Application for Deep Carbonate Reservoirs

Abstract

Deep learning has been utilized for gas-bearing prediction in recent years due to its powerful nonlinear fitting capacity; however, the scarcity of log labels severely restricts its application. Given this key issue, this research proposes a two-branch neural network for gas-bearing prediction that employs domain-adapted data augmentation. In the first step, an unsupervised domain adaptive approach based on latent space is used to augment the dataset. The variational deep embedding (VaDE) is trained to map the original seismic data to a roughly orthogonal latent space and then expand the labeled dataset by transforming the latent nuisance attributes. In the second stage, a two-branch neural network is constructed using long short-term memory (LSTM) and convolutional neural network (CNN), which learn the gas-bearing features from 1-D time and 2-D time traces, respectively. Finally, the augmented dataset is employed to train the two-branch neural network that is subsequently used for the detection of gas-bearing deep marine formations in the Sichuan Basin of China. The root mean square error (RMSE) and R-square ( $R^{2}$ ) of the predicted probabilities and labels for the test set are 0.03 and 0.99, respectively, and the predicted gas-bearing profile is consistent with known geology knowledge, demonstrating the effectiveness of the proposed method.