Quantifying Uncertainty in Gas-Bearing Prediction Using Multicomponent Seismic Data Derived From a Parallel Linked Bayesian Neural Network

Longitudinal and converted shear wave seismic data depict unique and abundant seismic response characteristics of gas reservoirs and help to elucidate the multicomponent seismic attributes required for predicting gas-bearing distributions. Although machine learning (ML) methods, including deep learning (DL), show potential for predicting gas-bearing reservoirs, most conventional ML (CML) methods combine longitudinal and converted shear wave seismic attributes as inputs for learning; however, this approach cannot fully extract the unique characteristics of gas reservoirs. In addition, the prediction results are deterministic, with no room for uncertainty quantification. To address this issue, we developed a parallel linked Bayesian neural network (PLBNN) model for multicomponent seismic gas-bearing prediction and uncertainty quantification. First, we used unsupervised ML methods to optimize longitudinal and converted shear wave seismic attributes, reduce redundant information, and extract feature data. Then, we separately input the obtained feature data of longitudinal and converted shear waves into the feature-extraction layer of the PLBNN to extract high-dimensional features of the target gas reservoir. The extracted features were then fused through the feature-fusion module for gas reservoir prediction and uncertainty quantification. Finally, we input the uncertainty quantified using the Bayesian approximation method of Monte Carlo (MC) dropout into the network framework for model parameter optimization to further improve model prediction performance and reduce uncertainty. This method exhibited excellent predictive ability under strong noise conditions when using synthetic data. When using actual data, the model was deemed suitable for gas reservoir prediction in unexplored areas. Compared with conventional deep neural network (DNN) and Bayesian neural network (BNN) models, the proposed model better utilizes the characteristics of multicomponent seismic data to predict gas-bearing distributions with higher accuracy and lower uncertainty.