An Intelligent Approach for Gas Reservoir Identification and Structural Evaluation by ANN and Viterbi Algorithm—A Case Study From the Xujiahe Formation, Western Sichuan Depression, China

Abstract

Gas reservoir identification using seismic data has become a major focus of geophysical exploration. This study presents a gas reservoir identification and structural evaluation method using artificial neural networks (ANNs) and the Viterbi algorithm to improve processing efficiency and evaluate gas reservoir structural control. Initial identification was conducted using deep neural networks (DNNs). Composite seismic attributes sensitive to the multi-component seismic response characteristics of gas reservoirs were obtained. Subsequently, a model expansion dataset and network hyperparameter optimization strategy were employed to assess the optimal DNN model for ReLUactivation with nine hidden layers (3–5–7–7–7–9–9–11–11–11–1). The training model was run with the three composite attributes as input to predict the gas-bearing probability distribution. Considering the importance of evaluating geological structural characteristics, an automatic horizon tracking method using the Viterbi algorithm was proposed to evaluate the structural factors of gas reservoirs. Finally, the ANN-based gas reservoir identification results were comprehensively evaluated based on structural characteristics, thus, reducing the uncertainty, or multiple solutions, predicted by mathematical methods. This scheme was successfully applied to assess synthetic and real data, demonstrating the consistency between the predicted gas reservoir areas and true situation. The effective implementation of this scheme improves processing efficiency and provides a new way to shorten the exploration cycle of a gas reservoir.