A Symmetric Deep Learning Approach for Dynamic Reserve Evaluation of Tight Sandstone Gas Wells

Traditional dynamic storage calculation methods face challenges such as difficult data acquisition and prolonged testing periods. To address the industry’s need for rapid yet accurate estimation of single-well dynamic reserves in tight sandstone gas formations, a deep learning architecture combining convolutional neural network (CNN) and long short-term memory (LSTM) network is proposed. This model enables fast and accurate reserve evaluation, outperforming other machine learning methods in overall capability while achieving a symmetric improvement in both training efficiency and prediction accuracy—reaching up to 95.9%. Based on this model, dynamic reserves of gas wells in the Sulige Gas Field were predicted. The single-well dynamic reserve test showed a relative error of less than 10%, and the method demonstrated strong stability and high precision in localized multi-well group tests, with errors distributed symmetrically within a narrow margin. All results satisfy engineering standards. The feasibility of the method has been verified, proving it can deliver fast and accurate gas well dynamic reserve predictions, greatly reduce evaluation costs, and enhance work efficiency.