Production Forecasting of Deep Shale Gas Wells Using the ROA-Transformer-Mamba Hybrid Network

Abstract The production of shale gas wells is influenced by multiple factors, including geological conditions, fracturing techniques, and production strategies. Its production patterns exhibit pronounced nonlinearity, temporal variability, and multi-scale fluctuations. In particular, deep shale gas wells often follow a complex trend characterized by “high initial production, rapid decline, and low stable output,”making it difficult for traditional decline models to accurately fit and predict their dynamic behavior.To improve the accuracy and generalization capability of production prediction in deep shale gas wells, this study proposes a hybrid deep learning model that integrates the Rabbit Optimization Algorithm (ROA), Transformer, and Mamba modules. The model first employs 1D convolution to extract local features, followed by a Transformer to capture global dependencies, and further utilizes the Mamba module to model long-term temporal dependencies and dynamic state transitions. The ROA is applied to optimize key hyperparameters, thereby enabling efficient analysis of complex nonlinear time-series data.Experimental results demonstrate that the proposed Transformer-Mamba model achieves favorable accuracy and stability, showing strong potential for generalization. It provides an effective technical foundation and valuable reference for production forecasting in deep shale gas reservoirs.