Abstract: During the development of middle and deep gas reservoirs in South Sichuan, conventional reservoir engineering methods—such as fracture propagation, stress-induced analysis, and numerical simulation—render productivity prediction of infilling wells laborious and ineffective in addressing variations in production capacity across different production stages, with stringent application conditions. In order to quickly and accurately predict the production capacity of infilling wells, this study classifies the “three-stage” declining trend observed in the production pressure curves of existing wells into: (1) a drastic decline period, regarded as the initial water production stage; (2) a rapid decline period; and (3) a slow decline period, both considered part of the later gas production stage. The Grey Wolf Optimizer(GWO) algorithm, a fast optimization algorithm with adaptive capabilities and an information feedback mechanism, is applied for hyperparameter optimization of the Long Short-term Memory (LSTM) neural network. Two stage-specific models were constructed, with the number of hidden layer neurons, dropout rate, and batch size determined by the optimal solutions obtained via GWO. The number of iterations was selected based on the loss curve and performance metric curve, while a linear warm-up strategy was used to dynamically adjust the learning rate, facilitating high-speed training and the formation of a staged productivity prediction model. Example studies show that the GWO-optimised LSTM neural network model achieves rapid convergence with a preset learning rate of 0.002 and 450 iterations, ultimately reaching a performance index of 0.923. Compared to the conventional LSTM neural network model, the average absolute errors during the early and later stages are reduced by 1.290 m³/d and 0.213 × 10⁴ m³/d, respectively. Compared with numerical simulation fitting results, the average absolute error in gas production prediction is reduced by 0.24 × 10⁴ m³/d. Therefore, the improved LSTM neural network model demonstrates excellent performance in capacity prediction across different production stages, and the stage-specific productivity variations in infilling wells within middle and deep shale gas reservoirs in South Sichuan. This provides a theoretical foundation for productivity prediction methods of infilling wells.

Key words: shale-gas, infilling well, neural networks, GWO, productivity prediction