MSAFP-Net: An Adaptive Fusion-Driven Multi-Scale Neural Network for Peak Production Forecasting in Gas Wells

This study seeks to overcome the limitations of traditional gas well production peak forecasting approaches, particularly in the context of handling multi-dimensional features, complex dynamic behaviors, and non-linear relationships. Accordingly, a deep learning-based multi-scale adaptive fusion perception network is proposed in this work. Specifically, a time-series processing framework based on multi-scale decomposition is first constructed, where wavelet transform is employed to perform multi-scale decomposition of the gas well production data, thereby effectively extracting both long-term trends and short-term fluctuations. Subsequently, a multi-dimensional feature collaborative encoding and adaptive fusion module is designed. This module jointly encodes the multi-dimensional features in gas well production forecasting. By utilizing a dual-path PatchTST encoder, it captures the long-term trends and short-term fluctuations within the time-series data. Finally, to enhance the accuracy of gas well production peak forecasting, a personalized multi-loss training function is developed. This mixed loss function balances the robustness to outliers with the control of percentage errors, effectively mitigating the sensitivity of conventional loss functions to outliers. Systematic experiments conducted on real gas well production samples demonstrate that the proposed model outperforms existing state-of-the-art models across various evaluation metrics. Furthermore, quantile accuracy analysis reveals that the proposed model consistently achieves the highest proportion of qualifying samples under error thresholds ranging from 5% to 20% while exhibiting stable adaptability across different production scenarios, including low, typical, and high production rates. The proposed method provides a high-precision, effective solution for gas well production peak forecasting, offering reliable decision support for production process optimization.