Accurate production forecasting of tight gas reservoirs plays a critical role in effective gas field development and management. Recurrent-based deep learning models typically require extensive historical production data to achieve robust forecasting performance. This paper presents a novel approach that integrates transfer learning with the neural basis expansion analysis time series (N-BEATS) model to forecast gas well production, thereby addressing the limitations of traditional models and reducing the reliance on large historical datasets. The N-BEATS model was pre-trained on the M4 competition dataset, which consists of 100,000 time series spanning multiple domains. Subsequently, the pre-trained model was transferred to forecast the daily production rates of two gas wells over short-term, medium-term, and long-term horizons in the S block of the Sulige gas field, China’s largest tight gas field. Comparative analysis demonstrates that the N-BEATS transfer model consistently outperforms the attention-based LSTM (A-LSTM) model, exhibiting greater accuracy across all forecast periods, with root mean square error improvements of 19.5%, 19.8%, and 26.8% of Well A1 for short-, medium-, and long-term horizons, respectively. The results indicate that the pre-trained N-BEATS model effectively mitigates the data scarcity challenges that hinder the predictive performance of LSTM-based models. This study highlights the potential of the N-BEATS transfer learning framework in the petroleum industry, particularly for production forecasting in tight gas reservoirs with limited historical data.