Abstract: Oil and gas production forecasting is a critical technical approach for optimizing field development strategies and enhancing recovery efficiency. This study systematically reviews the theoretical framework of production decline analysis, conducts a comparative evaluation of conventional empirical models and analytical methods in terms of their theoretical foundations, applicability, and limitations, and highlights innovative applications of machine learning in production prediction for complex reservoirs. The analysis suggests that: ① Traditional methods maintain robustness in conventional reservoirs but exhibit constrained performance in unconventional plays due to strong heterogeneity and multiphase flow nonlinearity; ② Data-driven models demonstrate superior predictive capabilities in unconventional reservoirs through automated feature extraction and spatiotemporal correlation modeling; ③ Physics-informed hybrid models effectively integrate data-driven advantages with physical mechanisms, delivering enhanced reliability under complex conditions and long-term forecasting. The study concludes that artificial intelligence significantly improves prediction accuracy and reliability, with machine learning and deep learning offering novel technical support for complex reservoir development. However, challenges persist in engineering applications, particularly in real-time computation and model interpretability, necessitating further interdisciplinary research to advance intelligent and sustainable development in the oil and gas industry.

Key words: Production forecasting; Decline model; Machine learning; Deep learning; Application analysis