Application of Dynamic-Static Neural Network Model Integrating Physical Constraints in EUR Prediction of Shale Gas Wells

Accurate estimation of estimated ultimate recovery (EUR) is critical for shale reservoir development but remains challenging due to the complex interplay of geological and production factors. This paper presents a hybrid machine learning framework that combines static geological parameters with dynamic production data to enhance EUR prediction. Key innovations include a dual physical constraint mechanism incorporating the Arps decline equation and Darcy's law, and a dynamic weighting strategy that adaptively balances static and dynamic feature contributions based on production stage. The model achieves an R² of 0.85 for wells with complete production history and 0.83 for those with limited dataa 5.7% improvement over conventional static methods in the Duvernay shale. Notably, using only 20 months of production data combined with static parameters, the model attains high prediction accuracy (<i>R</i> ² = 0.83), demonstrating strong performance even under data scarcity. This approach provides a reliable tool for EUR prediction in marginal or undeveloped oil fields, supporting informed investment decisions and optimized development.