AI-driven digital twin framework for real-time seismic reservoir monitoring and predictive hydrocarbon production optimization

Real-time reservoir monitoring and production optimization remain major challenges in modern hydrocarbon exploration and field development. Conventional reservoir management approaches rely on static geological models that are periodically updated using limited production and seismic information, often leading to delayed decision-making and increased operational uncertainty. This study proposes an AI-driven digital twin framework for real-time seismic reservoir monitoring and predictive hydrocarbon production optimization. The framework integrates 4D seismic data, well-log measurements, production history, seismic attributes, and reservoir simulation outputs within a dynamic digital twin environment. Machine learning models, including Long Short-Term Memory (LSTM) networks and Bayesian predictive analytics, are employed to continuously update reservoir conditions and forecast production performance under uncertainty. The proposed framework enables real-time monitoring of reservoir changes, prediction of fluid movement, detection of production anomalies, and optimization of production strategies. Results demonstrate that integrating digital twin technology with AI-driven seismic analysis significantly improves reservoir prediction accuracy, reduces operational uncertainty, and enhances hydrocarbon recovery efficiency. The study highlights the potential of intelligent digital twins for next-generation reservoir management and data-driven energy production systems.