A review of AI-driven seismic interpretation, digital twin technology and intelligent reservoir characterization for hydrocarbon exploration

Recent advances in artificial intelligence (AI), seismic interpretation, digital twin systems, and machine learning have significantly transformed hydrocarbon exploration and reservoir management [3, 23, 31, 35, 36]. Traditional seismic interpretation and reservoir characterization techniques often face challenges associated with large-scale data processing, interpretation uncertainty, structural complexity, and real-time reservoir monitoring limitations. This review paper presents a comprehensive overview of modern AI-driven seismic interpretation approaches and digital twin technologies applied to intelligent reservoir characterization and hydrocarbon exploration. The study reviews seismic attribute analysis, deep learning techniques, seismic inversion, digital twins, uncertainty quantification, and predictive reservoir analytics. Various machine learning algorithms including convolutional neural networks (CNNs), recurrent neural networks (RNNs), Long Short-Term Memory (LSTM) networks, and Bayesian learning approaches are analyzed in terms of their applications in fault detection, seismic facies classification, reservoir prediction, and production forecasting. Furthermore, this review examines the integration of digital twin frameworks with real-time seismic monitoring and intelligent reservoir management systems. The review highlights recent developments, key challenges, and future research opportunities in AI-assisted seismic interpretation and intelligent energy systems.