抄録
Intelligent and digital oilfields have emerged as a transformative paradigm for improving hydrocarbon recovery, enhancing operational safety, and supporting the low-carbon transition of the petroleum industry under increasingly complex reservoir conditions and stringent environmental requirements. Rapid advances in the Internet of Things, artificial intelligence, big data analytics, cloud computing, edge intelligence, and digital twin technologies have fundamentally reshaped oilfield development by enabling integrated sensing, real-time data fusion, intelligent prediction, and autonomous decision-making throughout the exploration, production, and management lifecycle. This review presents a comprehensive synthesis of recent developments by establishing a unified analytical framework that integrates system architecture, key enabling technologies, representative application scenarios, and engineering implementation strategies for intelligent and digital oilfields. Representative applications—including intelligent exploration and drilling, reservoir characterization and production optimization, predictive equipment maintenance, safety management, and low-carbon operation—are comparatively evaluated with respect to technical maturity, operational effectiveness, scalability, and industrial applicability. Particular attention is given to the convergence of physics-based modeling and data-driven artificial intelligence, highlighting physics-informed intelligent systems as a promising paradigm for enhancing prediction accuracy, model interpretability, decision robustness, and engineering reliability. The review further identifies the principal barriers to large-scale deployment, including heterogeneous data quality, limited interoperability and data governance, insufficient model generalization and explainability, inadequate integration of mechanistic and artificial intelligence models, cybersecurity risks, and organizational constraints. Finally, future research priorities are proposed, emphasizing autonomous decision-making, foundation models for subsurface intelligence, digital twin-enabled closed-loop optimization, cloud–edge collaborative computing, and low-carbon intelligent operations. By bridging petroleum engineering with digital intelligence and systems engineering, this review provides a systematic research roadmap and decision framework for advancing next-generation intelligent and digital oilfields from technological innovation to large-scale industrial deployment.
