抄録
Driven by the dual imperatives of global energy transition and high-quality development of the oil and gas industry, smart oilfields are rapidly evolving from digitalization toward autonomy, characterized by enhanced full-domain sensing, deepened data intelligence, strengthened cyber–physical integration, and increasingly autonomous closed-loop decision-making. Recent advances in the Internet of Things and sensing technologies have significantly expanded real-time data acquisition across oilfield production systems, while big data, cloud computing, and cloud–edge collaboration enable efficient governance and integration of multi-source heterogeneous data. Artificial intelligence and machine learning have demonstrated clear advantages over traditional approaches in key workflows such as reservoir characterization, production optimization, and predictive equipment diagnostics. Digital twin technologies further support multi-scale coupling and dynamic evolution of reservoir–wellbore–surface systems, providing a cyber–physical basis for autonomous decision-making. Despite differences in real-time performance, interpretability, scalability, and application scenarios, these technologies exhibit strong integration potential, including the synergy between mechanistic and data-driven models, the complementarity of edge intelligence and cloud computing, and the coupling of AI models with digital twins. However, challenges remain in data quality and security, model generalizability, system integration and standardization, and the alignment of organizational structures and workforce capabilities. Looking ahead, emerging technologies such as edge intelligence, reinforcement learning, federated learning, generative AI, and industry-specific foundation models are expected to enable cross-scale perception, cross-system collaboration, and autonomous decision-making. This article reviews the technological evolution from digitalized to autonomous smart oilfields, evaluates the strengths and limitations of core technology systems, and outlines future research directions for next-generation autonomous oilfields.
