From Sensing to Autonomous Control: Artificial Intelligence in Oil and Gas Pipeline Networks

Abstract

Oil and gas pipeline operators must simultaneously improve efficiency, strengthen safety, and reduce emissions. Artificial intelligence offers a practical route from fragmented monitoring toward coordinated, closed-loop control. This review condenses the operational bottlenecks, enabling algorithms, and deployment architecture of AI-enabled pipeline networks. It organizes intelligent control around four functions—perception, prediction and warning, decision optimization, and automated control—and maps multi-source data to suitable algorithms and cloud-edge execution. Priority applications include leak detection, robotic inspection, integrity assessment, demand forecasting, pressure and throughput optimization, compressor coordination, transient protection, and intelligent safety management of storage facilities. Five development directions are identified: deeper integration with digital twins, edge computing and the Internet of Things; systematic industry foundation models; low-carbon optimization; greater value from governed and securely shared data; and coordinated talent and innovation ecosystems. The central implementation principle is progressive autonomy: validated AI recommendations should first augment dispatchers, then move toward constrained closed-loop control as data quality, interpretability, cybersecurity, and fail-safe mechanisms mature.