AI-ASSISTED HYDROCARBON LEAKAGE DETECTION AND INCIDENT PREVENTION IN SAUDI ARABIA: ADVANCING ENVIRONMENTAL PROTECTION UNDER VISION 2030

Hydrocarbon leakage remains an operational, environmental, and safety concern across upstream, midstream, and downstream systems, while Saudi Arabia pursues digital industrialization and stronger environmental performance under Vision 2030. This review critically examines how artificial intelligence can strengthen leakage detection and incident prevention by integrating pressure and flow analytics, acoustic emission, distributed optical-fiber sensing, infrared imaging, drones, corrosion prediction, transient simulation, and asset-health data. This integrative review compares sensing modalities, machine-learning architectures, validation practices, and deployment constraints rather than treating algorithmic accuracy as the sole measure of value. The evidence indicates that AI is most useful when it fuses complementary signals, accounts for changing operating regimes and connects detection outputs to maintenance and emergency decisions. Acoustic and pressure-based methods offer continuous internal surveillance but are sensitive to operational transients; optical, thermal, and aerial methods improve spatial awareness but depend on line of sight, atmospheric conditions, or inspection frequency. Predictive corrosion and failure models extend the system from leak recognition to prevention, although scarce labeled failures, class imbalance, and inconsistent reporting limit field generalizability