ABSTRACT

This paper explores how Wood has leveraged advancements in artificial intelligence (AI) to address key challenges in managing safety critical elements (SCEs) in the oil and gas industry. SCEs serve as barriers to major accident events (MAEs) and are regulated in Australia through the safety case regime. The study focusses on three key areas of SCE management: identification, computerised maintenance management system (CMMS) verification, and reliability assurance. (1) SCE identification: AI and defined logic verify existing SCE classification and barrier identification, achieving up to 100% accuracy. (2) CMMS verification: AI creates a line of sight between performance standard requirements, CMMS assets, maintenance activities, and procedural tasks, with up to 80% accuracy in automated mapping. (3) Reliability assurance: AI assesses SCE reliability and identifies risks through data analysis, processing over 50,000 safety observations. The integration of digital and AI techniques streamlines engineering processes while maintaining rigorous quality assurance by subject matter experts. The study includes findings from Australian and Middle Eastern energy operators, demonstrating significant reductions in SCE audit durations and high accuracy in barrier identification and task mapping. The results show that AI, combined with engineering expertise, enhances SCE management by improving safety assurance and reducing resource requirements, providing a foundation for enhanced safety management across the energy sector.