LEVERAGING ARTIFICIAL INTELLIGENCE FOR INTEGRATED PROCESS SAFETY RISK ANALYSIS AND VISUALIZATION IN PETROLEUM OPERATIONS

Process safety accidents in petroleum operations can result in catastrophic consequences, including fatalities, injuries, environmental pollution, asset damage, and production losses. Such incidents are often associated with the gradual accumulation of safety risks arising from impaired safety-critical barriers, inadequate risk monitoring, and ineffective operational decision-making. Despite the availability of process safety risk assessment frameworks, petroleum facilities continue to experience challenges in identifying, monitoring, and managing cumulative risks due to fragmented data systems and the dynamic nature of barrier impairments. This study proposes a conceptual framework for applying Artificial Intelligence (AI) in process safety cumulative risk assessment and visualization within petroleum operations. The proposed framework focuses on developing an intelligent web-based software system capable of automatically collecting safety-critical barrier impairment data from multiple operational and maintenance information systems, processing and analysing the data using AI techniques, and providing real-time visualization of cumulative risk levels. The framework is intended to improve risk identification, enhance situational awareness, support proactive safety interventions, and facilitate risk-based operational decision-making. Furthermore, it seeks to assist petroleum facility managers in prioritizing maintenance activities, optimizing work execution, and reducing the likelihood of major process safety incidents. The study provides a conceptual foundation for developing intelligent digital solutions that strengthen process safety management and promote safer, more reliable, and sustainable petroleum operations.