Abstract In extensive drilling operations, maintaining safe, efficient, and consistent rig performance is a formidable challenge. Traditional methods rely on manual interpretation of diverse data sources—such as HSE observations, CCTV footage, and operational metrics—resulting in delays and missed opportunities for proactive measures. To overcome these limitations, Pertamina Hulu Rokan pioneered an AI-driven solution, enabling real-time, holistic rig performance analysis and unlocking unprecedented efficiency and safety improvements. The developed fuzzy model incorporates nine critical parameters into a unified framework for systematic risk assessment. This collaborative effort by the Drilling Performance, HSE, and Analytics Teams resulted in 32 field-specific inference rules. Leveraging Sugeno's defuzzification method, the model computes a Rig Safety Index (RSI) on a scale from 25 to 100. The RSI provides a quantifiable, dynamic metric for evaluating rig safety and proactively identifying potential hazards across diverse operational conditions, ensuring actionable insights for enhanced safety performance. Over six months, the fuzzy model was validated on more than 80 rigs, encompassing drilling, workover, and routine service operations under diverse field conditions. The Rig Safety Index (RSI) consistently identified and mitigated high-risk scenarios, providing a standardized benchmark for safety across varying rig types and operational environments. Transitioning from manual workflows to an AI-driven fuzzy expert system proved transformative. The system mimics human cognitive reasoning to evaluate high-risk scenarios with greater precision, streamlining decision-making and improving safety compliance. Operational efficiency was enhanced through the seamless integration of automated risk assessments and actionable insights. The deployment of an interactive Power BI dashboard further amplified these benefits, offering intuitive visualization of performance metrics and risk patterns. This empowered field teams to monitor trends, predict potential issues, and implement proactive measures effectively. The results underscore the value of adopting AI-powered solutions for rig performance management, demonstrating significant improvements in safety, efficiency, and data-driven decision-making across diverse operational conditions. This paper introduces a novel AI-driven fuzzy expert system that bridges the gap between subjective HSE evaluations and fragmented operational metrics. By unifying diverse data into a cohesive Rig Safety Index (RSI), it minimizes delays and enhances predictive capabilities. Unlike traditional methods, this scalable approach leverages AI to offer real-time, actionable insights tailored to complex drilling environments, empowering engineers to proactively optimize safety and performance with unprecedented precision.
Hoviari et al. (Mon,) studied this question.