In the oil and gas industry, ensuring the continuous operation of offshore rotating equipment is essential for maintaining production efficiency and minimizing costs. Traditional maintenance approaches, such as reactive and time-based methods, often fall short when addressing the challenges of offshore environments. These approaches typically rely on fixed schedules or respond only after failures occur, leading to increased downtime and higher maintenance costs.
Artificial intelligence in predictive maintenance sectors
Artificial intelligence (AI) and machine learning (ML)-driven predictive maintenance offer a promising shift toward proactive maintenance strategies. By combining real-time and historical data with ML algorithms, predictive maintenance systems can forecast equipment failures before they happen. This approach not only improves equipment reliability and maximizes uptime but also reduces operational disruptions.
This article summarizes a project by Murphy that involved implementing an AI/ML-based predictive maintenance method in the Gulf of Mexico. The project focused on production-critical rotating equipment such as turbines, compressors, and pumps. By integrating data from multiple sources and using predictive models, the project aimed to improve operational reliability. However provide valuable insights to maintenance teams. It also explored the transformative potential of predictive maintenance in offshore environments while addressing the various challenges it presents.
Murphy embarked on a transformative journey by partnering with a service provider to apply AI/ML-based predictive analytics to rotating equipment. The project aimed to explore and trial predictive maintenance with the goal of maximizing equipment uptime and receiving early failure warnings for critical production equipment. The AI/ML-based predictive maintenance solution was deployed across two deepwater platforms in the Gulf of Mexico for a 24-month period, monitoring critical production equipment such as turbines, gas compressors, pumps, and glycol systems.
The project was executed in several planned phases, each contributing to the overarching goal of enhancing operational reliability and efficiency.
