An AI-based predictive maintenance system has been introduced onboard the ultra-deepwater drillship Saipem 12000 to improve operational efficiency and offshore safety.
The rollout is part of a wider innovation program that Saipem said is aimed at extending artificial intelligence and data analytics across its fleet, using continuous monitoring to support reliability.
The system combines real-time operational data with AI algorithms to track equipment condition, anticipate potential failures and schedule maintenance interventions before issues occur. The approach is intended to cut downtime and reduce management costs.
The solution was developed with ADC Energy, a rig and vessel assurance specialist. Through continuous data analysis, the platform is designed to detect anomalies early and enable targeted maintenance planning, supporting safer and more reliable operations.
Saipem is also implementing a separate predictive maintenance project on Saipem 7000, one of the world’s largest semi-submersible crane vessels. This initiative focuses on diesel generators, which are critical for onboard power production.
Using IoT sensors and machine learning models, the Saipem 7000 project is designed to identify early signs of potential failures and improve maintenance planning to support operational continuity. The work is being developed with BIP, an international consulting firm specializing in technological innovation and data science, and is scheduled to be tested in the coming months.
The company said these initiatives highlight its effort to integrate AI, predictive analytics, and advanced digital tools into offshore energy operations to enhance safety, efficiency, and sustainability.
