Radically reduced time-to-insight compared to traditional physics-based model
In the demanding realm of subsea operations, flexible risers are critical for connecting seabed infrastructure to surface vessels. Traditional monitoring using Finite Element Analysis (FEA) models is slow and computationally intensive, hindering real-time assessment of riser health. This has driven the need for faster, more accurate solutions to predict stress and fatigue, ensuring operational reliability and preventing costly downtime.
The Challenge: Inefficient Traditional Monitoring
Subsea companies face the challenge of maintaining flexible risers in harsh marine environments. Existing FEA models, while accurate, are resource-intensive and time-consuming, making them unsuitable for real-time monitoring. This limits the ability to proactively address potential issues, increasing the risk of equipment failure.
The Solution: Machine Learning for Real-Time Predictions
Together with our customer, Arundo developed a multi-stage machine learning model to predict riser stress and fatigue. Trained on FEA simulation data, this model accurately assesses riser conditions based on vessel motions. It achieves over 98% accuracy compared to FEA simulations but operates 1000 times faster. This speed enables real-time decision-making, optimizing both design and operations. The model is deployed with a user-friendly visual tool, enhancing accessibility for engineers.
The Impact: Rapid Insights, Reduced Costs
The machine learning solution provides 1000x faster insights than traditional methods, significantly reducing processing time. This leads to:
- Improved Riser Life Prediction: Accurate stress monitoring enabled better prediction of riser lifespan, improving maintenance planning.
- Cost Savings: Optimized maintenance and prevention of failures generate substantial cost savings.
- Enhanced Service Offerings: The company can offer advanced, data-driven services to clients.
Conclusion
Arundo's machine learning approach to flexible riser monitoring provides real-time insights, overcoming the limitations of traditional FEA models. This innovation enhances operational efficiency, reduces costs, and improves the reliability of subsea infrastructure, marking a new era of data-driven decision-making in the offshore industry.
Do you also need to complement your FEA simulations with faster models? Read more about our enabling technology or get in touch to discuss your particular use case.
