Harnessing AI and Operational Data to Predict Vessel Workability in Challenging Offshore Conditions

Offshore platform supply vessels face unpredictable weather that can disrupt schedules and threaten crew safety. Traditionally, vessel planning relied on weather forecasts and operational requests, focusing mainly on significant wave height and wind speed. But these factors alone do not capture the full picture of vessel motion and workability alongside platforms. During 2022 and 2023, a project explored whether machine learning could use vessel operational data to predict workability more accurately for platform supply vessels in the Southern North Sea. This blog post breaks down the key insights and practical benefits of applying AI to improve predictability, reduce idle time, and strengthen schedule robustness in offshore operations.

Platform supply vessel operating in challenging sea conditions

Understanding the Challenge of Vessel Workability

Vessels operating in the North Sea face complex meteorological conditions. High waves and strong winds can make it unsafe or impossible to work alongside platforms, causing delays and increasing idle time. Before this project, vessel motion was not directly considered in planning. Instead, decisions were based on:

• Significant wave height (Hsig) and Wave Periods (Tp)

• 2D Spectra

• Wind speed (Ws)

• Weather forecasts from Infoplaza Marine Services

  
  

This approach left gaps in predictability because it did not account for how vessels actually move and respond to conditions when moored or working near platforms. Without this insight, planners could not fully anticipate when operations would be safe or need to pause, impacting crew safety and schedule robustness.

How AI and Operational Data Improve Predictions

The project collected detailed operational data from vessels, including motion sensors, engine performance, and environmental conditions. Machine learning models were trained to analyse this data and predict workability windows more precisely. Here are three ways AI added value:

1. Integrating Vessel Motion into Workability Assessments

Instead of relying solely on wave height and wind speed, the AI models incorporated vessel motion metrics such as roll, pitch, and heave. This gave a clearer picture of how the vessel behaves in real time, which directly affects crew safety and operational feasibility.

2. Learning from Historical Patterns

The models used historical operational data to identify patterns where vessels successfully worked or had to stop. This learning helped improve predictability by recognising subtle combinations of conditions that impact workability but are not obvious from weather data alone.

3. Providing Dynamic, Real-Time Predictions

Unlike static weather forecasts, the AI system could update predictions continuously as new data arrived. This allowed planners to adjust schedules proactively, reducing idle time and improving schedule robustness.

Vessel motion sensors capturing & visualising real-time operational data using FFT

Practical Benefits for Offshore Operations

Applying AI to predict vessel workability brought several tangible improvements:

• Enhanced crew safety by better identifying unsafe conditions before operations begin.

• Reduced idle time through more accurate scheduling that avoids unnecessary waiting.

• Improved schedule robustness by anticipating weather impacts and adjusting plans dynamically.

• Better resource allocation as planners can optimise vessel deployment based on reliable workability forecasts.

• Increased operational efficiency by minimising disruptions and delays.

  
  

For example, one vessel reported a 15% reduction in idle time during peak weather seasons after adopting AI-based predictions. This translated into cost savings and smoother project execution.

Steps to Implement AI-Based Workability Prediction

Organisations interested in adopting similar solutions can follow these steps:

• Collect comprehensive operational data, including vessel motion, engine status, and environmental sensors.

• Partner with data scientists to develop machine learning models tailored to vessel types and operating areas.

• Integrate AI predictions into planning workflows so that schedulers can use real-time insights.

• Train crews and planners on interpreting AI outputs and adjusting operations accordingly.

• Continuously monitor and refine models using new data to maintain accuracy and relevance.

  
  

Offshore workability planning board

Moving Forward with AI for Offshore Vessel Operations

This project demonstrated that combining vessel operational data with machine learning can significantly improve the predictability of vessel workability. This approach supports safer working conditions, reduces costly idle time, and strengthens schedule robustness in challenging offshore environments.