Downtime in offshore wind operations significantly impacts construction efficiency, project timelines and costs. But forecasting weather downtime is a challenge. Realistic statistical models that incorporate granular operational and weather data are required for accuracy.
To address an industry gap identified by developers, contractors and OEMs, Spinergie has risen to meet this challenge. Our model leverages AIS vessel location data and detailed weather records to measure weather impact on offshore installation campaigns.
Current industry-standard calculations, notably P50/P70 estimation methods, frequently underestimate actual downtime due to oversimplified weather assumptions. These methods fail to reflect the detailed variations observed in the field, leading to discrepancies between planned and actual project timelines.
Our model addresses this shortcoming by integrating detailed vessel activity computed by analysis of Automatic Identification System (AIS) signals from offshore installation vessels, wind turbine locations, and weather models from NOAA and Copernicus. This approach allows us to benchmark installation performance and accurately predict delays due to weather and technical factors.
The weather downtime model methodology
The methodology comprises three main steps:
- Determining weather thresholds: The model identifies campaign-specific weather thresholds by analyzing AIS data from installation vessels. Installation durations are segmented, isolating periods of optimal productivity. Our statistical approach highlights the weather thresholds in which operations can continue as planned.
Weather downtime thresholds determination process applied to a North Sea turbine installation campaign. AIS locations are colored by the performance of the installation.
- Classifying downtime: Once thresholds are established, the model further categorizes downtime:
- Offshore weather downtime: Periods at the worksite impacted by weather that exceeds identified thresholds.
- Technical downtime: Poor installation performance at the worksite under favorable weather conditions, typically due to mechanical or operational disruptions.
- Waiting on weather: Vessels stationary offshore or ashore due to adverse conditions at the installation site.
- Nominal time: Normal operational periods unaffected by weather or technical issues.
- Operational analysis: The model evaluates performance across various parameters, including vessel types (e.g., jack-ups or floaters), component installation (turbine or foundation), region, and seasonal variations.
For ensured accuracy, our modelled outputs are benchmarked against actual contractor performance data and existing literature.
What we have learned so far
The model’s initial findings have highlighted significant regional and seasonal disparities. For instance, European offshore wind farms completed between 2020 and 2023 averaged a median total downtime of 0.89 days per turbine installation. The East Anglia 1 campaign (2019-2020), notably executed during winter, averaged 1.97 days per turbine, indicating substantial productivity loss due to adverse seasonal conditions.
Further, Taiwan experienced significantly higher average "waiting on weather" times, up to 152% more than North Sea installations, emphasizing regional differences and the necessity for customized planning. Taiwan's offshore installation schedules face frequent disruption due to a long typhoon season and high monsoonal wave activity, both of which reduce operational windows.
By comparing North Sea projects, we found out that nominal performance (productive time) was similar between projects once all types of downtimes were accounted for.
Next steps and the way forward
This weather downtime model will enhance current industry benchmarks and provide developers and OEMs with strategic data when it comes to optimizing project planning. The model’s granularity offers valuable insights for improved contractor selection and operational scheduling, reducing the financial impacts of weather-related delays.
Future refinements include integrating additional operational parameters, such as wave period variations and specific crane capabilities, further enhancing predictive accuracy. Our continued collaboration with industry players will drive iterative improvements, ensuring the model remains aligned with practical industry needs.
Though initially developed for offshore wind turbine installations, the methodology's flexibility enables its application across other weather-sensitive offshore operations, such as cable laying, pipeline installation, or crew transfer activities, making it a highly adaptable tool within the offshore industries.
To find out more about how Spinergie’s offshore wind downtime analysis can benefit your operations, get in touch: insights@spinergie.com
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