Preliminary foundation design can be overly conservative for offshore wind farms. This is often as a result of there being insufficient geotechnical data or a site being particularly complex to model accurately. What developers and designers require is greater certainty of soil conditions site-wide. A quantitative approach to ground modelling can help to address these needs.
Over the last two decades, our Survey & GeoEngineering team has been generating ground models to determine sub-seabed geological conditions to help characterise sites. The results inform turbine foundation design for offshore wind farms. A recent project in the Asia-Pacific region has proved revelatory. Given the complexity of this development, our team has applied an advanced statistical geo-cellular method to reduce the uncertainties in the soil conditions at locations where geotechnical data is unavailable. Higher uncertainties result in increased expected foundation costs for a wind farm. Engineers must design foundation solutions with levels of conservatism dependent on uncertainty, which can mean designing larger foundations and increased costs. Constraining geotechnical conditions can help reduce the expected costs for foundations.
Cone Penetrometer Tests (CPT) are in situ geotechnical tests, where an instrumented cone is pushed into soil formations at a fixed penetration rate in order to provide a continuous profile of parameters correlated to soil classification. Many ground models try to determine expected CPT responses across an OWF. In many cases, without seismic inversion or advanced statistical analysis these ‘synthetic CPT’s’ can be overly simplistic. We believe the methodology used by Vysus Group to quantitatively produce synthetic CPTs is the first of its kind for the renewable energy sector.
The approach is already demonstrating value for our client as the project progresses. Proven for use at a complex OWF site, the model’s application could benefit the sector more widely. At a point when the world requires as much renewable energy as possible to meet de-carbonisation targets, and offshore wind farm sizes are rapidly increasing, we believe this advanced solution couldn’t be more timely.
Taking ground modelling further
For complex sites, you can only push a conventional ground model so far until the outputs become less reflective of local conditions. A conventional ground model has limited ability to model vertical and lateral variations in soil conditions within soil units. However, in any stratigraphic unit there can be changes in past deposition, lithology and geotechnical conditions. A 2017 study of Dogger Bank in the south central North Sea by the British Geological Survey and Norwegian Geotechnical Institute highlights the sheer complexity of soil conditions at a site once deemed to be a simple ‘layer cake of Quaternary sediments’.
The quantitative model introduces a new level of flexibility to better predict geotechnical conditions at extensive OWF sites. By adopting an advanced statistical modelling technique, we can predict changes in soil types and properties within any single mapped unit across a site. This is presented as a 3D model with varying levels of confidence.
Same raw inputs as standard models
The method requires no more data than traditional ground modelling inputs to deliver significant improvements. Similar to conventional approaches, the process is based on mapped sub-seabed formations, but additional data analysis and stochastic simulations are performed within these layers. Although highly sophisticated, the quantitative model takes just a few months to be up and running.
Exploring the new advantages
Quantitative ground modelling can benefit offshore wind farm developments globally.
It can act as a prediction tool, enabling more accurate preliminary foundation design to proceed prior to gaining a full set of actual geotechnical data. It can assist with micro-siting and potentially remove the need for additional geotechnical data in some instances. Additionally, quantitative modelling helps developers identify areas of highest soil condition uncertainty to support future site-investigation planning.
Our team is incredibly excited about how this quantitative modelling method can support a world moving to net zero energy. To discover more, just send your email to: email@example.com