Comparison of Different Prediction Methods to Derive Synthetic CPT Profiles - An Offshore Wind Farm Case Study from the German North Sea
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The further development of offshore windfarm areas in various countries plays a major role in the energy transition. As the windfarm areas tend to expand, and the amount of ground truthing data is limited, the estimation of geotechnical parameters at unknown locations, integrating all available site investigation data becomes a necessary tool and is of increasing interest for the industry. This is especially relevant for a time and cost efficient, area wide site characterization. Therefore, the proper integration and correlation of geotechnical and geophysical data is a key factor for a reliable subsurface model building. Here, we present a modeling framework which incorporates geological, geotechnical, and geophysical information to derive synthetic Cone Penetration Testing (CPT) profiles using offshore windfarm site investigation data from the German North Sea. We combine geological interpretation, cone penetration testing data and 2D seismic reflection data. The geophysical data and the geological information are used to guide geotechnical parameter prediction. Additionally, seismic horizons are used as structural information to constrain the prediction. Seismic attributes (e.g., Acoustic Impedance) are used as secondary information to guide CPT parameter prediction. For evaluation, we compare several prediction techniques, with different level of complexity, highlighting advantages and disadvantages, to decide on the most appropriate modelling strategy for the corresponding dataset, especially in the case of a pre-investigation campaign with limited ground truth data. To validate the results, CPT parameters are predicted onto a representative 2D seismic line and a leave-one-out cross-validation (blindtest) is performed on all available CPT. The developed and proposed workflow for synthetic CPT prediction is applicable and adaptable to a wide range of windfarm site investigation datasets and builds a basis for deciding on an appropriate method in early windfarm site characterization.