Probabilistic site characterisation using monitored spudcan installation data

Predicting spudcan penetration is essential for reducing risk during the installation of an offshore mobile jack-up platform. However, predictions made prior to installation often deviate from those recorded in practice, mainly because inherent uncertainties in seabed soils are not adequately considered. In this paper, the characteristics of the seabed are quantified using monitored spudcan installation data by developing a Bayesian framework coupled with Metropolis algorithm-based Markov chain Monte Carlo (MCMC) simulation. Applied to sand overlying clay conditions multiple readings from the load–penetration curve are used to derive statistical interpretations of seabed parameters such as sand thickness, sand friction angle and the undrained shear strength profile of clay. Illustrative examples demonstrate the proposed method using data from geotechnical centrifuge tests and a monitored offshore jack-up. The accuracy of the method is also retrospectively examined against a database of 66 centrifuge tests. The results show that the proposed method can provide consistent soil parameters with reduced uncertainty regardless of the influence of the updating sequence, number of MCMC chains, scale factors and the prior distributions applied. This method offers the offshore industry a new methodology to statistically interpret seabed conditions by incorporating monitored data. Potentially it could be combined with spatial interpolation methods to infer ground conditions and more accurately predict future spudcan installations.