Bayesian prediction of peak resistance of a spudcan penetrating sand-over-clay

Abstract

Assessing the potential for a punch-through failure during spudcan installation in sand-over-clay is crucial for reducing risk in the operations of mobile jack-up platforms. Typically, in the offshore industry, the peak penetration resistance and the depth at which it occurs are determined deterministically without rigorously considering the uncertainties in the soil. This paper proposes a probabilistic approach to estimate the peak resistance and the corresponding depth, as well as a Bayesian method of incorporating installation data to update the predictions. Instead of a single value in the deterministic analysis, a range of the potential peak resistances and depths can be estimated by accounting for the uncertainties in the soil, the spudcan's geometry and in the calculation method itself, with a database of 66 geotechnical centrifuge tests characterising the model. This prior probability is then updated using the monitored data, allowing a real-time update of the probabilities associated with candidate values of peak resistance and depth during the installation. The advantage of such a probabilistic updating model is shown in a retrospective simulation of a mobile jack-up platform in sand-over-clay conditions in the Gulf of Mexico. The results show that the prior estimation can be effectively refined by incorporating the monitored data. The proposed method provides a powerful tool for assisting decision-making during the installation of jack-ups offshore.