Probabilistic Analyses of Floating Offshore Marine Drilling Operations Using Active Learning Methods

Abstract Garlid et al. 1 previously showed that it is feasible to determine marine riser disconnect criteria for a mobile offshore drilling unit (MODU) using the structural reliability analysis (SRA) method. The method was presented through a case study of a notional MODU moored, with static thruster assist, in shallow water and subjected to harsh environments. The case study was performed with a series of simplifications that might introduce unwanted conservatism for the end user, such as co-linear environmental forces and static thruster settings. Further, only ten environmental load cases were used to populate the response surfaces, leading to a relatively coarse response surface grid. Still, many time domain (TD) analyses were needed to populate the response surfaces that describe the relevant short-term response statistics. Although more computationally expensive, the SRA method offers several advantages over the conventional frequency-domain method currently employed in the industry. These advantages include the possibility of considering non-linear effects more accurately when considering extreme responses and the possibility of considering the coupling effects between the different components, such as the MODU and marine riser. Therefore, the SRA method is more accurate, allowing operators to run more efficient marine operations and possibly extending operational windows. In this paper, the authors will develop the SRA method further by utilizing active learning when performing the response surface generation to significantly reduce computational efforts. This paper will use the Kriging model to model the response surface. Stochastic noise in the sampling points is included in the model to avoid potential over-fitting. A significant reduction in analysis effort could make the SRA method closer in performance in terms of computational efforts against the conventional frequency domain, but with the added advantages of a high-fidelity method that can consider full-coupling and non-linear effects.