With day-rates of several hundred thousand USD, small delays in the original schedule might amass to considerable costs. On the other hand, pushing the limits of the load capacity of the riser-stack and wellhead may compromise the integrity of the well itself, and such a failure is not an option.
Advanced simulation techniques may reduce uncertainty about how different weather scenarios influence the system’s integrity, and thus increase the acceptable weather window considerably. However, real-time simulations are often not feasible and the stochastic behavior of wave-loads make it difficult to simulate all relevant weather scenarios prior to the operation.
This paper outlines and demonstrates an approach which utilizes probabilistic machine learning techniques to effectively reduce uncertainty.