Data-driven dynamic risk analysis of offshore drilling operations

Abstract

A data-driven dynamic risk analysis methodology is proposed here. The methodology is applied to offshore drilling operations. Modern drilling rigs are highly instrumented to monitor real time operational data. This provides sufficient data for time dependent risk analysis of drilling operations. The probabilistic relationships (structure) among the primary operational (drilling) parameters are modelled using the Bayesian Tree Augmented Naïve Bayes (TAN) algorithm. The developed model is used to predict time dependent probability of kick, and is continuously updated based on the current state of the key drilling parameters. The real-time probability of kick is used to model blowout risk as a function of time. The dynamic risk profile generated from the model is useful in operational decision making to prevent accidents and enhance the safety of drilling operations. The proposed dynamic risk methodology is tested and verified using actual drilling operational data.