Dynamic Bayesian Network-Based Risk Assessment for Arctic Offshore Drilling Waste Handling Practices

Abstract

The increased complexity of Arctic offshore drilling waste handling facilities, coupled with stringent regulatory requirements such as zero "hazardous" discharge, calls for rigorous risk management practices. To assess and quantify risks from offshore drilling waste handling practices, a number of methods and models are developed. Most of the conventional risk assessment approaches are, however, broad, holistic, practical guides or roadmaps developed for off-the-shelf systems, for non-Arctic offshore operations. To avoid the inadequacies of traditional risk assessment approaches and to manage the major risk elements connected with the handling of drilling waste, this paper proposes a risk assessment methodology for Arctic offshore drilling waste handling practices based on the dynamic Bayesian network (DBN). The proposed risk methodology combines prior operating environment information with actual observed data from weather forecasting to predict the future potential hazards and/or risks. The methodology continuously updates the potential risks based on the current risk influencing factors (RIF) such as snowstorms, and atmospheric and sea spray icing information. The application of the proposed methodology is demonstrated by a drilling waste handling scenario case study for an oil field development project in the Barents Sea, Norway. The case study results show that the risk of undesirable events in the Arctic is 4.2 times more likely to be high (unacceptable) environmental risk than the risk of events in the North Sea. Further, the Arctic environment has the potential to cause high rates of waste handling system failure; these are between 50 and 85%, depending on the type of system and operating season.