A conditional dependence-based marine logistics support risk model

Abstract Industries and researchers have renewed interest in the Arctic as well as the sub-Arctic regions due to the proven hydrocarbon reserves. The main challenges of operations in these regions arise due to their remoteness and extreme weather conditions. These conditions also put major challenges to plan emergency logistics support, which is currently offered either by helicopters or marine vessels. This paper analyzes the risk-based marine logistics support model in an offshore facility operating in the far northern (sub-arctic) region. A Bayesian network (BN) approach is used to develop the risk model considering interdependencies and conditional relationships among the contributing factors. Exploration in the Flemish Pass Basin located offshore Newfoundland and Labrador, Canada, is selected as a case study to demonstrate the methodology. The study identifies the critical elements of a marine logistics operation that need attention to reduce its associated risk. The corresponding safety measures are identified and implemented into the risk model. Appropriate risk management strategies are proposed to support marine logistics operations.

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