Analyzing seasonal effects on logistical risks associated with servicing an oil production facility in the Flemish Pass

Abstract

There are field discoveries in the oil and gas industry that require significant investment to develop, and the Bay du Nord and Baccalieu fields in the Flemish Pass are no exception. Long-distance, deep-water production facilities are expected to increase marine logistical risks in an area that already is known for its harsh weather conditions, and the implications of not properly understanding and mitigating these risks are the safety of those involved in the operations and millions of dollars in material damage. This research is focused on more accurately modelling the seasonal effects of changing environmental conditions on the failure rate of marine logistics operations. This work is an extension of previous research in the field and is focused on further model development and refinement. Fault tree and Bayesian network models are compared, with applicable data uncertainty considerations. Model refinements to reflect conditional dependencies more accurately, and a process for establishing mitigation efforts and accident prevention strategies are then proposed. This thesis details data acquisition, application of seasonal conditions inputs, and a quantitative technique for analyzing pre-emptive barriers to logistics operation failure. The final product is a strategy for estimation of logistics operation failures