A simulation-based decision support tool for arctic field logistics

• Main Author: Ulstein, Mads
• Other Authors: Ehlers, Soren, Norges teknisk-naturvitenskapelige universitet, Fakultet for ingeniørvitenskap og teknologi, Institutt for marin teknikk
• Format: Master Thesis
• Language: English
• Published: Institutt for marin teknikk 2014
• Subjects: Arctic
• Online Access: http://hdl.handle.net/11250/238817

As the global demand for energy continues to rise, the oil and gas industry investigates new opportunities for oil and gas production in more remote areas. Promising estimation of oil and gas reserves in the Arctic areas has resulted in an increased focus on oil extraction in the Higher Norths. For the logistic providers and ship yards, this area represent a new era in offshore operations and vessel design. The Arctic area offers challenges to offshore vessels but also to the offshore operation itself. Increased distances to shore and harsh environmental conditions sets new demands to the offshore supply chain. Platform supply vessels(PSVs) are an important part of the offshore logistic supply chain, providing offshore installations with necessary cargo and personnel. Accurate delivery is of high importance in order to ensure continuously production. Weather or not an offshore operation can be conducted, is restricted by environmental conditions at site. Exceedance of these limitations will affect the feasibility and duration of a voyage. In such cases, an oil operator may be forced to hire a vessel from the spot-marked. Although, offshore field logistics represent an important segment in offshore operations, the literature is scarce on the business side.It is clear that the current situation require new technological solutions, but also new and improved business models to ensure sustainability. In this paper we address the impact of the Arctic environment on PSVs.It has been investigated if a simulation model could be used to determine the operational duration and optimal fleet composition of PSVs in the Arctic environment. To determine the capability of the model, two case studies were presented.