The oil and gas industry is currently the largest industry in Norway, and it is expected to remain so in years to come. Only 44 % of the projected total recoverable resources have been extracted, and new discoveries are still being made. In order for this industry to maintain its position, new resources must be proven, and this requires exploration drilling. During these operations, the operators require an extensive supply of equipment and services, and are therefor highly dependent on safe and reliable supply services. Furthermore, there is an increasing focus on cost-effectiveness for offshore activities, and it is believed that an optimal upstream supply logistic will provide a significant reduction in these cost levels. This report presents an academic study of supply logistic strategies for exploration drilling operations on the Norwegian Continental Shelf (NCS). The aim of this study is to utilize optimization to determine the most cost-effective supply operation for exploration drilling for both planned and unplanned demands, and use this to initiate a discussion on the supply logistics. Offshore supply operations are complex, and good logistics and planning are therefore essential elements in achieving excellence. Traditionally, experienced logistic personnel perform the scheduling and route planning manually. But due to the significant amount of variables that must be considered, manual planning may fail to investigate all possible solutions. Therefore, optimization can be used as a decision support tool, and aid the planners in their work.The problem presented in this thesis is a planning problem in which the operator is responsible for the inventory management, and the routing and scheduling of the deliveries. These problems are classified as Inventory Routing Problem (IRP). This methodology enables the planners to evaluate both the optimal inventory levels and the routing and scheduling decisions, which provides a supply chain that optimize both aspects. The thesis addresses the tactical and operational aspect of the traditional supply chain, and a mathematical model will determine the optimal supply alternatives for both planned (deterministic) and unplanned (stochastic) demands. The mathematical model that is developed for this study is a mixed integer, two-stage recourse model. The model is implemented in the commercial software FICOTM Xpress Optimization Suit, and tested on a case study in which four offshore facilities require supply services. The solution from the case study yields that the four offshore installations can be serviced by two PSVs during a time horizon of five days, and all the installations should have two visits each. To address the issue with unexpected demands that makes the estimated deterministic stock levels insufficient, late deliveries are performed. The preferred alternative for the late deliveries is to use helicopters. However, as the amount of the unexpected demands increases, an alternative that combines an additional storage at the onshore base and the spot chartering of an additional PSV, becomes the preferable solution. The cost of the estimated planned deliveries is 88,707 $, and the estimated cost of the late deliveries is 17,514 $. The cost saving of using late deliveries compared to the risk of downtime, is estimated to be approximately 20 %, this is therefor the preferred solution. Still, the cost of the late deliveries might get extensive. If information about the real demands can be revealed during the planning of the initial schedules, these demands can be incorporated in the schedules, which has a potential cost saving of 16 %.
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