Offshore oil and gas facilities require regular cargo shipments of food, water, fuel and other commodities from supply vessels. Some of these vessels also assist with oil off-takes, where oil stored at a production facility is transferred via hose to a waiting tanker. Scheduling the vessel fleet to enable efficient cargo delivery and oil off-take is critical to business productivity.
A decision support model was constructed to schedule a series of round trips for support vessels so that all servicing and off-take requirements were met, while minimising travel cost and trip duration. Operational restrictions included vessel capacity, speed and suitability (not every vessel is suitable for every facility), varying cargo demands at the facilities, specific time windows for cargo transfer and oil off-take at each facility (such as restrictions on night-time loading), and off-take equipment constraints (only some support vessels have the equipment needed to support oil off-take). The model was validated with real data provided by Woodside Energy, Australia’s largest independent oil and gas company.
A decision support model identified the best trip start
times and sequence of routes between facilities to ensure that cargo delivery and off-take requirements were met, with the best compromise between travel cost (less travel is better) and trip duration (less time, including waiting time at facilities, is better). The model was also used to calculate vessel utilisation rates for each feasible schedule, to assess fleet efficiency and different options for future fleet replacements. This provided a rigorous mechanism for assessing different fleet configurations and fleet upgrade options.
With the major decisions about the size and nature of the support vessel fleet made, these types of optimisation models can also be used on an operational basis, as scheduling tools to make day-to-day decisions on vessel routes in response to changing circumstances such as equipment breakdowns or cyclones curtailing offshore activities. Equally, the methodology can be applied to any supply chain scheduling problem, from managing harvesters in agriculture to autonomous haul trucks on mine sites.
