Optimising offshore supply vessel (OSV) fleet

The challenge

Determining the best way to configure the fleet and improve efficiency

This energy company had a fleet of ice-class vessels supplying a number of operational platforms working year-round, including in icy winter conditions. The company was embarking on a 5-year strategic planning exercise and wanted an evidenced-based approach to determining the best way to configure its fleet and optimise performance, with a view to reducing operational cost.

Offshore supply logistics is complex, requiring the interplay of static (number of vessels) and dynamic variables (cargo) including a range of difficult-to-predict factors (weather) over a long period. Successful operations need to account for:

1.   Operational logistics for a wide breadth of activities include transporting personnel, equipment, supplies, and materials
2.   Weather and environmental factors
3.   Port operations – availability and transfer and turnaround time (TAT)
4.   Ship to-platform transfers
5.   Platform storage and operational constraints across the operation lifecycle of the platform e.g. drilling, production, maintenance, and exploration
6.   Emergency response and standby requirements for operations
7.   Flexibility and redundancy requirements
8.   Maintenance and repairs – both vessel and platform

The solution

Using predictive simulation to understand the impact of different fleet configurations

Employing our Twinn Witness predictive simulation software (formerly part of Lanner), Royal HaskoningDHV modelled the intricate supply chain from port-to-platform operations. Bolstered by Witness' track record and ready-to-deploy models for the Oil and Gas sector, we were able to maintain an aggressive schedule in support of key upcoming decision gates.

Our expert consulting team guided a series of stakeholder workshops to create an array of fleet configurations. Each configuration underwent rigorous evaluation within a secure environment, highlighting performance trajectories across a set of key performance indicators. Promising configurations were then subjected to intense stress tests, scrutinizing their resilience across an array of sensitivities.

The culmination of this deployment crystallized into a singular preferred configuration, which balanced opex costs with service imperatives. Notably, our versatile simulation ecosystem delved further, quantifying the ramifications of trimming non-productive time and expanding platform storage capacity. 

The impact

Evidence for maintaining the current fleet configuration – with operational optimisations

The simulation decisively pinpointed the fleet arrangement jeopardising production, while confirming the recommended configuration. This empowered the client to forego fleeting cost-cutting measures, averting substantial long-term production setbacks. The outcome provided a data-driven blueprint for fleet prerequisites and unearthed promising avenues for operational enhancement.