An Artificial Bee Colony optimization-based approach for sizing and composition of Arctic offshore drilling support fleets considering cost-efficiency

TL;DR

This paper introduces an Artificial Bee Colony algorithm-based method for optimizing Arctic offshore drilling support fleet size and composition, focusing on cost-efficiency by considering various operational and accidental costs.

Contribution

It presents a novel optimization approach that integrates multiple operational factors and accidental costs for fleet sizing in Arctic offshore drilling, validated through case studies.

Findings

The approach effectively identifies cost-efficient fleet configurations.

Some solutions closely match real-world fleet compositions.

Sensitivity analysis highlights the importance of accident cost considerations.

Abstract

This article presents an optimization-based approach for sizing and composition of an Arctic offshore drilling support fleet considering cost-efficiency. The approach studies the main types of duties related to Arctic offshore drillings: supply, towing, anchor handling, standby, oil spill response, firefighting, and ice management. The approach considers the combined effect of the expected costs of accidental events, the versatility of individual support vessels, and ice management. The approach applies an Artificial Bee Colony algorithm-based optimization procedure. As demonstrated through case studies, the approach may help to find a range of cost-efficient fleet compositions. Some of the obtained solutions are similar to corresponding real-life fleets, indicating that the approach works in principle. Sensitivity analyses indicate that the consideration of the expected costs from accidental events significantly impacts the obtained solution, and that investments to reduce these costs may improve the overall cost-efficiency of an Arctic offshore drilling support fleet.