Offshore Subsea IMR Operations: Review of the Automation Potential

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Authors: Jon S. Larsen, Simon Pedersen, Jesper Liniger, Fredrik Fogh Sørensen
Cited by: 2
References: 69

Abstract

The demand for green energy continues to rise, with predictions indicating a 62-fold growth in floating wind. This offshore expansion, especially from wind, requires a corresponding increase in inspection, maintenance, and repair (IMR) operations. For offshore turbines, the marine segment accounts for half of fixed turbine O&M costs. One avenue to reduce these costs is through unmanned underwater vehicles (UUV) and automation. UUVs are becoming more specialized and automated, but most tasks remain only partially automated. It is estimated that the global energy cost in 2050 can be reduced by 0.5-0.6% from increased unmanned underwater vehicle automation, with wind contributing 0.4-0.5%. This study assesses automation levels and task frequencies across application domains, defined as types of offshore infrastructure, to calculate an automation priority score (APS), a weighted product of automation level and task frequency, indicating where economic and development benefits are. The automation level refers to a UUVs ability to operate without human intervention while performing tasks, regardless of industry use. The current highest APS is found in pipelines and jacket structures, with the lowest in wind turbines and dams. A sensitivity analysis evaluates the effects of increasing automation levels and anticipated annual growth. The study concludes that the wind sector will represent the highest automation priority in the future, providing the greatest economic incentive, especially for mooring lines. Visual inspection will increase due to AI, crawler-based UUVs will dominate circular structures IMR tasks, and autonomous underwater vehicle (AUVs) with subsea stations will handle frequent or long-range tasks.

Fields of Study & Subjects

Offshore Engineering and Technologies Oil and Gas Production Techniques Wave and Wind Energy Systems

Publication details

Journal / source
Robotics and Autonomous Systems
Publisher
Elsevier BV
Volume
199
Pages
105365–105365
Publication date
28 January 2026
Language
EN