Offshore oil platforms are critical infrastructures that require regular inspection to detect corrosion and maintain structural integrity. While traditional manual inspections are prone to human bias and high operational costs, recent advancements in automated inspection using 360-degree imagery have shown promise. This study presents a comprehensive evaluation of state-of-the-art metric monocular depth estimation methods—Depth Anything V2, ZoeDepth, Metric3Dv2, and Patchfusion—applied to 360-degree images of offshore oil platforms, a novel application in this domain. Metric depth estimation may also benefit downstream tasks such as corrosion and object detection by providing additional spatial context. Our comparative analysis assesses the performance and suitability of these methods in the context of the unique visual characteristics of offshore industrial environments and panoramic imagery. The findings offer valuable insights into the limitations and strengths of current approaches and serve as a basis for future work aimed at improving depth estimates, including domain-specific fine-tuning. This work contributes to ongoing efforts to enhance the efficiency, accuracy, and safety of structural health monitoring in challenging industrial settings. Code is available at https://github.com/DiMorten/depth_offshore_LAGIRS2025.