Abstract
Atmospheric corrosion is the biggest asset integrity threat to offshore Oil and Gas (O&G) platforms in the Gulf of Mexico (GoM). Manual inspection of an offshore platform’s topside equipment is costly, time-consuming, and labor-intensive. Moreover, manual inspection findings are subjective and provide incomplete asset coverage, leading to increased risk of unplanned shutdowns due to missed repairs. Computer vision and machine learning algorithms can be used to detect and classify corrosion, allowing for the objective and comprehensive management of corrosion across a facility. Detected corrosion is associated with equipment and reported, enabling high-risk equipment (i.e., high likelihood and/or consequence of failure) to be targeted for remediation, significantly reducing the risk of unplanned downtime. This paper covers the first-in-industry application of an AI-based system to improve corrosion management and inspection processes. A case study is presented, where the AI-based corrosion management system is deployed across a large offshore O&G platform in the GoM. The impacts of this new technology for corrosion management are demonstrated, in practice. Machine learning and computer vision algorithms are leveraged to greatly improve inspection, maintenance, and management processes, reducing the operating costs and risks associated with offshore O&G platforms.
