Automated Corrosion Mapping AI & Machine Learning

Abstract Objective Risk reduction and increased Fabric Maintenance efficiency using Artificial Intelligence and Machine Learning algorithms to analyze full-facility imagery for atmospheric corrosion detection and classification. Following imagery capture and processing, deficiencies are identified, and targeted mitigation strategies are executed at greatly reduced cycle time and cost. Methods, Procedures, Process A pre-mobilization facility scan plan is generated to maximize imagery quality, including high elevation scan positions, to ensure thorough and comprehensive analytics. Data from all scan positions are stitched together in a point cloud and aligned for accuracy relative to each location. Finalized imagery and point clouds are then tagged with unique piping line numbers per design, fixed equipment tags, or unique asset identification. The Machine Learning algorithm is intensely trained with manual ground truth inputs prior to analysis. The algorithm analyzes each pixel throughout the facility and detects, classifies, and reports on all identified corrosion, tagging faults to specific piping or equipment. Results, Observations, Conclusions Atmospheric corrosion is the number one Asset Integrity threat in the Gulf of Mexico. Utilizing this tool, we can have a comprehensive and objective analysis of a facility’s health in a matter of weeks from the time of data collection. Data collection for a large deep-water, spar facility requires approximately 12 days with 8 data scanning personnel. Conventional manual inspections incur higher risk, higher cost, and reporting is much less objective considering the number of inspectors involved and the duration of a full-facility campaign. Finally, all results are published in a user-friendly dashboard that can be filtered by process type, equipment type, corrosion severity, and many other criteria as the user requires. Each fault is associated with the specific equipment identification and the user can navigate to see the imagery of the corrosion in a 3D, photogrammetric environment. Remediation strategies can be collated into work packs for fabric maintenance teams, further Nondestructive Examination (NDE) assessment, or work orders for replacement. Fabric maintenance efficiencies are substantially realized by targeting decks, blocks, or areas with the highest aggregate surface areas of corrosion (on process equipment or structurally, as selected by the user) and concentrating remediation efforts on at-risk equipment. Novel/Additive Information This application of Artifical Intelligence and Machine Learning is a first-in-industry approach to having a comprehensive understanding of facility coating integrity and external corrosion threats. HSE analysis, Risk awareness, and targeted remediation strategies will make the Asset Integrity program more efficient, proactive, and reduce down-time across the Gulf of Mexico related to atmospheric corrosion.