AI-Powered Inspection App for Offshore Oil and Gas Facilities: A Practical Application of Real-Time Structural Assessment

Abstract Visual inspection remains a fundamental method for assessing the condition of offshore oil and gas facilities; however, inspection outcomes are often influenced by inspector experience and inconsistent reporting practices. This paper presents an AI-assisted inspection methodology that integrates computer vision, knowledge-based reasoning, and natural language processing to support structural condition assessment. A deep-learning image model is trained on annotated inspection photographs to detect visible anomalies such as corrosion and cracking. Detected findings are interpreted using a knowledge graph that encodes inspection criteria, component relationships, and condition logic derived from established inspection practices. A constrained natural language model processes the structured outputs to emulate human reasoning and generate consistent inspection narratives. The proposed approach demonstrates how AI models can be trained and combined to assist anomaly identification, reasoning, and documentation while maintaining human oversight. The methodology provides a structured foundation for generating inspection reports and supports improved consistency, traceability, and efficiency in offshore inspection workflows. From inspection image upload to preliminary report generation, the AI-assisted workflow produced structured inspection reports in under two minute for individual findings, compared with hours typically required for manual report drafting. For inspections comprising on the order of tens of anomalies, full report generation was reduced from weeks of manual effort to approximately one hour, subject to engineer review.