A Physics-Informed, Retrieval-Augmented Industrial Agent for Early Opportunity Detection and Operational Support in Offshore Gas Networks

Abstract

Abstract Large offshore gas pipeline networks generate continuous process measurements, alarms, operational records, reporting demands, and external-event indications. The bottleneck is often converting a candidate signal into a useful technical assessment. This paper presents a physics-informed, retrieval-augmented industrial agent that combines contextual knowledge, approved data-access tools, physical-consistency guidance, and a configurable language-model adapter in a governed asynchronous workflow. The system provides evidence-based decision support under human operational authority. The system was evaluated over a frozen 10.5-day window containing 403 consecutive work items, supplemented by a stratified domain-expert review and a paired comparison with conventional operational communications. The logs showed rapid governed processing and reliable publication through the primary enterprise channel. In the 40-case expert review, 39 outputs were fully or partially useful, and finalization was adequate in 37 cases. In eight strictly matched event-specific pairs, the agent reduced notification latency by approximately 90% relative to the conventional communication path. Deployment also showed that formatted process tables, operational records, transcriptions, and external-event context could be combined in a single workflow. Productivity, competency, and cognitive workload were not quantified. Performance remained sensitive to source and tool availability, trigger coverage, measurement observability, endpoint integration, configuration quality, and premature finalization. Overall, the system shortened the path from a candidate signal to an evidence-based assessment for human review.