This paper presents domain-adapted large language models (augmented via specialized prompting, retrieval, or fine-tuning with process safety knowledge) for the automated classification of root causes from unstructured offshore process incident narratives. It tackles domain-specific challenges including technical terminology, multi-label causal attribution, hallucination risks, and alignment with established taxonomies or investigation frameworks.
Relevance to PSM: Offshore facilities present complex, high-consequence process safety risks (hydrocarbon releases, well control events, fires/explosions). Automating accurate and auditable root cause classification accelerates investigation cycles, enables consistent application of lessons across assets, supports quantitative trending of causal categories, and frees subject-matter experts to focus on higher-value judgment and corrective action development—while incorporating guardrails for safety-critical use.
Note: Users of the ABS Group Root Cause Map™ will find this paper presents an interesting extension of that technique.
