Application of Bayesian network and artificial intelligence to reduce accident/incident rates in oil & gas companies

The paper combines machine learning/keyword analysis with Bayesian network modeling to extract insight from oil & gas incident reports. Narratives are labeled and grouped, mapped to CCPS process safety management categories, and analyzed with Bayesian networks and cross‑correlation to identify dependencies and prioritize improvement actions (e.g., which management elements most influence incident outcomes). For PSM incident investigation, it demonstrates a repeatable, data-driven method to convert incident databases into actionable causal insights and element prioritization.