AI/NLP Engine to Detect Serious Injury & Fatality (SIF) Precursors in OIL's Unsafe-Act/Unsafe-Condition and Near-Miss Reports

Official description

• Background OIL collects large volumes of UA/UC observations, near-miss and incident reports through its HSSE platform but these are triaged manually after certain time intervals such as monthly, quarterly etc.However, Global best practice (DEKRA Martin & Black 2015; EEI SIF Precursor model; VelocityEHS 2024 PSIF classifier) has established that low-severity incidents do not share the same causes as fatalities - non-fatal US accidents fell 51% over 15 years while fatalities fell only 25.5%.Leading operators therefore separately flag the ~20–25% of reports carrying genuine fatal potential. Problem Description Build a prototype that ingests OIL's free-text safety reports and automatically a) Classifies each as SIF-potential vs non-SIF-potential b) Tags it to the relevant IOGP Life-Saving Rule (e.g., Energy Isolation, Hot Work,Confined Space, Line of Fire) c) Surfaces recurring precursor patterns (activity, location, barrier failure) via a dashboard. Expected Outcome/Solution A working AI/NLP with an interactive dashboard that ranks sites/activities by SIF-precursor density and auto-maps to Life-Saving Rules, enabling HSE to focus interventions where fatal potential is highest. Relevant Data Availability (if any) OIL's UA/UC observations, near-miss and incident reports.