# Predictive Maintenance in O&G 30-40% of O&G budgets go to maintenance, yet equipment failures persist. Harsh environments (high pressure, corrosive gases, extreme temps) cause sensor drift, leading to both false positives and missed threats. ## The Numbers - Detecting bearing anomaly 18 days early prevents $700K production loss - Shell: AI predictive maintenance found 65 control valves needing repair that traditional methods missed - 40% reduction in maintenance costs through AI (Shell case) - BP: $10M annual savings from predictive systems ## The AI Moat Time-series anomaly detection trained on facility-specific patterns: - Vibration signatures - Temperature profiles - Pressure curves - Flow patterns - IoT sensor health monitoring The moat is in the data. Each facility has unique baselines. Generic models won't cut it. ## Links - [[F&G Control Systems]] - [[Multi-Modal Anomaly Detection]] - [[F&G Safety Opportunity MOC]] --- #opportunity #maintenance #ai