AI-Driven Early Event Detection in VRU Compressors

Unplanned compressor trips in Vapor Recovery Units (VRUs) reduce uptime, increase flaring, and raise emissions.
This case study shows how an AI + Fault Tree Analysis approach provided early event detection and guided root cause analysis, improving reliability while supporting ESG goals.
emissions. 

What’s Inside:

• 10–15% improvement in VRU availability
• ~12% reduction in flaring and associated emissions over 3 months
• Targeted maintenance with RCA-based insights
• Standardized, auditable predictions for engineers & sustainability teams

Why It Worked:

• Built on historian data and OH notifications
• Combined machine learning with Fault Tree Analysis
• Delivered real-time dashboards with RCA paths

Fill out the form to access the full case study

AI applied to upstream production. Proven. Measurable. Sustainable.