Gas compressors are critical to oil and gas production, where pressure instability or equipment failure can result in lost production and
potentially require wells to be shut in to limit flaring.
Traditional DCS monitoring typically evaluates individual measurements against predefined alarm limits. In this case, however, the
developing issue was reflected in changing relationships across multiple pressure and control parameters, making it difficult to
identify from any single measurement alone.
VROC was running 256 live AI models across the compressor train's critical operating data. Rather than monitoring each pressure
independently, the multidimensional Deviation model learned how key process parameters normally behaved in relation to one
another. Three days before the trip, the models began identifying abnormal relationships between upstream pressures, main gas
header pressure and associated pressure control valves.
The analysis highlighted abnormal behavior associated with a malfunctioning pressure control valve (PCV). As the valve became
unable to maintain header pressure, other control valves responded to compensate, progressively driving the system toward the low
suction pressure condition that ultimately resulted in the compressor trip.
No single parameter told the complete story. VROC detected a change in the normal relationship between multiple compressor and
pressure-control parameters, helping isolate the malfunctioning PCV as a key contributor to the developing low-pressure condition.
Earlier warning
The multidimensional Deviation model detected abnormal behavior 3 days before the sudden trip, providing
additional time for the reliability and operations teams to investigate.
Faster problem identification
By analyzing relationships across multiple parameters, VROC helped identify the malfunctioning
PCV as a bad actor within the wider process, rather than relying on individual threshold alarms.
Estimated cost avoidance
The availability of a second compressor enabled a timely compressor swap, helping avoid an
estimated USD $72K in production loss, equivalent to approximately 1,200 bbl.
VROC’s multidimensional AI identifies changes across interacting process parameters, helping teams find emerging bad actors and investigate issues before they escalate
Explore Deviation Management | Talk to Our Team
Related Case Study
Detecting a Faulty Pressure Transmitter 4 Days Before a Gas Compressor Trip
See how VROC helped distinguish abnormal instrumentation from a genuine compressor process condition.
Industry: Oil and Gas
Solution: Predictive Maintenance & Reliability
Use Case: Deviation Management
Facility: Offshore Platform
Asset: Gas Compressor
Gain real-time visibility, predict failure earlier, optimize performance, and take control of your operations with VROC’s integrated solutions.