An offshore oil and gas platform detected a gas moisture level of 243 ppmv, above the required limit of 200 ppmv, with the reading continuing to increase.
Elevated moisture can contribute to CO₂ corrosion, increasing the risk of leakage and process-safety incidents. Under the site's operating requirements, three consecutive days of high moisture would also require the platform to shut down.
Conventional troubleshooting would typically focus first on the dehydration system and could require a multidisciplinary team working over several weeks to isolate the cause. With limited time available, the operator needed a faster way to determine what was actually driving the high moisture condition.
The team built a no-code AI model in VROC to analyze available historical and real-time data from the dehydration process together with data from surrounding systems.
Rather than limiting the analysis to the moisture analyzer or glycol system, the model evaluated relationships across the wider operation and identified a gas well as a leading contributor to the high moisture condition.
Although the well and moisture analyzer sat within seemingly separate processes, the team acted on the AI insight and shut in the identified well.
The response was immediate: gas moisture dropped from 243 ppmv to 144 ppmv within minutes, providing strong operational confirmation that the contributing source had been correctly identified.
VROC identified a gas well outside the immediate dehydration process as a leading contributor to the high moisture condition.
When the well was shut in, moisture dropped from 243 ppmv to 144 ppmv within minutes, validating the insight and returning the reading below the required limit.
Rapid root-cause identification
AI analysis helped the team move beyond conventional troubleshooting of the dehydration system and identify a contributing source across the wider operation.
Process-safety risk reduced
Shutting in the identified well rapidly reduced moisture below the required limit, helping reduce the risk of CO₂ corrosion, leakage and a potential LOPC event.
Customer-confirmed cost avoidance
The client attributed close to USD $1M in value to leakage avoidance associated with the intervention.
“Technically you couldn’t get this particular decision to be made as fast as that.” – Head of Offshore Operations
VROC analyzes relationships across assets and processes to identify the factors contributing to abnormal conditions — even when the cause sits outside the system where the problem appears.
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Industry: Oil & Gas
Solution: Predictive Maintenance & Reliability
Use Case: Time to Failure Prediction
Facility: Offshore Platform
Asset/System: Gas Dehydration / Glycol System
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