Benefits at a Glance

Improve planning with Auto AI

AI prediction with 5 days lead time, compared to client who identified the failure 2-3 days prior

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Prediction allowed the client to schedule a pump changeover, to keep the system running and prevent a complete site shutdown

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Up to GBP 1M failure prevention if failure not caught in time for trip and restart


VROC was contacted by a Tier 1 FPSO Platform to assist with tracking asset degradation on Water Injection Pumps. After being impressed by the VROC platform, we were quickly asked to commence AI predictive maintenance modelling on the Produced Water Pumps to monitor the system, predict trips and track performance degradation.

The Solution

VROC commenced detailed AI predictive maintenance modelling on three Produced Water Pumps. The AI models looked at live and historical data analysing what normal and abnormal operation looked like, tracking the slightest deviation. We found some significant anomalies unfolding in the live data for Pump A.

A Produced Water Pump failure was detected by the AI. Investigating the data further the models of the seal pressure and temperature clearly showed the reduction of seal pressure and temperature from normal, revealing a mechanical seal leakage. The Produced Water Pump failure was subsequently confirmed by the client.


VROC successfully identified in advance the first ever failure to occur on this Produced Water Pump. The Produced Water Pump was subsequently taken offline for maintenance on the leaking Mechanical Seal, and the client was able to schedule a pump changeover before the mechanical seal failed, preventing a complete plant shutdown.

Potential savings: GBP30,000 per incident to repair if caught early. Potential for a Produced Water Plant trip savings GBP500,000 to GBP1,000,000 production rate at time of incident, if not caught in time for trip and restart.

VROC was quickly engaged to commence predictive maintenance modelling on the remaining functioning Produce Water Pumps to ensure that any potential failures would be predicted in advance, preventing failures which would disrupt production.

Our client was extremely happy with the results of the modelling, which was able to detect the degradation more than 5 days before a failure. This has given them great confidence in the VROC platform to help predict and avoid any future performance issues.

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