The simultaneous review of the slug catcher and the subsurface AI models has shown that our system provides a high level of confidence in monitoring and predicting undesireable activities, which can lead to potential sand events across topside critical equipment.
VROC was asked to use AI modelling to investigate three major subsurface instability events recorded over the course of six months by a top tier oil and gas operator in the North Sea. The purpose of the study was to identify subsurface instability and potential sand production events by observing the wellhead critical pressure and temperature values, as well as the top-side equipment performance.
The slug catcher vessel was the first of the equipment in the top-side processing plant responsible for gathering the well product immediatley after receiving the flow from subsea flowlines and risers. The slug catcher is highly sensitive to subsurface activities.
VROC trained AI models on the historical data from subsurface, including various subsurface pressures, temperatures and valve configurations, as well as the slug catchers sensors including level, pressure, temperature etc.
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