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.
The AI models were able to predict slug catcher level and pressure, as well as critical downhole pressure and temperature parameters at all points in time based on what the AI model has learnt of past subsurface and topside operations. The models generated by VROC are highly accurate and therefore can be used to compare the parameters predicted values with their actual values. When there is a deviation between the expected values and the actual values, it is possible to conclude that there has been a change to the slug catcher parameters, such as level and pressure, which can indicate anomalies occurring in the downhole environment leading to potential sand events;
The VROC AI platform and modelling of observed information at the surface – slug catcher, separator, choke manifold, etc; can be used to predict subsurface instability and subsequent effects on well delivery. This also allows operators to change flow parameters to optimise hydrocarbon recovery while providing time to mitigate any damage mechanism on surface and downhole equipment.
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The efficient deployment, continuous retraining of models with live data and monitoring of model accuracy falls under the categorisation called MLOps. As businesses have hundreds and even.
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