We present a data-driven root cause analysis of slug flow in a subsea field. The asset experienced severe slugging in a riser, which limited production throughput. The results were used in combination with simulator studies and engineering experience to create a better understanding of the underlying root cause for slugging.
A selection of signals was investigated as possible drivers behind slug severity. Focus was put on well-specific signals such as pressures, temperatures and flow rates, in addition to total flow rates, pipeline pressures and temperatures, and settings on the topside facility. Total liquid rate, especially the water component, is isolated as an important driver for slugging, while ruling out other signals believed to be important before the analysis, such as production from individual wells. The results were aligned with the field engineers’ experience. Actions were implemented to reduce water production, and this led to reduced slugging. Close collaboration between data scientists and field engineers was essential to guide the search towards actionable evidence.
The novelty of this approach lies in utilizing machine learning techniques to model and analyze historical production data in order to find drivers behind events such as slug flow. This makes it easier for field engineers to leverage all available information to optimize production.