The complete paper discusses the successful application of a data-driven approach to analyze production data and identify root causes of slugging in a subsea production system on the Norwegian Continental Shelf. The approach used machine-learning techniques to model and analyze historical production data to identify the drivers behind slug flow. The results were used in combination with simulator studies and engineering experience to create a better understanding of the underlying root cause and to make it easier for field engineers to leverage all available information to reduce slugging and optimize production.
Slugging Challenges in Offshore Fields
Subsea production systems, characterized by deep wells and pipeline-riser setups, are especially prone to slug flow. Severe slugging, which can occur in the riser as a result of a pipeline topology-induced low-point angle at the base of the riser, produces only one slug in the riser at a time, and its length can reach that of the entire riser.
