This article, written by JPT Technology Editor Judy Feder, contains highlights of paper OTC 29925, “Slug-Flow Root-Cause Analysis: A Data-Driven Approach,” by Anders T. Sandnes, Vidar Uglane, and Bjarne Grimstad, Solution Seeker, prepared for the 2019 Offshore Technology Conference Brasil, Rio de Janeiro, 29-31 October 2019. The paper has not been peer reviewed. Copyright 2019 Offshore Technology Conference. Reproduced by permission.

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.

Keywords:
machine learning, reservoir surveillance, production control, artificial intelligence, subsea system, production logging, production monitoring, flow metering, complete paper, slug severity
Subjects:
Artificial intelligence, Downhole and wellsite flow metering, Information Management and Systems, Offshore Facilities and Subsea Systems, Pipelines, Flowlines and Risers, Production logging, Risers, Well & Reservoir Surveillance and Monitoring
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2019. Offshore Technology Conference
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