DNV GL Develops ML Solution For Faster, Accurate Mooring Line Failure Detection

DNV GL, the technical advisor to the oil and gas industry, has developed a solution that reduces the risk of offshore floating vessel mooring line failure going undetected by replacing physical sensors with a machine learning algorithm that accurately predicts line failure in real time.

The company’s Smart Mooring solution addresses growing industry concern about the high frequency of mooring line failure and a vessel’s subsequent loss of station. Over the past two decades, more than 20 incidents have been reported globally involving failure of permanent mooring systems on floating structures. In the most severe cases, vessels have drifted and risers have ruptured, causing extended field shutdown, and risk to life, property and the environment.

Results from a numerical case study of a turret moored floating production, storage and offloading vessel (FPSO) with more than 4,000 test cases have demonstrated that DNV GL’s Smart Mooring solution can accurately identify when a mooring line has failed. Multiple pilot studies will be conducted on other offshore floating vessel types over the remainder of this year.

DNV GL develops machine learning solution for faster, more accurate mooring line failure detection in offshore operations
Image Credits: dnvgl.com

“Our Smart Mooring solution can be deployed to predict a mooring system’s response to various operating conditions. It determines when a mooring line has failed, more accurately and cost-effectively than physical tension sensors currently used to detect anomalies. Conservatively, we estimate it is half the cost to implement our solution versus installing a mooring line tension monitoring system for a brownfield operation,” said Frank Ketelaars, Regional Manager, the Americas, DNV GL – Oil & Gas.

Tension sensors can be difficult and costly to maintain, and field experience suggests that they can be prone to failure within the first few years of installation. DNV GL’s Smart Mooring solution can be used instead of replacing failed sensors in brownfield offshore operations, or as a complete alternative to implementing sensor technology in greenfield offshore oil and gas developments.

DNV GL’s experts developed the Smart Mooring solution by training a machine learning model to interpret the response of a vessel’s mooring system to a set of environmental conditions and are then able to determine which mooring line has failed.

Vivek Jaiswal, Senior Engineer, DNV GL – Oil & Gas is presenting a paper on ‘Mooring Line Failure Detection using Machine Learning’ at the Advances in Mooring Technology session, Room 600 of the OTC Conference in Houston, TX, on Monday 6 May at 14:00 – 16:30.

Reference: dnvgl.com

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Marine Insight News Network is a premier source for up-to-date, comprehensive, and insightful coverage of the maritime industry. Dedicated to offering the latest news, trends, and analyses in shipping, marine technology, regulations, and global maritime affairs, Marine Insight News Network prides itself on delivering accurate, engaging, and relevant information.

About Author

Marine Insight News Network is a premier source for up-to-date, comprehensive, and insightful coverage of the maritime industry. Dedicated to offering the latest news, trends, and analyses in shipping, marine technology, regulations, and global maritime affairs, Marine Insight News Network prides itself on delivering accurate, engaging, and relevant information.

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