The international oil & gas advisory and classification society DNV GL has developed an algorithm that reduces the risk of offshore floating vessel mooring line failure going undetected.
DNV GL said that the solution entailed 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 drifted, and risers ruptured causing extended field shutdown and risk to life, property, and the environment, DNV GL said.
Results from a numerical case study of a turret moored FPSO with more than 4,000 test cases demonstrated that DNV GL’s Smart Mooring solution could 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.
Frank Ketelaars, regional manager for the Americas at DNV GL – Oil & Gas, said: “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.”
According to the firm, 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 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.
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