DNV GL, a major of 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.
Specifically, DNV GL’s ‘Smart Mooring’ solution marks the rising concerns the industry has about the high frequency of mooring line failure, and a vessel’s subsequent loss of station.
In the past 20 years, up to 20 incidents have been globally reported 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.
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Thus, SQE Marine has provided a sample of toolbox meeting in order to be used as guidance on board, in favour of the seafarers’ safety.
Moreover, trials of the Smart Mooring solution, showed that the application can accurately identify when a mooring line has failed. Yet, multiple pilot studies will be conducted on other offshore floating vessel types over the remainder of this year.
Frank Ketelaars, Regional Manager, the Americas, DNV GL – Oil & Gas commented
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.
Also, tension sensors are expensive and difficult to maintain, whereas experience in the field shows that they are eager to fail within the first few years of installation.
Consequently, 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.
Concluding, the Class’s experts developed the smart solution by trialling 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.
