DNV GL has developed a solution that is designed to reduce 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 Norwegean 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.
“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.
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