OTC 2019: Smart Mooring system predicts line failure

DNV GL has developed a technique that is said to lessen the risk of offshore floating vessel mooring line failure being missed.

Offshore staff

HOUSTON – DNV GL has developed a technique that is said to lessen the risk of offshore floating vessel mooring line failure being missed.

The Smart Mooring solution involves replacing physical sensors with a machine learning algorithm that is said to accurately predict line failure in real time.

According to DNV GL, there is increasing concern in the industry about the frequency of mooring line failure and the subsequent loss of station.

More than 20 incidents have been reported during the past two decades involving failure of permanent mooring systems on floating structures. In some cases, the vessels drifted and their risers ruptured, leading to extended field shutdowns, with associated risks to life, the offshore facilities, and the environment.

However, a numerical case study of a turret moored FPSO with more than 4,000 test cases proved that Smart Mooring solution could accurately identify when a mooring line had failed.

DNV GL plans various pilot studies on other types of offshore floating vessels later this year.

“Our Smart Mooring solution can be deployed to predict a mooring system’s response to various operating conditions,” said Frank Ketelaars, regional manager, the Americas, DNV GL – Oil & Gas.

“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.”

Tension sensors can be tricky and costly to maintain, and potentially subject to failure within the first few years of installation. Smart Mooring can be deployed as an alternative to replacing failed sensors in brownfield offshore settings or to implementing sensor technology in greenfield offshore developments.

DNV GL ‘trained’ a machine learning model to interpret the response of a vessel’s mooring system to a set of environmental conditions and then determine which mooring line had failed.

Vivek Jaiswal, senior engineer, DNV GL – Oil & Gas will present a paper at OTC today on this development at the Advances in Mooring Technology session, Room 600, 14:00-16:30.

05/06/2019

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