Machine Learning Can Make Mooring Safer and More Cost Effective

Failure of mooring systems is a persistent problem for floating offshore facilities such as floating production, storage, and offloading (FPSO) vessels. On average, about two incidents of permanent mooring systems failing were reported annually between 2000 and 2013, according to various analysts. One of these studies found that nearly half of such incidents reported over a decade involved failures of multiple mooring lines. Another estimated that 150 mooring lines were repaired or replaced in the same period. 

For example, the Gryphon Alpha FPSO vessel came off station in a storm in the UK North Sea in 2011 when four of its 10 mooring lines parted. This reportedly cost an estimated $1.8 billion to reinstate, and another $300 million later that year when five of 10 lines parted and the vessel again came off station. 

The consequences of mooring system failure can be dangerous and costly. In the severest cases, vessels have drifted and risers connecting floating structures to subsea systems have ruptured. Such incidents have resulted in extended field shutdowns and raised risk to life, property, and the environment. Despite this, no formally published study has yet quantified both the likelihood of failure and its consequences at specific points along a mooring line. 

Traditional Ways of Detecting and Predicting Mooring Line Failure Have Limitations 

Traditional methods of failure detection rely on either a "watch circle" approach or the measurement of mooring line tension. 

In essence, the watch circle method establishes a ring inside of which the vessel is assumed to operate with all mooring lines intact. However, this approach lacks accuracy and reliability and is not very practical in operational use. Physical tension sensors on mooring lines are expensive and problematic to maintain. Field experience suggests that they are prone to failure within a few years of installation. 

An Approach Based on Machine Learning Has Advantages 

Given the considerations described, there is a need for a robust method. One approach is suggested by recent advances in machine learning, which are igniting interest in intelligent digital methods for anomaly detection, structural integrity assessment, and virtual sensors.

DNV GL specialists in Houston have applied such methods to train machine-learning models to accurately identify mooring line condition. The result is Smart Mooring, the company’s novel alternative method using machine learning. It uses the floating vessel’s GPS and six degrees-of-freedom acceleration data to detect the condition of the mooring system in near real-time. 

“Our tests show that, for determining when a mooring line has failed, this advanced solution is more accurate and cost effective than physical tension sensors that detect anomalies,” said Frank Ketelaars, regional manager, the Americas, DNV GL. 

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