As fatigue life assessment of flexible risers is a concern for operators offshore Brazil, DNV GL proposes a robust tool based on an artificial neural network (ANN). This tool would use as input a big data set of environmental data that most offshore units already have from years of monitoring and provide reliable results in terms of fatigue damage of flexible risers. Estimating fatigue life in a reliable and less time-consuming manner will support cost-effective decision making on riser lifetime extension.
Integrity management is a major concern for flexible risers operating in offshore Brazil and one of the main issues is related to fatigue life assessment. The industry is interested in estimating the fatigue damage of the risers during operation to obtain more reliable results of accumulated damage and to have additional information allowing for integrity assessment and life extension evaluation in a more cost-efficient way.
DNV GL proposes a robust tool based on an Artificial Neural Network (ANN) to provide reliable results in terms of fatigue damage of flexible risers. A big data set of environmental data already available for most offshore units from years of monitoring will be used as input into the tool.
Obtain fatigue life in a more reliable, cost-efficient and less time-consuming manner, to support decision making (e.g. on possible life extension of the risers).
Estimate fatigue life of riser in a reliable way and less time-consuming, reducing money-loss and giving reliable results to support decision on extend lifetime of risers in a more cost-efficient way.