This paper presents a low capital expenditure (CAPEX) monitoringsolution for flexible pipeline fatigue integrity assessment. This approachfocusses on the processing of floating unit (FU) motion informationavailable in field, to predict remaining service life of operating flexiblepipelines. The solution is built from a supervised statistical learningalgorithm, called kriging, calibrated and validated on design data andcertified design tools. The monitoring solution is then performed feedingin field real time motion reference unit data, within this calibrated model.Accounting for uncertainties, this solution aims to provide accurateprediction of risers service life.
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