Fatigue Prediction for Extended Riser Life and Improved Vessel-Response Analysis

Abstract

This article, written by JPT Technology Editor Judy Feder, contains highlights of paper OTC 29531, “Flexible-Riser Fatigue Counter Developed From Field Measurements and Machine-Learning Techniques,” by Christoffer Nilsen-Aas, Jan Muren, and Håvard Skjerve, 4Subsea, et al., prepared for the 2019 Offshore Technology Conference, Houston, 6–9 May. The paper has not been peer reviewed. Copyright 2019 Offshore Technology Conference. Reproduced by permission. This paper presents a fatigue-prediction methodology designed to extend the life of unbonded flexible risers and improve the accuracy of floating production, storage, and offloading (FPSO) vessel response analysis. The methodology combines measured-motion-response, maritime-environment, and process data to improve traditional time-domain dynamic analysis models, along with machine-learning (ML) techniques to develop a heading model for the FPSO. Introduction Life extension of unbonded flexible risers is a challenge because of uncertainties associated with key parameters driving deterioration, such as how the riser was designed, manufactured, and installed and how it is operated. Fatigue service life is calculated during the design phase using models based on field-specific environmental data derived for the area and loads for the required design conditions per project specifications and international standards. Using real measured data from the riser motions and from environmental sources can reduce uncertainties significantly, improving life-extension analysis for fatigue and other failure drivers. These data, when combined with high-end engineering assessments and considerations on data reliability, provide better insight and contribute to continued operations with acceptable risk for the risers. The complete paper presents a methodology that uses measured environmental data and FPSO and riser response data in an ML environment to build more-realistic riser-response and fatigue-prediction models. A case example is presented for the risers suspended from the FPSO Fluminense producing from the BijupirÁ and Salema fields offshore Brazil. Because FPSO heading is important for vessel dynamics, especially roll, and the vessel dynamics are a key factor in the riser dynamics at this field, initial focus was on predicting vessel heading relative to swell. The heading model developed by ML showed good agreement and was used as a key tool in a traditional fatigue analysis. This analysis was based on historical sea states from the last 2 years. The results show that the fatigue analysis from the design phase is conservative and lifetime extension is achievable. Because the fully instrumented measurement campaign ended after 4 months, the work focused on using all captured data to provide improved insight and develop both traditional simulation and ML models. For future fatigue predictions based on the developed fatigue counter, the objective is to maintain accuracy with less instrumentation. In the present phase, FPSO and riser-response data from the 4-month campaign have been used to establish a correlation between riser behavior, environmental data, and FPSO heading and motion. Calibration of a traditional numerical model is performed using measurement data along with a direct waves-to-fatigue prediction based on modern ML techniques. The ongoing work illustrates how real environmental data and response monitoring can reduce uncertainties and improve and extend flexible-riser life.