Fatigue Analysis and Intelligent Prediction for the Contact Sections of Steel Catenary Riser in Deep Water Environments

The deep-water steel catenary riser (SCR) touches the seabed first and the interaction is complex, which makes the steel catenary riser prone to fatigue damage. Therefore, fatigue analysis has become a research hotspot of steel catenary risers. However, the existing pipe-soil mechanism model combined with fatigue calculation analysis method lacks comparative verification with the operation site, and does not have continuous prediction characteristics. In this paper, a fatigue analysis method based on field monitoring data of steel catenary riser is proposed, which greatly improves the accuracy of fatigue prediction. Based on the application of deep-water steel catenary riser in the field, a finite element simulation model is established, and the monitoring data is used to carry out the characterization inversion work and modify the mechanical model. The mechanical response data set of the modified model under multiple working conditions is established. The deep learning algorithm is used to train the production stress spectrum of the set. Combined with the fatigue analysis method, the fatigue life of the steel catenary riser is calculated to meet the 30-year non-docking requirement. It provides theoretical basis and technical support for the application safety assessment and optimization design of deep-water steel catenary riser.