A multibody system including a drilling riser system, tensioners and a floating platform is key equipment for offshore oil and gas drilling. Most of the previous studies only focus on the drilling riser system rather than the multibody system. Mechanical characteristics of the deepwater drilling riser system cannot be analyzed accurately in a simplified model. Therefore, a three-dimensional multibody analysis program is developed. The static and dynamic characteristics of the deepwater drilling riser system under different platform motions are analyzed based on the developed program. The results show that the static displacement of the riser system with tensioners is smaller than that without tensioners, which means the tensioners can suppress the deformation of the riser system. Under surge and sway motions of the platform, the dynamic displacement of the riser system with tensioners is also smaller than that without tensioners due to the tensioner suppression effect. Besides, the heave motion induces a uniform axial vibration of the riser system, while roll and pitch motions excite the riser system to vibrate laterally. Compared with the stress amplitude due to surge and sway motions, the stress amplitude of the riser system due to heave, roll and pitch motions is relatively small but cannot be neglected.

The fatigue reliability assessment of deepwater risers plays an important role in the safety of oil and gas development. Physical-based models are widely used in riser fatigue reliability analyses. However, these models present some disadvantages in riser fatigue reliability analyses, such as low computational efficiency and the inability to introduce inspection data. An improved fatigue reliability analysis method was proposed to conduct the fatigue reliability assessment of deepwater risers. The data-driven models were established based on response surface methods to replace the original physical-based models. They are more efficient than the physics-based model, because a large number of complex numerical and iterative solutions are avoided in fatigue reliability analysis. The annual crack growth model of the riser based on fracture mechanics was established by considering the crack inspection data as a factor, and the crack growth dynamic Bayesian network was established to evaluate and update the fatigue reliability of the riser. The performance of the proposed method was demonstrated by applying the method to a case. Results showed that the data-driven models could be used to analyze riser fatigue accurately, and the crack growth model could be performed to analyze riser fatigue reliability efficiently. The crack inspection results update the random parameters distribution and the fatigue reliability of deepwater risers by Bayesian inference. The accuracy and efficiency of fatigue analysis of deepwater risers can be improved using the proposed method.