Optimization Design of Deepwater Steel Catenary Risers Using Genetic Algorithm

This work presents the implementation of genetic algorithms in the op- timization design of deepwater Steel Catenary Risers (SCRs) with discrete design variables. For deepwater developments, riser system requirements become a sig- nificant factor in the cost of the overall oil field investment. Hence, riser design must consider safety before reducing costs. Firstly, three dimensional nonlinear SCRs models were constructed by finite element method; In addition, nonlinear characteristics of soil/structure interaction are also included according to regula- tions. Secondly, a steel catenary riser is analyzed for several typical ocean envi- ronmental conditions to prove the vast potential of the proposed strategy as a de- sign tool; thirdly, SCRs design based on genetic algorithm were applied for given design parameters such as riser thickness, coating properties and constraints. Compared with conventional design method, the optimized configuration not only can cut the cost while satisfied all constraints, but also reduce the maximum von Mises stress. According to the above analysis, the optimization strategy base on genetic algorithm is a useful tool for SCRs design, and that the proposed method for selection of optimum design variables will enable an engineer to identify de- signs with minimum costs in an efficient way.