Session: 04-02-02 Rigid Risers II

Paper Number: 103743

103743 - Steel Lazy Wave Riser Optimization Using Particle Swarm Optimization Algorithm 

Steel lazy wave risers have become a preferred riser solution as offshore oil and gas developments are happening in deeper waters and harsher environments. Steel lazy wave risers work well with FPSOs even though FPSO motions are worse compared to other floating platforms such as Spars and Semisubmersibles. FPSOs offer a tremendous advantage compared to other floating platforms as they can be used in regions with inadequate pipeline infrastructure to transport oil and gas to shore. The targeted placement of buoyancy modules on steel lazy wave risers improves the strength and fatigue performance compared to steel catenary risers making them feasible for use with FPSO’s. Also, buoyancy modules on steel lazy wave risers help reduce the top tension compared to steel catenary risers which gains significance in ultra-deep water developments where top tensions for steel catenary risers make them an infeasible riser solution.

 

Steel lazy wave riser configuration design is dependent on a few parameters such as hang-off angle, length to start of buoyancy section, length of buoyancy section, type of buoyancy and buoyancy factor. Theoretically, there are infinite solutions for a steel lazy wave riser configuration. Hence, the design of steel lazy waver riser is an optimization problem in which the parameters that govern the design can be varied to get an optimized configuration. In this paper, the particle swarm optimization algorithm which is a bio-inspired algorithm is used to find an optimal solution for steel lazy wave riser configuration. The solution space is searched for an optimal steel lazy waver riser configuration based on an objective function which is a function of strength response, fatigue response and cost. Solutions with poor interference response are penalized in the algorithm and rejected as part of the solution space search process. Finding an optimal solution automatically will help reduce the overall cost of risers which is a significant part of any offshore development. Also, it will help in making the riser design efficient from strength and fatigue point of view in addition to making it robust and streamlined.

Presenting Author: Mayank Lal TechnipFMC

Presenting Author Biography: Mayank Lal has more than 14 years of experience in the Oil and Gas industry and 3 years of experience in the Automotive industry. Mayank has extensive experience in the design and analysis of risers including Steel Lazy Wave Risers, Steel Catenary Risers, TTR's, FSHR's, flexible risers and umbilicals. Mayank currently works as a Lead Engineer at TechnipFMC. He holds a Ph.D. from Texas A&M University in Mechanical Engineering. Mayank has published several papers in the area of optimization of risers using artificial intelligence tools.

Authors:

Mayank Lal TechnipFMC
Anurag Yenduri TechnipFMC
Abhilash Sebastian TechnipFMC