Application of Genetic Algorithms to the Synthesis of Riser Configurations

Abstract

The purpose of this work is to describe the application of Genetic Algorithms in the search of the best configuration of catenary riser systems in deep waters. Particularly, an optimization methodology based on genetic algorithms is implemented on a computer program, in order to seek an optimum geometric configuration for a steel catenary riser in a lazy-wave configuration. This problem is characterized by a very large space of possible solutions; the use of traditional methods is an exhaustive work, since there is a large number of variables and parameters that define this type of system. Genetic algorithms are more robust than the more commonly used optimization techniques. They use random choice as a tool to guide a search toward regions of the search space with likely improvements. Some differences such as the coding of the parameter set, the search from a population of points, the use of objective functions and randomized operators are factors that contribute to the robustness of a genetic algorithm and result in advantages over traditional techniques. The implemented methodology has as baseline one or more criteria established by the experience of the offshore engineer. The implementation of an intelligent methodology oriented specifically to the optimization and synthesis of riser configurations will not only facilitate the work of manipulating a huge mass of data, but also assure the best alternative between all the possible ones, searching in a much larger space of possible solutions than classical methods.

Tailoring the particle swarm optimization algorithm for the design of offshore oil production risers

“…This fact had already been recognized in previous works (Vieira et al 2003;de Lima et al 2005;Vieira 2008), where Genetic Algorithms-GA were employed in the development of optimization procedures for lazy-wave risers.…”

“…The method is implemented in an in-house computational tool oriented to the design of risers, referred here as the OtimRiser tool. This tool incorporates other optimization methods based on evolutionary concepts presented in previous works (Vieira et al 2003;de Lima et al 2005;Albrecht 2005;Vieira 2008), including Genetic Algorithms, Micro-Genetic Algorithms, PSO and Evolutionary Strategies, applied to the design of different offshore systems including risers and mooring lines for floating production systems.…”

Computação Evolucionária em Problemas de Engenharia

“…Neste capítulo são sintetizados alguns dos resultados obtidos em trabalhos anteriores (Vieira et al, 2003;de Lima et al, 2005;Vieira et al, 2008), onde métodos de computação evolucionária foram empregados no desenvolvimento de uma ferramenta para a otimização de congurações de risers. Nas duas próximas seções será descrita a modelagem do problema e, mais adiante, na Seção 4.1, serão apresentados resultados de estudos de casos.…”

A hybrid fuzzy/genetic algorithm for the design of offshore oil production risers

“…All remaining riser properties, including the geometric characteristics of the buoys, the riser sections and material properties, are defined in Reference [18].…”

“…A previous work [18] has presented results of the application of standard GAs in the synthesis of riser configurations. In this section, we present the application of the proposed hybrid fuzzy-GA approach in the design of lazy-wave SCR configurations.…”