A Multiobjective Genetic Algorithm for Blowout Preventer Test Scheduling Optimization

P.A.A. Garcia, C.M.C. Jacinto, and E.A.L. Droguett (Brazil)


Multiobjective optimization, genetic algorithm, cost, availability, safety system, well engineering.


In oil and gas well construction, safety characteristics are in constant increasing, and, at same time, the cost associated with them too. Blowout preventer (BOP) is a safety system associated to well constructions in field development. With the experience gathered by Petrobras, some researches are carried to estimate optimum periodic testing intervals and costs associated to BOP components. In nowadays, with the evolution of computational technology, improvements in optimization methods can be done. One of the bigger computing progresses is related to evolutionary algorithms, which are founded on specimens’ evolution. One of these algorithms are the genetic algorithms (GA), developed by John Holland at University of Michigan. The first monograph were entitled “Adaptation in Natural and Artificial Systems”. In the present paper one presents an application of a kind of GA to solve a multiobjective problem. So, a multiobjective genetic algorithm (MOGA) approach to BOP test-scheduling optimization is utilized, and a simple example is carried out to demonstrate the capability of the proposed approach. The motivations to use a GA approach are associated to the combinatorial characteristics of the surveillance test-scheduling problems. The results obtained with the proposed approach shown the powerfulness of MOGA in this kind of problems.

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