Investigação sobre meta-heurísticas híbridas para o problema de otimização associado ao Gas Lift

Abstract

Gas lift is a technique that uses an external source to supplement gas formation for lifting fluids in oil wells. The amount of injected gas cannot be excessive, as it may lead to decreased production. One of the objectives in this context is the optimization of gas allocation in order to maximize oil production. Some algorithms presented for this problem are based on metaheuristics. Each technique has strengths and weaknesses. The hybridization of metaheuristics aims to extract the best potential from each technique and minimize weaknesses. This undergraduate thesis presents memetic algorithms based on the hybridization between the Genetic Algorithm and the Simulated Annealing and Multi-start algorithms.