Genetic Algorithms and Ant Colony Approach for Gas-lift Allocation Optimization

Abstract

Continuous gas-lift is one of the most commonly practiced artificial lift techniques. It assists production enhancement by continuous injection of high-pressure gas into the well tubing, which lightens the oil column. Either gas limitation or compressor capacity makes it impossible to make all the network wells produce at the optimum rate; hence the need to determine the optimal gas distribution. Gas allocation optimization is a type of nonlinear function maximization with gas injection rates as decision variables subject to physical restrictions. Various optimization methods are applied in previous works among which genetic algorithm (GA) has proposed the best efficiency for large networks. In this work, various methods are performed as a comparison to GA. Besides, ant colony optimization (ACO) is applied to the network as a new optimization tool in oil industry, as a possible alternative for GA, already proved its capability in the optimization of water distribution networks. The literature demonstrated the application capability of GA and ACO for gas-lift allocation optimization in small networks. The results proved an availability of GA and ACO for this problem, and it showed the applicability to the optimization of large-scale networks. The results are compared to similar calculations in the literature by other optimization techniques, which show promising agreement.