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 net works. 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.

A Novel Approach to the Gas-Lift Allocation Optimization Problem

“…Kosmidis et al (2005) used mixed integer linear programming to optimize the gas distribution of a gas lift system and the genetic algorithm model was applied by Ray and Sarker (2007) to deal with gas allocation problems. Finally, Zerafat et al (2009) applied an ant colony optimization (ACO) method to allocate the restricted available gas to a group of wells.…”

A comparative study of several metaheuristic algorithms for optimizing complex 3-D well-path designs

“…Among the algorithms evaluated, ACO is more suited to discrete and network optimization problems. For example, the application of ACO is gas allocation in gas lift operations (Zerafat et al 2009).…”

A study on the effects of surrounding faults on optimisation of improve oil recovery process by using a genetic algorithm self-code

“…To avoid getting stuck on a set of local optimum due to premature convergence, a specific amount of timed mutation is applied by flipping alleles or bits on the chromosome strings (Tayfur et al, 2009). These advantages allow the GA to be a strong, stochastic and streamlined optimisation method and for the noticed advantages it is clear that GA code is a good solution for optimisation process (Ray and Sarker, 2007;Zerafat et al, 2009). In the well placement optimisation, testing of each solution corresponds to perform a simulation for each well configuration over all realisations of the geological model.…”