Application of particle swarm optimization and genetic algorithm for optimization of a southern Iranian oilfield

Abstract

Abstract Optimization of the placement and operational conditions of oil wells plays an important role in the development of the oilfields. Several automatic optimization algorithms have been used by different authors in recent years. However, different optimizers give different results depending on the nature of the problem. In the current study, a comparison between the genetic algorithm and particle swarm optimization algorithms was made to optimize the operational conditions of the injection and production wells and also to optimize the location of the injection wells in a southern Iranian oilfield. The current study was carried out with the principal purpose of evaluating and comparing the performance of the two most used optimization algorithms for field development optimization on real-field data. Also, a comparison was made between the results of sequential and simultaneous optimization of the decision variables. Net present value of the project was used as the objective function, and the two algorithms were compared in terms of the profitability incremental added to the project over twelve years. First, the production rate of the producers was optimized, and then water alternating gas injection wells were added to the field at locations determined by engineering judgment. Afterward, the location, injection rate, and water alternating gas ratio of the injectors were optimized sequentially using the two algorithms. Next, the production rate of the producers was optimized again. Finally, a simultaneous optimization was done in two manners to evaluate its effect on the optimization results: simultaneous optimization of the last two steps and simultaneous optimization of all decision variables. Results showed the positive effect of the algorithms on the profitability of the project and superiority of the particle swarm optimization over the genetic algorithm at every stage. Also, simultaneous optimization was beneficial at finiding better results compared to sequential optimization approach. In the end, a sensitivity analysis was made to specify the most influencing decision variable on the project’s profitability.

Keywords

• Field development optimization
• Genetic algorithm
• Particle swarm optimization
• Sequential optimization
• Simultaneous optimization