Using modern heuristic algorithms for optimal control of a gas lifted field

Abstract

In the gas lift, gas injection rate has an optimum value and increasing or decreasing its amount decreases the oil production rate. This optimum point changes in production time and creates an optimum trajectory. There has previously been much research in this area, but none have used a powerful tool in a dynamic model to find a good optimal path for maximizing the production or NPV in a gas-lifted field. In addition, gas allocation in a time step changes the production of different wells and the pattern of fluid flow and also reservoir pressure decline in the reservoir. This can affect the optimum gas allocation of the next time step, but this has not been analyzed in any of the previous works. In this paper, first, a model of reservoir and well of a gas lift well is developed and using some experimental data points the best-fitted correlations are selected. Then, two common and famous heuristic algorithms (genetic algorithm, simulated annealing), and two recently introduced optimization algorithms (Moth Swarm Algorithm and Grasshopper Optimization Algorithm) are used to find the optimal path of the injection lift gas rates of the wells. Finally, the results of these four algorithms are compared with each other and some other allocation scenarios. In addition, different aspects of the injection and production rates of the different wells and their cumulative rates and NPV paths are analyzed. The results illustrated that using MSA has an optimum point with higher production. In addition, MSA finds a specific control path with a need for lower lift gas and also a smaller compressor. It also has a really different control path with other optimization algorithm paths. In addition, in this paper, a new long term instability in flow is observed, which was explained by the drainage area of each well. This control path of each well was discussed and it was concluded that GA can find the most stable path.