Optimization of Lift Gas Allocation using Evolutionary Algorithms

Abstract

In this paper, the particle swarm optimization (PSO) algorithm is proposed to solve the lift gas optimization problem in the crude oil production industry. Two evolutionary algorithms, genetic algorithm (GA) and PSO, are applied to optimize the gas distribution for oil lifting problem for a 6-well and a 56-well site. The performance plots of the gas intakes are estimated through the artificial neural network (ANN) method in MATLAB. Comparing the simulation results using the evolutionary optimization algorithms and the classical methods, proved the better performance and faster convergence of the evolutionary methods over the classical approaches. Moreover, the convergence rate of PSO is 13 times faster than GA's for this problem.

A New Energy-Aware Method for Gas Lift Allocation in IoT-Based Industries Using a Chemical Reaction-Based Optimization Algorithm

“…Based on fitness value, produced chromosomes replaced others in the parent population. López, Koç [31] presented the lift gas allocation based on particle swarm optimization (PSO) and genetic algorithm (GA). In the proposed approach, the efficiency graphs of the wells with various stages of gas injections were calculated.…”

“…In this experiment, the number of generations (0-250) was performed at a speed of 2000 million instructions per second, whose fitness function was in the range between (1-0). To test the convergence, the proposed algorithm is compared to the ABCO [32], GA_PSO [31] and GA_SA [34] in 180 repetitions. As the number of generations increases, the fitness function reaches a constant value.…”

Gas lift optimization in the oil and gas production process: a review of production challenges and optimization strategies

“…The Genetic algorithm (GA) is one of the most important meta-heuristic algorithms which was first introduced by Holland in 1975 (Bordbar et al, 2020;Kesavan et al, 2020). It is a type of evolutionary algorithm, which is commonly used in artificial intelligence (AI) and computing (López et al, 2019).…”

“…PSO optimizes a problem by improving the candidate solution iteratively. Kennedy and Eberhart first introduced the algorithm in 1995 (López et al, 2019). In the PSO algorithm, two main parameters are being updated in each iteration: the velocity term and position term.…”