Application of Oil Well Measures Optimization Base on Improved Shuffled Flog Leaping Algorithm
Jiali Wang · Journal of Changchun University of Science and Technology · 2014
Aiming at the defect that artificial arrangements for oilfield traditional measures work is computationally intensive,time-consuming and the absence of a high economic profit,a optimization model is established.The target of the model is maximizing the economic benefits.The model is used to increase injection of annual targets,increase production,hydrous and lapse rate as constraint conditions,and is solved by a improved shuffled flog leaping algorithm based on cloud model theory. The population is initialized through reverse learning mechanism,the better value around the global best individual and subgroup optimal individual in SFLAis solved by the normal cloud particle operator in this algorithm.Finally,the individual were mutated to jump out of local optimal solution by using the theory of chaos.Through comparison of classical function extremal optimization,the performance of improved algorithm is better than PSO algorithm and SFLA algorithm,The actual test data show that optimal optimization measures obtain very good effect in practical application.