基于改进元胞粒子群的油田措施调整方案优化

Optimization of Oilfield Stimulation and Adjustment Scheme Based on the Improved Cellular Particle Swarm

针对油田实际生产情况,以措施增油量、措施增液量和措施增注量为约束条件,以当年产出投入比最大化为目标,构建一种多约束条件下的措施调整规划模型,并将元胞自动机理论引入到粒子群算法中,利用佳点集理论初始化种群,结合云模型和混沌算法改进个体进化方式,提出一种改进元胞粒子群算法对模型进行求解。结合油田的实际数据和4种优化算法对模型进行实例仿真结果对比,仿真结果表明该算法获得了很好的求解精度和速度,具有很好的实际应用价值。

In view of the actual production situation of the oilfield,measure adjustment plan model based on multi constraint conditions is established by taking oil production increment,fluid production increment and injection increment as the constraint condition,by taking maximum output and investment ratio as target,and cellular automaton theory is introduced into the particle swarm optimization algorithm.The initialization population is initialized by using the best point set theory,and individual evolution method is improved based on cloud model and chaos algorithm.Based on the above theory,an improved cellular particle swarm optimization algorithm is proposed to solve the model.With the actual data of oilfield and four kinds of optimization algorithms,the simulation results are compared.The results show that the proposed algorithm has good accuracy and speed,and it has good practical value.