基于粒子群优化算法的感应测井反演

THE INDUCTION LOGGING INVERSION BASED ON PARTICLE SWARM OPTIMIZATION

针对传统感应测井线性迭代反演受初始模型影响,易陷入局部最优解的特点,设计一种基于粒子群优化的非线性全局最优化反演方法。利用该方法对不同厚度储层模型进行反演研究,在无噪声情况下,反演结果和模型基本一致;在加入5%、10%和15%随机噪声后,反演仍取得良好效果。数值实验结果表明,该反演方法不依赖于初始模型,具有较好的全局寻优和抗噪声能力,能有效反演感应测井数据。

This paper proposes a particle swarm optimization inversion algorithm for avoiding the dependency on initial model and local solution. This algorithm is applied to induction logging inversion on the models of different thickness layers,and yields consistent results with the models in the noise-free case. When noises of 5%,10% and 20% are added to the models,the results of inversions remain fairly good. Numerical experiment results demonstrate that this particle swarm optimization inversion algorithm has advantages of being independent of initial models,capable of global optimization and anti-noise,and making induction logging data inversion more effective.