基于粒子群算法的页岩孔隙结构反演及横波速度预测

Shale pore structure inversion and shear wave velocity prediction based on particle swarm optimization( pso) algorithm

提出了基于粒子群算法的页岩孔隙纵横比反演以及横波速度预测的方法.基于岩石物理模型,建立岩石纵、横波速度与密度、孔隙度和矿物组分等参数之间的定量关系,利用传统遍历搜索方法和粒子群算法两种方法计算最佳孔隙纵横比,使理论纵波速度与实际纵波速度的误差最小,并以孔隙纵横比作为约束进行横波速度预测,将预测结果与实测横波速度对比,验证了粒子群算法的有效性和精确性.反演结果表明页岩部分的孔隙结构比围岩部分的孔隙结构更加的稳定,利用粒子群算法的预测结果比利用传统算法的预测结果更加准确.

It is proposed based on particle swarm optimization （pso） algorithm of shale pore aspect ratio inversion and the method of shear wave velocity prediction. Based on rock physics model, the establishment a quantitative relationship of P-wave and S-wave velocity of rock and the parameters such as density, porosity, and mineral components and so on,using traditional traversal search method and particle swarm algorithm two kinds of method to calculate the best pore aspect ratio to the theory of wave speed and actual speed of the minimum error, and using the pore aspect ratio as the constraint of shear wave velocity prediction, the prediction results and the measured shear velocity contrast, verified the effectiveness and accuracy of the particle swarm optimization（pso）. The results of inversion show that the pore structure of shale section more than part of the pore structure of surrounding rock stability, the predictive results ofusing the particle swarm optimization（pso）is more accurate than the predictive results ofusing traditional algorithm.