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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.
A method based on Particle Swarm Optimization (PSO) for the inversion of shale pore-to-aspect ratio and the prediction of shear wave velocity is proposed.Based on the petrophysical model, the quantitative relationship between rock longitudinal and shear wave velocity and density, porosity and mineral composition and other parameters is established. The traditional traversal search method and the particle swarm optimization algorithm are used to calculate the optimal aspect ratio, which minimizes the error between the theoretical longitudinal wave velocity and the actual longitudinal wave velocity. The S-wave velocity is predicted by using the aspect ratio as the constraint. The predicted result is compared with the measured S-wave velocity , The particle swarm optimization algorithm is validated and validated.The inversion results show that the pore structure of the shale is more stable than the pore structure of the surrounding rock and the prediction using the PSO is more accurate than the prediction using the traditional algorithm .