基于可变临界孔隙度Nur模型的页岩气层横波速度预测

准确的横波测井速度是影响地震叠前属性分析及反演质量的重要参数，在缺乏偶极横波测井情况下需要用Krief、Pride等数学模型预测横波，近年来临界孔隙度模型在计算骨架的弹性模量中应用效果较好。在前人研究的临界孔隙度模型的基础上，发展出了可变临界孔隙度的Nur模型，并将遗传算法应用于可变临界孔隙度的计算，最终估算得到了页岩气层的横波速度。实例应用表明，遗传算法可以计算得到沿井眼不同深度的临界孔隙度，而且预测得到的横波与偶极横波测井（DSI）一致性好，证明该方法应用于页岩气的横波估算中是可行的。

Estimation of shear wave velocity in shale gas layer based on variable critical porosity Nur model

Accurate shear wave logging velocity is a necessary parameter for pre-stack seismic attribute analysis and pre-stack seismic inversion. In the absence of dipole shear wave logging, it is necessary to use Krief, pride and other mathematical models to predict shear waves. In recent years, critical porosity has been well applied in calculating elastic model quantities of skeleton. Based on the critical porosity model, genetic algorithm (GA) is investigated to achieve a variable critical porosity through the borehole. The shear wave velocity is finally estimated by using the critical porosity and the GA-derived parameters. A case study shows that genetic algorithm can calculate the continuous critical variable porosity, and the predicted shear wave slowness is well consistent with DSI log. This method can be applied to tight sandstone reservoir.