Accurate prediction of S-wave velocity is of great significance in many aspects, such as inversion, migration, brittleness index calculation, etc. Under normal circumstances, the more types of known input parameters there are, the more accurate the rock’s specific situation, and the more accurate the predicted S-wave velocity. However, considering the actual situation, the types of parameters obtained through logging curves are relatively limited. Some parameters cannot be measured and calculated, which limits the accuracy of S-wave prediction. Therefore, if the parameters can be predicted, a more accurate underground situation is able to described by these parameters. Through rockphysical analysis, the pore aspect ratio and capillary pressure coefficient can affect the velocity. In this way, a new orthorhombic (ORT) rockphysical modeling process considering the pore aspect ratio and capillary pressure coefficient is proposed. The model consists of VTI anisotropy from compaction or textural alignment of minerals, and HTI anisotropy from high-angle fractures caused by stratum pressure, thus showing ORT anisotropy. The inputs of the model can be multiple minerals. And the pore structure and the modulus of the mixed fluids in the pores are considered. We use inverse theory (quantum genetic algorithms) to obtain the pore aspect ratio and capillary pressure coefficient and finally calculate the S-wave velocity through the above parameters. The calculation results in a shale reservoir show that the predicted S-wave velocity is in good agreement with the real logging data. This shows that the proposed rockphysical modeling process and inverse algorithm method are effective.

横波速度的准确预测在反演、偏移、脆性指数计算等诸多方面都具有重要意义。一般情况下，已知输入参数的种类越多，岩石的具体情况就越准确，并且预测的 S 波速度越准确。但考虑到实际情况，通过测井曲线获取的参数类型相对有限。有些参数无法测量和计算，限制了横波预测的准确性。因此，如果这些参数是可以预测的，就可以用这些参数来描述更准确的井下情​​况。通过岩石物理分析，孔隙纵横比和毛管压力系数会影响速度。这样，提出了一种新的考虑孔隙纵横比和毛细管压力系数的斜方晶系 (ORT) 岩石物理建模过程。该模型包括来自矿物压实或结构排列的 VTI 各向异性，以及来自地层压力引起的高角度裂缝的 HTI 各向异性，从而显示 ORT 各向异性。模型的输入可以是多种矿物。并考虑了孔隙结构和孔隙中混合流体的模量。我们利用逆理论（量子遗传算法）得到孔隙纵横比和毛细管压力系数，最后通过上述参数计算出横波速度。某页岩储层的计算结果表明，预测的横波速度与实际测井资料吻合较好。