Existing shear-wave velocity prediction methods for organic-rich rocks have not simultaneously taken the effects of organic matter (kerogen) distribution and pore shape on the velocity into consideration. To solve this problem, the current paper proposes a shear-wave velocity prediction method for organic-rich rocks based on the Kuster–Toksöz (KT) rock physics model and chaotic quantum particle swarm optimisation (CQPSO) algorithm. In this method, the organic-rich rocks are considered equivalent to a mixture of minerals, kerogen particles and fluid-containing pores, in which kerogen particles and pores are both equivalent to ellipsoid-shaped inclusions. The effect of kerogen distribution and pore shape on velocity is described according to the change in the aspect ratio of ellipsoids. We used the error between the predicted and measured P-wave velocities to establish the inverse objective function, and introduced the CQPSO algorithm to calculate the equivalent kerogen particle and the equivalent pore aspect ratios. Based on this, the shear-wave velocity is predicted using inverted parameters. Compared with three existing single-parameter adaptive methods, the prediction results show that the proposed method is superior and can be satisfactorily implemented for laboratory measurement and on logging data.

现有的富含有机物岩石的剪切波速度预测方法并未同时考虑有机质（干酪根）的分布和孔隙形状对速度的影响。为了解决这个问题，本文提出了一种基于库斯特-托克斯（KT）岩石物理模型和混沌量子粒子群优化（CQPSO）算法的富含有机物岩石的剪切波速度预测方法。在这种方法中，富含有机物的岩石被认为等同于矿物，干酪根颗粒和含流体孔隙的混合物，其中干酪根颗粒和孔隙均等同于椭圆形夹杂物。根据椭球体长径比的变化描述了干酪根分布和孔形状对速度的影响。我们利用预测和测得的P波速度之间的误差建立逆目标函数，并引入CQPSO算法来计算等效干酪根颗粒和等效孔隙长径比。基于此，使用倒置参数预测剪切波速度。与现有的三种单参数自适应方法相比，预测结果表明，该方法具有较好的优越性，可以较好地应用于实验室测量和测井数据记录。