Quantitative inversion of reservoir properties plays a crucial role in the efficient development of tight sandstone reservoirs. Based on the theories of statistical rock physics and extended elastic impedance (EEI), a complete workflow for quantitative prediction of reservoir properties is developed and the key steps thereof are provided here. The square of S-velocity to P-velocity ratio (K), which is consistent with geological setting of a study area, is obtained from statistics of well data, the value of angle \(\chi\) most relevant to lithology and porosity is produced automatically by evaluating correlations of reservoir parameters with EEI, and the formulas that fit EEI with clay volume and with porosity are obtained. Then, angle \(\chi\) is applied to relevant EEI reflection coefficient data from amplitude variation with offset attribute cubes, the EEI related to clay volume and porosity is obtained through model-based constrained inversion on these data cubes, fitting formulas from well analysis are used to convert EEI data cubes to clay volume and porosity data cubes, and finally, the parameters of a tight sandstone reservoir are predicted quantitatively. The prediction results are consistent with production data from existing wells, indicating that the method proposed here is reliable.

储层性质的定量反演在致密砂岩储层有效开发中起着至关重要的作用。基于统计岩石物理理论和扩展弹性阻抗（EEI）理论，开发了定量预测储层物性的完整工作流程，并在此提供了关键步骤。S速度与P速度之比（K）的平方与研究区域的地质背景一致，是从井数据统计中获得的，与岩性和孔隙度最相关的角度\（\ chi \）的值通过评估储层参数与EEI的相关性，可以自动生成油藏，并得出与油藏体积和孔隙度相符的EEI公式。然后，角度\（\ chi \）将其与具有偏移属性的多维数据集的振幅变化相关的EEI反射系数数据应用于EEI反射系数数据，通过对这些数据多维数据集进行基于模型的约束反演，获得与粘土体积和孔隙率相关的EEI，使用井分析的拟合公式将EEI数据多维数据集转换为最后，定量预测了致密砂岩储层的参数。预测结果与现有油井的生产数据一致，表明本文提出的方法是可靠的。