The Permian coal seams in eastern Yunnan and western Guizhou are thin, numerous, and staggered with other thin coal seams. Depicting the fine characteristics of coal reservoirs is pivotal for the safe and efficient exploitation of coal and coalbed methane (CBM), and is important for transparent mining. To improve the inverse resolution and accuracy of predicting reservoir thickness, this study used the model-based acoustic impedance (AI) inversion method that utilizes seismic and logging data. This method changes seismic data, reflecting stratigraphic interfaces, into AI data, reflecting lithologic structures. Moreover, it avoids the relevant assumptions of wavelets and reflection coefficients. Compared with other inversion methods, model-based AI inversion strengthens the description of thin reservoir horizontal and vertical changes. The results showed that comprehensively using intermediate-frequency seismic information and high-low-frequency logging data greatly broadens the seismic data frequency band and improves the dominant frequency of the reflected wave. Furthermore, the AI profile resolution and the prediction accuracy of the physical parameters for the target geological body can be improved. A cross-validation comparing the inverted thickness and measured thickness of borehole cores was applied to achieve fine prediction (error of appropriately 0.02–0.4 m), providing a basis for CBM development.

滇东、黔西二叠系煤层薄、数量多、与其他薄煤层交错。刻画煤储层精细特征对于煤炭及煤层气安全高效开采至关重要，对透明开采具有重要意义。为了提高储层厚度预测的反演分辨率和精度，本研究采用了基于模型的声阻抗（AI）反演方法，该方法利用地震和测井数据。该方法将反映地层界面的地震数据更改为反映岩性结构的AI数据。而且，它避免了小波和反射系数的相关假设。与其他反演方法相比，基于模型的AI反演加强了对薄层储层水平和垂直变化的描述。结果表明，综合利用中频地震信息和高低频测井资料，大大拓宽了地震资料频带，提高了反射波的主频。此外，还可以提高AI剖面分辨率和目标地质体物理参数的预测精度。通过对比反演厚度与实测岩心厚度的交叉验证，实现精细预测（误差在0.02~0.4 m左右），为煤层气开发提供依据。可以提高AI剖面分辨率和目标地质体物理参数的预测精度。通过对比反演厚度与实测岩心厚度的交叉验证，实现精细预测（误差在0.02~0.4 m左右），为煤层气开发提供依据。可以提高AI剖面分辨率和目标地质体物理参数的预测精度。通过对比反演厚度与实测岩心厚度的交叉验证，实现精细预测（误差在0.02~0.4 m左右），为煤层气开发提供依据。