Application of model-based acoustic impedance inversion in the prediction of thin interbedded coal seams: a case study in the Yuwang colliery, Yunnan Province

Abstract

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.

KEYWORDS:

• Reservoir prediction
• thin interbedded coal seams
• model-based AI inversion
• CBM
• geophysical exploration

Data availability statement

The data that supports the findings of this study are available in the supplementary material of this article.

Disclosure statement

No potential conflict of interest was reported by the author(s).

Additional information

Funding

This work was supported by the National Key Research and Development Program of China under grant number 2018YFC0807803; and the Open Fund of the State Key Laboratory of Coal Resources and Safe Mining under grant number SKLCRSM20KFA07.