Seismic characterization and spatial distribution analysis of coal gangue in the deep coalbed of the Northeastern Ordos Basin

Abstract

Deep coalbed methane exploration in the Ordos Basin holds significant potential, yet the frequent development of coal gangue severely hinders productivity and complicates horizontal well deployment. Accurately characterizing the spatial distribution of these thin coal gangue layers remains a critical challenge, particularly when constrained by limited seismic resolution and sparse well control. To address this challenge, we propose a novel paleogeomorphology-constrained stochastic seismic inversion workflow applied to the Benxi Formation in the YL area. This approach integrates petrophysical analysis with paleogeomorphic restoration, identifying natural gamma as the most sensitive lithological indicator. Under hierarchical geomorphic constraints, geostatistical simulation was utilized to predict the three-dimensional spatial probability of coal gangue occurrence. Quantitative validation demonstrates a robust correlation between coal gangue thickness and natural gamma response, with the inversion results achieving an accuracy of 81.82% in blind well validation. Spatially, gangue development is controlled by paleotopography, with higher probabilities concentrated in paleo-highs and slopes associated with stronger hydrodynamic conditions, while paleo-depressions exhibit superior coal continuity. This study not only overcomes the resolution limitations of traditional inversion in sparse-well areas but also provides a rigorous quantitative geological basis for sweet-spot identification and trajectory optimization in deep coalbed methane development.

Keywords

Ordos Basin / Coal gangue-sensitive log parameter / Seismic characterization of deep coal gangue / Coal gangue distribution pattern / Deep coalbed methane

Funding

This study was supported by the Sichuan Science and Technology Program (No. 2025ZNSFSC0004), the National Natural Science Foundation of China (No. 42241206), and the National Natural Science Foundation of China (No. U24B2022).

Conflict of interest

The authors declare that they have no known financial or personal conflicts of interest that could have influenced the work reported in this paper.

Author contributions

Conceptualization: Yucong Huang

Data curation: Yucong Huang

Formal analysis: Zelei Jiang

Funding acquisition: Xuri Huang

Methodology: Zelei Jiang

Project administration: Dong Zhang

Resources: Xuri Huang

Supervision: Xuri Huang, Dong Zhang, Yong Wu

Validation: Zelei Jiang

Visualization: Zelei Jiang

Writing-original draft: Zelei Jiang

Writing-review & editing: Zelei Jiang, Xuri Huang

Availability of data

The data in this study are not openly available as they are confidential data provided by PetroChina Changqing Oilfield Company.

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