Abstract: Microstructures in coalbed methane (CBM) reservoirs are concealed and highly heterogeneous, which directly impacts horizontal well placement and productivity. To address the limitations of conventional methods in characterizing their small-scale, weak seismic responses, this study integrates artificial intelligence with multiple seismic attributes, including most positive curvature, maximum likelihood, and phase tuning, for microstructure identification. This method enhances the characterization of low-relief structures (relative relief <20 m, mostly <10 m) and low-grade faults (throw ≤10 m, and even <5 m), both of which affect CBM well productivity. Application of the proposed method to CBM reservoirs of the Permian Shanxi Formation in the central-eastern Qinshui Basin identifies low-grade faults and low-relief structures with improved accuracy and high confidence, which significantly enhances engineering response capabilities. Comprehensive analysis suggests that well placement should be at least 500 m away from local negative microstructures and avoid densely populated net-shaped low-grade faults. This method provides technical support for the integrated seismic-geological-engineering development of CBM.