Fractures are important storage spaces and flow pathways in tight reservoirs, but small-scale fractures with weak seismic responses are difficult to characterize with band-limited post-stack seismic data. This study proposes a high-resolution multiscale seismic method for predicting fracture distribution based on basis pursuit frequency extension (BPFE)-enhanced multi-attribute fusion. BPFE represents each trace sparsely on a wedge dictionary built from a zero-phase Morlet wavelet, which is derived from the average dominant frequency of the data. It then re-synthesizes the basis pursuit denoising solution with a time-compressed wavelet, thereby extending the effective bandwidth without estimating the seismic wavelet. Dip-steered third-generation coherence and ant-tracking attributes are subsequently normalized and fused with explicit weights according to their attribute-sensitive scales, and objective rules are provided for selecting the BPFE parameters. Synthetic tests with known reflectivity show that BPFE reproduces the ideal high-resolution section with correlation coefficients of 0.978 (noise-free) and 0.882 (signal-to-noise ratio of 10 dB). BPFE increases the dominant frequency from 30.8 to 49.3 Hz and reduces the tuning thickness from 16 to 11 ms without systematic phase rotation. At 10 dB, it also raises the area under the receiver operating characteristic curve for detecting weak bed-confined discontinuities from 0.580 to 0.687, outperforming spectral whitening, spiking deconvolution, inverse-Q filtering and sparse-spike inversion in this respect. Applied to the tight sandstone reservoir in the northeastern Sichuan Basin, the workflow delineates main faults, fault-associated fracture zones and small-scale discontinuities more completely than coherence, ant tracking or their fusion without BPFE, which is consistent with the actual geological understanding. The workflow offers a wavelet-estimation-free approach for characterizing weak-response small-scale fractures, although its generalization requires further field validation.
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Wang et al. (2026) studied this question.
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