Fracture prediction based on W-transform inversion spectral decomposition

Abstract

Conventional ant-tracking technology is effective for fault identification, but its application is constrained by sensitivity to data quality and inadequate continuity of fault tracking results. This limitation creates an urgent need for optimization. To address this issue, this study proposes a frequency-divided ant-tracking method based on W-transform inversion spectral decomposition (WT-ISD) to improve the accuracy of fracture prediction in complex structural areas. By integrating the flexible time–frequency localization of the W-transform with sparse inversion theory, this method markedly improves the resolution and focusing performance of time–frequency spectra. This is achieved through the construction of an overcomplete dictionary and the imposition of L1 norm constraints. The proposed method not only provides a novel high-resolution time–frequency analysis tool for seismic signal processing but also establishes a theoretical basis for subsequent fine geological interpretation via its frequency-divided processing workflow. Taking the Raphia S Block in the Bongor Basin as a case study, this research first adopts WT-ISD to obtain high-resolution spectral decomposition results, generating single-frequency data volumes corresponding to different frequency components. Subsequently, structural smoothing filtering and boundary enhancement processing are applied to highlight discontinuity information. Then, the ant-tracking algorithm is introduced, combined with regional structural attitude constraints, to realize the identification of multi-scale fractures. Finally, red–green–blue attribute fusion technology is used to integrate responses from different frequencies and construct a fault distribution model. Practical application results indicate that this method not only improves the accuracy and spatial continuity of fracture prediction but also enhances its anti-noise capability. In particular, it exhibits excellent identification performance for large-, medium-, and small-scale fractures corresponding to 20 Hz, 35 Hz, and 50 Hz, respectively. This study verifies that the proposed method can provide reliable technical support for multi-scale fracture detection in complex structural areas and demonstrates important application value for fracture prediction in oil and gas exploration.

Keywords

Frequency-divided ant-tracking / Fracture prediction / W-transform / Inversion spectral decomposition / Red–green–blue attribute fusion

Funding

This research was supported by the Open Fund Project (Tenth Batch) (Grant No. K2024-03) of the Key Laboratory of Oil and Gas Resources and Exploration Technology, Ministry of Education, Yangtze University, China.

Conflict of interest

The authors declare they have no competing interests.

Author contributions

Conceptualization: Ying Jia, Xilin Qin Formal analysis: Xiuwei Wang, Xilin Qin Funding acquisition: Ying Jia, Xiuwei Wang Investigation: Ying Jia, Jun Wang Methodology: Xilin Qin, Yinglong Li Project administration: Ying Jia, Xiuwei Wang Validation: Yinglong Li Visualization: Xilin Qin, Yinglong Li Writing–original draft: Ying Jia, Yinglong Li Writing–review & editing: Ying Jia, Xilin Qin

Availability of data

Data are available from the corresponding author upon reasonable request.

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