This analysis employs sequential optimization and geophysical methods to enhance micro fault prediction in gas reservoirs, indicating potential for new trap types.
With the continuous improvement of exploration level, the remaining trap scale of the Kelasu structural belt is becoming smaller and facing more complex geological conditions. Therefore, this study focuses on the Bozi Dabei area as the key breakthrough point to carry out micro fault characterization and quantitative evaluation of boundary fault sealing. The research results are of great significance for supporting the conception of new trap types and implementing the discovered trap scale. There are currently four main methods for identifying micro faults within gas reservoirs: conventional geological methods, logging methods, geophysical methods, and structural stress field simulation methods. This study mainly uses the method of sequence level optimization combined with azimuthal micro fault earthquake prediction to characterize micro faults in the Bozi and Dabei regions. The core algorithm of this technology is to determine the density of faults based on the upper limit frequency and optimize the fault sequence based on the extension length of the fault plane. This method determines the characteristics of internal micro faults in gas reservoirs, fully utilizing the advantageous features of micro faults in various seismic attribute data in the depth domain. Through coherent calculation, upper frequency constrained filtering, and planar curvature guided flow calculation, micro faults are characterized. The results of sequential optimization have directionality, significant improvement in linear fracture characteristics, strong interpretability, and reflect a significant improvement in the accuracy of hidden micro fracture features compared to traditional coherence. The research results of this study are of great significance in supporting the conception of new trap types and implementing the scale of discovered traps, achieving effective identification of micro faults within gas reservoirs.
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Song et al. (2025) studied this question.
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