Research on three-dimensional seismic attribute fusion technology for characterizing fractures in granite geothermal reservoirs

This paper is the result of geothermal survey engineering.

Objective It is necessary to predict the characteristics, spatial distribution and density of natural fractures in granite reservoirs using high−precision seismic data and feature analysis methods, which provide scientific evidence for the construction of Enhanced Geothermal System (EGS).

Methods This study adopted advanced 3D seismic techniques, including wideband, wide−angle, high−density sampling, as well as advanced processing methods such as structural−oriented filtering and multi−window dip scanning. The application of these techniques greatly improved the signal−to−noise ratio of seismic data, further optimizing the detection effect of the granite interior. At the same time, this study also combines 3D seismic attributes such as seismic attribute volume, maximum likelihood volume, coherence volume, curvature volume, variance volume, and ant volume with multi−azimuth constraints to accurately predict the spatial distribution density of fracture clusters.

Results Through comprehensive attribute analysis and drilling imaging logging results, we reveal the regularity of the development of northeast and northwest fractures in granite−type hot dry rock reservoirs. These results are highly consistent with drilling imaging logging results, further verifying the accuracy and reliability of this research method.

Conclusions This study successfully predicted the characteristics, spatial distribution, and density of natural fractures in granite−type hot dry rock reservoirs through high−precision seismic data and feature analysis methods. This method provides important evidence for fracture detection, drilling deployment, and mining plan determination in EGS engineering construction.