Identification and distribution patterns of the ultra-deep small-scale strike-slip faults based on convolutional neural network in Tarim Basin, NW China

Abstract

The isolated fracture-vug systems controlled by small-scale strike-slip faults within ultra-deep carbonate rocks of the Tarim Basin exhibit significant exploration potential. The study employs a novel training set incorporating innovative fault labels to train a U-Net-structured CNN model, enabling effective identification of small-scale strike-slip faults through seismic data interpretation. Based on the CNN faults, we analyze the distribution patterns of small-scale strike-slip faults. The small-scale strike-slip faults can be categorized into NNW-trending and NE-trending groups with strike lengths ranging 200–5000 m. The development intensity of small-scale strike-slip faults in the Lower Yingshan Member notably exceeds that in the Upper Member. The Lower and Upper Yingshan members are two distinct mechanical layers with contrasting brittleness characteristics, separated by a low-brittleness layer. The superior brittleness of the Lower Yingshan Member enhances the development intensity of small-scale strike-slip faults compared to the upper member, while the low-brittleness layer exerts restrictive effects on vertical fault propagation. Fracture-vug systems formed by interactions of two or more small-scale strike-slip faults demonstrate larger sizes than those controlled by individual faults. All fracture-vug system sizes show positive correlations with the vertical extents of associated small-scale strike-slip faults, particularly intersection and approaching fracture-vug systems exhibit accelerated size increases proportional to the vertical extents.