Modeling of High-Sand-Ratio and Low-Connectivity Reservoirs Based on Self-Attention Single-Image Generative Adversarial Network

High sand-ratio and low-connectivity reservoirs are commonly developed in deep-water depositional environments. Well-developed muddy interlayers reduce reservoir connectivity and form multiple discrete sandbody units, thereby offering good potential for layered development. However, due to limited research and insufficient data, modeling such complex reservoir structures remains challenging. The existing multiple-point statistics (MPS) method can utilize limited training images for geological modeling, but under high sand-ratio conditions, it often produces models with excessively high connectivity, failing to accurately represent reservoir characteristics. To address this issue, this study proposes a self-attention-enhanced single-image generative adversarial network (SA-SinGAN). Based on the original 2D SinGAN, the method was extended to 3D modeling by incorporating a self-attention mechanism and trained layer by layer to capture multi-scale geological features. Experimental results show that the FID score of SA-SinGAN is 143.75, compared with 175.61 for the MPS method. In terms of average connectivity error, MPS yields 0.42, which is significantly higher than 0.13 for SA-SinGAN, while the average NTG error is similar. SA-SinGAN can more accurately reproduce the low-connectivity characteristics of reservoirs while maintaining randomness, outperforming MPS in modeling performance. This demonstrates the applicability of SA-SinGAN for modeling complex reservoirs.