Efficient generation of digital rock CT images using LoRA-enhanced stable diffusion models
Kunyao Li, Haijiang Li, Ali Dheyaa Khudhair, Jun Yan, Bin Wang
Abstract
Digital rock analysis (DRA) is fundamental for geo-energy research, enabling the characterisation of microstructures for applications like hydrocarbon recovery, carbon storage, and groundwater modelling. Although 2D CT images provide valuable pore-scale data, the scarcity of real-world datasets limits the effectiveness of advanced analysis. Generative AI presents a promising approach for synthesizing high-quality rock images but faces key challenges, including high computational demands, insufficient evaluation metrics, and the trade-off between image fidelity and diversity. To address these limitations, this study proposes the use of Low-Rank Adaptation (LoRA) for fine-tuning stable diffusion models, significantly reducing computational requirements while maintaining image quality. A systematic investigation was conducted to evaluate the influence of LoRA training parameters, including rank and learning rate, on the quality of generated images. Image outputs were assessed using both standard generative metrics, such as Kullback-Leibler Divergence (KID), and domain-specific metrics, including porosity, pore count, and pore area distributions. The optimised LoRA-enhanced diffusion model achieved a 92.6% reduction in KID relative to baseline models, while also improving inference speed. Building on these advancements, this study demonstrates that the LoRA-enhanced diffusion model significantly improves neural network extrapolation in incomplete data scenarios through statistically consistent synthetic generation. Despite control challenges, this approach reduces costs and enables diverse applications, bridging fundamental rock physics with practical energy research.