Pore-scale imaging of subsurface formations is costly and limited to discrete depths, creating significant gaps in reservoir characterization. To address this, we present a conditional Generative Adversarial Network (cGAN) framework for synthesizing realistic thin section images of carbonate rock formations, conditioned on porosity values derived from well logs. The model is trained on 5,000 sub-images extracted from 15 petrography samples over a depth interval of 1992-2000m, the model generates geologically consistent images across a wide porosity range (0.004-0.745), achieving 81% accuracy within a 10\% margin of target porosity values. The successful integration of well log data with the trained generator enables continuous pore-scale visualization along the wellbore, bridging gaps between discrete core sampling points and providing valuable insights for reservoir characterization and energy transition applications such as carbon capture and underground hydrogen storage.

翻译：地下岩层孔隙尺度成像成本高昂且仅限于离散深度，导致储层表征存在显著空白。为解决此问题，本文提出一种条件生成对抗网络（cGAN）框架，用于合成碳酸盐岩地层真实薄片图像，其生成过程以测井数据提取的孔隙度值为条件。该模型基于1992-2000米深度区间内15个岩石学样本提取的5,000张子图像进行训练，能够在宽孔隙度范围（0.004-0.745）内生成地质一致性图像，并在目标孔隙度值10%误差范围内达到81%的准确率。测井数据与训练后生成器的成功整合，实现了沿井筒连续的孔隙尺度可视化，填补了离散岩心采样点之间的空白，为储层表征及碳捕集与地下储氢等能源转型应用提供了重要见解。