用井下数据生成碳酸盐岩显微图像，填补储层表征空白。
Well Log-Guided Synthesis of Subsurface Images from Sparse Petrography Data Using cGANs
- 基于井眼孔隙度数据，用条件生成对抗网络合成岩石薄片图。
- 在0.004~0.745孔隙度范围生成真实图像，精度达目标值10%内81%。
- 适合地质建模、碳封存与地下储氢等能源转型研究者使用。
地下储层微观成像成本高且局限于离散深度，导致储层表征存在显著空白。为此，我们提出一种条件生成对抗网络（cGAN）框架，基于井下孔隙度数据合成碳酸盐岩储层的逼真薄片图像。模型在1992-2000米深度区间内，利用15个岩心样本提取的5000个子图像进行训练，可在0.004至0.745的广泛孔隙度范围内生成地质一致的图像，实现目标孔隙度值10%误差范围内的准确率达81%。通过将井下数据与训练好的生成器融合，可实现井筒沿线连续的孔隙尺度可视化，弥合离散岩心采样点间的空缺，为储层表征及碳捕集、地下氢气储存等能源转型应用提供关键洞察。
原文摘要 · Abstract (English)
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.
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