用扩散模型生成地质数据，提升碳封存模拟的精度与可靠性。
Integrating Score-Based Diffusion Models with Machine Learning-Enhanced Localization for Advanced Data Assimilation in Geological Carbon Storage
- 结合扩散模型与机器学习定位，优化地下储层参数估计。
- 5000个样本下保持高方差，数据匹配效果接近传统方法。
- 适合关注碳封存风险评估与不确定性量化的研究者。
准确刻画地下非均质性对安全高效实施地质碳封存（GCS）项目至关重要。本文提出一种融合基于得分的扩散模型与机器学习增强定位的框架，用于通道型储层在二氧化碳注入过程中的数据同化。采用大规模集合（N_s = 5000）生成渗透率场，由扩散模型生成，状态由简单机器学习算法计算，以改进集成平滑器多数据同化（ESMDA）的协方差估计。基于地质统计模型FLUVSIM生成的先验通道型渗透率场，应用该方法于使用DARTS模拟的二氧化碳注入场景。结果表明，基于机器学习的定位在保持显著更高集合方差的同时，实现与不使用定位相当的数据匹配质量。该框架对提升GCS项目中不确定性量化可靠性具有实际意义。
原文摘要 · Abstract (English)
Accurate characterization of subsurface heterogeneity is important for the safe and effective implementation of geological carbon storage (GCS) projects. This paper explores how machine learning methods can enhance data assimilation for GCS with a framework that integrates score-based diffusion models with machine learning-enhanced localization in channelized reservoirs during CO$_2$ injection. We employ a machine learning-enhanced localization framework that uses large ensembles ($N_s = 5000$) with permeabilities generated by the diffusion model and states computed by simple ML algorithms to improve covariance estimation for the Ensemble Smoother with Multiple Data Assimilation (ESMDA). We apply ML algorithms to a prior ensemble of channelized permeability fields, generated with the geostatistical model FLUVSIM. Our approach is applied on a CO$_2$ injection scenario simulated using the Delft Advanced Research Terra Simulator (DARTS). Our ML-based localization maintains significantly more ensemble variance than when localization is not applied, while achieving comparable data-matching quality. This framework has practical implications for GCS projects, helping improve the reliability of uncertainty quantification for risk assessment.
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