用多样岩石物理模型提升碳封存监测的预测可靠性
Enhancing Robustness Of Digital Shadow For CO2 Storage Monitoring With Augmented Rock Physics Modeling
- 通过引入多种岩石物理模型增强预测集合
- 显著提高对均匀与斑块状饱和分布的区分能力
- 适合关注碳封存安全性的地质与能源研究者
为实现气候目标，国际气候变化委员会强调需具备每年清除十亿吨级二氧化碳的技术，地质碳封存（GCS）是核心手段。该技术将捕获的CO2注入深层地质构造长期储存，需精确监测以确保封存安全并防止泄漏。时移地震成像对追踪CO2迁移至关重要，但难以捕捉多相流体的复杂行为。数字阴影（DS）利用机器学习驱动的数据同化技术（如非线性贝叶斯滤波、生成式AI），提供更精细且考虑不确定性的监测方法。通过融合储层属性不确定性，DS框架可提升CO2迁移预测精度，降低运营风险。然而，数据同化依赖于对储层性质、岩石物理模型及初始条件的假设，若不准确，将影响预测可靠性。本研究证明，通过在预测集合中加入多样化岩石物理模型，可有效缓解错误假设的影响，显著提升预测准确性，尤其在区分均匀与斑块状饱和模式方面表现突出。
原文摘要 · Abstract (English)
To meet climate targets, the IPCC underscores the necessity of technologies capable of removing gigatonnes of CO2 annually, with Geological Carbon Storage (GCS) playing a central role. GCS involves capturing CO2 and injecting it into deep geological formations for long-term storage, requiring precise monitoring to ensure containment and prevent leakage. Time-lapse seismic imaging is essential for tracking CO2 migration but often struggles to capture the complexities of multi-phase subsurface flow. Digital Shadows (DS), leveraging machine learning-driven data assimilation techniques such as nonlinear Bayesian filtering and generative AI, provide a more detailed, uncertainty-aware monitoring approach. By incorporating uncertainties in reservoir properties, DS frameworks improve CO2 migration forecasts, reducing risks in GCS operations. However, data assimilation depends on assumptions regarding reservoir properties, rock physics models, and initial conditions, which, if inaccurate, can compromise prediction reliability. This study demonstrates that augmenting forecast ensembles with diverse rock physics models mitigates the impact of incorrect assumptions and improves predictive accuracy, particularly in differentiating uniform versus patchy saturation models.
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