标题： 利用增强的岩石物理建模提高二氧化碳封存监测中数字阴影的鲁棒性 显示英文标题

标题： Enhancing Robustness Of Digital Shadow For CO2 Storage Monitoring With Augmented Rock Physics Modeling

摘要： 为满足气候目标，政府间气候变化专门委员会（IPCC）强调了需要能够每年去除十亿吨二氧化碳的技术，其中地质碳储存（GCS）起着核心作用。 GCS涉及捕获二氧化碳并将其注入深层地质构造中进行长期储存，需要精确的监测以确保封存并防止泄漏。 时移地震成像对于追踪二氧化碳迁移至关重要，但通常难以捕捉多相地下流动的复杂性。 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和生成式人工智能，提供了一种更详细、具有不确定性意识的监测方法。 通过结合储层属性中的不确定性，DS框架提高了二氧化碳迁移的预测，降低了GCS操作中的风险。 然而，数据同化依赖于关于储层属性、岩石物理模型和初始条件的假设，如果这些假设不准确，可能会损害预测的可靠性。 本研究证明，通过引入多种岩石物理模型来增强预测集合，可以减轻错误假设的影响，并提高预测准确性，尤其是在区分均匀饱和模型与斑块饱和模型方面。 显示英文摘要

摘要： 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.