用3D数字影子技术提升碳封存监测精度，更准判断二氧化碳迁移路径。
Advancing Geological Carbon Storage Monitoring With 3d Digital Shadow Technology
- 构建3D数字影子框架，融合地震与井下数据实时追踪二氧化碳饱和度。
- 相比2D方法，3D监测空间精度显著提升，完整捕捉二氧化碳迁移范围。
- 适合关注碳封存安全与风险评估的科研人员和工程团队。
地质碳封存（GCS）是实现全球气候目标的关键技术，通过将二氧化碳封存于深层地质构造中。其有效性与安全性依赖于利用先进时序地震成像对地下二氧化碳迁移进行精准监测。数字影子（Digital Shadow）框架整合现场数据，包括地震与钻井测量，用于追踪二氧化碳饱和度随时间的变化。基于生成式AI与非线性集合贝叶斯滤波的机器学习辅助数据同化技术，在更新二氧化碳羽流数字模型的同时考虑储层属性的不确定性。相较于二维方法，三维监测显著提升了地质碳封存评估的空间准确性，全面捕捉二氧化碳迁移范围。本研究在原有不确定性感知的二维数字影子框架基础上，引入三维地震成像与储层建模，进一步优化了二氧化碳封存项目中的决策制定与风险控制能力。
原文摘要 · Abstract (English)
Geological Carbon Storage (GCS) is a key technology for achieving global climate goals by capturing and storing CO2 in deep geological formations. Its effectiveness and safety rely on accurate monitoring of subsurface CO2 migration using advanced time-lapse seismic imaging. A Digital Shadow framework integrates field data, including seismic and borehole measurements, to track CO2 saturation over time. Machine learning-assisted data assimilation techniques, such as generative AI and nonlinear ensemble Bayesian filtering, update a digital model of the CO2 plume while incorporating uncertainties in reservoir properties. Compared to 2D approaches, 3D monitoring enhances the spatial accuracy of GCS assessments, capturing the full extent of CO2 migration. This study extends the uncertainty-aware 2D Digital Shadow framework by incorporating 3D seismic imaging and reservoir modeling, improving decision-making and risk mitigation in CO2 storage projects.
Thank you to arXiv for use of its open access interoperability. PaperDance 不是 arXiv 官方产品；中文卡片由大模型生成，请以原文为准。