History matching is crucial for reliable numerical simulation of geological carbon storage (GCS) in deep subsurface aquifers. This study focuses on inferring highly complex aquifer permeability fields with multi- and intra-facies heterogeneity to improve the characterization of CO2 plume migration. We propose a deep learning (DL)-based parameterization strategy combined with the ensemble smoother with multiple data assimilation (ESMDA) algorithm to formulate an integrated inverse framework. The DL model is employed to parameterize non-Gaussian permeability fields using low-dimensional latent variables in a Gaussian distribution, thereby mitigating the non-Gaussianity issue faced by the ensemble-based ESMDA inverse method and simultaneously alleviating the computational burden of high-dimensional inversion. The efficacy of the integrated DL-ESMDA inverse framework is demonstrated using a 3-D GCS model, where it estimates the non-Gaussian permeability field characterized by multi- and intra-facies heterogeneity. Results show that the DL model is able to represent the highly complex and high-dimensional permeability fields using low-dimensional latent vectors. The DL-ESMDA framework sequentially updates these low-dimensional latent vectors instead of the original high-dimensional permeability field to obtain posterior estimations of the permeability field. The resulting CO2 plume migration closely matches historical measurements, suggesting a significantly improved model reliability after history matching. Additionally, a substantial reduction in uncertainty for future plume migration predictions beyond the history matching period is observed. The proposed framework provides an effective approach for reliable characterization of CO2 plume migration in highly heterogeneous aquifers, enhancing GCS project operation and risk analysis.

中文翻译：

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基于深度学习的地质参数化，用于复杂含水层中 CO2 羽流迁移的历史匹配


历史匹配对于深部地下含水层地质碳储量 （GCS） 的可靠数值模拟至关重要。本研究的重点是推断具有多相和多相内非均质性的高度复杂的含水层渗透场，以改进 CO2 羽流迁移的表征。我们提出了一种基于深度学习 （DL） 的参数化策略，结合具有多重数据同化的集成平滑器 （ESMDA） 算法来构建一个集成的逆框架。DL 模型用于使用高斯分布中的低维潜在变量参数化非高斯磁导率场，从而缓解基于集成的 ESMDA 逆方法面临的非高斯问题，同时减轻高维反演的计算负担。使用 3-D GCS 模型证明了集成的 DL-ESMDA 逆框架的有效性，其中它估计了以多相和相内非均质性为特征的非高斯渗透率场。结果表明，DL 模型能够使用低维潜在向量表示高度复杂和高维的磁导率场。DL-ESMDA 框架按顺序更新这些低维潜在向量，而不是原始的高维磁导率场，以获得磁导率场的后验估计。由此产生的 CO2 羽流迁移与历史测量值非常吻合，表明在历史匹配后模型可靠性显著提高。此外，观察到历史匹配期之后未来羽流迁移预测的不确定性大幅降低。 所提出的框架为高度非均质含水层中 CO2 羽流迁移的可靠表征提供了一种有效的方法，从而加强了 GCS 项目的运行和风险分析。