美国北达科他州布罗姆溪地层的盆地尺度多存储项目建模：统一模型与不确定性量化
Basin-Scale Modeling of Multiple Storage Projects in the Broom Creek Formation, North Dakota, USA: Unified Model and Uncertainty Quantification
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中文总结 AI 辅助
本研究针对美国北达科他州布罗姆溪地层构建含5个项目的盆地尺度统一模型，结合多级处理与深度学习替代模型量化碳封存不确定性及项目间干扰，为碳封存规模化提供支撑。
中文摘要 AI 辅助
随着碳封存业务规模扩大，量化相邻项目间的相互作用至关重要。然而，许多现有的盆地尺度研究是确定性的或依赖假设场景，此外，未充分考虑相邻项目的模拟可能仅适用于初始许可阶段。本研究针对美国北达科他州布罗姆溪地层开发了一个盆地尺度统一模型，该模型包含总共5个（现有及规划中的）项目，涉及18口注入井，最大潜在注入速率为30 MTPA。该统一模型通过整合公开可用的地质与项目数据构建，包含44×10⁶个网格单元，运行需时超过两天。为实现更快的模拟，提出了一种多级处理方法，该方法结合了通过约束满足问题求解构建的四级嵌套局部网格加密，以及粗区域渗透率的优化幂平均。在验证粗模型的准确性后，利用其在广泛的地质实现和模型参数范围内量化关键感兴趣量（QoI）的不确定性，包括50年后的总CO₂注入量和羽流面积。此外，开发了一个深度学习替代模型，用于全局敏感性分析以识别QoI方差的主要贡献因素。还对比了统一模型与独立项目模拟的结果，以量化项目间干扰，其在某些情况下可能相当显著，例如某项目的中位数总CO₂注入量从独立模拟的271 MT降至统一模型的192 MT。
英文摘要
Quantifying the interactions between neighboring projects will be essential as carbon storage operations expand in scale. However, many existing basin-scale studies are deterministic or rely on hypothetical scenarios. In addition, simulations that do not fully account for neighboring projects may suffice for initial permitting. In this study, we develop a unified basin-scale model for the Broom Creek Formation in North Dakota. The model includes a total of five (existing and planned) projects, involving 18~injection wells with a maximum potential injection rate of 30~MTPA. The unified model, constructed by integrating publicly available geological and project data, contains $44\times10^6$ cells and requires over two days to run. To enable faster simulations, we present a multilevel treatment that combines four-level nested local grid refinement, constructed through the solution of a constraint-satisfaction problem, with optimized power averaging for permeability in coarse regions. After demonstrating the accuracy of the coarse model, we use it to assess uncertainty in key quantities of interest (QoIs), including total CO$_2$ injected and plume area after 50~years, across a wide range of geological realizations and model parameters. In addition, we develop a deep learning surrogate model, which is used in global sensitivity analyses to identify dominant contributors to QoI variance. Comparisons between the unified model and standalone project simulations are presented to quantify inter-project interference. This can be substantial in some cases, e.g., for one project, the median total CO$_2$ injected decreases from 271~MT (standalone) to 192~MT (unified model).
