Numerical modeling of geochemistry associated with geologic CO₂ storage involves many conceptual and quantitative uncertainties. In this study, a time efficient arbitrary polynomial chaos (aPC) expansion approach was proposed to do global sensitivity analysis of mineral dissolution and precipitation modeling in geologic carbon storage scenarios. To demonstrate the workflow of the aPC approach, a numerical model to predict permeability evolution of a Lower Tuscaloosa sandstone core exposed to CO₂ saturated brine was used. The modeled sandstone core permeability by the aPC approach was 2095.5 mD ± 504.5 mD after 180 days of CO₂ exposure. The measured permeability of the core after 180 days of CO₂ exposure was 1925.0 mD, which was within the uncertainty range. K_eq (SiO₂ (am)) was the most important modeling parameter that influenced permeability results, implying that SiO₂ (am) is a key mineral that governs permeability evolution of sandstone in geologic carbon storage scenarios. The aPC approach can reduce 99% of simulation time needed to do global sensitivity analysis of a complicated geochemical model, compared with traditional Monte Carlo approach.

中文翻译：

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任意多项式混沌（aPC）展开在地质储碳条件下矿物溶解和降水建模的全局敏感性分析中的应用

与地质CO ₂储存相关的地球化学数值模拟涉及许多概念和定量不确定性。在这项研究中，提出了一种时效性高的任意多项式混沌（aPC）扩展方法，以对地质碳存储场景中的矿物溶解和降水建模进行全局敏感性分析。为了证明aPC方法的工作流程，使用了一个数值模型来预测暴露于CO ₂饱和盐水中的塔斯卡卢萨下部砂岩岩心的渗透性演变。在暴露180天的CO ₂之后，通过aPC方法模拟的砂岩芯渗透率是2095.5 mD±504.5 mD 。CO ₂ 180天后测得的岩心渗透率暴露为1925.0 mD，处于不确定范围内。K _eq（SiO ₂（am））是影响渗透率结果的最重要的建模参数，这表明SiO ₂（am）是控制地质碳储藏情景中砂岩渗透率演变的关键矿物。与传统的蒙特卡洛方法相比，aPC方法可以减少对复杂地球化学模型进行全局敏感性分析所需的99％的仿真时间。