Estimating mineral reactive surface areas in geologic media remains one of the key challenges limiting the accuracy of reactive transport modeling (RTM) predictions of subsurface processes, particularly those controlling the fate of carbon dioxide (CO₂) during geologic storage. Although there have been numerous attempts to combine imaging and experimental techniques to estimate mineral reactive surface area for use in RTM predictions of geologic CO₂ storage, these techniques have yet to be adapted to basaltic reservoirs, which have pore structure, mineralogy, and chemical composition that is unique compared to their more often-studied sedimentary counterparts. Here, we address this issue by quantifying fluid-accessible mineral surface areas through image analysis of scanning electron microscope (SEM) backscatter electron images (high-resolution 500 nm/pixel) and Raman spectroscopic mapping of a basaltic rock sample from the Eastern Snake River Plain, Idaho. To evaluate whether the determined pore fluid-accessible mineral surface area accurately reflects reactive surface area, a micro-continuum scale RTM was developed and compared with a high-temperature, high-pressure flow-through CO₂ mineralization experiment conducted on the characterized basalt. Importantly, simulations employing the image-derived pore fluid-accessible mineral surface areas match the experimental effluent chemistry well within uncertainties. These mineral surface areas were then used to parametrize a field-scale model representative of the Cascadia basin, NE Pacific, to evaluate impacts of surface area variations on mineral carbonation. Simulations were carried out using variations in image-derived surface areas that cover one to two orders of magnitude increase and decrease in surface area, analogous to previously reported magnitudes of difference between total and reactive surface areas. Carbonation efficiency in terms of CO₂ volume mineralized over the simulated period was tracked and compared. Simulations with surface area increased and decreased by two orders of magnitude show basalt carbonation efficiency that is three times faster and six times slower, respectively, than predictions with image-derived mineral surface area. These sensitivity analyses demonstrate that accurate quantification of mineral surface area is crucial for efforts to predict CO₂ mineralization, and that efforts such as those employed here can dramatically reduce the uncertainty of field-scale predictions of basalt carbonation.

估计地质介质中的矿物反应表面积仍然是限制地下过程反应传输模型 (RTM) 预测准确性的主要挑战之一，特别是那些控制地质储存期间二氧化碳 (CO _{2 ) 命运的预测。}尽管已经多次尝试结合成像和实验技术来估计矿物反应表面积以用于地质 CO _{2的 RTM 预测}储存，这些技术尚未适用于玄武岩储层，与更经常研究的沉积对应物相比，玄武岩储层具有独特的孔隙结构、矿物学和化学成分。在这里，我们通过扫描电子显微镜 (SEM) 反向散射电子图像（高分辨率 500 nm/像素）的图像分析和来自东蛇河的玄武岩样品的拉曼光谱映射来量化流体可接近的矿物表面积来解决这个问题平原，爱达荷州。为了评估确定的孔隙流体可及矿物表面积是否准确反映反应表面积，开发了一种微连续尺度 RTM，并与高温高压流通 CO _{2进行了比较}对特征化的玄武岩进行矿化实验。重要的是，使用图像衍生的孔隙流体可接近矿物表面积的模拟在不确定性范围内与实验流出物化学非常匹配。然后将这些矿物表面积用于参数化代表东北太平洋卡斯卡迪亚盆地的现场尺度模型，以评估表面积变化对矿物碳化的影响。使用图像衍生表面积的变化进行模拟，这些变化覆盖表面积增加和减少一到两个数量级，类似于先前报道的总表面积和反应表面积之间的差异量级。以 CO _{2计的碳酸化效率}跟踪和比较模拟期间矿化的体积。表面积增加和减少两个数量级的模拟显示玄武岩碳化效率分别比图像衍生矿物表面积的预测快三倍和慢六倍。这些敏感性分析表明，准确量化矿物表面积对于预测 CO ₂矿化的努力至关重要，并且像这里所采用的努力可以显着降低玄武岩碳化的现场规模预测的不确定性。