We perform a set of flow and reactive transport simulations within three-dimensional fracture networks to learn the factors controlling mineral reactions. CO$_2$ mineralization requires CO$_2$-laden water, dissolution of a mineral that then leads to precipitation of a CO$_2$-bearing mineral. Our discrete fracture networks (DFN) are partially filled with quartz that gradually dissolves until it reaches a quasi-steady state. At the end of the simulation, we measure the quartz remaining in each fracture within the domain. We observe that a small backbone of fracture exists, where the quartz is fully dissolved which leads to increased flow and transport. However, depending on the DFN topology and the rate of dissolution, we observe a large variability of these changes, which indicates an interplay between the fracture network structure and the impact of geochemical dissolution. In this work, we developed a machine learning framework to extract the important features that support mineralization in the form of dissolution. In addition, we use structural and topological features of the fracture network to predict the remaining quartz volume in quasi-steady state conditions. As a first step to characterizing carbon mineralization, we study dissolution with this framework. We studied a variety of reaction and fracture parameters and their impact on the dissolution of quartz in fracture networks. We found that the dissolution reaction rate constant of quartz and the distance to the flowing backbone in the fracture network are the two most important features that control the amount of quartz left in the system. For the first time, we use a combination of a finite-volume reservoir model and graph-based approach to study reactive transport in a complex fracture network to determine the key features that control dissolution.

翻译：我们在一系列三维裂缝网络中进行流动和反应性输运模拟，以探究控制矿物反应的因素。二氧化碳矿化需要含二氧化碳的水、一种矿物的溶解，进而导致含二氧化碳矿物的沉淀。我们的离散裂缝网络（DFN）部分填充了石英，石英逐渐溶解直至达到准稳态。在模拟结束时，我们测量域内每条裂缝中剩余的石英量。我们观察到存在一个小型裂缝骨架，其中石英完全溶解，导致流动和输运增强。然而，根据DFN拓扑结构和溶解速率，这些变化存在巨大差异，这表明裂缝网络结构与地球化学溶解影响之间存在相互作用。在本工作中，我们开发了一个机器学习框架，用于提取支持以溶解形式进行的矿化的重要特征。此外，我们利用裂缝网络的结构和拓扑特征，预测准稳态条件下剩余石英体积。作为表征碳矿化的第一步，我们使用此框架研究溶解过程。我们研究了各种反应和裂缝参数及其对裂缝网络中石英溶解的影响。我们发现，石英的溶解反应速率常数和裂缝网络中流动骨架的距离是控制系统内剩余石英量的两个最重要特征。首次，我们结合有限体积储层模型和基于图的方法，研究复杂裂缝网络中的反应性输运，以确定控制溶解的关键特征。