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We perform a set of flow and reactive transport simulations withinthree-dimensional fracture networks to learn the factors controlling mineralreactions. CO$_2$ mineralization requires CO$_2$-laden water, dissolution of amineral that then leads to precipitation of a CO$_2$-bearing mineral. Ourdiscrete fracture networks (DFN) are partially filled with quartz thatgradually dissolves until it reaches a quasi-steady state. At the end of thesimulation, we measure the quartz remaining in each fracture within the domain.We observe that a small backbone of fracture exists, where the quartz is fullydissolved which leads to increased flow and transport. However, depending onthe DFN topology and the rate of dissolution, we observe a large variability ofthese changes, which indicates an interplay between the fracture networkstructure and the impact of geochemical dissolution. In this work, we developeda machine learning framework to extract the important features that supportmineralization in the form of dissolution. In addition, we use structural andtopological features of the fracture network to predict the remaining quartzvolume in quasi-steady state conditions. As a first step to characterizingcarbon mineralization, we study dissolution with this framework. We studied avariety of reaction and fracture parameters and their impact on the dissolutionof quartz in fracture networks. We found that the dissolution reaction rateconstant of quartz and the distance to the flowing backbone in the fracturenetwork are the two most important features that control the amount of quartzleft in the system. For the first time, we use a combination of a finite-volumereservoir model and graph-based approach to study reactive transport in acomplex fracture network to determine the key features that controldissolution.

Learning the Factors Controlling Mineralization for Geologic Carbon Sequestration