  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.
