Data-driven Surrogate Model Approach for Improving the Performance of Reactive Transport Simulations

Geochemical simulation models are the computational bottleneck for coupled reactive transport simulations. We investigate the use of a data-driven surrogate model in place of a geochemical simulation model to speed up the run-times of reactive transport simulations. This is a challenge because the surrogate model needs to use results of its predictions as inputs at each subsequent simulation step. We test the suitability of surrogate model approach on a popular reactive transport benchmark problem, often used for evaluating simulation models. We show that the concept is feasible and can make the simulations many times faster, however several open areas for future work remain.