Accelerated reactive transport simulations in heterogeneous porous media using Reaktoro and Firedrake

This work investigates the performance of the on-demand machine learning (ODML) algorithm introduced in Leal et al. (Transp. Porous Media 133(2), 161-204, 2020) when applied to different reactive transport problems in heterogeneous porous media. This approach was devised to accelerate the computationally expensive geochemical reaction calculations in reactive transport simulations. We demonstrate that even with a strong heterogeneity present, the ODML algorithm speeds up these calculations by one to three orders of magnitude. Such acceleration, in turn, significantly advances the entire reactive transport simulation. The performed numerical experiments are enabled by the novel coupling of two open-source software packages: Reaktoro (Leal 2015) and Firedrake (Rathgeber et al. ACM Trans. Math. Softw. 43(3), 2016). The first library provides the most recent version of the ODML approach for the chemical equilibrium calculations, whereas, the second framework includes the newly implemented conservative Discontinuous Galerkin finite element scheme for the Darcy problem, i.e., the Stabilized Dual Hybrid Mixed (SDHM) method Ntifiez et al. (Int. J. Model. Simul. Petroleum Industry, 6, 2012).

Automated summary

This work investigates the performance of the on-demand machine learning (ODML) algorithm in different reactive transport problems in heterogeneous porous media. The results show that even with strong heterogeneity, the ODML algorithm can significantly accelerate the calculations, thereby improving the efficiency of the entire reactive transport simulation.