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

Abstract

This work investigates the performance of the on-demand machine learning (ODML) algorithm introduced in Leal et al. (Transp. Porous Media133(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 Núñez et al. (Int. J. Model. Simul. Petroleum Industry, 6, 2012).

Validating the Nernst–Planck transport model under reaction-driven flow conditions using RetroPy v1.0

“…The equilibrium calculations are performed at every time step and for every discretized volume of the simulation domain, and calculations of similar chemical states can be cached for efficiency. Efficient lookup tables and machine learning approaches of chemicalequilibrium calculations are in active research (Huang et al, 2018;Savino et al, 2022;Laloy and Jacques, 2022), and significant speedups in solving reactive transport problems have been achieved (Kyas et al, 2022;Bordeaux-Rego et al, 2022). In particular, Reaktoro employs on-demand machine learning and physics-based interpolation, which is able to conserve mass and control interpolation accuracy (Leal et al, 2020).…”

Artificial Intelligence for Earth System Predictability (AI4ESP) (2021 Workshop Report)

“…The computational costs associated with modeling chemical processes in reactive transport simulations can substantially compromise performance, such as when rich chemical descriptions of fluids and rocks are considered. In this case, the on-demand ML strategy presented in Leal et al (2020) and Kyas et al (2022) can speed up the sheer number of chemical equilibrium and kinetics calculations by one to three orders of magnitude and provide significant overall speed up.…”

Validating the Nernst–Planck transport model under reaction-driven flow conditions using RetroPy v1.0

“…The equilibrium calculations are performed at every time step and for every discretized volume of the simulation domain, and calculations of similar chemical states can be cached for efficiency. Efficient lookup tables and machine learning approaches of chemical-equilibrium calculations are in active research (Huang et al, 2018;Savino et al, 2022;Laloy and Jacques, 2022), and significant speedups in solving reactive transport problems have been achieved (Kyas et al, 2022;Bordeaux-Rego et al, 2022). In particular, Reaktoro employs on-demand machine learning and physics-based interpolation, which is able to conserve mass and control interpolation accuracy (Leal et al, 2020).…”

“…Due to the simplicity and effectiveness in applying the SNIA coupling between the transport solvers and the chemical equilibrium codes, this coupling method is widely adopted in the following software: OpenGeoSys (Shao et al, 2009;Naumov et al, 2022), poreReact (coupling of OpenFOAM (Weller et al, 1998) and Reaktoro) (Oliveira et al, 2019), CSMP++GEM (Yapparova et al, 2019), FEniCS-Reaktoro (Damiani et al, 2020), Osures (Moortgat et al, 2020), FEniCS-based Hydro-Mechanical-Chemical solver (Kadeethum et al, 2021), PorousFlow (based on the MOOSE Framework (Permann et al, 2020)) (Wilkins et al, 2021), IC-FERST-REACT (Yekta et al, 2021), COMSOL and PHREEQC (Jyoti and Haese, 2021), GeoChem-Foam (coupling of OpenFOAM and PHREEQC) (Maes and Menke, 2021), coupling of Reaktoro and Firedrake (Rathgeber et al, 2016) (Kyas et al, 2022), and P3D-BRNS (Golparvar et al, 2022). For reviews of reactive transport codes and the underlying coupling approaches, we refer the reader to the publications by Gamazo et al (2015); Damiani et al (2020).…”