A higher-order finite element reactive transport model for unstructured and fractured grids

Abstract

This work presents a new reactive transport framework that combines a powerful geochemistry engine with advanced numerical methods for flow and transport in subsurface fractured porous media. Specifically, the PhreeqcRM interface (developed by the USGS) is used to take advantage of a large library of equilibrium and kinetic aqueous and fluid-rock reactions, which has been validated by numerous experiments and benchmark studies. Fluid flow is modeled by the Mixed Hybrid Finite Element (FE) method, which provides smooth velocity fields even in highly heterogenous formations with discrete fractures. A multilinear Discontinuous Galerkin FE method is used to solve the multicomponent transport problem. This method is locally mass conserving and its second order convergence significantly reduces numerical dispersion. In terms of thermodynamics, the aqueous phase is considered as a compressible fluid and its properties are derived from a Cubic Plus Association (CPA) equation of state. The new simulator is validated against several benchmark problems (involving, e.g., Fickian and Nernst-Planck diffusion, isotope fractionation, advection-dispersion transport, and rock-fluid reactions) before demonstrating the expanded capabilities offered by the underlying FE foundation, such as high computational efficiency, parallelizability, low numerical dispersion, unstructured 3D gridding, and discrete fraction modeling.

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

“…Figure 1 illustrates the simulation procedure, where the fluid flow, the species transport, and the equilibrium steps are solved consecutively. 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 Open-FOAM (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), GeoChemFoam (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).…”

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

“…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).…”

POET (v0.1): speedup of many-core parallel reactive transport simulations with fast DHT lookups

“…Traditionally, the reactive transport community has rather been focused on implementing parallelism of specialized simulators (e.g., Steefel et al, 2015;Beisman et al, 2015;Moortgat et al, 2020) in order to efficiently scale when computing on large HPC facilities. Since the parallelization of flow and transport is much harder than chemistry, these simulators rely on fixed domain partitioning, and the computational load of chemistry is addressed by using more CPUs.…”