Impact of Microheterogeneity on Upscaling Reactive Transport in Geothermal Energy

Abstract

Reaction rates for different minerals are usually measured in ideal conditions in batch experiments, where the impact of pore morphology and hydrodynamics have been fully neglected. Such reaction rates are used at continuum-scale (Darcyscale) models without the impact of pore structure on upscaled reaction rates under flow conditions. Therefore, to address the gap from batch experiments to upscaled reaction rates in continuumscale models, a pore-network model coupled with geochemical modeling has been developed. As a case study, we simulate the geochemical reactions of geothermal energy storage/recovery in sandstone rocks by coupling PhreeqcRM (a geochemistry model) with a pore-network model. The main purpose is to delineate the impact of pore morphology and dynamic conditions on upscaling of reaction rates using the surface-weighted and volume-weighted averaging. The results show that the kaolinite reaction rate in porous media highly depends on both the flow rate and spatial distribution of reactive pores. We evaluate the impact of correlation between the reactive pores and pore size distribution on upscaled reaction rates. Results indicate that if reactive pores do not belong to the main flow path, then upscaling the geochemical reactions based on the continuum-scale or batch experiments would be erroneous. In such a scenario, the discrepancy between volume-averaged and surface-weighted average reaction rates are highlighted. Moreover, increasing the injection flow rate results in lower average concentration of different species in the effluent, while it results in higher reaction rates in porous media. This research provides insights into the complex aspects of flow-based reaction rates versus the batch reaction rates. That has a significant impact on continuum-scale modeling of reactive transport for applications such as geothermal energy and enhanced oil recovery.

Signature of Geochemistry on Density‐Driven CO Mixing in Sandstone Aquifers

“…In this work, geochemistry is modeled by coupling PhreeqcRM (Charlton & Parkhurst, ; Parkhurst & Wissmeier, ) with an in‐house convective‐diffusive transport simulator. Geochemical reactions are coupled with flow equations by sequential noniterative approach (SNIA) (Barry et al, ; Charlton & Parkhurst, ; Carrayrou et al, ; Erfani et al, ; Farajzadeh et al, ; Kanney et al, ). Solid‐phase reactions are considered as kinetic reactions, and aqueous‐phase reactions are assumed to proceed instantaneously (equilibrium condition).…”

Dynamics of CO2 Density‐Driven Flow in Carbonate Aquifers: Effects of Dispersion and Geochemistry

“…Confidential manuscript submitted to <Water Resource Research> transport simulator. Geochemical reactions were coupled with the flow equations by sequential non-iterative approach (SNIA) [Kanney et al, 2003;Carrayrou et al, 2004;Charlton and Parkhurst, 2011;Farajzadeh et al, 2012;Erfani et al, 2019]. As the carbonate mineral reactions are fast and the convective flow is relatively slow, LEA was followed for all geochemical reactions including solid and aqueous phase reactions.…”

A Triple Pore Network Model (T-PNM) for Gas Flow Simulation in Fractured, Micro-porous and Meso-porous Media

“…Despite PNM simplifications, it is still interesting due to the low computational cost, especially with the Fig. 1 Network structure of the previous single PNMs and D-PNMs compared to the presented T-PNM, a single PNM (Blunt 2001;Fatt 1956), b D-PNM with meso-pores coupled to a lattice micro-pores (Bekri et al 2005), c dual with meso-pores and parallel micro-pores and throats (Bauer et al 2012), d a fully twoscale PNM with micro-and meso-pores (Jiang et al 2013), e dual with extra micro-throats to connect the meso-pores (Bultreys et al 2015), f dual with meso-pores and fractures with variable aperture (Hughes and Blunt 2001), g dual with meso-pores and fractures with fixed aperture (Erzeybek and Akin 2008), h D-PNM with extra bypassing fracture links (Liu et al 2017), i dual with unstructured network and variable geometry of fracture (Jiang et al 2017), j the presented T-PNM which includes meso-and micro-pores, meso-and micro-throats, fracture nodes, and fracture links have been accomplished by researchers to enhance the capability and reliability of PNMs (Baychev et al 2019) to realistically simulate and predict the properties of porous materials (Garboczi 1990;Joekar-Niasar and Hassanizadeh 2012;Xiong et al 2016), from hydraulic permeability (Dong and Blunt 2009) and mass diffusivity (Burganos and Sotirchos 1987;Erfani et al 2019) to electrical (Friedman and Seaton 1998) and thermal conductivities (Plourde and Prat 2003).…”