Kinetic modeling of laboratory CO₂‐exposure experiments performed on whole rock reservoir samples

Abstract

Mineralogical and geochemical observations from laboratory CO2‐exposure experiments on reservoir rocks are compared with predictions from geochemical modeling that was performed using PHREEQC software. The Pitzer‐based Eq 3/6 thermodynamic database, provided by Quintessa Ltd., was applied. For kinetic modeling, a Lasaga‐type rate equation was implemented and different models were parameterized taking kinetic rate law parameters from literature. Based on previous modeling studies a modified inverse modeling approach is presented here. This comprises several different Fe‐proxies and improved statistical ranking preferences that were implemented in particular to better match modeled and measured concentrations of dissolved K+, Fe2+ and Al3+. Compared to the previous approach, the presented modeling results are in good (better) agreement with experimental data. Systematic discrepancies between modeling and observation still occur regarding K‐bearing mineral phases and corresponding K+ brine concentrations. Despite missing correlation between K+ and Cl− concentrations, potential reasons for these discrepancies may be increased K+ brine concentrations during the experiments due to dissolution of K‐rich salt(s), such as sylvite. Much better matches were generated for dissolved Fe2+ concentrations. Goethite mainly controls the chemical behavior of dissolved Fe2+ in kinetic simulations. Based on both the available equilibrium and kinetic modeling results, the ultimate fate of dissolved Al3+ and the analysis of Al‐bearing mineral phases potentially controlling dissolved Al3+ brine concentrations cannot be conclusively determined. The overall best ranked kinetic model comprises anhydrite, dolomite, goethite, K‐feldspar and kaolinite. Despite minor inconsistencies dissolved Fe2+, Al3+ and Si4+ were in particular much better reproduced by the best ranked kinetic models.

Thermodynamic Modeling of Aqueous Electrolyte Systems: Current Status

“…Topical issues associated with CO 2 (g) dissolved in aqueous solutions have also attracted considerable attention, especially regarding solubility. ,− There has been special interest in the general importance of electrolyte interactions, scrubbing technologies, , potential scale-formation, ,, modeling chemical processes in rock pore waters, , the exploitation of saline aquifers for carbon capture and storage, − and ocean acidification. ,, Such investigations typify the need for generalized thermodynamic modeling capabilities in aqueous chemistry for simulating the variety and complexity of the many prospective practical applications.…”

Hydraulic and Mechanical Impacts of Pore Space Alterations within a Sandstone Quantified by a Flow Velocity-Dependent Precipitation Approach

“…Geochemical fluid-rock interactions such as precipitation and dissolution of minerals alter the microstructure of rocks, and thereby affect their physical behaviour at the macro scale. The prediction of the resulting changes in effective hydraulic and mechanical rock properties is of paramount importance for numerous natural geochemical systems and commercial applications such as geothermal energy production [ 1 , 2 ], hydrocarbon exploration and exploitation [ 3 , 4 ], nuclear waste disposal [ 5 , 6 ] as well as energy and gas storage [ 7 , 8 , 9 ]. Within the context of subsurface utilisation, the interaction of hydraulic, mechanical and chemical processes may be triggered or preferably prevented depending on the geological application: Precipitation of minerals reduces the permeability, what could negatively impact the injectivity and productivity of a reservoir [ 10 , 11 ], but on the other hand mineral growth can seal potential leakage pathways, e.g., in the context of CO 2 storage [ 12 , 13 ].…”

Revising the Static Geological Reservoir Model of the Upper Triassic Stuttgart Formation at the Ketzin Pilot Site for CO2 Storage by Integrated Inverse Modelling

“…With the availability of new data, the static geological model has been continuously further developed, revised and matched against field observations to enable reliable short-to long-term predictions using coupled numerical multiphase flow, hydromechanical and hydrochemical models [14,15,[19][20][21]. In this context, substantial efforts have been undertaken to integrate data from ongoing on-site field tests and continuous observations as well as laboratory experiments on the Stuttgart Formation reservoir rocks with numerical simulations [12,14,19,20,[22][23][24][25][26][27][28][29][30][31][32]. Recent activities especially focussed on integration of the 3D seismic survey data with other geophysical findings and numerical simulations [33][34][35][36], also in view of defining conformance criteria between geophysical monitoring and numerical model predictions to be considered in German and EU-wide regulations [37].…”