Geochemical effects of SO2 during CO2 storage in deep saline reservoir sandstones of Permian age (Rotliegend) – A modeling approach

International Journal of Greenhouse Gas Control 46 (2016) 116–135

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Geochemical effects of SO2 during CO2 storage in deep saline reservoir sandstones of Permian age (Rotliegend) – A modeling approach Svenja Waldmann ∗ , Heike Rütters Federal Institute for Geosciences and Natural Resources (BGR), Stilleweg 2, 30655 Hanover, Germany

a r t i c l e

i n f o

Article history: Received 31 March 2015 Received in revised form 22 December 2015 Accepted 4 January 2016 Keywords: CO2 storage Sulfur dioxide SO2 solubility Geochemical modeling Rotliegend Impurities

a b s t r a c t Geochemical modeling was used to assess the impact of sulfur dioxide (SO2 ) in impure carbon dioxide (CO2 ) streams on fluid–rock interaction during storage in siliciclastic rocks of Permian age (Rotliegend). We focused on the impact of SO2 on the porosity evolution, as well as on the trapping of CO2 as solid mineral phase. Due to the lack of a validated approach to calculate SO2 solubility in highly saline brine at CO2 storage conditions, different calculation approaches were tested against experimental literature data. Our novel approach employs a pressure and temperature adjusted Henry’s constant with salinity correction. Depending on the solubility calculation approach and on the consecutive reactions of dissolved SO2 , different SO2 concentrations in the brine were calculated. Based on the results of these solubility calculations, a low and a high SO2 concentration case were defined for geochemical modeling to assess the impact of different SO2 concentrations on fluid–rock interactions in a CO2 storage reservoir. An exemplary Rotliegend sandstone composition was chosen reflecting compositions of potential target horizons in the North German Basin. Short-term dissolution of carbonates and, in the presence of SO2 , subsequent precipitation of sulfur-bearing minerals results in a decrease in porosity. A precipitation of sulfates near the injection well may lower injectivity. With time, dissolution of alumosilicates further provides cations to the aqueous solution for a precipitation of secondary carbonates. The presence of SO2 modifies this long-term interplay between silicate and carbonate minerals. The higher the SO2 concentration in the brine, the lower the amounts of newly formed carbonates, i.e. the mineral trapping of CO2 , and the lower the overall porosity decrease. Hence, the assessment of SO2 dissolution in the formation brine at subsurface conditions is crucial for predicting mineral reactions and porosity evolution during geological storage of impure CO2 . © 2016 Elsevier Ltd. All rights reserved.

1. Introduction Geological storage of carbon dioxide (CO2 ) in the deep subsurface is discussed as one option for reducing CO2 emissions of large stationary point sources, such as power stations or other industrial plants, into the atmosphere (IPCC, 2005). The composition of CO2 streams captured from fossil-fueled power plants depends, among other things, on the fuel and the capture technology (IEAGHG, 2011). Beside CO2 , different associated incidental substances (“impurities”) may be present such as sulfur dioxide (SO2 ), oxygen (O2 ), nitrogen (N2 ), hydrogen (H2 ), water (H2 O), and carbon monoxide (CO) (e.g. Kather and Kownatski, 2011; Kather et al., 2013).

∗ Corresponding author. Present address: TNO, Princetonlaan 6, 3584 CB Utrecht, The Netherlands. E-mail address: [email protected] (S. Waldmann). http://dx.doi.org/10.1016/j.ijggc.2016.01.005 1750-5836/© 2016 Elsevier Ltd. All rights reserved.

Our study investigates the impact of SO2 in CO2 streams on geochemical interactions between brine and exemplary rock material of Permian age (Rotliegend) during geological storage of CO2 by reaction path modeling. A batch model was set-up using the code PHREEQC to (i) study major geochemical reactions in the presence of CO2 and SO2 , (ii) identify parameters influencing these reactions, and (iii) assess implications of simulated geochemical reactions on rock properties relevant to CO2 injection and geological storage. Since the mineralogical composition of Rotliegend sandstones in the North German Basin can be highly variable (Gaupp and Okkerman, 2011; Glennie et al., 1978), sensitivity analyses were performed with modified primary mineral assemblages and brine compositions. The aim of these sensitivity studies was to assess implications of different rock compositions for mineralization of CO2 and porosity evolution in the reservoir rock. A SO2 concentration of 0.005 vol.% in the injected CO2 stream was used that is derived from typical CO2 stream compositions resulting from oxyfuel combustion or post combustion capture

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(Kather and Kownatski, 2011; Kather et al., 2013). The work was performed as part of the collaborative project “CO2 purity for capture and storage (COORAL)” that investigated requirements for CO2 quality along the entire carbon dioxide capture and storage process chain for an optimization of CO2 stream composition (Rütters et al., 2015). 1.1. Dissolution of CO2 in saline water and its geochemical implications Rotliegend sandstones of Permian age located in the North German Basin are typically found under conditions of pressures ≥5 MPa and temperatures ≥323 K. At these pressure and temperature conditions, the solubility of CO2 in pure water is approximately five to ten times higher than at atmospheric conditions (Duan et al., 2006; Duan and Sun, 2003; Stewart and Munjal, 1970). The presence of dissolved species within the formation water generally decreases CO2 solubility. Formation waters of Rotliegend sandstones from the North German Basin are highly saline brines, containing mainly sodium chloride, with total concentrations of dissolved solids >165 g/L (Hoth et al., 2005; Rockel et al., 1997; Tesmer et al., 2007). For calculating CO2 solubility in saline water, an equation of state (Duan et al., 1992) and numerical models (Duan et al., 2006; Duan and Sun, 2003; Spycher and Pruess, 2005) are needed that are applicable at the relevant subsurface conditions. The dissolution of CO2 in water and the subsequent dissociation of the formed carbonic acid produce acidic solutions. This acidification initiates mineral reactions comprising both mineral dissolution and precipitation on different time scales (Gaus et al., 2005; Lu et al., 2010; Pham et al., 2011; Raistrick et al., 2009). Trapping of CO2 by formation of solid carbonate minerals is the safest form of long-term CO2 storage in the subsurface. The total amount of mineralized CO2 depends most notably on the pore network, the pore connectivity, the physicochemical properties of the formation water as well as on the types of minerals and their spatial distribution within reservoir sandstones (Kampman et al., 2014; Waldmann et al., 2014). Mineral reactions triggered by the presence of dissolved CO2 may also have a short-term influence on rock chemistry and the evolution of porosity and permeability during the injection period (Kharaka et al., 2006; Higgs et al., 2007). 1.2. Dissolution of SO2 in saline water and its geochemical implications The preferential dissolution of SO2 in comparison to CO2 significantly increases the acidity of the formation water (Goldberg and Parker, 1985) and thereby enhances mineral reactions, if the fluid–rock system is not capable of buffering the additional amount of protons. For the prediction of SO2 -related geochemical reactions, SO2 solubility at subsurface conditions (i.e. high pressure (p), temperature (T), and salinity (S)) needs to be known. In general, SO2 solubility increases with increasing pressure (Crandell et al., 2010; Rabe and Harris, 1963; Rumpf and Maurer, 1992; Weisenberger and Schumpe, 1996). It decreases with increasing temperature and salinity (Millero et al., 1989; Xia et al., 1999) relative to pure water and atmospheric conditions. Solubility of SO2 was experimentally analyzed in pure water at T ≤ 403 K and p ≤ 2.5 MPa (Harris and Meyers, 1987; Rabe and Harris, 1963), in sodium chloride solutions (0.1–5.9 mol/L NaCl) and mixed electrolyte solutions at T ≤ 03 K and p p ≤ 3.7 MPa (Millero et al., 1989; Rodriguez-Sevilla et al., 2002; Weisenberger and Schumpe, 1996; Xia et al., 1999). All experimental measurements were conducted at lower pressures than relevant for geological storage of CO2 in the deep subsurface. The initial physical dissolution equilibrium (Eq. (1)) is shifted by reactions of dissolved SO2 with water and other solutes (Eqs. (2)–(6)) leading to higher amounts of SO2 that enter the aqueous

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phase. Eqs. (2) and (3) describe reactions in an oxidant-free system (Goldberg and Parker, 1985): SO2 (g) → SO2 (aq)

(1)

SO2 (aq) + H2 O ↔ H2 SO3 H2 SO3 ↔

SO2− 3

(2)

+

+ 2H

(3) Fe3+

Mn4+ )

If oxygen or other oxidants (e.g. or are present in the formation water, sulfuric acid may be formed (Eq. (4)), which completely dissociates (Eq. (5)), and consequently strongly lowers the pH value (Millero, 1986). SO2 (aq) + H2 O + 1⁄2O2 ↔ H2 SO4 +

H2 SO4 ↔ 2H

+ SO2− 4

(4) (5)

The oxidation to sulfuric acid (Eqs. (4) and (5)) was identified as an important process during storage of impure CO2 in Fe-rich sandstones (Garcia et al., 2012; Palandri et al., 2005). However, according to Knauss et al. (2005) and Ellis et al. (2010), a complete oxidation of SO2 is unlikely due to a low oxygen fugacity of about 10−64 MPa in deep saline formations (Helgeson et al., 1993). Another important reaction of SO2 in aqueous solutions is its disproportionation (Eq. (6)) that is especially known from volcanic and hydrothermal systems (Getahun et al., 1996; Kusakabe et al., 2000; Symonds et al., 2001): 4SO2 (aq) + H2 O ↔ 3H2 SO4 + H2 S

(6)

There is no validated thermodynamic model available in the literature for calculating the dissolution behavior of SO2 in saline water at pT conditions of interest for geological storage of CO2 . In consequence, different approaches are currently used, each with specific advantages as well as shortcomings. Each approach yields different initial SO2 concentrations in the aqueous phase that are then available for mineral reactions. Often Henry’s constant is used to predict concentrations of dissolved SO2 at selected pT conditions and salinities (Crandell et al., 2010; Rodriguez-Sevilla et al., 2002; Rumpf and Maurer, 1992; Xia et al., 1999). However, Henry’s law (and Henry’s constant) only describe(s) the physical dissolution of SO2 (g) in pure water (Eq. (1)). In the code PHREEQC the dissolution behavior of SO2 is calculated using the Peng–Robinson equation of state (Peng and Robinson, 1976) and a Henry’s law based equilibrium constant in combination with simulations of chemical reactions of dissolved SO2 . We have set up a novel approach for calculating SO2 solubility using a pressure and temperature adapted Henry’s constant with a consecutive salinity correction of SO2 concentrations following Crandell et al. (2010), Rodriguez-Sevilla et al. (2002), Rumpf and Maurer (1992) and Xia et al. (1999). Resulting aqueous SO2 concentrations were compared to experimental data of Xia et al. (1999) and to SO2 (aq) concentrations calculated by the approach implemented in the code PHREEQC. Based on these two approaches, two cases of SO2 concentrations in the formation water – a high and a low SO2 case – were derived for geochemical modeling to study impacts of different concentrations of dissolved SO2 on geochemical reactions in a CO2 storage reservoir. This is important, since SO2 concentrations may differ locally within one CO2 storage reservoir on a meter-scale due to different transport processes that affect migration and dissolution of CO2 and SO2 in the formation water (e.g. Ellis et al., 2010). 1.3. Rotliegend sandstones–mineral inventory and formation water composition Rotliegend sandstones located in the North German Basin may offer potential target horizons for CO2 storage depending on the

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reservoir quality, the presence of a thick and regional seal, the proximity to faults and the abundance of hydrocarbon wells (van der Meer and Yavuz, 2009; Ramirez et al., 2010). The mineralogical composition of Rotliegend sandstones in the North German Basin can be highly variable due to their depositional environment, burial history, and diagenetic evolution (Gaupp and Okkerman, 2011; Glennie et al., 1978; Platt, 1993; Pudlo et al., 2012). Thus, for modeling with an exemplary rock type, a mineralogical composition was derived from a statistical analysis of petrographic data sets of Rotliegend sandstones from the North German Basin. 1.3.1. Mineral inventory 1.3.1.1. Detrital components. Detrital components comprise different minerals as well as various types of rock fragments. In Rotliegend sandstones, quartz is present as monocrystalline and polycrystalline quartz, and as chert. Detrital feldspar occurs dominantly as K-feldspar with negligible amounts of anorthite and albite. The most abundant volcanic rock fragments can be classified based on their chemical composition as acidic to intermediate types, reflecting a mixture of quartz, K-feldspar, albite, and muscovite (Gast and Gebhardt, 1995; Gaupp et al., 1993; Gebhardt et al., 1995; Kohlhepp, 2012; Pudlo et al., 2012; Rieke, 2001; Schöner, 2006). Rare mafic volcanic rock fragments contain high amounts of plagioclase instead of K-feldspar, and chlorite is a common component (Pudlo et al., 2012). The composition of metamorphic rock fragments is, as for volcanic grains, highly variable. Sedimentary rock fragments contain mainly quartz and minor amounts of Kfeldspar and albite, as reported by Schöner (2006) and Waldmann (2011). Detrital clay-rich components may occur as a clay matrix after burial and compaction (Hug, 2004) and comprise different types of clay minerals, quartz, feldspar, and iron oxides (MontesHernandez and Pironon, 2009; Pudlo et al., 2012; Wolfgramm, 2002). 1.3.1.2. Authigenic minerals. Carbonates are volumetrically abundant cements in Rotliegend sandstones and are reported in almost all data sets considered in this study. In most data sets, authors distinguish between calcite and dolomite (Rieke, 2001; Schöner, 2006; Wolfgramm, 2002), whereas Kohlhepp (2012) reports only the total amount of undefined carbonates which have been further identified as calcite by Pudlo et al. (2012). Waldmann (2011) reports contents of Fe-rich carbonates in addition to calcite and dolomite. Anhydrite is detected by different authors (cf. Table 1), whereas pyrite is reported only by Wolfgramm (2002). Sr and Ba may occur as substitutes in anhydrite or as pure celestite (SrSO4 ) and barite (BaSO4 ). Besides anhydrite, barite was reported by Deutrich (1993), Kohlhepp (2012), and Schöner (2006). Clay minerals can be distinguished as illite, chlorite, and kaolinite. According to Gaupp (1996) two main types of authigenic illite are typical for Rotliegend sandstones: (i) tangential illite coatings covering detrital grain surfaces and (ii) illite meshworks with platy or fibrous habitus. Illite coatings may vary in composition and are often formed from precursor smectite or illite/smectite mixed layers during diagenesis (Wilson, 1992; Worden and Morad, 2003). Such illite coatings may be intercalated with iron oxide minerals in red beds (Chukhrov, 1973; Gaupp, 1996; Waldmann, 2011; Walker, 1967). Ziegler (2006) indicated up to 35% residual smectite in irregular stratified mixed layer minerals in Rotliegend sandstones from the Southern North Sea. In general, the composition of smectite is highly variable due to the environmental conditions during formation and further transformation to illite with Na, Ca, Mg, and Fe being common substitutes (Deer et al., 1992). The chemical composition of natural chlorites mainly depends on the substitution between Mg and Fe and can be represented by binary combinations of the end-members clinochlore and chamosite (Deer et al., 1992). Chlorites from Rotliegend sandstones are covering a

wide range of compositions (Deutrich, 1993; Pudlo et al., 2012) and can be classified following Curtis et al. (1985) and Foster (1962) as Fe-rich clinochlore, Mg-rich chamosite, and chamosite. Authigenic kaolinite appears in pore spaces and/or as replacements of detrital feldspars (Gaupp et al., 1993; Ziegler, 2006). 1.3.2. Formation water composition Formation water compositions of Rotliegend horizons have been studied in detail at a few localities in the center of the North German Basin where waters have been produced with gases from deep reservoirs (Gaupp et al., 2008; Lüders et al., 2005, 2010; Rieken, 1988; Schmidt Mumm and Wolfgramm, 2002; Schöner et al., 2008). The amount of total dissolved solids (TDS) in these waters generated from Late and Early Permian sediments range between 210 and 330 g/L, with approximately 270 g/L on average (De Lucia et al., 2012; Hoth et al., 2005; Lüders et al., 2010; Rockel et al., 1997; Tesmer et al., 2007). The formation waters are mainly characterized by high concentrations of Cl, Na, Ca, Mg, and K with minor concentrations of elements like Ba, Br, Fe, Li, Mn, Pb, Rb, S, Sr, and Zn (De Lucia et al., 2012; Lüders et al., 2010). 2. Methods 2.1. Dissolution of SO2 in saline water The amount of SO2 dissolved in saline water was calculated using two different approaches: (i) by a pT adjusted Henry’s constant (Crandell et al., 2010; Rodriguez-Sevilla et al., 2002; Rumpf and Maurer, 1992; Xia et al., 1999) with salinity correction and (ii) by an approach based on Henry’s law implementing the EOS of Peng and Robinson (1976) in combination with interactions parameters for Pitzer’s equation (Pitzer, 1973) with the code PHREEQC. From these calculations, which are described in detail in Sections 2.1.1–2.1.3, two cases of SO2 concentrations in the aqueous phase were derived: a low and a high SO2 concentration case. The two cases were used to assess the impacts on mineral dissolution and precipitation reactions in a CO2 storage reservoir (see Section 2.4.4). 2.1.1. Solubility calculation using an adapted Henry’s constant As one approach for SO2 solubility calculation, a pT adjusted Henry’s constant with salinity correction was used. Henry’s constant (Eq. (7)) relates the concentration ci of a gas in an aqueous phase to its partial pressure pi and fugacity i : Kh =

ci i · pi

(7)

Henry’s law is valid for dissolution of gas in pure water if solubility and partial pressure of the gas are low and if the temperature is sufficiently smaller than the critical temperature of the respective gas (Prausnitz et al., 1999). However, typical target horizons for CO2 storage contain (highly) saline formation waters, and exhibit high temperatures and pressures. In consequence, Henry’s constant is not applicable and has to be adjusted to these conditions. The adjustment of the Henry’s constant to high pressure is performed using the Krichevsky–Ilinskaya equation (Eq. (8)) applicable for moderately high pressure (Prausnitz et al., 1999) provided that the partial molar volume of the solvent at infinite dilution is independent of pressure (Lüdecke and Lüdecke, 2000): ln

s v¯ ∞ (ps ) f2 A 2 · (P − P1 ) · (x12 − 1) + = ln H2,11 + x2 R·T R·T

(8)

with 1 being the solvent (here: water) and 2 the gas, f2 the fugacity of a gas, x1 the molar fraction of the solvent in liquid phase, x2 the molar fraction of gas in liquid phase. A is the Margules’s constant (cm3 · 105 Pa/mol), R the universal gas constant (105 Pa · cm3 /mol K), T is the temperature (K), v¯ ∞ 2 the

Table 1 Detected detrital components and authigenic minerals (indicated with tickmark) of Upper Rotliegend sandstones from the North German Basin using microscopical “point-counting” analysis of thin sections (PC) for 300 points and X-ray diffraction analysis (XRD). For each mineral/component a conversion to mineral phases used in geochemical modeling is listed (vol.%). Component

PC Deutrich (1993)

XRD Förster et al. (2013)

PC Kohlhepp (2012)

PC Rieke (2001)

PC Schöner (2006)

PC Waldmann (2011)

PC Wolfgramm (2002)

√ √ √ √ √ √

√

√ √

√ √

√ √

√ √ √ √ √

√ √ √ √ √

√ √ √ √ √

√ √ √ √ √ √

√

√

√

√

√

√

Felsic volcanic RF

√

Mafic volcanic RF

√

Metamorphic RF

Sedimentary RF Other RF Diagenetically replaced RF Mica Heavy minerals Matrix Authigenic minerals Quartz K-Feldspar Plagioclase Carbonate (undefined) Calcite Dolomite Ankerite/siderite Anhydrite Barite Pyrite Illite coatings Illite/kaolinite Illite Chlorite Kaolinite Titan oxide Iron oxide

√

√

√ √ √ √

√ √

√

√

√

√ √ √ √ √

√

√

√ √

√ √

√ √

√

√

√ √ √

√ √ √

√ √

√

√

√ √

√ √ √ √ √ √

√ √ √

√

√ √

√

√ √ √ √

√ √ √

√ √ √ √

√

√

√ √ √ √

√

√

√ √

√ √

√ √

√ √

√

√

√ √ √ √

√

√

√

Conversion/recalculation for geochemical simulation

Considered in modeling

Quartz Quartz Quartz K-feldspar Anorthite 45% Qz, 30% Kfsp, 20% Alb, 5% Musc 45% Qz, 30% Kfsp, 20% Alb, 5% Musc 30% Qz, 20% Alb, 30% An, 15% Chl, 5% Hem 45% Qz, 25% Kfsp, 25% Alb, 2.5% Musc, 2.5% Chl 70% Qz, 20% Kfsp, 10% Alb Not defined further Not defined further Muscovite Not defined further 90% Illite, 5% Chlorite, 5% Qz

x x x x x x

Quartz K-feldspar Albite 97% Calcite, 3% Dolomite Calcite Dolomite Ankerite Anhydrite Barite Pyrite 90% Illite, 10% Iron oxide 95% Illite, 5% Kaolinite Illite Chlorite Kaolinite Titan oxide Hematite

x x x

x

x

x x x x x x x x

S. Waldmann, H. Rütters / International Journal of Greenhouse Gas Control 46 (2016) 116–135

Detrital components Monocrystalline Qz Polycrystalline Qz Chert K-feldspar Plagioclase Volcanic RF (undefined)

Detection of detrital components and authigenic minerals by different methods and authors

x x x x x x

Qz, quartz; Kfsp, K-feldspar; Alb, albite; An, anorthite; Musc, muscovite; Ill, illite; Chl, chlorite; Kaol, kaolinite; Hem, hematite; Cc, calcite; Dol, dolomite; RF, rock fragments.

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partial molar volume of dissolved gas in liquid phase at infinite dilution (cm3 /mol). P is the pressure (105 Pa), and P1s is the satu(ps )

ration vapor pressure of water (105 Pa). The term H2,11 describes Henry’s constant at a reference pressure of 0.1 MPa and is calculated by using the temperature dependent adjustment from Lide and Frederikse (1995) based on the Henry’s constant (at 298 K) Kh = 1.4 mol/kg · 105 Pa and the constant d(ln(H))/d(1/T) = 2900 K. The Margules’s constant A describes the discrepancy between the behavior of a real solution in comparison to an ideal one (Atkins and de Paula, 2006). The partial molar volume of SO2 is taken from Brelvi and O’Connell (1972) for calculating the behavior of SO2 in mixtures at infinite dilution. This approach, presented by Krichevsky and Ilinsakya, is independent of pressure as long as the temperature (here, 363 K) is well below the critical temperature of the gas (Tc (SO2 ) = 430.8 K) (Prausnitz et al., 1999). The saturation vapor pressure of water is calculated using a quadratic empirical equation after Wagner and Kretzschmar (2008). For calculating the fugacity coefficient of a real gas (cf. Eq. (7)) the reduced second virial coefficient was applied following the approach of Tarakad and Danner (1977) and Tsonopoulos (1974). With increasing ionic strength of the water, the solubility of a gas generally decreases (Weisenberger and Schumpe, 1996). In moderately saline solutions (salt concentrations ≤2 mol/L), this effect can be described using the Seˇcenov constant that is applicable for a temperature range from 273 K to 363 K. By means of gas- and ion-specific constants, the ratio between SO2 concentrations in pure and saline water can be calculated (Weisenberger and Schumpe, 1996). For the gas-specific constant hG,0 the value given in Rodriguez-Sevilla et al. (2002) was used. Note that this approach only considers physical dissolution of SO2 (see Eq. (1) in Section 1.2). 2.1.2. Calculation of SO2 dissolution using the Peng–Robinson equation of state and simulations of consecutive reactions Dissolution of SO2 was also calculated with the code PHREEQC (Parkhurst and Appelo, 2013) using a Henry’s law based approach with the implemented Peng–Robinson EOS (Peng and Robinson, 1976) applied for calculations of the fugacity coefficient of SO2 . The Peng–Robinson EOS is applicable for temperatures ≤∼500 K and pressures ≤∼20 MPa. Note that the equilibrium constant (log K) used in this approach is, strictly speaking, only applicable to solubility calculations in pure water. For the simulation of consecutive reactions of SO2 in the aqueous solution, a database was used that is applicable for brines with high ionic strength (“Pitz ext database”; Moog and Mönig, 2010). This database includes interaction parameters for Pitzer’s equation (Pitzer, 1973) for the systems MCl(2) -H2 SO4 -H2 O and CO2 -H2 SO4 -H2 O (M = H+ , K+ , Ca2+ , Mg2+ , Fe2+ , and Fe3+ ). In order to consider the formation and dissociation of sulfurous acid in saline water, the database was supplemented by interaction parameters for the system SO2 -NaCl-H2 O given in Rumpf and Maurer (1992), Rumpf et al. (1993), and Xia et al. (1999). The equilibrium constants for the first and second dissociation step of sulfurous acid for 273 K given in Xia et al. (1999) were used in combination with analytical expressions for the temperature adaption of equilibrium constants (log K) available in the database from the Lawrence Livermore National Laboratory (Version: thermo.com.V8.R6.230; “LLNL database”). SO2 disproportionation reactions are not adequately defined in the “Pitz ext database” and were disregarded for SO2 solubility calculations in this study. 2.1.3. Testing approaches for calculating SO2 dissolution 2.1.3.1. Comparison of calculated solubility values with experimental data. To test the reliability of the two approaches, calculations were performed for pTS conditions chosen for experimental

investigations of SO2 dissolution by Xia et al. (1999). Calculation results were compared to experimental data of these authors. More specifically, amounts of dissolved SO2 were calculated at T = 353 K, a salinity of 2.942 mol/kgw NaCl, at a pressure range between 0.01 and 2.5 MPa considering four different cases: Case A “pTS adapted Henry’s constant (Eq. (1))”: Physical dissolution only calculated by the approach described in Section 2.1.1. Case B “pTS adapted Henry’s constant (Eqs. (1) and (2))”: To account for shifting of the initial dissolution equilibrium by reactions of dissolved SO2 in the aqueous phase, the amount of SO2 calculated in case A is doubled. Case C “Peng–Robinson & Pitzer (Eqs. (1)–(3))”: Calculation of SO2 dissolution with the code PHREEQC (see Section 2.1.2) considering physical dissolution of SO2 , formation and dissociation of sulfurous acid; Case D “Peng–Robinson & Pitzer (Eqs. (1)–(5))”: Calculation of SO2 dissolution with the code PHREEQC (see Section 2.1.2) considering the oxidation of sulfurous acid to sulfuric acid in addition to the processes considered in case C. 2.1.3.2. Adaptation of pe values. Since the code PHREEQC is not capable of simulating redox reactions adequately in combination with Pitzer’s interaction parameters (Plummer and Parkhurst, 1990), the pe value, i.e. the negative logarithm of electron activity (=−log10 (ae)), is kept constant at the default value of pe = 4 (in cases C + D). Note that often more reducing conditions, i.e. lower pe values, prevail in subsurface reservoirs, but higher values may be encountered if oxidants are present in the formation water. Hence, simulations for case D were also performed with manual adaptations of pe values between 3.7 and 10.7 following simulation results for case D obtained by using the Debye-Hückel based “LLNL-database”. 2.2. Determination of an exemplary mineral inventory of Rotliegend sandstones To develop an exemplary rock composition of Upper Rotliegend sandstones from the North German Basin (Fig. 1), 484 literature petrography data sets derived from microscopical “point-counting” on thin sections and X-ray diffraction analysis (Deutrich, 1993; Förster et al., 2013; Kohlhepp, 2012; Rieke, 2001; Schöner, 2006; Waldmann, 2011; Wolfgramm, 2002) were used. The studied sandstones were deposited under different environmental settings (e.g. aeolian dune sediments, fluvial and dry sandflat deposits) and are located between 606 m and 5997 m depth at present (George and Berry, 1993; Plein, 1978). Reported information on abundances of different detrital components and authigenic minerals was collated. From this collation, an average content (arithmetic mean value) of each detrital component and each authigenic mineral was calculated. 2.3. Data processing for geochemical simulations 2.3.1. Conversion to mineral contents In Rotliegend sandstones, a high natural variability of the chemical composition of minerals occurs. Such mineral compositions almost exclusively represent binary mixtures of different compositional end-members. In contrast, in thermodynamic databases as used for geochemical simulations, mainly pure end-member minerals are defined. Hence, for some minerals, stoichiometric formulas in the databases have to be compared to natural occurrences in Rotliegend sandstones to select (or adapt) thermodynamic data for geochemical modeling (Table 1). In addition, rock fragments consisting of agglomerates of different minerals have to be

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121

Fig. 1. Distribution of Rotliegend deposits in the Central European Basin System with indicated study areas of different authors (see legend and numbers 1–7). Major fault systems (solid lines) and thicknesses of Rotliegend deposits (dashed lines) are indicated. Paleogeographic sketch adapted from Schöner (2006).

recalculated in terms of their mineral content (Table 1). For example, sedimentary rock fragments were defined in this study to contain 70 vol.% quartz, 20 vol.% K-feldspar, and 10 vol.% albite. Composition of rock fragments listed as “others” or “diagentically replaced” in the literature are not considered for the calculation of overall mineral contents. Note that due to the high variability of the composition of rock fragments, values used in this study are inevitably a simplification. 2.3.1.1. Chlorites. For some minerals, the evaluated chemical composition is not reflected in the entries in any of the available databases, e.g. for chlorite: Based on their composition, chlorites from Rotliegend sandstones can be classified as a binary combination of the end-members clinochlore [(Mg10 Al2 )(Si6 Al2 O20 )(OH)16 ] and chamosite [(Fe10 Al2 )(Si6 Al2 O20 )(OH)16 ] (Deer et al., 1992) with an average ratio of 46 mol% clinochlore and 54 mol% chamosite. This average ratio was gained from chlorite composition data from Deutrich (1993), Pudlo et al. (2012) and Waldmann (unpublished data) based on 102 measured data points. However, in the “LLNL-database” clinochlore-14 A and daphnite-14A are defined as [(Mg5 Al2 Si3 O10 )(OH)8 ] and [(Fe5 Al2 Si3 O10 )(OH)8 ], respectively, whereas chamosite is listed as [(Fe2 Al2 SiO5 )(OH)4 ]. Thus as the best possible approximation, a clinochlore-14A/daphnite-14A mixture [(Mg2.3 Fe2.7 Al2 Si3 O10 )(OH)8 ] is used in geochemical modeling. This mixture will be named chlorite in the following. The thermodynamic data calculated for this mixture are given in section 2.4.2. 2.3.1.2. Illites. Quantitative analyses of illites (n = 84) by Electron Microprobe Analysis (Deutrich, 1993; Dittmann, 2008) show that the composition of natural illites, calculated on the basis of 24 oxygen and 8 tetrahedral cations, is in a good agreement with the stochiometric formulae [K0.6 Mg0.25 Al1.8 Al0.5 Si3.5 O10 (OH)2 ] in the “LLNL database” (Fig. 3).

2.3.1.3. Carbonates. Due to the highly variable carbonate composition in Rotliegend sandstones (Fig. 4), ankerite (CaMg0.3 Fe0.7 (CO3 )2 ) is used, beside calcite and dolomite, as primary minerals as representative of Fe-rich carbonates (see Section 2.4).

2.3.2. Uncertainty assessment of average mean mineral contents After conversion of mineral agglomerates to mineral phases listed in available databases (Fig. 2) and transformation of naturally occurring solid solutions into a mixture of the respective end-members, an overall average content (arithmetic mean value) of each mineral phase was derived. Uncertainties of the arithmetic mean values were assessed using Gaussian error propagation (Eq. (9); Table 2):

  j  2 2 Mineralm = K ·  (vol%i · xi ) + (xi · vol%i ) ,

(9)

i=1

where Mineralm is the individual mineral phase that was defined for geochemical modeling, K is the k-factor (sigma environment) which is defined as 2, vol.%i is the amount of each detrital (e.g. rock fragments) and authigenic (e.g. calcite) component, x is the percentage of each mineral phase in detrital rock fragments (Table 1), and j is the total number of minerals in the rock. For uncertainty analysis of the point counting data (vol % i ), standard deviation charts from Howarth (1998) were used. Since only two of the total 484 samples were analyzed by X-ray diffraction, no discrete uncertainty was assumed for these values. The composition of detrital rock fragments can be highly variable due to their type of source rock (e.g. granitic, basaltic). Hence, an error (xi ) of 30% is chosen to account for these uncertainties.

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75% Quartile 50% Quartile Outlier

Content (vol.%)

No. of samples (n)

Fig. 2. Volumetric contents of mineral phases in Rotliegend sandstones calculated on the basis of literature data (petrographic analysis) represented as “box-whisker plots”. The maximum and minimum of each “whisker” is defined as 1.5 times of the “box size”. Furthermore, the number of samples is given in which each mineral was detected. Qz = quartz, Kfsp = K-feldspar, Alb = albite, An = anorthite, Musc = muscovite, Cc = calcite, Dol = dolomite, Ank = ankerite, Anhy = anhydrite, Bar = barite, Pyr = pyrite, Chl = chlorite, Kaol = kaolinite, Hem = hematite.

2.4. Geochemical modeling Geochemical interactions between CO2 , SO2 , saline water, and rock material were simulated by using the code PHREEQC (Version v03; Parkhurst and Appelo, 2013) in a zero dimensional kinetic batch reaction model. For the simulations, a database needs to be selected and the initial mineral assemblage (primary minerals) and potentially formed minerals (secondary minerals), composition of the gas phase and aqueous phase need to be defined as input parameters. The unit used in the code PHREEQC to define the compositions of both the solid phase and the aqueous phase is the molality in moles of substances per kilogram water (mol/kgw). In the following, the expressions “concentration” and “amount” are used to distinguish between compositions of aqueous and solid phase, respectively. Fig. 3. Cation contents of illites (based on 24 O atoms, i.e. per formulae unit – pfu) in Rotliegend sandstones in the North German Basin (Deutrich, 1993; Dittmann, 2008). The stoichiometric formula of illite as listed in the “LLNL database” is included for comparison.

(Fe,Mn)CO3 Rotliegend samples

Sid eri te

Dolomite Ankerite Siderite Calcite Magnesite

Fe hD -ric olo mi te/ ke

An

Calcite

rite

CaCO3

MgCO3

Dolomite

Fig. 4. Chemical composition of diagenetic Rotliegend carbonates from the North German Basin based on electron micro probe analysis (EMPA) data (Schöner, 2006; Waldmann, 2011). The stoichiometric formulae of the carbonates calcite, dolomite, ankerite, and siderite in the “LLNL database” and the “TOUGHREACT database” are included for comparison (black symbols).

2.4.1. Initial model set-up Firstly, the defined assemblage of primary minerals is equilibrated in the batch models with a sodium chloride solution (5.5 mol/kgw; see Section 2.4.4). Secondly, pre-calculated amount(s) of gas(es), i.e. CO2 or CO2 + SO2 , is/are added as a gas Table 2 Calculated arithmetic mean contents (vol.%) of different minerals in Rotliegend sandstones based on 484 literature data sets. The 95% confidence interval (2) is estimated based on the Gaussian error propagation. Mineral

Mean content (vol.%)

Confidence interval (2␴)

Albite Anhydrite Ankerite/Siderite Anorthite Barite Calcite Chlorite Dolomite Fe-Oxide Illite Kaolinite K-feldspar Mica Others Pyrite Quartz

1.70 2.20 3.10 2.20 0.10 4.40 1.10 2.80 2.20 6.40 2.10 7.70 0.50 1.40 0.40 65.2

5.31 3.47 2.66 2.22 0.21 3.49 3.80 2.59 2.18 3.39 2.05 6.16 4.98 3.53 0.58 9.17

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123

Table 3 Equilibrium constants (log K) for clinochlore-14A and daphnite-14A at different temperatures (K) (Marini, 2007) and calculated log K values at 363 K for a mixture of clinochlore-14A (46%) and daphnite-14A (54%) based on a polynomial function. Component

Log K at different temperatures

Clinochlore-14A Daphnite-14A Clin-14A/daphn-14A

273.01 K

298 K

333 K

373 K

423 K

363 K

76.7345 60.2452 67.8911

67.2391 52.2821 59.2175

55.7725 42.5135 48.6615

45.152 33.3581 38.8268

36.6423 24.1876 29.0353

– – 41.0007

phase (see Section 2.4.4). Added gas(es) is/are forced to dissolve completely and are immediately equilibrated with the aqueous phase. Afterwards, mineral dissolution and precipitation reactions are calculated. In the models, fully water saturated conditions and a rock porosity of 10% were assumed resulting in a rock–water ratio of 25:1. Models were run at T = 363 K and p = 32 MPa for a simulation time of 10,000 years. These pT conditions reflect the Rotliegend scenario defined by Rütters et al. (2015). Primary minerals were defined based on the collated exemplary composition of Rotliegend sandstones (see Section 2.4.4; Table 2). While dissolution kinetics of primary minerals is considered, precipitation and (re-)dissolution of secondary minerals is linked to equilibria only. In consequence, the code PHREEQC calculates the precipitation of secondary minerals as an instantaneous event without considering precipitation kinetics. This simplified approach was chosen to ensure numerical stability when running numerically complex models.

2.4.2. Thermodynamic databases used for geochemical modeling The thermodynamic database from the Lawrence Livermore National Laboratory (Version: thermo.com.V8.R6.230; “LLNL database“), based on Debye-Hückel expressions, is used for the simulations in this study. Usually, for aqueous solutions with higher ionic strengths as typical for Rotliegend formation waters, a specific ion interaction approach should be used (Harvie et al., 1984; Harvie and Weare, 1980; Pitzer, 1973; Plummer et al., 1988). However, the available PHREEQC database, which is based on this approach, lacks data for some elements important for silicate reactions like Al and Si (Bartels et al., 2005). Furthermore, due to the lack of internally consistent data for important redox couples in this database, simulation results of redox (mineral) reactions in highly saline water need to be interpreted with caution (Plummer and Parkhurst,

1990). As the best compromise, the “LLNL database” is used in this study. For modeling, chlorite is represented by a mixture of 46 mol% clinochlore-14A and 54 mol% daphnite-14A (see Section 2.3.1). An equilibrium constant (log K) for the mixture [(Mg2.3 Fe2.7 Al2 Si3 O10 )(OH)8 ] was calculated as the weighted average of the equilibrium constants of clinochlore and daphnite at 363 K. The log K values of these end members at 363 K were derived from an exponential fitting of equilibrium constants at different temperatures (273, 298, 333, 373 and 423 K) given in Marini (2007) (Table 3). The “LLNL database” was supplemented with the calculated log K value. 2.4.3. Kinetic mineral reactions Kinetics of mineral dissolution reactions were implemented in the input file for the code PHREEQC by including the rate law equation after Lasaga (1998): −E dm q = −SA · Ae ⁄RT · f (ai ) · (1 − ˝pi ) i dt

(11)

with dm/dt as the reaction rate (mol/s), SA the specific mineral surface area (m2 /kilogram water), Ae being the Arrhenius preexponential factor (mol/m2 s), E the activation energy (J/mol), R the gas constant (J/mol K), T the absolute temperature (K), a the activity of species i, ˝ is the saturation rate (=log (Ion activity product Q/Equilibrium constant K)), and pi and qi are the reaction order. The mineral rate constants and the Arrhenius activation energies are adapted from Palandri and Kharaka (2004). The specific surface areas used are included in Table 4. 2.4.4. Input parameters 2.4.4.1. Mineral assemblage. For the definition of primary minerals (Table 4), an exemplary composition of sandstones of Permian

Table 4 Initial volumetric (vol.%) and molar contents (m0 ; mol/kgw), corresponding stochiometric formulae and specific surface areas (SSA; m2 /g) of primary and secondary minerals used for geochemical modeling. Mineral

Vol. content

m0 (mol/kgw)

Formula

SSA (m2 /g)

Reference for SSA

Primary Albite Anhydrite Ankerite Anorthite Calcite Clin-/daphn-14A Dolomite Hematite Illite Kaolinite K-feldspar Quartz

1.64 4.35 3.00 2.13 4.26 1.06 2.80 2.13 6.40 2.10 7.70 62.4

1.34 7.75 3.62 1.71 9.41 0.40 3.09 5.77 3.57 1.67 5.59 225

NaAlSi3 O8 CaSO4 CaMg0.3 Fe0.7 (CO3 )2 CaAl2 Si2 O8 CaCO3 Mg2.3 Fe2.7 Al2 Si3 O10 (OH)8 CaMg(CO3 )2 Fe2 O3 K0.6 Mg0.25 Al1.8 Al0.5 Si3.5 O10 (OH)2 Al2 Si2 O5 (OH)4 KAlSi3 O8 SiO2

0.02 0.07 0.16 0.15 0.15 7.95 0.14 0.14 46.0 13.1 0.15 0.01

Brantley and Mellott (2000) Brantley and Mellott (2000) Waldmann et al. (2014) Brantley and Mellott (2000) Waldmann (2011) Zazzi, 2009 Karaca et al. (2006) Waldmann et al. (2014) Macht et al. (2010) Dogan et al. (2006) Brantley and Mellott (2000) Waldmann et al. (2014)

SiO2 Mg5 Al2 Si3 O10 (OH)8 Ca0.165 Mg0.33 Al1.67 Si4 O10 (OH)2 Mg0.495 Al1.67 Si4 O10 (OH)2 FeS2 S

*

Secondary Chalcedony Clinochlore-14A Montmor-Ca Montmor-Mg Pyrite Elemental Sulfur *

0 0 0 0 0 0

0 0 0 0 0 0

Not indicated, since precipitation of secondary minerals is calculated based on saturation indices only.

* * * * *

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Table 5 Calculated compositions of aqueous phase after 1st equilibration between sodium chloride solution (5.5 mol/kgw) and a surplus of each primary mineral (200 mol/kgw) and 2nd equilibration of solution of 1st equilibration with concentrations of the primary minerals in initial rock composition (Table 5). Parameter/element

pH pe Al C Ca Cl Fe K Mg Na S Si

Parameter value/element concentration in aqueous phase 1st equilibration

2nd equilibration

6.81 −3.54 5.4 × 10−07 28.7 2.97 5.55 7.4 × 10−03 9.9 × 10−07 0.06 0.24 1.39 2.4 × 10−04

6.08 −2.54 5.0 × 10−08 28.3 2.74 5.55 0.13 1.5 × 10−03 0.06 0.34 1.60 2.4 × 10−04

age (Upper Rotliegend) in the North German Basin was established and converted into a mineral assembly (see Sections 2.2 and 2.3; Table 2). In this initial mixture of primary minerals anhydrite represents the total sulfate content, ankerite represents the Ferich carbonates, and chlorite is assumed to be a mixture between clinochlore and daphnite (see Section 2.4.2). For calculating the amounts of primary minerals (per kilogram water), a water density of 1100 kg/m3 was used. Beside all minerals listed as “primary minerals” (Table 4), the following additional secondary minerals were included: chalcedony, clinochlore-14A, montmorillonite, pyrite, and elemental sulfur. Chalcedony is selected instead of crystalline quartz to allow super-saturation of the solution with respect to quartz. Dawsonite precipitation was suppressed following recommendations of Hellevang et al. (2013). 2.4.4.2. Formation water composition. Before calculating kinetic mineral dissolution in the presence of CO2 or CO2 + SO2 , a sodium chloride brine (270 g NaCl/L equal to 5.5 mol/kgw) was equilibrated with the chosen minerals (200 mol/kgw of each mineral phase) in a first modeling step to gain an initial formation water composition (Table 5). The resulting aqueous solution was exposed in a second modeling step to the initial mineral assemblage (Table 4). The simulations on impacts of CO2 and CO2 + SO2 were run with the formation water composition gained in this second step. 2.4.4.3. Gas phase composition. The amount of CO2 dissolved in saline water (5.5 mol/kgw NaCl) was calculated at T = 363 K and p = 32 MPa by using the solubility calculation model after Duan et al. (2006) as 0.63 mol/kgw. From an SO2 concentration in the CO2 stream of 0.005 vol.% two cases (“scenarios”) of overall amounts of SO2 dissolving in the aqueous phase were derived (see Section 2.1): Scenario 1 ”pTS adapted Henry’s law constant (Eqs. (1) and (2))“: overall amount of SO2 entering the aqueous phase is 0.04 mol/kgw. Scenario 2 ”Peng–Robinson & Pitzer (Eqs. (1)–(5))“: overall amount of SO2 entering the aqueous phase is 2.33 mol/kgw. These calculated amounts of SO2 were added to the model as a gas phase and forced to dissolve completely in the aqueous phase by setting the log (partial pressure [atm]) = 10 in the input file. 2.4.5. Sensitivity analyses Input data for pressure, temperature, initial mineralogy, and formation water composition, were changed within specific ranges to address general uncertainties in model calculations of the impact

Base case

Carbonate dominated sandstone

S u m c a r b o n a te c o n te n t ( v o l . % )

124

Case for sensitivity analysis

Case A

Sulfate dominated sandstone Case B Sum sulfate content (vol.%)

Fig. 5. Volumetric contents of total carbonates (as sum of calcite, dolomite, and Febearing carbonates) and total sulfates (as sum of anhydrite and negligible amounts of barite) in Rotliegend sandstones from the North German Basin and initial rock compositions of base case model and cases A and B used in sensitivity analyses.

of SO2 on CO2 storage. Since higher temperature and pressure conditions generally result in increased rates and a higher total mass transfer in mineral reactions, we focused in this study on the influence of changes in rock and formation water composition. 2.4.5.1. Rock composition. The impact of the initial rock composition on modeling results was investigated by changing the initial carbonate and sulfate contents. The original model for this study is based on a carbonate content of 10.0 vol.% and an anhydrite content of 4.6 vol.% (Fig. 5). Two sensitivity tests were performed to account for the natural variability in cement composition: Sensitivity case A considers a carbonate-dominated system with a carbonate content of 26.3 vol.% and minor amounts of primary anhydrite (1.1 vol.%). In contrast, sensitivity case B considers 32.7 vol.% of anhydrite and only 2.0 vol.% of carbonates. The same volumetric ratio of calcite:dolomite:ankerite of 43:27:30 as in the original model was assumed. The sensitivity analyses were performed for the CO2 –SO2 scenarios S1 and S2. In Rotliegend sandstones, illite can be transformed during burial from precursor smectite minerals to mixed-layer illite/smectite minerals with varying illite:smectite ratios. In the “LLNL database” smectite is e.g. defined as “smectite-high-Fe-Mg” with the formula Ca0.025 Na0.1 K0.2 Fe2+ 0.5 Fe3+ 0.2 Mg1.15 Al1.25 Si3.5 O10 (OH)2 . To address mixed-layer illite-smectite minerals, a mixture of 65 vol.% illite (2.32 mol/kgw) and 35 vol.% smectite-high-Mg-Fe (0.76 mol/kgw) was assumed in sensitivity analysis. The values are based on illite/smectite abundances in Rotliegend sandstones in the southern North Sea area as reported by Ziegler (2006). 2.4.5.2. Formation water composition. In order to determine the influence of the formation water composition on the calculated precipitation of sulfur bearing minerals, sensitivity analyses were performed with an adapted fluid composition considering the

Table 6 Minor element concentrations in Rotliegend brines from the North German Basin (De Lucia et al., 2012) for sensitivity analyses. Element

Concentration (g/l)

Concentration (mol/kgw)

Ba Mn Pb Sr Zn

0.14 0.94 0.10 1.90 0.38

1.15 × 10−03 1.92 × 10−02 4.83 × 10−04 2.44 × 10−02 6.53 × 10−03

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Amount of dissolved SO2 (mol/kgw)

12

125

pTS adjusted Henry's constant (Eq. 1) pTS adjusted Henry's constant (Eq. 1-2)

10

Peng-Robinson & Pitzer (Eq. 1-3) Peng-Robinson & Pitzer (Eq. 1-5)

8

Experimental data after Xia et al. (1999) (Eq. 1-3) 6 4 2 0 0

0.5

1

1.5

2

2.5

Partial pressure of SO2 (MPa) Fig. 6. SO2 solubility (mol/kgw) in saline water (2.942 mol/kgw NaCl) at a temperature of 353 K as a function of pressure (MPa) calculated by different approaches (explanations of different approaches are given in text) in comparison to experimental data of Xia et al. (1999).

minor elements Ba, Mn, Sr, Pb, and Zn (Table 6) and a possible formation of the minerals barite (BaSO4 ) and celestite (SrSO4 ).

dissolved SO2 (Fig. 7). However, at p > 1 MPa the numerical method used by the code PHREEQC failed to converge.

2.5. Porosity calculation

3.2. Geochemical simulations of water–rock interactions and impacts of CO2 and CO2 + SO2

The porosity (ϕ) of the rock material was calculated via the change of the molar rock volume in relation to the initial value using the following equation: ϕ = 1 − (1 − ϕinitial ) • 

j i=1

V

R  (c ·m i

H2 O )·Mi



(12)

ıi

where ϕinitial is the initial porosity value (%), VR is the initial rock volume (m3 ), ci the relative amount of each mineral phase i (mol/kgw), mH2 O is the mass of water (kg), Mi is the molar mass of each mineral (kg/mol), ıi is the mineral density (kg/m3 ), and j is the total number of minerals within the rock. 3. Results

Simulations were performed for (i) a base case model without the addition of gas(es) and in presence of (ii) pure CO2 and (iii) CO2 + SO2 . For the latter, two scenarios, a low SO2 concentration case (scenario S1 – 0.04 mol/kgw SO2 ), and a high SO2 concentration case (scenario S2 – 2.33 mol/kgw SO2 ) were simulated. Since a thermodynamic equilibrium state was not reached between the selected initial mineral assemblage and the pre-equilibrated formation water, mineral reactions in the base case model without an addition of gases need to be considered to deduce impacts of CO2 and SO2 . Mainly related to the instantaneous availability of CO2 or CO2 + SO2 in our batch models, some mineral reactions may occur very rapidly in the simulations. To allow a full assessment of all simulated reactions including these very rapid reactions, modeling results are given from 1 × 10−8 to 10,000 years in all figures.

3.1. Dissolution of SO2 in brine To test the reliability of the different approaches to calculate SO2 dissolution, dissolved amounts of SO2 were calculated by different approaches at the physicochemical conditions used in the experiments by Xia et al. (1999). The results of the different calculation approaches were compared to the experimental values (Fig. 6). Considering only physical dissolution of SO2 by using an adjusted Henry’s constant (case A “pTS adapted Henry’s constant (Eq. (1))”) underestimates the amount of SO2 dissolved in saline water at p > 0.2 MPa in comparison to experimental results by Xia et al. (1999) (Fig. 6). Reactions of SO2 in the aqueous phase shift the initial dissolution equilibrium and increase the overall amount of SO2 dissolving in the aqueous phase. Up to a pressure of ∼1 MPa results of case B “pTS adapted Henry’s law constant (Eqs. (1) and (2))” agree well with the calculated amounts using the approach implemented in the code PHREEQC based on the Peng–Robinson EOS (case C “Peng–Robinson & Pitzer (Eqs. (1)–(3))”; Fig. 6). Both approaches yield slightly higher dissolved amounts of SO2 in comparison to the experiments. If the formation of sulfuric acid is included in the calculations (case D “Peng–Robinson & Pitzer (Eqs. (1)–(5))”), a total of approximately eleven times more SO2 dissolves in the aqueous phase at p = ∼0.1 MPa. Also at higher pressures, the total amount of dissolved SO2 calculated in the case D is significantly higher than in the case C and in the experiments (Fig. 6). Manual adaptation of pe values (see Section 2.1.3) for simulations with the thermodynamic database based on Pitzer’s interaction parameters results in higher calculated amounts of

3.2.1. Geochemical reactions in base case In the base case, i.e. in the model without gas addition, ankerite, anorthite, and chlorite dissolve almost completely within approx. 100 years resulting in calcite, dolomite, and hematite precipitation (Fig. 8). Furthermore, the silicates kaolinite, albite, and quartz

Fig. 7. Calculated amounts of dissolved SO2 as a function of pressure for a constant pe value (−log10 (ae− )) = 4 and an adapted pe value which is calculated separately using the “LLNL database”. For p > 1 MPa the code PHREEQC does not find a numerical solution.

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Fig. 8. Calculated changes of carbonate and hematite amounts with time for the base case model without gas, a pure CO2 model, and CO2 + SO2 models: 0.04 mol/kgw (S1) and 2.33 mol/kgw (S2). Note that concentration curves of pure CO2 and CO2 + SO2 (S1) models largely overlap.

precipitate at the expense of chlorite and anorthite. K-feldspar and illite remain stable over the entire simulation time of 10,000 years (Fig. 9). 3.2.2. Impact of pure CO2 on geochemical reactions When pure CO2 is added to the model, partial dissolution of calcite takes place within the first days and minor amounts of ankerite precipitate. Over time ankerite dissolution, as in the base case, can be recognized. Due to the interim formation of ankerite in the pure CO2 case, dolomite precipitation is slowed down (Fig. 8).

After about two months, complete chlorite dissolution and moderate dolomite precipitation is observed (Figs. 8 and 9). Overall, the presence of pure CO2 results in noticeable differences in simulated final calcite and ankerite concentrations after 10,000 years in comparison to the base case (Fig. 9). In contrast, feldspar and clay mineral reactions are only weakly affected by the presence of CO2 : Anorthite dissolution is slightly accelerated compared to the base case, resulting in earlier and enhanced kaolinite precipitation, also at expense of albite, which is newly formed in the presence of CO2 .

Fig. 9. Calculated changes of feldspar and clay mineral amounts with time for the base case model without gas, a pure CO2 model, and CO2 + SO2 models: 0.04 mol/kgw (S1) and 2.33 mol/kgw (S2). Note that concentration curves of pure CO2 and CO2 + SO2 (S1) models largely overlap.

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127

Fig. 10. pH values before and after addition of CO2 (and SO2 ) (right) and calculated changes of pH values with time (left) for the base case model without gas, the pure CO2 model, and CO2 + SO2 models: 0.04 mol/kgw (S1) and 2.33 mol/kgw (S2). Note that pH curves of pure CO2 and CO2 + SO2 (S1) models largely overlap.

3.2.3. Influence of SO2 on geochemical reactions Very similar mineral reactions, as described for the pure CO2 model, are obtained for scenario S1 (Figs. 9 and 10). In this low SO2 concentration case, the presence of SO2 has only minor influence on modeled water–rock interactions. This is because the pH value, that is initially significantly lower in comparison to the pure CO2 model, is very rapidly buffered by carbonate dissolution at nearly the same pH value as in the pure CO2 case (Fig. 10). If higher amounts of dissolved SO2 (scenario S2) are considered in the simulation, SO2 has a pronounced impact on specific mineral reactions and modifies reaction trends observed in the base case and in the other two models. In the high SO2 concentration case, at the very beginning of the simulation the aqueous phase is oversaturated with respect to elemental sulfur, which is stable in the model as a solid phase for a few seconds (Fig. 11). Subsequently, pyrite precipitates at the expense of elemental sulfur and ankerite. As one major difference to the base case and the pure CO2 case, hematite dissolution starts after about one day resulting in an intense ankerite formation in scenario S2. After approximately three months the trend reverses and hematite precipitates at the

expense of ankerite (Fig. 8). Precipitation of other sulfate and sulfide minerals that are defined in the “LLNL database” is not calculated despite their saturation indices being close to zero (Fig. 11). The low pH value in the presence of CO2 and higher amounts of SO2 (Fig. 10) result in an acceleration of anorthite and chlorite dissolution with subsequent kaolinite precipitation in comparison to the pure CO2 model (Fig. 9). Another important reaction in the high SO2 case is the instantaneous dissolution of calcite and the subsequent precipitation of anhydrite. While the presence of low SO2 concentrations has only little influence on the simulated total carbonate contents, the presence of a high SO2 amount results in a strong partial dissolution of primary carbonate minerals and impedes precipitation of new (secondary) carbonates (Fig. 8). This impediment is caused by a number of mineral precipitation reactions in the presence of SO2 : Anhydrite serves as sink for Ca and sulfate, whereas Fe is predominantly trapped as hematite and pyrite rather than as ankerite. In summary, if Ca is available in the solution (e.g. due to carbonate or plagioclase dissolution) in addition to sulfate, anhydrite is formed preferentially to calcite and other Ca-bearing carbonate minerals. As the formation of carbonates is one sink for CO2 during storage,

6

14

B

A 4

10

Saturation Index

Amount (mol/kgw)

12

Anhydrite

8 6 4

2

Gypsum 0

Bassanite -2

S

Thenardite

-4

2

S 0 -08 1e

Pyrite 1e

-06

0.0001

0.01

1

100 10000

Glauberite -6 -08 1e

1e

-06

0.0001

0.01

1

100 10000

Time (years)

Time (years) Anhydrite_Base case

Pyrite_Base case

S_Base case

Anhydrite_CO2

Pyrite_CO2

S_CO2

Anhydrite_CO2+SO2 (S1)

Pyrite_CO2+SO2 (S1)

S_CO2+SO2 (S1)

Anhydrite_CO2+SO2 (S2)

Pyrite_CO2+SO2 (S2)

S_CO2+SO2 (S2)

Fig. 11. (A) Calculated changes of anhydrite, pyrite, and elemental sulfur (S) amounts with time for the base case model without gas, the pure CO2 model, and CO2 + SO2 models: 0.04 mol/kgw (S1) and 2.33 mol/kgw (S2). Note that concentration curves of pure CO2 and CO2 + SO2 (S1) models largely overlap. (B) Variation of saturation indices of different sulfate and sulfide minerals with time for the CO2 + SO2 model (S2).

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Fig. 12. (A) Changes of total carbonate amounts with time for the base case model without gas, the pure CO2 model, and CO2 + SO2 models:0.04 mol/kgw (S1) and 2.33 mol/kgw (S2) as difference () to initial total carbonate concentration of 16.1 mol/kgw. Note that concentration curves of pure CO2 and CO2 + SO2 (S1) models largely overlap. (B) Total carbonate concentrations after ten and 100 years of simulation time for the different models relative to the present day amount.

high amounts of SO2 in the formation water may this way (locally) lower the amount of mineral-trapped CO2 (Fig. 10). 3.2.4. Porosity evolution Based on changes in the molar volumes of the mineral phases, porosity evolutions were estimated for the different models. In the base case model, initial porosity of 10% starts to be decreased after 0.01 year reaching a minimum value of 6.3% after 70 years before a slight re-increase to 7.9% can be recognized. Similarly, in the presence of CO2 and also in the low SO2 concentration case (scenario S1), initial porosity is decreased to a slightly lesser extent after 50 years (Fig. 13). If a high amount of SO2 (scenario S2) is present, initial porosity is partly reduced by the precipitation of sulfates and sulfides. A further decrease in porosity until a simulated time period of about 50 years is observed in scenario S2 that is smaller than in the other models due to the stronger dissolution of primary carbonates in comparison to the other models (Fig. 12). After approx. 50 years the overall trend is reversed and porosity increases again in all 11 CO2 and Scenario 1

10

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models due to the partial dissolution of calcite, dolomite, hematite, and ankerite. In scenario S2, these reactions result in a porosity re-increase of 0.7% whereas in the other two models, porosities re-increase by 0.2%. 3.2.5. Sensitivity analysis of geochemical models with CO2 -SO2 mixtures 3.2.5.1. Influence of the primary carbonate and sulfate content on CO2 mineral trapping. According to petrographic analysis, natural Rotliegend reservoir sandstones may be dominated by either carbonate or sulfate cements or by a mixture of these (Fig. 5). Accordingly, two sensitivity tests were performed for the CO2 -SO2 scenarios S1 and S2: considering a carbonate dominated system (sensitivity case A) and a sulfate dominated system (sensitivity case B). Sensitivity case A: A higher content of primary carbonate cements in the initial rock composition has a minor influence on modeling results (Fig. 14): Slightly lower amounts of carbonates are newly formed in this case compared to the original CO2 -SO2 scenarios S1 and S2, while the higher carbonate content has only negligible influence on the total amount of newly formed anhydrite. Sensitivity case B: Higher initial anhydrite contents result in lower amounts of newly formed carbonates in comparison to the original scenario S1 (Fig. 14). In contrast, in scenario S2 (high SO2 case), earlier and highly intense carbonate precipitation occurs between about two months and six years if the modified initial mineral assemblage is used. This precipitation is caused by (i) accelerated anorthite dissolution compared to the original S2 scenario due to lower pH values resulting in a higher supply of Ca to the aqueous phase and by (ii) partial dissolution of hematite and release of Fe. Due to this carbonate precipitation, porosity is reduced to a smaller extent. Overall, the initial cementation of the reservoir rock had a significant influence on mineral reactions and subsequent porosity changes on different time scales.

Time (years)

Base case

CO2+SO2 (S1)

CO2

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Fig. 13. Changes of total porosity with time for the base case model without gas, the pure CO2 model, and CO2 + SO2 models: 0.04 mol/kgw (S1) and 2.33 mol/kgw (S2). Note that porosity curves of pure CO2 and CO2 + SO2 (S1) models largely overlap.

3.2.5.2. Selection of primary clay minerals – illite versus smectite. Sensitivity tests were performed for scenarios S1 and S2 with smectite instead of illite as initial clay mineral. For the low SO2 case (scenario S1), the results show that within 200 years smectite is transformed into illite if additional K is available in solution, e.g., from partial K-feldspar dissolution. A surplus of Al und Si in solution

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Fig. 14. Sensitivity analysis for carbonate dominated (Case A: carbonate content: 26.3 vol.%, primary anhydrite content: 1.1 vol.%) and sulfate dominated sandstones (Case B: anhydrite content: 32.7 vol.%, carbonate content: 2.0 vol.%): Changes of total carbonate (as sum of calcite, dolomite and ankerite concentrations; Case A) and total sulfate (as anhydrite concentration; Case B) concentrations with time as differences () to the respective total concentrations of the CO2 + SO2 scenarios S1 and S2 (based on anhydrite content: 4.6 vol.% and carbonate content: 10.0 vol.%). Negative values indicate dissolution with respect to the initial amount, positive values represent precipitation.

results in the precipitation of kaolinite and, due to the additional supply of Na from smectite, albite. However, in the long-term, illite is transformed into kaolinite. For the high SO2 scenario (scenario S2), no precipitation event of illite is simulated, and smectite is directly transformed to kaolinite within 200 years. In both scenarios, Mg is incorporated into dolomite, with Ca being mainly provided by ankerite dissolution. Due to ankerite dissolution Fe2+ is provided to the aqueous phase and oxidized to Fe3+ that, in turn, is then incorporated in hematite. However, even with the supply of further cations from smectite dissolution in the sensitivity model of scenario S1 and S2, minor additional amounts of carbonates are formed within the first ∼50 years and no significant difference in porosity changes occurs in comparison to the original models. With on-going time (>50 years), less carbonates are formed in the sensitivity scenario S2 in comparison to the original S2 model, resulting in a slightly increasing porosity in the sensitivity test. In contrast, a significant porosity difference is not observed for the low SO2 sensitivity test for the time period >50 years (data not shown). In summary, the selection of smectite instead of illite as primary clay mineral does not have any major impact on the overall formation of newly formed carbonates. 3.2.5.3. Consideration of minor elements in formation water composition. In the models described in the previous sections only main elements were considered in sulfate and sulfide mineral reactions. However, there are a number of sulfur-bearing minerals and solid solutions between specific mineral end-members in natural rocks containing additional elements like Sr and Ba. In order to determine the sensitivity of modeling results to the consideration of additional elements, the initial aqueous phase was supplemented by small amounts of Ba, Mn, Pb, Sr, and Zn. For the high SO2 case (scenario S2), this supplementation results in the precipitation of small amounts of barite and celestite, whereas amounts of other S-containing phases (anhydrite, pyrite, and elemental sulfur) are not affected (Fig. 15). For the low SO2 case (scenario S1), only negligible amounts of barite and celestite are formed (data not shown). Volumetrically, the formation of barite and celestite is of little importance, i.e. it does not alter the trends in porosity changes observed in the other models.

4. Discussion 4.1. Dissolution of SO2 and its implications for geochemical reactions Since no validated approach for SO2 solubility calculation is available for the pressure, temperature, and salinity considered in the Rotliegend scenarios, various approaches to calculate the amount of dissolved SO2 were tested in this study. The calculation approach using an pT-adapted Henry’s constant with salinity correction only provides a lower estimate of SO2 solubility since it only considers physical dissolution of SO2 . Furthermore, there are uncertainties and limitations to the applicability of some of the parameters used in this approach. For example, no pressure range for the application of the Krichevsky–Ilinskaya equation is given in Prausnitz et al. (1999). In comparison to the calculation based on Henry’s constant only, consideration of formation and dissociation of sulfurous acid leads to a slightly increased calculated amount of dissolved SO2 . Much higher values are obtained if oxidation to sulfuric acid is taken into account. Overall, the performed calculations indicate that, firstly, more accurate approaches to calculate SO2 dissolution behavior at subsurface conditions are needed. Secondly, the presence of oxidants in the storage reservoir may strongly affect the total amount of SO2 dissolving in the formation water that is available for geochemical reactions in the storage reservoir. The results from this study clearly show that the type and extent of mineral reactions triggered by the presence of SO2 depend on its overall amount in the formation water. In a real storage reservoir, initial SO2 concentration within the CO2 stream, diffusive behavior of SO2 in the supercritical phase, solubility and subsequent reactions of SO2 in the formation water, among other processes, control the amount of SO2 locally available for geochemical reactions. The significance of the different processes mentioned above is still subject to ongoing debate. For example, according to the diffusion models by Crandell et al. (2010) and Ellis et al. (2010), 73–90% of the injected SO2 may be retained in the CO2 stream after 1000 years. This results in a gross marginal acidification of the formation water, with strong brine acidification (pH <4.6) being limited to a region of

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Fig. 15. Sensitivity analysis for minor elements (Ba, Mn, Sr, Pb, and Zn) for the CO2 + SO2 scenario S2. (A) Changes of concentrations of anhydrite, pyrite and elemental sulfur with time. (B) Changes of concentrations of newly formed barite and celestite with time.

approximately five meters from the center of injection (Ellis et al., 2010). In contrast, Xu et al. (2007) predicted an acidified zone with a diameter of approx. 100 m diameter. An additional influence on mineral reactions may be related to the fact that the dissolution of SO2 into brine causes a strongly acidic front dominated by charged species. Because of the electrostatic forces between these charged species, protons stay closer to their production site in comparison to HSO4 − and SO4 2− which exhibit faster diffusion rates (Ovaysi and Piri, 2013). In consequence, enhanced mineral dissolution reactions may occur at the two-phase boundary CO2 /SO2 -aqueous phase.

4.2. Exemplary “average” mineral inventory A detailed evaluation of petrography data from Rotliegend sandstones reflects natural variations in types and contents of rock-forming minerals. Contents and chemical composition of main authigenic minerals are generally well detected and described. In contrast, the data collation revealed inaccuracies in the recalculation of detrital rock fragments that are agglomerates of different minerals, as separate minerals for geochemical simulations. In summary, the higher the detrital rock fragment content, the more uncertain are bulk sandstone compositions. Accessory detrital components reported for Rotliegend sandstones comprise white mica, biotite, chlorite, and heavy minerals. In this study, these components are not considered for geochemical modeling, as their volumetric proportion is small in coarse-grained clastic sediments. However, in storage reservoirs where these minerals are more abundant, they should be taken into account because they may serve as (additional) sources for cations that may be involved the formation of carbonate minerals (Baines and Worden, 2004). Overall, detailed knowledge of mineral types, their chemical composition and amounts is indispensable for the prediction of chemical processes in storage reservoirs. The spatial distribution of minerals in the rock may also have a major influence on predicted mineral reactions since late diagenetic phases may shield earlier precipitates and/or detrital grains (Waldmann et al., 2014). This aspect is not considered in the models used in this study that aims to identify reaction tendencies for different mineral types in the presence of CO2 and of CO2 on combination with different amounts of SO2 .

4.3. Geochemical simulations of water–rock interactions 4.3.1. Model set-up and input parameters Geochemical batch simulations using a mean rock composition provide information on reaction tendencies for different mineral types in the presence of CO2 and of CO2 + SO2 and allow for deducing influencing factors as shown in this study. In the models, CO2 (and SO2 ) dissolve(s) instantaneously in the aqueous phase without any further supply with time. In real storage reservoirs, CO2 and SO2 dissolve only gradually into the formation water, whereas in the model initial pH values are lowered instantaneously and, in case of SO2 , sulfur species are immediately available. Compared to a natural storage reservoir, simulated early mineral reactions, e.g. the precipitation of anhydrite at expense of carbonates, occur much faster in the model. Similarly, in batch experiments by Pearce et al. (2015), SO2 dissolution was kinetically controlled with rapid initial dissolution followed by a further more gradual supply. Their simulations with a fixed amount of SO2 lead to an overestimation of sulfate concentration in the aqueous solution and to a higher predicted pH value compared to experimental results. In addition, the batch model set-up used in our study may lead to a consumption of SO2 and related species during simulation time, thereby enabling reactions that would not occur otherwise. This needs to be considered when interpreting long-term simulation results. In the geochemical simulations performed in this study, the precipitation of secondary minerals is calculated by the code PHREEQC as an instantaneous event controlled by saturation indices only and without considering kinetics. This approach is critical for the formation and precipitation of clay minerals like illite or kaolinite. Hence, mineral precipitation may occur more rapidly in our models compared to a fully kinetic approach (Hellevang et al., 2013; Noguera et al., 2011). Mineral reactions indicate disequilibria between rock and fluids. The observation that no formation water composition could be calculated that is in equilibrium with all initially chosen minerals may be related to the fact that the initial mineral assemblage reflects a calculated average composition of Rotliegend sandstones. In natural systems, the presence of minerals is not only controlled by the thermodynamic stability of each of the mineral phases, but also by e.g. the kinetic reactivity, as well as the spatial mineral distribution and accessibility. Hence, natural mineral assemblages do not necessarily represent thermodynamic equilibrium states as assumed

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in our model. In consequence, the prediction of water–rock interactions related to the presence of CO2 and SO2 must be performed in relation to a base case in which no gas is added to the model. As the database used contains only end members of naturally occurring solid solutions, as is the case for Mg-Fe chlorites in Rotliegend sediments, an equilibrium constant was calculated as the weighted average of the end-member constants (see Section 2.4.2). The calculated log K value is to be considered as an approximation only, because the dissolution behavior of a solid solution differs from that of a mixture of its pure end-members (e.g. Gailhanou et al., 2009). 4.3.2. Impact of SO2 on CO2 –water–rock interactions In a real storage reservoir, the magnitude of acidification caused by SO2 dissolution in the formation water may vary locally depending e.g. on the transport behavior of SO2 in the CO2 plume and the composition, redox state, and flow rate of the formation water (see Section 4.1). Hence, we investigated impacts of different amounts of SO2 on fluid–rock interactions during geological storage of CO2 using a low and a high SO2 concentration scenario resulting from the same SO2 concentration in the CO2 stream of 0.005 vol.%. 4.3.2.1. Carbonate minerals. As a consequence of CO2 dissolution, the pH value dropped down from 7 to around 4, initiating the partial dissolution of carbonates that stabilize the pH value at a value of approx. 5. The higher the SO2 content, the lower the pH value and the lower the amount of newly formed carbonates, since anhydrite is preferentially formed instead of Ca-bearing carbonates in our models. In general, the precipitation of carbonates is influenced in particular by the availability of divalent cations. A low pH value (in presence of SO2 ) enhances the dissolution of silica minerals thereby supplying divalent cations to the solution (Garcia et al., 2012; Mandalaparty et al., 2011; Min et al., 2015). The availability of SO4 2− (from SO2 oxidation) may result in the precipitation of sulfates such as anhydrite or gypsum and in the removal of Ca from the aqueous solution making it unavailable for carbonate precipitation (Chopping and Kaszuba, 2012; Garcia-Rios et al., 2014). The latter is reflected in our simulations. However, the formation of Fe-carbonates may be favored if Fe-rich primary minerals like hematite and/or Fe-chlorite dissolve and supply Fe3+ ions to the aqueous solution. These Fe3+ ions may be reduced by SO2 leading to the formation of Fe2+ -bearing minerals like Fe-rich carbonates (Luquot et al., 2012; Palandri and Kharaka, 2005; Palandri et al., 2005; Pearce et al., 2015). Mineral dissolution may affect the mechanical stability of the reservoir sandstone due to a removal of e.g. carbonate cements between the grains. However, carbonate dissolution may only be locally important as newly formed anhydrite may (re-)fill pore space and potentially re-increase mechanical stability of rocks (Pape et al., 2005). 4.3.2.2. Sulfate minerals. Higher amounts of SO2 lower the pH value to strongly acidic conditions, initiating short-term dissolution of carbonates and, depending on the available amount of SO2 , the precipitation of anhydrite in our models (see above). The formation of anhydrite, as in our high SO2 case, has also been reported in experimental and modeling natural analog studies for a carbonate reservoir where high concentration of aqueous sulfur complexes occur (Chopping and Kaszuba, 2012; Kaszuba et al., 2011). No significant precipitation of other sulfate minerals is calculated in our study, neither in the original models nor in the sensitivity analyses despite them having saturation indices close to zero. Nevertheless, various sulfate minerals should be taken into consideration as secondary minerals in geochemical simulations since precipitation of minerals like barite, celestite, or PbSO4 may provide an additional sink for dissolved SO4 2− e.g. in feldspar-rich sandstones.

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Gypsum may precipitate instead of anhydrite if the reaction temperature is <343 K (Xu et al., 2007). However, the analytical expression for the temperature dependency of the equilibrium constant in the “LLNL database” indicates formation of gypsum at lower temperatures (<318 K) only. In contrast, experimental investigations suggest the formation of gypsum rather than anhydrite even at higher temperatures, i.e. between 333 and 393 K, at pressures from 10 to 12 MPa at different SO2 concentrations in the CO2 stream. Gypsum formation has also been recognized in experimental investigations and numerical simulations for limestones (Garcia-Rios et al., 2014), in siliciclastic reservoir sandstones (Mandalaparty et al., 2011; Pearce et al., 2015) and basaltic rocks (Schaef et al., 2014). The distinction between the formation of either anhydrite or gypsum is of great importance for a prediction of porosity changes because of the higher molar volume of gypsum (74.9 cm3 /mol) compared to anhydrite (45.8 cm3 /mol). In CO2 –SO2 models for geological storage in sandstone aquifers, Xu et al. (2007) identified the precipitation of alunite (K-Al sulfate) in the immediate vicinity of the injection well resulting in a major immobilization of “sulfur” from SO2 during the initial injection period. The natural occurrence of alunite minerals is related to (extremely) acidic and oxidizing environments (Jamieson et al., 2005, Desborough et al., 2010; Elwood Madden et al., 2012). The formation of alunite has also been predicted by Schaef et al. (2014) for mineralization experiments with basalt exposed to CO2 , SO2 , and O2 . The supply of O2 appears to have favored the precipitation of alunite in their experiments. Thus, alunite formation should be taken into account if O2 is injected together with CO2 and SO2 , like it would preferentially occur in CO2 streams gained from e.g. oxyfuel post combustion capture processes. The precipitation of sulfate minerals in the vicinity of the injection well and a consequent decrease in porosity may affect CO2 injectivity and thus lead to higher injections costs. This study indicates decreasing porosities by approx. 3.5% within the first 50 years of simulation in the presence of high amounts of SO2 . The highest decreases are expected to occur near the injection well. 4.3.2.3. Iron-containing minerals and sulfides. The availability of different sulfur species and the prevailing redox conditions in scenario S2 result in an interim precipitation of pyrite and elemental sulfur due to partial hematite dissolution and the supply of ferrous iron. Experimental work on hematite dissolution in the presence of sulfide and CO2 shows a similar reaction tendency (Garcia et al., 2012; Murphy et al., 2011; Palandri and Kharaka, 2005; Palandri et al., 2005). A precipitation of elemental sulfur, as in our scenario S2, is expected only in systems with very high sulfate concentrations that are not likely to occur during injection and storage of impure CO2 in reservoir sandstones. For example, Parmentier et al. (2013) identified a precipitation of elemental sulfur and anhydrite at expense of calcite in batch experiments with a pure SO2 phase. Palandri et al. (2005) note that pyrite and native sulfur are metastable phases in the presence of hematite. Following initial hematite dissolution, precipitation of newly formed hematite occurs in our models after about one year of simulation time. The reaction is caused by a slight decrease in the pH value due to kaolinite and albite precipitation and changes in redox conditions to a dominantly oxidizing environment. 4.3.2.4. Silica minerals. In all models, due to the low pH values caused by the dissolution of CO2 or CO2 + SO2 , dissolution of anorthite and chlorite as well as the precipitation of albite and kaolinite are the earliest simulated silica reactions. The formation of kaolinite from feldspars was observed in models from Gunter et al. (2000), Gaus et al. (2005), and Lagneau et al. (2005), in laboratory experiments by Allan et al. (2011) and Mandalaparty et al. (2011), but is not calculated in the numerical simulations performed by

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Wigand et al. (2008) for pure CO2 . In the latter study, albite, K-feldspar, and kaolinite were relatively stable phases whereas anorthite dissolved completely releasing Ca, Al, and silica into the solution. Due to this Ca release, calcite was newly formed simultaneously to anorthite dissolution. This relationship was also recognized in natural CO2 gas fields, namely the Vert le Grand field, Paris Basin, France (Baines and Worden, 2004), and the Australian Otway Basin (Watson et al., 2003). Albite may be formed by substitution reactions of Ca with Na in anorthite minerals. In contrast to the simulated precipitation of kaolinite in our models, a formation of illite at the expense of feldspar is reported by several authors for natural feldspar (Land and Milliken, 1981; McAulay et al., 1993) and numerical simulations in the presence of CO2 (Barclay and Worden, 2000). Barclay and Worden (2000) pointed out that the formation of either illite or kaolinite is mainly linked to the activity of K+ and SiO2 , with kaolinite being stable in contact with pore fluids of low pH and low K+ activity. In the presence of sulfide in the pore fluids, anorthite dissolves faster and higher amounts of silica are released in the aqueous phase due to contemporaneous dissolution of quartz. These reactions result in enhanced albite and kaolinite precipitation in our models. Similarly, enhanced anorthite dissolution occurs at the presence of SO2 in our models. In contrast, precipitation of secondary Alsilicates may be slowed down due to complexation of S with Al that is then not available for e.g. kaolinite formation (Min et al., 2015). If smectite is present as a primary mineral as in our sensitivity tests, illite is temporarily formed due to transformation of smectite in the presence of CO2 and minor amounts of SO2 (scenario S1). In contrast, Xu et al. (2007) state that precipitation of Na-smectite occurs mainly in models with pure CO2 , but is not significant if SO2 is present. Likewise, smectite was formed at the expense of labradorite in dissolution experiments with pure CO2 (Caroll and Knauss, 2005). As in our study, these authors were not able to find modeling conditions, which account for smectite formation. These considerations underline that a careful selection of the primary mineral assemblage within the sandstone body is indispensable for adequately predicting geochemical reactions during storage of (impure) CO2 . In summary, the presence of SO2 within the CO2 stream only weakly influences long-term silica reactions, but may be of concern near the injection well if high amounts of fast reacting detrital anorthite are present in the reservoir sandstones. 4.3.3. Influence of SO2 on porosity evolution and mineral trapping Overall, the influence of SO2 within the CO2 stream is rather on short-term dissolution and/or precipitation behavior of sulfates, carbonates, and hematite than on long-term silica reactions. In particular, in the presence of SO2 , anhydrite is preferentially formed if Ca-bearing primary minerals dissolve. In addition, our results clearly emphasize the role of anorthite for porosity evolution that (partly) dissolves under acidic conditions within the first years of simulation, thereby providing Ca for the formation of anhydrite in the CO2 + SO2 models. The higher the amount of SO2 , the lower the amount of CO2 trapped as solid carbonate minerals on long-term due to the enhanced solubility of carbonates, like calcite and ankerite, at more acidic conditions preventing the secondary formation of these carbonates. Similarly, a short-term decreasing porosity near the injection well and a long-term increasing porosity in the presence of CO2 and SO2 was recognized in earlier publications (e.g. De Lucia et al., 2012; Kampman et al., 2013; Xu et al., 2007). Overall, the injection of impure CO2 streams containing SO2 results in overall porosity changes between 0.5 and 1.0% (shortand long-term, respectively) due to dissolution and precipitation reactions of distinct minerals.

5. Summary and conclusions We performed geochemical simulations to assess the influence of 0.005 vol.-% SO2 in an impure CO2 stream on water–rock interactions in a siliciclastic storage reservoir (Rotliegend deposits). For modeling we used an exemplary rock composition that was gained from statistical analysis of petrographic rock data. (Local) differences in concentrations of SO2 dissolved in saline formation water due to variations in transport and portioning phenomena within one storage reservoir were addressed by defining a “low SO2 case” (0.04 mol SO2 /kilogram water) and a “high SO2 case” (2.33 mol SO2 /kilogram water). The performed simulations of geochemical interactions between CO2 , SO2 , saline water, and rock material indicate that the presence of SO2 may have a significant influence on shortand long-term mineral reactions in such geological CO2 storage reservoirs. Short-term reactions include the dissolution of carbonates and anorthite and the precipitation of sulfates. These are of major interest for clogging effects near the wellbore due to mineral precipitation. Affected long-term reactions included the formation of secondary carbonates, i.e. the mineral trapping of CO2 for permanent geological storage. For the “low SO2 case” only small differences in mineral reactions and related porosity changes were found in comparison to the pure CO2 model. In contrast, for the “high SO2 case” specific additional short-term mineral reactions were observed such as the precipitation of sulfur-bearing minerals like anhydrite and pyrite, following the dissolution of primary calcite and hematite. Relative to the pure CO2 model, these mineral reactions lead to a short-term decrease in porosity by approx. 0.5%. In the long-term, a higher amount of SO2 leads to a reduced formation of secondary carbonates and, thus, to less fixation of CO2 in solid minerals. In addition, a higher porosity is retained in the “high SO2 case” compared to the pure CO2 and “low SO2 case” after a few hundred years. The overall modeling outcome from the base case, the CO2 and CO2 + SO2 models and from sensitivity runs indicates that the initial composition of the reservoir rock material is important for mineral trapping and porosity evolution over time: The higher the initial amount of Ca-, Mg-, and Fe-bearing minerals like anorthite, chlorite, and hematite, the higher the potential availability of dissolved species for sulfate or carbonate precipitation. In consequence, no generalized predictions of impacts of SO2 on fluid–rock interactions in Rotliegend sandstones can be made, since these interactions depend on various site-specific parameters. Furthermore, even within a storage reservoir the extent of mineral dissolution reactions is mainly controlled by water acidity and by local thermodynamic equilibria between mineral phases and the formation water. In consequence, to adequately predict the impact of SO2 in CO2 streams on fluid–rock interactions in a potential storage reservoir, the amount of dissolved SO2 , i.e. its availability for geochemical reactions, needs to be known at different locations within the reservoir and at different time points in the storage project’s life time. For this, a detailed assessment of transport processes related to the migration of SO2 in the storage reservoir as well as of SO2 dissolution behavior in the formation water is needed. Additionally, it should be noted, that even small concentrations of SO2 in an impure CO2 stream might have an influence on water–CO2 –mineral reactions if SO2 is concentrated locally, e.g. at the boundary between the impure CO2 -SO2 phase and the formation water. Acknowledgements The German Federal Ministry for Economic Affairs and Energy funded the work presented in this paper on the basis of a decision by the German Bundestag within the project “CO2 purity for

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capture and storage (COORAL)” (FKZ 0327790A) with third party funding by Alstom, EnBW, E.on, Vattenfall and VNG. The authors thank S. Stadler, T. Nowak, M. Hentscher, L. Wolf, F. May, and D. Rebscher for inspiring discussions. Finally we thank J. Birkholzer and two anonymous reviewers for their constructive comments and for significantly helping to improve this article. References Allan, M.M., Turner, A., Yardley, B.W.D., 2011. Relation between dissolution rates of single minerals and reservoir rocks in acidified pore waters. Appl. Geochem. 26, 1289–1301. Atkins, P.W., de Paula, J., 2006. Physikalische Chemie. Wiley-VCH, Weinheim, 1220 pp. Baines, S.J., Worden, R.H., 2004. The long-term fate of CO2 in the subsurface: natural analogues for CO2 storage. Geol. Soc. Lond. Spec. Publ. 233 (1), 59–85. Barclay, S.A., Worden, R.H., 2000. Geochemical modelling of diagenetic reactions in a sub-arkosic sandstone. Clay Miner. 35, 57–67. 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