Effects of impurities on subsurface CO2 storage in gas fields in the northeast Netherlands Bolourinejad, Panteha

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1 University of Groningen Effects of impurities on subsurface CO2 storage in gas fields in the northeast Netherlands Bolourinejad, Panteha IMPORTANT NOTE: You are advised to consult the publisher's version (publisher's PDF) if you wish to cite from it. Please check the document version below. Document Version Publisher's PDF, also known as Version of record Publication date: 2015 Link to publication in University of Groningen/UMCG research database Citation for published version (APA): Bolourinejad, P. (2015). Effects of impurities on subsurface CO2 storage in gas fields in the northeast Netherlands [Groningen]: University of Groningen Copyright Other than for strictly personal use, it is not permitted to download or to forward/distribute the text or part of it without the consent of the author(s) and/or copyright holder(s), unless the work is under an open content license (like Creative Commons). Take-down policy If you believe that this document breaches copyright please contact us providing details, and we will remove access to the work immediately and investigate your claim. Downloaded from the University of Groningen/UMCG research database (Pure): For technical reasons the number of authors shown on this cover page is limited to 10 maximum. Download date:

2 Chapter 6 Effect of reactive surface area of minerals on mineralization and carbon dioxide trapping in a depleted gas reservoir Based on publication in: Bolourinejad, P., ShoeibiOmrani, P. and Herber, R. (2014)Effect of reactive surface area of minerals on mineralization and carbon dioxide trapping in a depleted gas reservoir. International Journal of Greenhouse Gas Control 21, Bolourinejad, P., Sheibi Omrani, P.and Herber, R. (2015) Reply to discussion on the paper titled Effect of reactive surface area of minerals on mineralization and carbon dioxide trapping in a depleted gas reservoir. International Journal of Greenhouse Gas Control 34,

3 Chapter 6 6 Effect of reactive surface area of minerals on mineralization and carbon dioxide trapping in a depleted gas reservoir Abstract In this study, a long-term (up to 1000 years) geochemical modelling of subsurface CO 2 storage was carried out on sandstone reservoirs of depleted gas fields in northeast Netherlands. It was found that mineral dissolution/precipitation has only a minor effect on reservoir porosity. In order to validate this, we focused specifically on the reactive surface area of minerals which we measured by scanning electron microscopy. In this way we obtained distributions for the measured reactive surface areas of each individual mineral. Subsequent parameter analysis and Monte Carlo sampling of these distributions revealed that in the Rotliegend sandstones, the surface area of quartz has by far the largest effect on SMCO 2 (total amount of CO 2 sequestered as mineral). The proportional relation of SMCO 2 and quartz reactive surface area leads to the conclusion that CO 2 injection in a sandstone reservoir with fine grained quartz has a higher potential for mineral trapping of CO 2. In addition, using parameter analysis we also could determine the effect of surface area of each mineral on its own dissolution/precipitation mechanisms as well as on the other minerals. For example, the results showed that dawsonite precipitation is proportional to kaolinite and K-Feldspar surface area. 6.1 Introduction Long-term global temperature measurements have revealed a gradual increase in annual average temperature (0.76 C) over the last 150 years (Bachu, 2008; IPCC, 2007). In a business-as-usual scenario it is predicted that if this trend continues, the temperature rise by the end of this century will be in the range of C (Bachu, 2008). It is by now generally accepted that the main reason for global warming is the increase in atmospheric concentration of greenhouse gases like carbon dioxide (CO 2 ) and methane (CH 4 ) (Bachu, 2008). In order to battle the risk of climate change, a decision has been made in 1997 between developed countries to reduce greenhouse gas emissions by 5.2% (on average) by the period of (Kyoto Protocol) (Gale et al., 2001). Suggested ways to reduce emissions are fuel switching, energy efficiency improvement and utilization of renewable energy instead of fossil fuels. However, in order to meet the Kyoto goals, additional mitigation techniques need to be taken into account to reduce greenhouse gas emissions (Gale et al., 2001). Geological storage of CO 2 is considered as one of those technologies (Gunter et al., 2000). Following CO 2 injection into a porous and permeable reservoir, trapping modes are initially physical (stratigraphic and residual), followed by chemical (solubility and mineral) trapping as time progresses (IPCC, 2005). 80

4 Many researchers have evaluated the subsurface behaviour of CO 2 with regard to flow, storage capacity and rate of the different trapping mechanisms. Several simulation algorithms have been developed to analyse various aspects of geological storage of CO 2, including capacity, injectivity, mineralization, well integrity, etc. Examples of such simulators are CMG-GEM (Computer Modelling Group- Generalized Equation of State Model) (CMG, 2011) and TOUGHREACT (Transport of Unsaturated Groundwater and Heat) (Xu et al., 2011, 2004) Audigane et al. (2007) used TOUGHREACT for modelling of CO 2 injection in the Utsira formation above Sleipner gas/condensate field in the Norwegian North Sea and concluded that solubility trapping is the dominant mechanism for long-term CO 2 retention and mineral trapping yields only a minor contribution. Xu et al. (2007) and Zhang et al. (2011) utilized TOUGHREACT to investigate the effects of impurities (H 2 S and SO 2 ) coinjection with CO 2 in a sandstone formation. Zhang et al. (2011) concluded that additional H 2 S injection reduces the capacity for CO 2 solubility and mineral trapping compared to the CO 2 only injection. Furthermore, experimental studies have been carried out to assess the possibility of mineral trapping in the geological systems (Fu et al., 2009; Gunter et al., 1997; Kaszuba et al., 2003). In addition, operational CO 2 storage sites have been investigated and different methodologies have been used to monitor the subsurface behaviour of injected CO 2 (Liebscher et al., 2013; Ringrose et al., 2013; Wei et al., 2013). The physical and chemical behaviour of CO 2 in the subsurface is determined by many parameters such as mineral composition, porosity, permeability, temperature, pressure, brine composition and ion concentration in the reservoir as well as kinetic rate and reactive surface area of minerals (Goater et al., 2013; Hellevang et al., 2013; Jung and Wan, 2012; Luo et al., 2012). Bennion and Bachu (2008) studied the drainage as well as imbibition relative permeability relationship for a CO 2 - H 2 S brine system. Juanes et al. (2006) investigated the impact of relative permeability hysteresis on CO 2 storage. Alkan et al. (2010) investigated the impact of capillary pressure, salinity and in situ conditions following CO 2 injection in aquifers. Pruess and Müller (2009) studied the effects of drying-out and solid precipitation in the storage site. Bachu (2003) and Bachu and Bennion (2008) studied the impact of in situ pressure and temperature on the volumetric and transport factors of CO 2 and CO 2 -containing water. Luo et al. (2012) studied the effect of reactive surface area of calcite and anorthite on CO 2 mineralization and they concluded that mineralization trapping reduces from 11.8% to 0.65% when the reactive surface area of these minerals is reduced from 838 to 83.8 m 2 /m 3. The reactive surface area of minerals is an important input parameter in modelling studies since it affects the magnitude of mineral trapping in long-term CO 2 storage (Luo et al., 2012). According to Gouze and Luquot (2011) the parameterisation of reactive surface area is one of the most difficult aspects in reactive transport modelling. The reactive surface area of minerals is evolving during dissolution/ precipitation of minerals. When the fluid reacts with the primary matrix of the reservoir, significant changes in mineralogy and then rheology of the material can occur (Steefel et al., 2005). Also, the way in which the grains are positioned can affect the reactive surface area of minerals. However, this 81

5 parameter is not sufficiently addressed in published studies. In this paper, the behaviour of CO 2 is first modelled by utilizing literature values for reactive surface area of minerals (e.g. Xu et al., 2007). Following that, we experimentally measured the initial reactive surface area of minerals from the reservoir core samples using Scanning Electron Microscopy (SEM) images. This method provides better insight into the available reactive surface area of minerals than using a single mineral phase since it takes the positioning of the grains in the reservoir into account. When using the TOUGHREACT package to analyse the effect of surface area, we utilized a statistical method (Monte Carlo uncertainty analysis and parameter analysis) based on the distributions obtained for the measured reactive surface areas of each individual mineral. In this way we could use a full range rather than being dependent on a single value. 6.2 Model description Geological and hydrological conditions The geochemical simulation was performed on a modelled Permian Rotliegend reservoir, which is common for the majority of (nearly depleted) gas fields in northeast Netherlands, situated at depths of around 3000m. Initial pressure and temperature are set at 50 bars (i.e. depleted) and 100 C respectively. The storage reservoir is described using a radial (R, Z) model with 5380 m radius and 109 m thickness (Figure 6.1). The reservoir is divided into 6 layers with different porosity-permeability characteristics, based on actual field data. Figure 6.1 The representative reservoir division and grid mesh used in this study. The black arrows represent the injection points. Relative permeabilities for liquid and gas are taken from Van Genuchten (1980) and Corey (1954). Also the capillary pressure values were determined based on Van Genuchten (1980) (Table 6.1,Equation 6.1 to 6.5 ) K rl = (S* _{1-(1-[S*] (1/m) ) m } 2 ) Equation 6.1 S * =(S I -S Ir )/(1-S Ir ) Equation 6.2 Krg=(1-S) 2 (1-S 2 ) Equation 6.3 S=( S I -S Ir )/( S I -S Ir -S gr ) Equation 6.4 P cap =-P 0 ([S * ] -1/m -1) 1-m Equation

6 K rl and K rg are relative permeabilities for liquid and gas. S Ir, S gr are irreducible water and gas saturation respectively and m is the Van Genuchten (1980) function exponent for the retention curve (in this study m is assumed to be 0.457). All these parameters are specified in Table 6.1. Table 6.1 Geological and hydrogeological parameters for the Rotliegend sandstone reservoir model used in the simulations. Layer Depth (m) ϕ(%) a k(md) a Relative Permeability Capillary pressure S Ir S gr S Ir P o (KPa) a a ϕ and K are porosity and permeability, respectively. P o is strength coefficient. The make-up of the reservoir model is given in Figure 6.1. The reservoir was laterally divided into 100 cells. A radial grid is used with successive grid size ratio of Six injection blocks are placed at different depths (Figure 6.1). CO 2 is injected into the system from these blocks at a rate of 4 kg/s for the period of 31 years, yielding a total of 3.91 Mt injected CO 2. Following cessation of injection the reservoir is monitored for 1000 years Mineral composition and water geochemistry The primary mineralogy of the layers was obtained from x-ray diffraction (XRD) and SEM analysis of a selection of reservoir core samples from northeast Netherlands gas fields (Table 6.2). Table 6.2 Average mineralogy of the reservoir rock matrix. Mineral Weight percentage Quartz % Kaolinite 5.14% K-feldspar 2.43% Dolomite 2.02% Albite 1.64 % Anhydrite 0.81% Illite 0.7% TOUGHREACT requires the specification of secondary minerals. In our case dawsonite and calcite are selected as potential secondary carbonate minerals, which can be precipitated by reactions between rock-brine-co 2. This selection is based on our previous 83

7 experimental study results on the same gas fields (The experimental detail is presented in section 0. Also daswonite precipitation is shown in Figure 2.6). According to Waldmann (2011) the brine composition we used in this study (derived from actual well data) agrees well with other data sets from the North Sea Formation Atlas (Warren and Smalley, 1994) for the Permian Rotliegend reservoir. This composition is taken as a starting point and is subsequently equilibrated by means of geochemical modelling with the initial mineral composition (Table 6.2) resulting in aqueous concentrations as given in Table 6.3. Table 6.3 Brine composition Component Concentration (mol/kg H 2 O) Cl Na Ca Mg SO K SiO 2(aq) HCO * -5 H * AlO *10-8 Fe *10-20 O 2(aq) 1.00* Numerical method and mineral kinetic parameters In this study, we used TOUGHREACT (Xu et al., 2004), which is a non-isothermal reactive geochemical code(xu et al., 2007). The code is based on the existing multiphase flow and heat flow code TOUGH2 (Pruess, 2004) with addition of geochemical transport equations. ECO2N (Spycher and Pruess, 2005) is used as fluid property module providing the thermo-physical properties of the water and CO 2 mixture under conditions applicable for CO 2 storage (high pressure/ high temperature)(xu et al., 2007). Flow and transport in geological media in TOUGHREACT is based on space discretization by means of integral finite differences (IFD) (Narasimhan and Witherspoon, 1976). The program uses a sequential iteration approach for coupling between fluid transport and geochemical reactions (Yeh and Tripathi, 1991). The general rate law for mineral dissolution/precipitation is taken from Lasaga et al. (1994) (Equation 6.6) r n =±K n A 1-( Q n K n ) θ η Equation 6.6 In here, r n is the kinetic rate (positive values indicate dissolution and negative values precipitation), k n the reaction rate constant of minerals (mol/(m 2 s)), A n the reactive surface area (m 2 ) of minerals per kg H 2 O, Q n the reaction quotient and K n the equilibrium constant for a mineral-water reaction written for the destruction of one mole of mineral. The parameters θ an η should be determined experimentally. When experimental data is 84

8 not available they are commonly assumed to be unity (Xu et al., 2007; Zheng et al., 2009; Xu et al., 2011). By this assumption Equation 6.6 reaches a form applicable to transition state theory (TST) (Lasaga, 1981). The kinetic rate constant can be obtained by the summing of three mechanisms as presented in Equation 6.7 Equation 6.7 (Palandri and Kharaka, 2004). k=k 25 nu exp[ -E nu R ( 1 T )]+k 25 H exp[ -E H R ( 1 T )]a nh H +k 25 OH exp[ -E OH R ( 1 T )]a noh OH Equation 6.7 The subscripts nu, H, OH stand for neutral, acid and base mechanisms respectively. E is activation energy (J/mol), K 25 is rate constant at 25 C (mol/(m 2 s)), R is the gas constant (J/(mol. K)), T is temperature ( K), a is activity of the species and n is the power constant (Xu et al., 2012). The data for kinetic rates and activation energy are taken from Palandri and Kharaka (2004) and displayed in Table 6.4. Since data for mineral precipitation and nucleation rate are few for most minerals (Hellevang et al., 2013) parameters for neutral ph rates in Table 6.4 were employed to describe precipitation (Xu et al., 2007). Mineral precipitation and dissolution differ in aspects like nucleation, crystal growth and Ostwald ripening processes (Steefel and Van Cappellen, 1990). These aspects of mineral precipitation are omitted from the current model (Xu et al., 2007). Table 6.4 Minerals and their kinetic parameters in acidic, neutral and basic conditions as used in the numerical simulation. Mineral (chemical formula) Quartz (SiO 2 ) Albite (NaAlSi 3 O 8 ) K-feldspar (KaAlSi 3 O 8 ) Dolomite (Ca Mg (CO 3 ) 2 ) Kaolinite (Al 2 Si 2 O 5 (OH) 4 ) Illite (K 0.6 Mg 0.25 Al 1.8 (Al 0.5 Si 3.5 O 10 ) (OH) 2 ) Dawsonite (NaAl(CO 3 ) (OH) 2 ) Surface area (m 2 /m 3 ) K 25 (mol/(m 2 s)) E (kj/mol) Acid Neutral Base Acid Neutral Base E E E E E E E E E E E E E E E E E

9 Following Xu et al. (2007) the anhydrite and calcite minerals are assumed to react at equilibrium because their reaction rates are fast compared to the modelling time frame (Zheng et al., 2009). Also, Hellevang et al. (2013) compared ankerite precipitation by equilibrium or with kinetically controlled assumptions and showed that in the long term the two models are well comparable (at 100 bar). Porosity is calculated during every time step from the change in mineral volume fraction related to their dissolution / precipitation. Together with the porosity calculation, permeability is also calculated in every time step. This can be done using one of the different porosity-permeability equations which are implemented in the modeling software such as cubic law, Carman-Kozeny equation and Verma - Pruess permeabilityporosity relation (Verma and Pruess, 1988; Xu et al., 2012; Bear, 1972). In our study, we used Carman- Kozeny equation for the porosity permeability relation of the reservoirs based on experience from literature studies. This equation has been used effectively in literature in various modeling studies for different mineral compositions (Xu and Pruess, 2001; Zhang et al., 2011; Nghiem et al., 2004; Xu et al., 2007). The mineral surface areas listed in Table 6.4 are based on the work of Sonnenthal et al. (2005) and were calculated by assuming a cubic array of truncated spheres constituting the rock framework (Xu et al., 2007). The smaller grain size of the clay minerals led to bigger values for their reactive surface area. However, in reality the mineral surface areas in reservoir rocks are much more complicated and need to be measured accurately. 6.3 Results CO 2 plume The CO 2 in supercritical phase, the CO 2 dissolved in brine and the brine acidity (ph level) are shown in Figure 6.2 during the injection (31 years) and dispersion period (1000 years). The injection of CO 2 increases the pressure up to a maximum value of 212 bars. When CO 2 injection ceases, the pressure slightly drops to 204 bars, predominantly due to the solubility trapping. The injected CO 2 forms a plume rising up in the reservoir sandstone. After 1000 years the plume has spread laterally to about 3800m away from the injection point. The mixing time for the complete dissolution of gas is estimated to be between 1600 to years (Audigane et al., 2007a). The injected CO 2 gradually dissolves in the brine and produces a maximum dissolved CO 2 mass fraction of CO 2 dissolution in water increases the aqueous-phase density, giving rise to buoyant convection which leads to the downward migration of water (Weir et al., 1996). According to Okuyama et al. (2013) the lateral and downward migration of CO 2 -dissolved water probably induces a counter flow pushing up the less affected formation water (lower density) to the top part of the reservoir. The dissolved CO 2 in water leads to formation of a weak carbonic acid and makes the ph drop to a value of 4.3. In the short term (during injection) this happens mainly in the area where supercritical CO 2 is present. Brine convection and mixing is important at a later stage when the ph drops also in the regions without supercritical CO 2 (Audigane et al., 2007a). 86

10 Figure 6.2 Changes in (a) CO 2 saturation (%) (b) Mass fraction of dissolved CO 2 and (c) water ph obtained from model simulations Analysis of minerals The variation in the amount of all primary and secondary minerals present in the system is shown in Figure 6.3. Figure 6.3 Change in total volume of the main minerals in the reservoir with time. At the end of the simulation, albite and K-feldspar volumes have decreased by 12.4% and 9.0% respectively. The amount of dissolution for dolomite and kaolinite was less than for feldspar minerals: 2.4% and 3.5% respectively. Illite and quartz volumes have increased by 66.0% and 0.10% respectively. The carbonate phases responsible for mineral sequestration of CO 2 are fixed at calcite and dawsonite. The contribution of calcite was significantly higher than that of dawsonite (98.9%). The dissolution of minerals could increase the total pore volume of the reservoir by a factor of 0.2% whilst the average permeability increases by 0.26%. The amount of mineralized CO 2 reached a maximum 87

11 value of 3.22 kg CO 2 per m 3 (total injected CO 2 was 3.91 Mt) at the end of simulation (Figure 6.4). Figure 6.4 Spatial distribution of the total amount of CO 2 sequestered as mineral (SMCO 2 ) in kg per m 3 of rock after 1000 years. The bullets represent the injection perforations. The mesh of the model is shown as an overlay. 6.4 Discussion Effect of reactive surface area on the mineralization In view of the uncertainties in the reactive surface area of the minerals present in the reservoir rock we decided to focus on this parameter when validating the modelling results with regard to the predicted mineralization processes and amount of total sequestered CO 2 as a mineral (SMCO 2 ). For this purpose, experiments were performed to actually measure the reactive surface area of the minerals by making use of SEM images. More than 400 images were taken from the minerals present in the Rotliegend core samples. A summary of the assumed mineral crystallographic structures, minimum, maximum and mean values for surface area of each mineral is given in Table 6.5. Table 6.5 Mineral structural shape, range of grain size and surface area. Mineral Shape Min grain size (µm) Max grin size (µm) Min surface area (m 2 /m 3 ) Mean(P50) surface area (m 2 /m 3 ) Max Surface area (m 2 /m 3 ) Quartz Sphere Kaolinite Hexagonal Dolomite Cubic Albite Prismatic K-feldspar Prismatic Illite Prismatic Dawsonite Prismatic However, in reality the minerals did not always conform to these structures and hence a geometrical modification has been made for the reactive surface area measurement. In addition, dawsonite was not primarily present in the reservoir samples and the images were taken from previous experiments for CO 2 -rock-brine interaction on the same core samples (The details of the experimental procedure is explained in section 0). The relation between grain size and surface area of each mineral is presented in Figure

12 Figure 6.5 Measured surface area for a selection of the minerals in the Permian Rotliegend sandstone samples. The nominal surface area values are determined based on the 50% quantile of the surface area distribution (P50). The distribution of measured surface area of minerals and their cumulative curve are shown in Figure

13 Figure 6.6 Distribution of surface area measurement from the experiments. The experimental data showed that the surface areas are an order of magnitude larger than literature values listed in Table 6.4. Also, a large variation in surface area values for each mineral has been obtained. Hence, when using a single value for surface area in the simulations, as practiced in earlier studies, the modelling output represents only one realization of a family of possible outcomes. It was therefore decided to further study the effect of surface area variations on the modelling output. Two approaches were used: parameter sensitivity analysis and random sampling analysis. In the parameter sensitivity analysis, the surface area of each mineral was varied within the range of the experimental measurements (Table 6.5), independently from other minerals. In total 29 simulations have been performed (i.e. 4 simulations for each mineral along with a nominal run with the P50 value of the surface areas which is utilized for all minerals). Random Monte Carlo sampling (Figure 6.7) was performed on the experimental surface area cumulative distribution for each mineral. Thus, the probability of each sampled data point was related to the probability distribution of each mineral obtained from experimental measurements. 90

14 Figure 6.7 Monte Carlo sampling from the experimental surface area measurements. In this set of analyses the surface areas of all the minerals were changed simultaneously in each simulation in contrary with the parameter analysis where in each simulation the surface area of only one mineral was changed. It should be mentioned that the monitoring period after stop of injection was limited to 250 years for these analyses in view of the large amount of calculation time (in total 100 simulations) Total sequestered CO 2 as a mineral The results of the analysis are presented in Figure 6.8, Figure 6.9 and Figure These analyses showed that the amount of SMCO 2 is proportional to the surface area of quartz and feldspars. This dependency was more significant in case of quartz: SMCO 2 increased 80% from 2.9 to 5.3 kg/m 3 by increasing the surface area of quartz by 329% from to m 2 /m 3 within the range of the experimental results (Figure 6.8a). However, for feldspar minerals SMCO 2 increases by less than 25% when increasing their surface area by 2133% from to m 2 / m 3 for albite and by 567% from to m 2 / m 3 for K-feldspar (Figure 6.8b and Figure 6.8c). In addition, SMCO 2 did not show any dependency on surface area of carbonate (dolomite and dawsonite) minerals since calcite is the most important mineral for CO 2 mineral trapping and it is assumed to be at equilibrium with water. For long time scales, fast reacting minerals such as calcite (CaCO 3 ) can be considered to react instantaneously and be regarded as equilibrium phase (Hellevang et al., 2013) 91

15 Figure 6.8 Variation of SMCO 2 with surface area of minerals. (a), (b) and (c) show the results of the parameter analysis, indicating an increase in SMCO 2 with increasing surface area of quartz, albite and K-feldspar respectively, (d) the dependency of SMCO 2 on quartz surface area derived by Monte Carlo random sampling. The strong dependency of SMCO 2 on quartz reactive surface area is caused by the increase in precipitation of quartz due to the increase of its reactive surface area (Figure 6.9a) and dissolution of feldspars and kaolinite in the reservoir. Precipitation of quartz creates a lack of Si and more dissolution of feldspars and kaolinite takes place through consumption of H + (Figure 6.9b and Figure 6.9c). To balance the resulting lack of H +, calcite precipitates (Figure 6.9d). The above leads to the conclusion that CO 2 injection in a sandstone reservoir with fine grained quartz has a higher potential for mineral trapping of CO 2 than in a reservoir with coarser grained quartz. 92

16 Figure 6.9 Variation of (a) quartz, (b) K-feldspar, (c) kaolinite and (d) calcite volume as a function of quartz surface area. The distribution of SMCO 2 from the Monte-Carlo simulations ranges between 3-9 kg/m 3 with a mean of 5.3 kg/m 3 (Figure 6.10). This is significantly higher than the 0.8 kg/m 3 SMCO 2 for the base case simulation with the surface area values from Xu et al. (2007). Figure 6.10 Distribution of total sequestered CO 2 as mineral after 250 years. This is as expected since the experimental surface area values are significantly higher than these base case values. Also, in Figure 6.11, a comparison has been made between our measured data and those of (Waldmann, 2011) who measured surface areas of Rotliegend sandstone minerals and (Snippe et al., 2012) who measured surface areas in Triassic sandstones of the Golden Eye gas condensate field, offshore UK. 93

17 Figure 6.11 Comparison of our measured data with some of the available surface areas in literature for different minerals. The data from our study are based on the P50 quantile. Dolomite and dawsonite are not measured by (Snippe et al., 2012).Albite and dawsonite are not measured by (Waldmann, 2011) Mineral reactions In order to assess the influence of mineral surface area on the dissolution/precipitation of that mineral, we can compare its final volume in the base case with the final volume using the experimentally determined surface area value (P50) (Figure 6.12). Figure 6.12 Comparison of the volume of minerals by utilizing base case surface area and the P50 of the measured surface area distribution. Quartz is not shown in the graph due to the dominant volume of this mineral. Its volume increased by 0.7% compared to the base case value. Using parameter analysis we also could determine the effect of surface area of each mineral on its own dissolution/precipitation mechanisms as well as on the other minerals. The mineral worth discussing here is dawsonite. Generally, dawsonite precipitation needs Na and Al. According to Audigane et al. (2007) and Worden (2006), dawsonite precipitation occurs by conversion of albite in contact with CO 2 and brine. Our modeling 94

18 results showed however that dawsonite precipitation was reduced when increasing the albite surface area (Figure 6.13a) which is not consistent with the conversion of albite to dawsonite. Our modeling results suggest that albite dissolution has an inverse relationship with kaolinite volume. That means by dissolution of albite, kaolinite precipitates or its dissolution reduces (Figure 6.13b). At the same time, dawsonite precipitation showed to be proportional to increasing K-feldspar and kaolinite surface area (Figure 6.13c and Figure 6.13d). Figure 6.13 (a) Reduction of dawsonite precipitation with increasing albite surface area (b) increase in kaolinite volume with increasing albite surface area. (c) increase in dawsonite volume with increasing K-feldspar surface area. (d) increase in dawsonite volume with increasing kaolinite surface area. This means that the source of aluminum for dawsonite precipitation was provided by dissolution of both K-feldspar and kaolinite. Figure 6.13c and Figure 6.13d indeed show an increase in the volume of dawsonite when increasing the surface area of K-feldspar and kaolinite. The Na source also can be provided from the brine. The chemical reactions related to the dissolution/ precipitation of calcite (Reaction 6.1), dolomite (Reaction 6.2), kaolinite (Reaction 6.3 and Reaction 6.4), dawsonite (Reaction 6.5 and Reaction 6.6), albite (Reaction 6.4) and K-feldspar (Reaction 6.5) as discussed in results and discussion section. CaCO 3 +CO 2 +H 2 O Ca HCO 3 Reaction 6.1 Calcite CaMg(CO 3 ) 2 +2CO 2 +2H 2 O Ca 2+ +Mg HCO 3 Reaction 6.2 Dolomite 2Na + +Al 2 Si 2 O 5 (OH) 4 +2CO 2 +H 2 O 2NaAlCO 3 (OH) 2 +2SiO 2 +2H + Reaction 6.3 Kaolinite Dawsonite 95

19 2NaAlSi 3 O 8 +3H 2 O+2CO 2 2Na + +2HCO - 3 +Al 2 Si 2 O 5 (OH) 4 +4SiO 2 Albite KAlSi 3 O 8 +Na + +CO 2 +H 2 O NaAlCO 3 (OH) 2 +3SiO 2 +K + K-feldspar Dawsonite Reaction 6.4 Reaction 6.5 Finally, the Monte Carlo analysis showed that the final volumes of albite, calcite and quartz after simulation are less dependent on surface area variations in comparison with other minerals (Figure 6.14). Figure 6.14 Output volume distribution of albite (a) calcite (b) and quartz (c) resulting from Monte Carlo sampling of surface area. For completeness, we have investigated the sensitivity of the results to the grid size in the model and repeated the simulations for three different grid sizes. The results of the simulations, in this case SMCO2 after four years for three different grid sizes of 4.5 m, 9 m, which is the grid size utilized in the simulations explained in the model description, and 18 m (size of the nearest cell to the injection well) were , and kg/m3, respectively. Using Richardson extrapolation (Richardson and Gaunt, 1927; Roache, 1994) the sensitivity to the grid size in the simulations can be quantified. The convergence ratio shows that the simulation has a monotonic convergence with the rate of The approximated exact value (extrapolated value of Richardson if the grid size approaches zero) will be This shows that the simulation results with the grid size as used in this study are within 0.05% of the extrapolated exact values. In this study, we specifically addressed the influence of reactive surface area on the mineral trapping of CO 2. The uncertainties in the modelling are raised by the limitation of the current reactive 96

20 transport modelling software to represent complex physical and chemical situation of the reservoirs. Some of these limitations are related to the lack of experimental data for the necessary input parameters of the models as discussed earlier. In other words, more experimental work is needed to reduce these uncertainties (Xu et al., 2007). We also agree with Xu et al. (2007) that additional modelling developments is needed. For instance, the presence of residual methane in the reservoir, both in the gas phase and dissolved in the brine, affects the migration of CO 2 as well as the reservoir capacity for CO 2 sequestration. The most desirable scenario for modelling of CO 2 sequestration in depleted gas fields is therefore to incorporate the presence of methane. However, as Tambach and Hellevang (2015) correctly point out, the modelling software has limitations since TOUGHREACT modules which can handle both CO 2 and CH 4 in one geochemical simulation are not (yet) publically available. We do appreciate the workarounds by using multiple TOUGH EOS modules, as applied in previous studies (Audigane et al., 2009; Tambach et al., 2014). Audigane et al. (2009) found an elegant approach by using two independent modelling scenarios in parallel: in Case A the module TOUGHREACT ECO2N is applied, considering brine and pure CO 2 only, in order to estimate mineral trapping from geochemical reactions. In Case B the module TOUGH2 EOS7C is applied on a mixture of brine, CO 2 and CH 4 in order to model structural and solubility trapping of CO 2 in combination with enhanced gas recovery. In this way a geochemical (Case A) and a physical approach (Case B) can be combined. Tambach et al. (2014) on the other hand modelled the initial gas production prior to CO 2 injection by using TOUGH2 EWASG which can handle water, NaCl and noncondensable gases like CH 4. In this way, the conditions after gas production and prior to CO 2 injection were established. Subsequently TOUGHREACT ECO2N was used to simulate the CO 2 injection and storage phase. However, as mentioned earlier, ECO2N does not take into account CH 4. Hence, in this study all the CH 4 was replaced by CO 2, essentially resulting in CO 2 injection into saline brine with an initial CO 2 content in absence of methane. In our study, constrained by software limitations, we have chosen our physical and chemical parameters such that they approximate subsurface conditions of depleted gas fields as close as possible. Given the potential uncertainties of geochemical parameters such as dissolution rates and activation energy of minerals, we consider the possible effects of methane to fall within this uncertainty range. As far as the geochemical effects are concerned, our assumption above is corroborated by our laboratory results, since we include in all our experiments a 2% methane component in the injected CO 2 in order to approximate the conditions of depleted gas fields as good as possible. The other limitation of the modelling software is the introduction of one single input value for certain parameters instead of a range. In our study we addressed this issue for the parameter reactive surface area which was determined specifically for each reservoir mineral in order to increase the accuracy of the CO 2 -brine-rock interaction over a long period of time. 97

21 Other physical and chemical aspects such as kinetic rate also affect the evolution of dissolution/ precipitation of minerals through time (Cohen et al., 2008). Building on our results for reactive surface area and following Hellevang et al. (2013) more research is needed on mineral nucleation and precipitation to increase the accuracy of predictions even further. 6.5 Conclusion A geochemical simulation study using TOUGHREACT software was conducted for the CO 2 storage in a depleted gas field with a Rotliegend sandstone reservoir. A 31 year injection period was simulated followed by 1000 year monitoring period which enabled us to describe the evolution of three main trapping mechanisms: stratigraphic, solubility and mineral trapping. Our results indicate that the total sequestered CO 2 as a mineral after 1000 years of simulation is 3.23 kg/m 3. The dissolution of minerals had only a minor effect on the porosity of the formation: a factor of 0.2% increase at the end of the simulation. One of the purposes of our study was to evaluate the possible effect of reactive surface area on mineralization processes. In order to do so, we measured the mineral surface area of the Rotliegend reservoir core samples and carried out in total 129 simulations covering the full range of measured surface areas. The injection period was similar to the base case but with less monitoring time (250 years) due to the large number of simulations. The results indicated that the increase in surface area of quartz and feldspar minerals significantly influences the mineral trapping process. The total sequestered CO 2 as mineral (SMCO 2 ) changed from 3 to 9 kg/m 3 after 250 years within the range of experimental surface areas. For comparison, the SMCO 2 of the base case after 250 years was 0.8 kg/m 3 due to utilization of the lower surface areas derived from literature (e.g. Xu et al., 2007). Additionally, dawsonite precipitation did reduce with increasing albite surface area. It was concluded that the aluminium source for the dawsonite precipitation was provided either by kaolinite or K-feldspar. In addition, calcite, quartz and albite final volumes showed less dependency on the mineral surface area. 98