3.1. Screening the RSA Combinations
The 87 combinations were simulated to study the ranges of uncertainty in mineral precipitation/dissolution and CO
2 mineral trapping caused by using different mineral RSA values.
Figure 3 and
Figure 4 present the simulation results of the simplified model and emphasize three RSA combinations, Case #85, Case #86, and Case #87, where all mineral RSA values were set at mid, low, and high, respectively (please refer to
Table A1 for the detailed RSA combinations). The predicted pH values of all cases exhibit a similar pattern (
Figure 3a). A drastic drop from the initial pH (~6.7) to the range of 4.0~4.4 during the first day is followed by a relatively slower recovery through the rest of the simulation period. While most of the curves overlap each other, the dashed dark green line, which indicates the results of Case #86, presents a deeper pH decrease in the beginning and a slower pH recovery rate. Comparing the much faster pH recovery rate of the other cases, it is obvious that the prolonged recovery time of Case #86 results from the lack of buffers, i.e., fewer accessible minerals to react with the H
+ introduced by CO
2.
Because the changes of the silicate minerals are much smaller than the total amount of silicate minerals in the model, the mineral reaction rate is a better parameter to illustrate the silicate mineral reactions. The predicted precipitation or dissolution of silicates at the center of the model are shown in
Figure 3b–d. For illite and quartz, Case #87 (dashed dark blue line) and Case #86 (dashed dark green line) is the most and the least reactive scenario for these two minerals, whereas Case #85 (dashed dark red line) overlaps with most of the medium cases. On the other hand, changes in kaolinite are more complicated. There are many crossovers and overlaps among results using different kaolinite RSA values, and the deviations between lines in the same color are much greater than those in
Figure 3b or
Figure 3d. This suggests that the reaction of kaolinite is controlled by not only the RSA of kaolinite but also the RSA of other minerals. Nevertheless, the high RSA cases (blue) predicted prompt and more intense reactions, while the low RSA cases (green) predicted delayed and more restricted reactions.
The simulation results of four carbonate minerals, ankerite, calcite, siderite, and dolomite, are shown in
Figure 4a–d, respectively. Please note that all 87 cases started with the same initial mineral amounts, and
Figure 4 presents mineral amounts since Day 1; therefore, the differences between the starting points of the lines in each subplot indicate the difference in the reactions that occurred during the first day of simulation. For example, the total amount of ankerite in the model was initially about 295 kmol, and dropped to 74 kmol in Case #87 (dashed dark blue line), whereas it dropped to about 245 kmol in Case #85 (dashed dark red line) and to 292 kmol in Case #86 (dashed dark green line), after the first day of simulation.
For all 87 cases, a monotonic dissolution of ankerite is observed, while all three other minerals show monotonic precipitation within the 600 years of simulation. The low RSA cases (green lines) once again exhibit the least reactivity, i.e., the slowest to deviate from the initial values. Interestingly, the prediction results fall into three clusters for each of these four minerals in the first year. In other words, the RSA value of the particular mineral, being either low, mid, or high, determines the reaction of this mineral in the first year, regardless of what RSA values are used for the other six minerals. Moreover, there is zero or small difference among predicted mineral amounts using the three levels of RSA values for ankerite (
Figure 4a) and siderite (
Figure 4c) in the later stage of the simulation period. The differences among the three clusters of lines are more obvious for calcite (
Figure 4b) and dolomite (
Figure 4d) after 300 days. At the end of the simulations, the cases with the high dolomite RSA value forecast much greater dolomite precipitation than the other cases.
Compared to the silicate minerals (
Figure 3b–d), a stronger clustering effect is observed in the carbonate minerals, especially in the first two years of simulation, suggesting that the reaction of the carbonate mineral (
Figure 4a–d) is more controlled by its own RSA value. The interference (or mutual dependence) between mineral reactions is weaker for the carbonate minerals than for the silicate minerals. As a result, Cases #85, #86, and #87 seem to be able to represent the median, minimum, and maximum reaction scenarios of all tested cases for all minerals.
The mineral trapping of CO
2 in the simplified model was evaluated with all 87 cases, and the results are presented in
Figure 5. There is a much greater deviation among total mineral trapping amount predictions than the predictions for each mineral, as shown in
Figure 3 and
Figure 4. No clear clustering is observed in
Figure 5. While the impact of RSA on each individual mineral is apparent and easier to interpret, its impact on the CO
2 trapping mechanism is rather complicated. This is likely due to the different abilities of each mineral to sequester CO
2. For example, one mole of precipitated calcite effectively secures one mole of CO
2, while for dolomite the ratio becomes 1:2. At the end of the simulation, the amount of CO
2 trapped in the minerals ranges from 2 kmol to 200 kmol. The results of three representative cases (#86, #86, and #87) are very close to the median, minimum, and maximum values of all cases, as shown in the dashed lines in
Figure 5. Therefore, these cases were selected for reactive transport simulations with the FWU reservoir model.
3.2. Reactive Transport Prediction with the Reservoir Model
As minerals precipitate or dissolve, it can change the porosity of the storage formation and thus affect fluid flow patterns and subsequent mineral reactions at new fluid-rock contacts. Therefore, we analyzed the porosity change due to mineral reactions in the FWU model.
Figure 6,
Figure 7 and
Figure 8 present the changes in porosity at three critical time steps: the end of the CO
2-EOR period, the end of the post-EOR CO
2 injection period, and the end of the simulation period, respectively.
After ten years of CO
2-EOR operation, porosity reduction is observed in all three cases.
Figure 6 shows changes of porosity due to mineral reactions at the top layer of the Morrow B sandstone using the RSA values of Case #86 (all low RSA values of seven minerals), Case #85 (all mid RSA values), and Case #87 (all high RSA values), respectively. In general, a dominant porosity reduction was observed for all cases. Specifically, Case #86 (
Figure 6a) exhibits the most restricted variation, while Case #87 (
Figure 6c) presents the greatest porosity change across the layer, showing a maximum porosity reduction of 1.86
10
−3 (or 0.9% of the initial porosity). However, very similar CO
2 global mole fraction distributions were predicted by three models (
Figure 7). This suggests that the mineral reactions have an insignificant impact on the CO
2 migration. Given that the CO
2 plume shapes are mostly identical among the three cases, the difference in porosity change is attributed to the mineral RSA values.
The CO
2-EOR period was followed by a post-EOR CO
2 injection period, during which all production wells were shut-in, and CO
2 was continuously injected via all injection wells for ten years.
Figure 7 presents the simulation results of porosity changes due to mineral reactions. There are visible changes in porosity reduction comparing
Figure 8b and
Figure 6b, and
Figure 8c and
Figure 6c. A clear expansion of areas with porosity loss is observed. The greater porosity reductions occur along the edges of the CO
2 plumes (
Figure 8c). Specifically, after ten years of CO
2 injection, the maximum porosity loss has been increased to 0.7% in Case #85 and 2.5% in Case #87, from 0.3% and 0.9%, respectively. However, the CO
2 flow patterns are still almost identical for all three cases (figure not shown). Therefore, the impact of mineral reactions on CO
2 flow remains insignificant during the post-EOR CO
2 injection period.
Figure 9 presents the estimated porosity loss at the end of the 600-year simulation period. The simulation results of using all low RSA values present almost no change in porosity due to mineral reactions (
Figure 9a). On the other hand, using all mid and high RSA values leads to greater porosity reduction, as shown in
Figure 9b,c. While there is only a slight expansion of porosity loss areas during the no-injection period, the maximum porosity loss due to mineral reactions increased to 1.19% and 5.04% for Case #85 and Case #87, respectively. It is worth noting that the predicted porosity changes in
Figure 6c and
Figure 8c are more profound than those in
Figure 9a,b, suggesting that using high RSA values leads to dramatically different porosity change predictions, hence mineral trapping of CO
2, over even a short time period. Comparing the distributions of CO
2 global mole fraction (
Figure 10) and porosity change (
Figure 9), it is clear that the mineral reactions had a nominal impact on the forecast of CO
2 migration in 600 years. It is interesting to note that there are lower porosity changes in the centers of the CO
2 plumes, where they exhibit very high (greater than 0.8) CO
2 global mole fractions (see
Figure 7 and
Figure 10). The areas with greater porosity reduction are located on the edges (or fronts) of the CO
2 plumes, suggesting that there are more intense geochemical reactions and thus more CO
2 trapped in minerals or aqueous ions. This is because mineral reactions require sufficient contacts between the minerals and the formation fluids, which is less likely to be present in areas with very high CO
2 saturation.
The performance of trapping mechanisms that are directly related to the geochemical reactions was evaluated, as shown in
Figure 11. As expected, and similar to the simplified model result (
Figure 5), there is a significant difference between the amount of CO
2 trapped in minerals in the FWU field-scale reservoir model (
Figure 11a). The mineral trapping first appeared as early as about 200 days (for Case #87), accelerated during the post-EOR CO
2 injection period (between the two vertical blue lines), and kept growing at a slower rate after CO
2 injection stopped. At the end of 600 years, the estimated amounts of CO
2 trapped in minerals are 3.25
10
6 kmol (1.43
10
5 metric tons), 0.8
10
6 kmol (3.52
10
4 metric tons), and 0.05
10
6 kmol (2.2
10
3 metric tons) for Case #87, Case #85, and Case #86, respectively. In other words, mineral trapping with all high RSA values is about four times more effective than with all mid RSA values, and 65 times more effective than with all low RSA values. At the end of 600 years, mineral trapping would contribute to 0.15%, 2.46%, and 9.44% of the total sequestered CO
2 at the FWU when using the low, mid, and high mineral RSA values, respectively. However, the mineral RSA values have much less impact on the CO
2 trapped in aqueous ions (
Figure 11b). The maximum difference between predictions is only about 0.02
10
6 kmol (880 metric tons), and only 0.12
10
6 kmol (5.28
10
3 metric tons) of CO
2 was sequestered in aqueous ions by the end of the simulation. Nevertheless, mineral reactions and aqueous ions are able to sequester at least around 3000 metric tons of CO
2 (in Case #86), which would be otherwise presented in other forms (i.e., supercritical phase or dissolved) if reactive transport was not taken into consideration.