A comparison of water chemistry from a CO<sub>2</sub>‐enhanced oil recovery project with reactive transport modeling of CO<sub>2</sub> injection into a carbonate reservoir
Bibliographic record
Abstract
Abstract A long‐term geochemical monitoring program was conducted at a CO2‐enhanced oil recovery site in central Alberta (Canada) to examine geological storage of CO2. The program included geochemical monitoring of reservoir brines, a mineralogical study, and using TOUGHREACT to compare model and field observations. CO2 was injected into a highly permeable carbonate reservoir at 78 tonnes/day for 2.5 years. Fluid and gas samples were obtained for geochemical characterization before, during, and 1.4 years after injection from a production well ∼915 m from the injection well at a depth of ∼936 m. Downhole water compositions were calculated using SOLMINEQ88 and compared to predictions from TOUGHREACT. Following CO2 breakthrough after ∼23 months, downhole pH decreased, HCO3−; and calcium concentrations increased, while magnesium concentrations changed marginally, indicating solubility and ionic trapping were occurring simultaneously. Trends in analyzed and modeled species were similar, but concentration levels were different. Calibrating the model to achieve better correlation between results was attempted but proved unsuccessful. The variances were likely caused by differences between ideal and reservoir mineral's kinetic parameters, surface area, thermodynamic parameters and sampling technique. Five‐hundred‐year simulations showed the CO2 plume migrating vertically due to capillary forces with no significant change in gas saturation below the caprock over the post‐injection period. The majority of CO2 remained trapped as a supercritical phase as the reservoir is composed of only carbonate minerals. The low reactivity of the reservoir is positive, in that the reservoir is not negatively impacted by dissolution and negative in that no mineral trapping occurs. © 2013 Society of Chemical Industry and John Wiley & Sons, Ltd
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".