An Integrated Numerical Study of CO2 Storage in Carbonate Gas Reservoirs with Chemical Interaction between CO2, Brine, and Carbonate Rock Matrix
Bibliographic record
Abstract
In light of the burgeoning interest in mitigating anthropogenic CO2 emissions, carbonate reservoirs have emerged as promising sequestration sites due to their substantial storage potentials. However, the complexity of CO2 storage in carbonate reservoirs exceeds that in conventional sandstone reservoirs, predominantly due to the rapid interactions occurring between the injected CO2, brine, and carbonate rock matrix. In this study, a numerical model integrated with the chemical CO2–brine–rock matrix interaction was developed to analyze the carbonate rock dissolution process and the physical property variations of different carbonate gas reservoirs during the CO2 injection and sequestration process. More specifically, a total of twenty scenarios were simulated to examine the effects of lithology, pore size, pore–throat structures, and CO2 injection rate on carbonate rock matrix dissolution and reservoir property variation. Calcite is significantly easier and quicker to react with CO2-solvated brine than dolomite; as a result, limestones exhibit an expedited rock dissolution and pore volume increase, along with a slower pressure buildup in comparison to dolomites. Also, the carbonate reservoir with a smaller pore size has a higher rock dissolution rate than one with a larger pore size. Furthermore, the simulation results show injected CO2 can modify the pore-dominant carbonate reservoir to a more pronounced extent than the fracture-dominant carbonate reservoir. Lastly, the carbonate rock dissolution is more obvious at a lower CO2 injection rate. The insights derived from this research aid evaluations related to CO2 injectivity, storage capacity, and reservoir integrity, thereby paving the way for environmentally and structurally sound carbon sequestration strategies.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| 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".