Status of CO<sub>2</sub>storage in deep saline aquifers with emphasis on modeling approaches and practical simulations
Bibliographic record
Abstract
Carbon capture and storage (CCS) is the only viable technology to mitigate carbon emissions while allowing continued large-scale use of fossil fuels. The storage part of CCS involves injection of carbon dioxide, captured from large stationary sources, into deep geological formations. Deep saline aquifers have the largest identified storage potential, with estimated storage capacity sufficient to store emissions from large stationary sources for at least a century. This makes CCS a potentially important bridging technology in the transition to carbon-free energy sources. Injection of CO2 into deep saline aquifers leads to a multicomponent, multiphase flow system, in which geomechanics, geochemistry, and nonisothermal effects may be important. While the general system can be highly complex and involve many coupled, nonlinear partial differential equations, the underlying physics can sometimes lead to important simplifications. For example, the large density difference between injected CO2 and brine may lead to relatively fast buoyant segregation, making an assumption of vertical equilibrium reasonable. Such simplifying assumptions lead to a range of simplified governing equations whose solutions have provided significant practical insights into system behavior, including improved estimates of storage capacity, easy-to-compute estimates of CO2 spatial migration and pressure response, and quantitative estimates of leakage risk. When these modeling studies are coupled with observations from well-characterized injection operations, understanding of the overall system behavior is enhanced significantly. This improved understanding shows that, while economic and policy challenges remain, CO2 storage in deep saline aquifers appears to be a viable technology and can contribute substantially to climate change solutions.
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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 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".