The relationship between kh and achievable rates of injection, and repercussions for large scale storage
Bibliographic record
Abstract
Geologic CO 2 storage (GCS) can provide meaningful reduction of CO 2 emissions if implemented with large injection rates. The traditional paradigm for GCS entails injection of supercritical CO 2 into a brine-filled formation with an implicit assumption that resident brine can be displaced through boundaries of the formation without adverse effects. In this paradigm, kh , the product of permeability and formation thickness, is the first order control on achievable injection rates, and the gradual buildup of pressure in the formation during the storage operation will reduce those rates. These two factors impose serious constraints on the overall storage rate. In contrast, examination of field-aggregated injection and production volumes during waterflooding operations in oil reservoirs reveals a notable lack of correlation between kh and achieved injection rates. This suggests that if CO 2 storage projects are operated in the same manner as waterflooded oil reservoirs, i.e. with both injection and extraction wells, located and operated to maximize rates, then material rates of storage can be achieved regardless of reservoir kh . When applied to a large set of storage formations, this mode of operation provides an otherwise unattainable overall rate of storage while greatly reducing risks associated with elevated pressure in storage regions.
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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 | 0.002 | 0.010 |
| 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 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 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".