Evaluating Reservoir Risks and Their Influencing Factors during CO<sub>2</sub> Injection into Multilayered Reservoirs
Bibliographic record
Abstract
Wellbore and site safety must be ensured during CO2 injection into multiple reservoirs during carbon capture and storage projects. This study focuses on multireservoir injection and investigates the characteristics of the flow-rate distribution and reservoir-risk evaluation as well as their unique influences on multireservoir injection. The results show that more CO2 enters the upper layers than the lower layers. With the increase in injection pressure, the risks of the upper reservoirs increase more dramatically than those of the low reservoirs, which can cause the critical reservoir (CR) to shift. The CO2 injection temperature has a similar effect on the injection flow rate but no effect on the CR’s location. Despite having no effect on the flow-rate distribution, the formation-fracturing pressures in the reservoirs determine which layer becomes the CR. As the thickness or permeability of a layer increases, the inflows exhibit upward and downward trends in this layer and the lower layers, respectively, whereas the inflows of the upper layers remain unchanged; meanwhile, the risks of the lower layer and those of the others decrease and remain constant, respectively. Compared to other parameters, the reservoir porosities have a negligible effect on the reservoir risks and flow-rate distributions.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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".