Bibliographic record
Abstract
Oil and gas reservoirs and deep saline aquifers are primary candidates for long-term geological sequestration of greenhouse and acid gases. Risk assessment for sequestration projects must include predictions of sequestration zone performance. These performance assessments will guide the selection of sequestration sites and/or operating parameters, such as injection pressure and rate, that mitigate leakage risks. If natural fractures or faults are present, then bottom-hole injection pressures higher than the minimum in-situ stress may open these fractures. Pressures higher than the fracture breakdown pressure will fracture the reservoir and/or caprock. In both cases, CO2 or acid gas will leak from the sequestration unit. Thus, it is essential to properly estimate the minimum stress and fracture breakdown pressure and devise injection strategies that will maintain pressures below these at all times. An extensive database of micro- and mini-fracture, leak-off and fracture breakdown pressures measured by industry in the Alberta Basin, Canada, and data from close to fifty acid-gas injection operations in the basin has been used to develop a methodology for estimating the minimum in-situ stress both at regional and local scales throughout the basin on the basis of these data. Minimum horizontal stress gradients close to 17 kPa/m have been estimated for much of the basin from leak-off tests conducted over depths
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| 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) | 0.002 | 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".