Injectivity of carbon dioxide in the St. Lawrence Platform, Quebec (Canada): A sensitivity study
Bibliographic record
Abstract
Abstract Injectivity of CO2 in the Bécancour deep saline aquifers, St. Lawrence Platform (Québec), was investigated using 2D radial numerical simulations with TOUGH2/ECO2N. In order to have an appropriate choice for the CO2 injection rate and the duration of injection, sensitivity analyses were carried out, considering different values of hydrodynamic, chemical‐petrophysical, and geometric parameters affecting CO2 injection in a brine reservoir. The parameterization analysis for capillary pressure and relative permeability models indicated large uncertainty for this case study. Simulations took into account Bécancour reservoir conditions in which the maximum pressure was limited to the fracturing pressure. The sensitivity analysis provides guidance on potential injection scenarios. To remain below fracturing pressure, intermittent 5‐year injection periods can be used, with a mass injection rate up to ∼ 20 kg/s, alternating with half‐year periods without injection. This scenario could give maximum CO2 storage in the aquifer. CO2 storage capacities in different phases were calculated versus time. This study shows that the northeastern reservoir block of the Bécancour area could host about 10 Mt CO2, which represents 15% to 50% of regional yearly CO2 emissions during about 60 and 20 years for the case of the injection formation permeability of 0.89 × 10−15 m2 and 4.17 × 10−15 m2, respectively. Finally, this modeling study will also be the basis for the design of a pilot CO2 injection test at the study site. © 2013 Society of Chemical Industry and John Wiley & Sons, Ltd
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| 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".