Feasibility of Injecting Large Volumes of CO2 into Aquifers
Bibliographic record
Abstract
Although it is recognized that deep aquifers offer a very large potential storage capacity for CO 2 sequestration it is not clear how to fill the storage with a large volume of CO 2 in a relatively short period of time. The typical benchmark for the rate of CO 2 injection is 1 Mt/year when studying storage performance. This rate is very low compare to the scale necessary for the storage technology to play a significant role in managing global emissions. In this study we perform numerical simulations of a large volume of injection, 20 Mt/year during 50 years of continuous injection resulting in a total sequestration of 1 Gt CO 2 . A sensitivity analysis of the results (plume area and CO 2 storage capacity) is presented within the range of aquifer parameters: thickness (50–100 m); permeability (25-100 mD); rock compressibility (from 9 10-10 to 2 10-9 (1/Pa)) as well as different injection arrangements. The implementation of this study to a particular case of injection of 1 Gt total over 50 years into the Nisku aquifer located in Wabamun Lake Area, Alberta, Canada [1] is presented. In this area, large CO 2 emitters including four coal-fired power plants with emission between 3 to 6 Mt/year each are present. The Nisku aquifer is believed to be a suitable choice for future sequestration projects. In this case study a few injection scenarios (number of wells and their placement, which control the ability to inject without exceeding the aquifer’s fracture pressure) are presented. The evolution of plume size and pressure field in the aquifer for these scenarios is shown. As opposed to the generic sensitivity study, the case study includes the heterogeneity of the aquifer and its dip angle. Both generic and Nisku studies have shown that the capacity of the reservoir in the case of large injection volumes should be evaluated not by available pore volume, but by ability to inject some amount without exceeding fracture pressure of formation.
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 | 0.000 | 0.001 |
| 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 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| 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".