Estimating Sustainable Long-Term Fluid Disposal Rates in the Alberta Basin
Bibliographic record
Abstract
Reliable regional-scale permeability data and minimum sustained injectivity rate estimates are key parameters required to mitigate economic risk in the site selection, design, and development of commercial-scale carbon sequestration projects, but are seldom available. We used extensive publicly available disposal well data from over 4000 disposal wells to assess and history-match regional permeability estimates and provide the frequency distribution for disposal well injection rates in each of 66 disposal formations in the Alberta Basin. We then used core data and laboratory analyses from over 3000 cores to construct 3D geological, geomechanical and petrophysical models for 22 of these disposal formations. We subsequently used these models and the history-matched regional permeability estimates to conduct coupled geomechanical and reservoir simulation modeling (using the ResFrac™, Palo Alto, CA, USA, numerical simulator) to assess: (i) well performance in each formation when injecting carbon dioxide for a 20-year period; (ii) carbon dioxide saturation and reservoir response at the end of the 20-year injection period; (iii) reliability of our simulated rates compared to an actual commercial sequestration project. We found that: (i) the injection rate from our simulations closely matched actual performance of the commercial case; (ii) only 7 of the 22 disposal formations analyzed appeared capable of supporting carbon dioxide injectors operating at greater than 200,000 tons per year/well; (iii) three of these formations could support injectors operating at rates comparable to the successful commercial-scale case; (iv) carbon dioxide presence and a formation pressure increase of at least 25% above pre-injection pressure can be expected at the boundaries of the (12 km × 12 km) model domain at the end of 20 years of injection.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| 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 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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 teacher head, 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".