Reservoir Characterization for CO<sub>2</sub>Sequestration: Assessing the Potential of the Devonian Carbonate Nisku Formation of Central Alberta
Bibliographic record
Abstract
The Wabamun Lake area of Central Alberta, Canada includes several large CO<sub>2<sub/> point source emitters, collectively producing more than 30 Mt annually. Previous studies established that deep saline aquifers beneath the Wabamun Lake area have good potential for the large-scale injection and storage of CO<sub>2<sub/>. This study reports on the characterization of the Devonian carbonate Nisku Formation for evaluation as a CO<sub>2<sub/> repository. Major challenges for characterization included sparse well and seismic data, poor quality flow tests, and few modern measurements. Wireline porosity measurements were present in only one-third of the wells, so porosity and flow capacity (permeability-thickness) were estimated using wireline electrical measurements. The Archie cementation factor appears to vary between 2 and 3, creating uncertainty when predicting porosity using the electrical measurements; however, high-porosity zones could be identified. The electrically-based flow capacity predictions showed more favorable values using a correlation with core than the relation based on drill stem and production tests. This behavior is expected, since the flow test flow capacities are less influenced by local occurrences of very permeable vuggy and moldic rocks. Facies distributions were modeled using both pixel and object methods. The object models, using dimensions obtained from satellite imaging of modern day environments, gave results that were more consistent with the geological understanding of the Nisku and showed greater large-scale connectivity than the pixel model. Predicted volumes show considerable storage capacity in the Nisku, but flow simulations suggest injection capacities are below an initial 20 Mt/year target using vertical wells. More elaborate well designs, including fracture stimulation or multi-lateral wells may allow this goal to be reached or surpassed.
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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 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 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".