Assessment of CO2 Flooding Potential for Bakken Formation, Saskatchewan
Bibliographic record
Abstract
Abstract The Bakken is an extremely tight formation, with the oil contained mostly in siltstone and sandstone reservoirs with low porosity and permeability. In Saskatchewan alone, there could be an estimated 25 to 100 billion barrels of Bakken oil in place. At present, the combination of horizontal well drilling and the new multi-stage fracturing and completion technologies has been the key to economically unlocking the vast reserves of the Bakken formation. The primary recovery factor, however, remains rather low due to high capillary trapping. While waterflooding could result in unfavorable injectivity issues, carbon dioxide (CO2) miscible flooding provides a promising option for increasing the recovery factor. Higher oil recovery factor can be achieved with CO2 injection through multi-contact miscibility that results in vanishing interfacial tension, viscosity reduction, and oil swelling. This paper conducted a numerical simulation work as an effective and economical means of evaluating CO2 flooding potential for enhanced oil recovery. Different strategies were tested to compare the effects on oil recovery of injection well pattern, continuous and cyclic injection, waterflooding and CO2 flooding, injected gas composition, and heterogeneity. The simulation results show that CO2 flooding presents a technically promising method for recovering the vast Bakken oil.
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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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 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".