Optimization of Miscible CO<sub>2</sub> Water-Alternating-Gas Injection in the Bakken Formation
Bibliographic record
Abstract
In this paper, optimization of miscible CO 2 water-alternating-gas (CO 2 -WAG) injection in the Bakken formation is experimentally studied. First, tight sandstone reservoir rock samples from the Bakken formation are characterized. Second, the vanishing interfacial tension technique is applied to determine the minimum miscibility pressure of the Bakken light crude oil and CO 2 at the actual reservoir temperature. Third, a total of nine coreflood tests are conducted through respective waterflooding, continuous miscible CO 2 flooding, and miscible CO 2 -WAG injection. In the miscible CO 2 -WAG injection, different WAG slug sizes of 0.125, 0.250, and 0.500 pore volume and different WAG slug ratios of 2:1, 1:1, and 1:2 are used to study their specific effects on the oil recovery factor (RF) in the Bakken formation. In addition, miscible CO 2 gas-alternating-water (CO 2 -GAW) injection is also tested as an opposite fluid injection sequence of the miscible CO 2 -WAG injection. It is found that, in general, the CO 2 enhanced oil recovery method is capable of mobilizing the light crude oil in the Bakken tight core plugs under the miscible conditions. The miscible CO 2 -WAG injection has the highest oil RF (78.8% in test 3), in comparison with waterflooding (43.2% in test 1), continuous miscible CO 2 flooding (63.4% in test 2), and miscible CO 2 -GAW injection (66.2% in test 8). Furthermore, using a smaller WAG slug size for CO 2 -WAG injection leads to a higher oil RF. The optimum WAG slug ratio is approximately 1:1 for the Bakken tight formation. More than 60% of the light crude oil is produced in the first two cycles of the miscible CO 2 -WAG injection. The CO 2 consumption in the optimum miscible CO 2 -WAG injection is much less than that in the continuous miscible CO 2 flooding.
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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) | 0.001 | 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 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 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".