Miscible CO2 Water-Alternating-Gas (CO2-WAG) Injection in a Tight Oil Formation
Bibliographic record
Abstract
Abstract In this paper, miscible CO2 water-alternating-gas (CO2-WAG) injection in the Bakken formation is experimentally studied and optimized. First, tight sandstone reservoir rock samples from the Bakken formation are characterized. Second, the saturation pressure, oil-swelling factor, CO2 solubility, CO2-saturated Bakken light crude oil density and viscosity are measured. Third, the vanishing interfacial tension (VIT) technique is applied to determine the minimum miscibility pressure (MMP) of the Bakken light crude oil and CO2 at the actual reservoir temperature. Last, a total of nine coreflood tests are conducted through respective waterflooding, continuous miscible CO2 flooding, and miscible CO2-WAG injection. In the miscible CO2-WAG injection, different WAG slug sizes of 0.125, 0.250, and 0.500 pore volume (PV) 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 CO2 gas-alternating-water (CO2-GAW) injection is also tested as an opposite fluid injection sequence of the miscible CO2-WAG injection. It is found that in general, CO2 enhanced oil recovery (CO2-EOR) method is capable of mobilizing the light crude oil in the Bakken tight core plugs under the miscible condition. The miscible CO2-WAG injection has the highest oil RF (78.8% in Test #3), in comparison with waterflooding (43.2% in Test #1), continuous miscible CO2 flooding (63.4% in Test #2), and miscible CO2-GAW injection (66.2% in Test #8). Furthermore, using a smaller WAG slug size of CO2-WAG injection leads to a higher oil RF. The optimum WAG slug ratio is approximately 1:1 for the Bakken tight oil formation. More than 60% of the light crude oil is produced in the first two cycles of the miscible CO2-WAG injection. The CO2 consumption in the optimum miscible CO2-WAG injection is much less than that in the continuous miscible CO2 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) | 0.000 | 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) | 0.001 | 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".