Miscible CO<sub>2</sub> Simultaneous Water-and-Gas (CO<sub>2</sub>-SWAG) Injection in the Bakken Formation
Bibliographic record
Abstract
In this paper, miscible CO 2 simultaneous water-and-gas (CO 2 -SWAG) injection in the tight Bakken formation is experimentally studied. The effective viscosities of high-salinity water and supercritical CO 2 mixtures with 12 different water volume fractions are measured at the actual reservoir conditions by using a capillary viscometer. A total of six coreflood tests with four different miscible CO 2 -EOR schemes are conducted in the tight reservoir core plugs collected from the Bakken formation (Canada). It is found that the measured effective viscosity of the saline water–CO 2 mixture increases with the water volume fraction and can be reasonably modeled by using the Arrhenius equation. The coreflood test results indicate that the miscible CO 2 -SWAG injection with an injected water–gas ratio (WGR) of 1:3 in volume has the highest oil recovery factor (RF). The miscible CO 2 water-alternating-gas (CO 2 -WAG) injection achieves a slightly higher oil RF than that of the miscible CO 2 flooding, whereas the waterflooding followed by the miscible CO 2 flooding has the lowest oil RF. In addition, the WGR shows strong effects on the fluid production trends of the miscible CO 2 -SWAG injection. A water bank might be formed ahead of the water–CO 2 mixture in the miscible CO 2 -SWAG injection with a higher injected WGR of 3:1 or 1:1. Furthermore, the mobility ratio of the injected fluid(s) to light crude oil is calculated based on the measured steady-state flow rate and pressure gradient in each coreflood test. In comparison with water or CO 2 alone, the water–CO 2 mixture has a lower mobility in the tight reservoir core plugs. Hence, the highest oil RF of the optimum CO 2 -SWAG injection with the lowest injected WGR of 1:3 is attributed to a well-controlled water–CO 2 mobility and a substantially weakened waterblocking effect.
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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 | 0.001 | 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 | 0.001 |
| 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".