Motion and Mobilization of Oil Slugs in a Capillary Model Under Stationary Conditions
Bibliographic record
Abstract
In this research, both experimental and numerical investigations are conducted to study the mechanism behind slug flow in a capillary tube. A 1.2 mm inner diameter capillary model is employed to represent the pore structure of porous media. A single oil slug is trapped in the tube and mobilized from a stationary condition by water injection. During the flow process, the flow behavior, the maximum driven pressure to mobilize the oil slug and the flow time to reach the maximum pressure are recorded and analyzed. The results show that the generation of a thin water film between the oil slug and the tube wall is essential in order to mobilize the oil slug from a stationary condition. During the process of water film generation, four different flow phenomena are observed: (1) Water film developing forward; (2) water film developing backward; (3) the leakage phenomenon; and (4) the oil slug breaking up. The appearance of these four phenomena is determined by both the oil slug length and the water injection velocity. Two models are created in the software package FLUENT. Based on the experimental settings, the flow behavior of the oil slug is simulated. The results indicate that both of these two models perform well in simulating oil slug shape variation. The impacts of diameter variation along the tube, water injection velocity and oil slug length are also simulated and analyzed. It can be summarized from the results that the maximum driven pressure magnitude is proportional to the water injection velocity and the oil slug length, and the flow time is inversely proportional to the water injection velocity. Quantitatively, the numerical results are consistent with the experimental results. This research imparts a better understanding of the mechanism behind oil slug flow. Moreover, it is a practical element for the further study of EOR technology.
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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 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".