Suppressing Frontal Instabilities and Stabilizing Miscible Displacements with Time-Dependent Rates for Improved Oil Recovery
Bibliographic record
Abstract
Abstract The frontal instabilities are a key control factor which can significantly affect the sweep efficiency and oil recovery in miscible flooding processes. Under unfavorable viscosity ratio between injection solvent and oil, the frontal instabilities are nearly unavoidable. However, how to suppress the instabilities, especially with low additional costs, should be carefully investigated. The present study examines the time-dependent displacement rates on flow instabilities in miscible flooding. Within the capacity of injection pumps, the injection rates are varied with time in a fast alternating manner. It is found that this kind of variable rates can stabilizing frontal instabilities by enhancing initial uniform mixing of solvent and oil. It therefore suppresses the later development of instabilities. Eventually, a much less unstable front is obtained when compared with the constant injection rate. Other parameters such as the amplitude of rates are also examined. The variations of propagation of front with time are analyzed for the change of rate strength. It is can therefore be concluded that this kind of time-dependent rate can improve oil recovery at very low additional rate within the capacity of pumps for the field EOR processes.
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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 | 0.001 |
| 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) | 0.001 | 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".