Enhancing Oil Recovery Through Vibration-Stimulated Waterflooding: Experimental Insights and Mechanisms
Bibliographic record
Abstract
Vibration-stimulated waterflooding (VS-WF) is a promising enhanced oil recovery (EOR) method, especially for reservoirs with high-viscosity or emulsified oil. This study explores the effect of low-frequency vibration (2 Hz and 5 Hz) on oil mobilization under constant pressure and flow rate, using both crude and emulsified oil samples. Vibration significantly improves recovery by inducing stick-slip flow, lowering the threshold pressure, and enhancing oil phase permeability while suppressing the water phase flow. Crude oil recovery increased by up to 24% under optimal vibration conditions, while emulsified oil showed smaller gains due to higher viscosity. Intermittent vibration achieved similar recovery rates to continuous vibration, but with reduced energy use. Statistical analysis revealed a strong correlation between pressure fluctuations and oil production in vibration-assisted tests, but no such relationship in non-vibration cases. These results provide insight into the mechanisms behind vibration-enhanced recovery, supported by analysis of pressure and flow rate responses during waterflooding.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.001 |
| 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".