External Vibration-Assisted Carbon Dioxide Sequestration in Heavy Oil Reservoirs: The Influences of Frequency and Cavity Distribution
Bibliographic record
Abstract
This study investigates the effect of external vibration stimulation on CO2 dissolution behavior in heavy oil reservoirs, focusing on the influence of vibration frequency and cavity distribution within porous media. Experiments reveal that 5 Hz vibration significantly enhances CO2 dissolution, while higher frequencies (10 Hz and 20 Hz) hinder the process. A more homogeneous and extensive distribution of oil-depleted cavities further improves dissolution rates, particularly in post-gas flooding scenarios. The dissolution process, observed under constant pressure conditions, is categorized into three stages: cavity filling, fast dissolution, and slow dissolution. Vibration stimulation effectively enhances the fast dissolution stage but has a minimal impact on the slow dissolution stage. Intermittent vibration shows mixed effects, improving dissolution at 100% oil saturation but reducing rates at 90% saturation due to cavity-induced flow disruptions. These findings demonstrate the potential of vibration-stimulated CO2 dissolution (VS-CO2 dissolution) as a novel technique for enhancing CO2 storage and heavy oil recovery in reservoirs. This study provides critical insights for optimizing vibration frequency and cavity distribution, paving the way for improved field applications of this innovative technology.
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 | 0.000 |
| 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".