Study on frequency optimization and mechanism of ultrasonic waves assisting water flooding in low-permeability reservoirs
Bibliographic record
Abstract
Water flooding is one of widely used technique to improve oil recovery from conventional reservoirs, but its performance in low-permeability reservoirs is barely satisfactory. Besides adding chemical agents, ultrasonic wave is an effective and environmental-friendly strategy to assist in water flooding for enhanced oil recovery (EOR) in unconventional reservoirs. The acoustic frequency plays a dominating role in the EOR performance of ultrasonic wave and is usually optimized through a series of time-consuming laboratory experiments. Hence, this study proposes an unsupervised learning method to group low-permeability cores in terms of permeability, porosity and wettability. This grouping algorithm succeeds to classify the 100 natural cores adopted in this study into five categories and the water flooding experiment certificates the accuracy and reliability of the clustering results. It is proved that ultrasonic waves can further improve the oil recovery yielded by water-flooding, especially in the oil-wet and weakly water-wet low-permeability cores. Furthermore, we investigated the EOR mechanism of ultrasonic waves in the low-permeability reservoir via scanning electron microscope observation, infrared characterization, interfacial tension and oil viscosity measurement. Although ultrasonic waves cannot ameliorate the components of light oil as dramatically as those of heavy oil, such compound changes still contribute to the oil viscosity and oil-water interfacial tension reductions. More importantly, ultrasonic waves may modify the micromorphology of low-permeability cores and improve the pore connectivity.
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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 | 0.000 | 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) | 0.000 | 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".