A study on enhancing oil recovery efficiency through bubble displacement based on microfluidic technology
Bibliographic record
Abstract
Abstract Tight sandstone contains a large number of oil and gas resources, but because of its ultra‐low porosity, permeability, and strong hydrophilicity, the oil recovery is low. Microfluidic technology, as an emerging research technique, offers advantages in visualizing fluid flow, reducing experimental reagent consumption, and accurately simulating the pore structure of sandstone using microfluidic chips. This study presents an effective research methodology for improving tertiary oil recovery efficiency in sandstone. By analyzing pore slice images of sandstone cores and employing image processing techniques, the study extracted characteristic dimensions of the sandstone and designed a microfluidic chip. A displacement system was constructed using high‐speed cameras, constant‐pressure pumps, and microscopes to monitor the oil displacement process. A bubble generation device based on ultrafiltration membranes was proposed to introduce generated bubbles into the microfluidic chip with a sandstone structure for oil displacement studies. Real‐time monitoring of the displacement process was conducted. Water and foam were used as displacing agents to investigate the displacement process in the microfluidic chip mimicking the sandstone core structure. Additionally, analysis and comparison were performed on foam formulation, surfactant concentration, and foam proportion, quantitatively evaluating the oil displacement efficiency under various experimental conditions. The proposed research is helpful for the understanding of the foam flooding process on a micro‐scale and of significant application potential for the enhanced oil recovery of sandstone reservoirs.
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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 | 0.000 |
| 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 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".