A Novel Foam Flooding for Enhanced Oil Recovery in Fractured Low-Permeability Reservoirs: Performance Evaluation and Mechanism Study
Bibliographic record
Abstract
Summary Foam flooding is an effective enhanced oil recovery (EOR) technology and has been widely applied in conventional reservoirs. However, its application in fractured low-permeability reservoirs is rarely reported. Hence, this study has conducted a series of laboratory experiments to investigate the application potential of foam flooding and clarify its EOR mechanism in fractured low-permeability reservoirs. Based on reservoir conditions, our laboratory has developed a novel foam system consisting of nano-SiO2 particles, water-soluble thixotropic polymer (WTP), and sodium dodecyl benzene sulfonate (SDBS). With the aid of nuclear magnetic resonance (NMR) technology, it was found that the foam flooding can significantly enhance the oil recovery in fractured low-permeability cores. The injected novel foams can plug the cracks in core samples and improve the imbibition in nanopores. As a result, more oil would be displaced from the nanopores and micropores. In addition, the foaming agent in the bulk solution can moderately reduce the oil-water interfacial tension (IFT) and alter the wettability of rock surface, improving the flowability of oil and the imbibition effect. Most importantly, the field tests in Ordos Basin have proved a promising EOR potential and appreciable financial rewards of the SDBS/nano-SiO2/WTP foam system applied in fractured low-permeability 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) | 0.001 | 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".