Quantitative Statistical Evaluation of Micro Residual Oil after Polymer Flooding Based on X-ray Micro Computed-Tomography Scanning
Bibliographic record
Abstract
Abstract Polymer flooding is an important tertiary recovery method. However, even after its application, more than 50% of the oil remains in the formation layers. It is thus important for efficient resource use that both residual oil distribution and the mechanisms associated with polymer flooding are better understood. In the work described in this paper, a series of in situ X-ray tomography experiments were carried out, and different displacement stages were imaged. Then, rock, water, oil, and polymer were identified in the imagery and quantified, while interchanges among the different types of residual oil, and their associated microscopic polymer displacement mechanisms, were discussed. We also studied the impact of permeability on polymer flooding efficiency, and our results showed that polymer flooding could mobilize residual oil that was not accessible via water flooding. Mechanistically, the polymer flood caused the big continuous residual oil clusters to split, dispersing them into smaller clusters. The polymer also changed fluid flow pathways, thus increasing sweep efficiency, while its viscoelasticity was helpful for recovering singlet and film residual types of oil. We found that the polymer could only drive oil locally, and that a large amount of residual oil remained in the middle of the core. We also noted that polymer flooding was more effective for higher permeability samples, which showed that, after polymer flooding, the recovery rate for the samples with permeabilities of 3989, 1543, and 814 millidarcys (mD) were enhanced by 10.13, 6.30, and 4.34%, respectively. Higher permeability samples remained as network-type and multiple-type residual oils after polymer flooding, while samples with lower permeability showed multiple-type and singlet-type residual oils. Thus, our results and conclusions showed that different enhanced oil recovery methods could be applied for further development.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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".