Classification of Flow Pathways for Waterflooding Operations in a Hydrocarbon Reservoir in Terms of Displacement Constraints and Incremental Oil Recovery
Bibliographic record
Abstract
A robust and pragmatical technique was developed to classify flow pathways during long-term waterflooding operations in a hydrocarbon reservoir. More specifically, pore structure analysis, wettability tests, relative permeability tests, and long-term waterflooding experiments were conducted and integrated. Then, effects of pore-throat structures, displacement rates, crude oil viscosities, and wettability on the oil displacement efficiency across different flow pathways were systematically investigated, allowing us to classify flow pathways into the primary and secondary ones. For the former, pore-throat structure significantly affects the efficiency of displacement: for mouth-bar microfacies, cores with larger pore-throat radii and lower fractal dimensions exhibit superior displacement performance, whereas, for point-bar microfacies, it exhibits greater sensitivity to variations in injection parameters. Increasing the injection rate from 0.2 mL/min to 0.5 mL/min can lead to a 7.31% improvement in oil recovery. Also, high-viscosity crude oil leads to an overall decline in displacement efficiency, with a more pronounced reduction observed in the point-bar microfacies, suggesting that complex pore-throat structures are more sensitive to viscous resistance. For the latter, wettability shows its dominant impact with an increase in oil recovery to 7.12% if the wettability index is increased from 0.17 to 0.21 in the point-bar microfacies.
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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 | 0.001 |
| 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 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| 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".