Effect of Wettability in Free-Fall and Controlled Gravity Drainage in Fractionally Wet Porous Media with Fractures
Bibliographic record
Abstract
Wettability was found to significantly influence the oil recovery behavior in heterogeneous porous media in gravity drainage processes. Despite the considerable efforts conducted on this issue, there are still challenging aspects remaining. The objective of this study is to investigate the wettability effects in fractured and homogeneous porous media during free-fall gravity drainage (FFGD) and controlled gravity drainage (CGD) conditions. Different test fluids (water and Varsol oil) were employed for completely water-wet, completely oil-wet, and fractionally wet porous media in this research. This experimental work enabled us to capture important characteristics of gravity drainage processes such as the production performance, matrix–fracture flow communication, and liquid production rate by the film-flow mechanism. It was concluded that the physical properties of the test fluids and the fractional wettability composition of the porous medium govern the fluid recovery mechanism during FFGD and CGD processes. The oil production rate by film flow was found to be appreciably affected by the packing composition when water-wet and oil-wet beads were randomly mixed. A percolation threshold of oil-wet beads was obtained at about 63%, after which no further change was experienced in the production rate magnitude when the film flow was a dominant recovery mechanism. Moreover, the results revealed that the wetting properties of the test fluid considerably affected the matrix-to-fracture transfer and the fluid saturation. The water-wet beads condition was favored in oil recovery for both FFGD and CGD processes. The oil-wet conditions provided greater film-flow rates and matrix-to-fracture transfer rates.
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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 | 0.000 | 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".