The Effect of Wettability and Pore Geometry on Foamed-Gel-Blockage Performance
Bibliographic record
Abstract
Summary Excessive gas and/or water production is a common problem encountered throughout the lifetime of oil-producing wells. High-producing gas/oil or water/oil ratios are normally responsible for both rapid productivity decline and increased operating costs caused by gas or water processing. The result is often a premature shut-in of wells because production has become uneconomical. Foamed gels have been used as selective barriers to counteract disproportionate gas/oil and/or water/oil ratios in oil production. However, research on the effects of critical parameters such as wettability of the porous medium and pore geometry on foamed-gel-blockage performance remains incomplete. In this work, microscale experiments, which involve the magnified observation of flowing and trapped foamed gel in etched-glass micromodels, were performed. The purpose of this research is to provide new insights into the sensitivity of foamed-gel-blockage performance as a function of porous-media wettability (strongly water-wet or strongly oil-wet systems) and pore geometry. The experimental results indicate that foamed gels presented higher blocking efficiency in strongly oil-wet systems than in strongly water-wet systems. Under these experimental conditions, foamed gels exhibited higher blocking efficiency at lower pore-body-/pore-throat-size aspect ratios. The plugging treatment exhibited stability after subsequent steps of gas and brine injection. Ultimately, these results indicate that the combination of foam and gel systems has technical advantages that make foamed gels superior mobility-control and plugging agents.
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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) | 0.001 | 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".