The Effect of Wettability and Pore Geometry on Foamed Gel Blockage Performance in Gas and Water Producing Zones
Bibliographic record
Abstract
Proposal Throughout the lifetime of oil producing wells, a common problem encountered is excessive gas and/or water production. High producing gas-oil or water-oil ratio is normally responsible for both rapid productivity decline, and increase in 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, knowledge of 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 micro-scale experiments, which involve the microscopic observation of flowing and trapped foamed gel in etched-glass micromodels were performed. The purpose of the research work is to provide new insights into the sensitivity of foamed gel blockage to porous media wettability and pore geometry. The experimental results indicated that foamed gels present higher blocking efficiency in oil-wet systems than in water-wet systems. Under experimental conditions, foamed gels exhibit higher blocking efficiency at lower pore body to pore throat aspect ratios. The plugging treatment exhibits stability after subsequent steps of gas and brine injection. Ultimately, the combination of foam and gel has both technical and economic advantages that make foamed gels superior mobility control 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 | 0.000 |
| 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".