Preformed-Particle-Gel Extrusion Through Open Conduits During Conformance-Control Treatments
Bibliographic record
Abstract
Summary Millimeter-sized (10 μm–mm) preformed particle gels (PPGs) have been used successfully as conformance-control agents in more than 5,000 wells. They help to control both water and CO2 production through high-permeability streaks or conduits (large pore openings), which naturally exist or are aggravated either by mineral solution or by a high injection pressure during the flooding process. This paper explores several factors that can have an important impact on the injectivity and plugging efficiency of PPGs in these conduits. Extensive experiments were conducted to examine the effect of the conduit inner diameter and the PPG strength on the ratio of the particle size to the opening diameter, injectivity index, resistance factor, and plugging efficiency. Five-foot tubes with four internal diameters were designed to emulate the opening conduits. Three pressure taps were mounted along the tubes to monitor PPG transport and plugging performance. The results show that weak gel has less injection pressure at a large particle/opening ratio compared to strong gel. PPG strength affected injectivity more significantly than did particle/opening ratio. Resistance factor increased as the brine concentration and conduit inner diameter increased. PPGs can significantly reduce the permeability of a conduit, and their plugging efficiency depends highly on the particle strength and the conduit inner diameter. The particle size of PPGs was reduced during their transport through conduits. Experimental results confirm that the size reduction was caused by both dehydration and breakdown. On the basis of the laboratory data, two correlations were developed to quantitatively calculate the resistance factor and the stable injection pressure as a function of the particle strength, particle/opening ratio, and shear rate. This research provides significant insight into designing better millimeter-sized particle-gel treatments intended for use in large openings, including open fractures, caves, worm holes, and conduits.
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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".