Gel plugging simulation with a new model and applications
Bibliographic record
Abstract
Injecting gel plugs into water-flooded wells can significantly reduce the water cut in wells and extend their operational lifespan. However, critical injection parameters, such as volume and speed, are often based on empirical estimates, leading to many wells being completely blocked following gel injection. This study introduces a new numerical gel component model that accurately simulates the gel flow process, enabling precise calculations of the required injection parameters. For this research, the gel compositional model was applied to two wells in the Tahe Oilfield. A detailed comparison between this new model, traditional polymer models, and historical data was conducted. The results show a 39% increase in oil production and a 19% improvement in water production accuracy. Furthermore, the new gel compositional model shows that gel migration distance and sealing volume strongly correlate with the amount of injected water and the karst background. Therefore, precise calculation of water invasion channels is essential before applying the gel plugging technique. This study shows that the success of gel water-shutoff techniques relies heavily on accurately simulating injection parameters, and the new simulation model provides a valuable reference for such technique applications.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".