Visual Experimental Study on Gradation Optimization of Two-Stage Gravel Packing Operation in Unconventional Reservoirs
Bibliographic record
Abstract
During the development of unconventional reservoirs with high sand production rate and fine silt content such as heavy oil and hydrate reservoirs, silt sand blockage problem is a serious issue. A two-stage gravel-packing sand control technique is applied to solve the silt sand blockage now. However, traditional experiments on this technique could not obtain the dynamic distribution law of intrusive sand in the gravel pack. In this study, a new visualization experiment based on hydrodynamic similarity criterion for studying particle blockage in gravel packs was conducted. Real-time monitoring of sand particle migration in the gravel pack could be achieved. Also, the stable penetration depth and the distributing disciplinarian of invaded particles could be determined. The results show that when the gravel-to-sand median size ratio of gravel bed I is less than five, the sand bridge can be formed at the front end of the gravel pack. This could prevent sand from further intruding. As the grain size of gravel bed II is increased, the flow velocity is reduced. Thus, the sand invading into gravel bed II tends to settle at the interface. A large amount of sand intrusion can happen to gravel pack II when the pore filling front breaks through the gravel bed I.
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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".