Feasibility of Proppant Flowback Control by Use of Resin-coated Proppant
Bibliographic record
Abstract
Proppant flowback is a problem in Xinjiang oilfield. It decreases production rate of a fractured oil well, corrodes surface and downhole facilities and increases production costs. Curable resin-coated sand is a common technique to control proppant flowback. This article presents an experimental investigation whether it is feasible to control proppant flowback by use of resin-coated sand and whether resin-coated sand has a negative effect on proppant pack conductivity. It included two kinds of experiments, Proppant flowback experiment measured critical flow rate while the Proppant pack conductivity one measured proppant conductivity. The experimental results of proppant flowback show that the critical flow rate of resin-coated sand is far greater than that of common sand which means proppant flowback would not happen by resin-coated sand tail-in. Compared to Xinjiang sand conductivity, resin-coated sand conductivity is far smaller though it declines slightly which means use of resin-coated sand would lead to conductivity loss and sequentially results in production impairment. Experimental results show that it is feasible to control proppant flowback by use of resin-coated sand and resin-coated sand would affect fracture conductivity of a fractured oil well. Based on the experimental results, resin-coated proppant conductivity can be improved by use of resin-coated ceramic or liquid-resin-coated proppant. The achievements can give a direction towards how to select a resin-coated proppant and how to improve resin-coated proppant.
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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) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 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".