Case Study: Mixing Proppant Sizes To Control Pressure-Dependent Leakoff
Bibliographic record
Abstract
Abstract One of the main factors affecting fracture treatment completion in the Cardium formation in North West Central Alberta is PDL. PDL is determined from minifrac analysis completed prior to the main treatment. PDL can be treated and controlled with a combination of increased fluid rates, additional 50/140 proppant (100 mesh) in the pad and increased gel loadings. Yet in certain areas even these precautions will not achieve successful placement of job design or effective fracture properties. Common practice is to design for 80 to 100 Tonnes of ceramic proppant into each of these reservoirs, with an additional 5 to 7 Tonnes of 50/140 proppant in the pad to control PDL. In most areas this works effectively for controlling the competing fracture leak-off during the treatment. In the Northwestern Alberta Cardium area this approach has not been as successful and has caused concern about total proppant placement. An alternative approach for controlling fluid leak-off during the treatment has been to mix in a small amount (usually 5% by weight) of 50/140 proppant with the ceramic proppant in the early stages of the treatments to control the fluid leak-off in these areas. Treatment design engineers recognize the fracture conductivity issues caused by mixing different mesh sizes. However, utilizing this approach has resulted in full job completion and this paper will discuss the specific treatment designs required to drastically limit fracture treatment screen-outs.
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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 | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".