Duvernay Proppant Intensity Production Case Study and Frac Fluid Selection
Bibliographic record
Abstract
Abstract The hundreds of Duvernay wells near Fox Creek, Alberta, Canada are usually fracture stimulated with either slickwater or hybrid slickwater-crosslinked water treatments with a large acid spearhead. There is a 14.6% higher 3-year average cumulative production total for slickwater treated wells versus hybrid fluid system treated wells. Additionally, the slickwater systems have an average 53.8% lower frac chemical cost compared to the 50/50 slickwater-crosslinked water hybrids frac stimulation systems. There is a very linear trend between the total volume of fluid pumped to the total 2 year BOE production; the higher the treatment fluid volume, the greater the stimulated reservoir volume (SRV) and the higher the resulting production. The production case study shows that wells treated with an average of 25,000 m3 of water have an 80% higher average 2-year cumulative production result compared to wells that only use an average of 13,000 m3. The average proppant tonnage per stage varies with 140 tonnes per stage outperforming 100 tonnes per stage by 31.6% cumulative BOE production per stage at 2 years. However larger tonnes per stage showing a diminishing benefit when looking at the 2 year BOE production totals. The total production versus total proppant used (1000 tonnes to 3500 tonnes per well) is also examined showing an overall total well production benefit. Comparing the total proppant placed versus cumulative BOE is shows a positive. It does appear that the more proppant that is placed the higher the production rate and total long term production.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| 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".