Polymer Reduction Leads to Increased Success: A Comparative Study
Bibliographic record
Abstract
Summary Recent advances in guar and crosslinker technologies have resulted in the development of high-viscosity crosslinked borate-fracturing fluids without increasing polymer loadings. These low polymer (LP) borate fracturing fluids are being used successfully in various formations previously believed to be too hot and or too deep for LP fracturing fluids. Historically, polymer loadings of 3.6 to 4.2 kg/m3 (30 to 35 lbm/1,000 gal) were commonly pumped in the Western Canadian Sedimentary basin (WCSB) for formations deeper than 2500 m and bottomhole temperatures greater than 80°C. These same formations are now fracture stimulated using the LP fluids with loadings as low as 1.8 kg/m3 (15 lbm/1,000 gal) with exceptional results. This paper demonstrates that LP fracture fluids can be used in place of fluids requiring higher polymer loadings with minimal changes to the overall design of the fracture treatment. The new fluid can be pumped on-the-fly at conventional pump rates and proppant concentrations because of the fluid's improved shear and temperature stability. The advantages of using a reduced-polymer fracturing fluid include increased production, lower treatment costs, and lower frictional pressure loss. This paper illustrates these advantages as it compares the LP fracture fluid with HP fracture fluids in more than 200 wells in the WCSB. The formations where LP fluids were used have depths of up to 3250 m and reservoir temperatures reaching over 100°C.
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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 | 0.002 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.008 | 0.001 |
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".