Polymer Reduction Leads to Increased Success: A Comparative Study
Bibliographic record
Abstract
Abstract Recent advances in guar and cross-linker technologies have resulted in the development of high viscosity cross-linked borate fracturing fluids without increasing polymer loadings. These Low Polymer borate fracturing fluids (LP) are successfully being utilized in various formations previously believed to be too hot and or too deep for low polymer fracturing fluids. Historically, polymer loadings of 3.6 – 4.2 kg/m3 (30-35 lb/1000gal) were commonly pumped in the Western Canadian Sedimentary Basin (WCSB) for formations deeper than 2500 meters and bottom hole temperatures greater than 80°C. These same formations are now fracture stimulated using the Low Polymer fluids with loadings as low as 1.8 kg/m3 (15 lb/1000gal) with exceptional results. This paper demonstrates that Low Polymer fracture fluids can be used in place of higher polymer fluids 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 due to 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 friction pressures. This paper illustrates these advantages as it compares the Low Polymer fracture fluid with High Polymer fracture fluids in over 200 wells in the WCSB. The formations where LP fluids were utilized have depths of up to 3250 meters 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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.007 | 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".