Research on the viscosity‐increasing mechanism and performance analysis of modified polysaccharide natural polymer fracturing fluid gel
Bibliographic record
Abstract
Abstract In this paper, a guar gum‐based hydraulic fracturing fluid suitable for low permeability oil and gas reservoirs was prepared using alkylamine guar gum (J580) as a thickening agent. The viscosification mechanism and comprehensive properties were analyzed to provide theoretical support for improving the fracturing fluid utilization rate and oil and gas field development in Changqing Oilfield. In this experiment, the properties and microstructure of thickener J580 were analyzed by infrared spectroscopy (FT‐IR), nuclear magnetic resonance ( 1 H‐NMR), X‐ray diffraction (XRD), and transmission electron microscope (TEM). The rheometer, dynamic filtration device, and acid erosion fracture conductive ability device were used to study the temperature and shear resistance performance, viscoelastic properties, dynamic filtration performance, and fracture conductive ability damage of the system. the gel breaking performance, formation water compatibility, and anti‐swelling performance of the system were measured according to the standard. The results show that J580 is an amine modified product of guar gum, and its viscosity increasing mechanism is similar to that of guar gum. The breaking time of the system is 375 s, the residue content is 413 mg/L, and the surface tension is 26.49 mN/m. At 90°C, the viscosity of the solution can be maintained at 180 mPa · s, loss modulus G " dominates, the anti‐swelling rate is 82.5%, the damage rate of the conductive ability is 22.40%, and the dynamic filtration coefficient is 1.3 × 10 −5 m/s 1/2 . The fracturing fluid system has good dynamic filtration performance, conductive ability, temperature and shear resistance, good structural strength, can easily break glue and return, and less residue content.
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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 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 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 | 0.000 |
| Open science | 0.000 | 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".