Preparation and performance analysis of nano‐crosslinking agent for sulphonated guar gum fracturing fluid
Bibliographic record
Abstract
Abstract In order to solve the problems of poor temperature resistance and low crosslinking efficiency of crosslinking agent for sulphonated guar gum fracturing fluid, in this paper, nano‐silica was reacted with 3‐aminopropyltriethoxysilane to obtain surface‐modified nano‐silica, which was then reacted with boric acid and n‐butyl titanate to obtain nano‐silica‐based boron‐titanium composite crosslinking agent. Its physical properties and structure were characterized by infrared (IR), laser particle size analysis, X‐ray diffraction (XRD), and atomic force microscopy (AFM). The sulphonated hydroxypropyl guar gum fracturing fluids formed by nano‐crosslinking agent were analyzed: When the temperature was uniformly increased from 25 to 120°C and the shear rate was 170 s −1 , the viscosity was finally constant at about 50 mPa · s, which indicated that the temperature and shear resistance were good; the system had a better filtration‐loss reduction performance; the average sedimentation rate of ceramic grains in the fracturing fluid system was 0.00872 cm · min −1 , indicating that the system had good sand carrying performance; the damage rate of fracturing fluid filtrate to the core was 23.33%; the gel breaking performance test showed that the fracturing fluid had good gel breaking performance. By analyzing the performance of the fracturing fluid, it can be seen that the nano‐crosslinking agent has the advantages of good temperature resistance and high cross‐linking efficiency compared with the traditional boron and titanium cross‐linking agents.
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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".