Preparation of biogum thickener and properties of recoverable fracturing fluid based on environmental protection
Bibliographic record
Abstract
Abstract A recyclable biogum thickener was developed and employed as the main agent to compound a fracturing fluid system in order to address the issue of the problematic recovery of fracturing fluid system. By using infrared (IR), nuclear magnetic resonance (NMR), X‐ray diffraction (XRD), and scanning electron microscopy (SEM), the physical characteristics and microstructure were examined. By using a rheometer, dynamic filtration loss, and an acid‐etching fracture conductivity device, respectively, the system's temperature and shear resistance, dynamic filtration loss, and fracture conductivity damage were studied. The system's gum‐breaking performance, formation water compatibility, and anti‐expansion performance were then measured in accordance with standards. As a result of shearing at 170 s −1 and 120°C for 2 h, the 4% biogum system's performance outperformed the other two systems in every way, according to the experimental findings. Its viscosity could also exceed 70 mPa · s. After breaking, the solution had a viscosity of 2.5 mPa · s, which was very compatible with the formation water. The system has a 90% anti‐swelling rate, a filtration loss coefficient of 5.14 × 10 −5 , strong infiltration ability, and negligible formation damage. The biogum system's recovery rate after field application is discovered to be around 50%.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 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".