Preparation and Antifiltration Performance of Organic Zirconium Crosslinker for Guar Gum‐Fracturing Fluid
Bibliographic record
Abstract
Aiming at the characteristics of low shear resistance and high filtration loss of guar gum‐fracturing fluid, a high‐efficiency organic zirconium crosslinker was prepared. In addition, the effects of different factors on the filtration coefficient and apparent viscosity of fracturing fluid were studied, and this study explores the micro mechanisms of various factors affecting the filtration coefficient from a molecular dynamics perspective. The results show that the antifiltration ability of guar gum‐fracturing fluid is inversely proportional to the fluid viscosity. The increase of crosslinker content and reservoir pressure is beneficial to improve the viscosity and antishear ability of guar gum‐fracturing fluid. When the crosslinker content is 0.25% and pressure is 30 MPa, the fracturing fluid parameters are 145 mPa·s and 2.1 × 10−2 m3 · min1/2, respectively, while the increase of reservoir temperature and the shear rate reduces the rheology and shear resistance of guar gum‐fracturing fluid, so that the viscosity and filtration coefficient of fracturing fluid at 180°C and 200 s-1 are 123 mPa·s and 2.3 × 10-2 m3·min1/2, respectively. This work not only paves a new avenue for synthesizing organic zirconium as a crosslinker but also provides an efficient measure to reduce the filtration of guar gum‐fracturing fluid on the oil and gas reservoirs.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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) | 0.000 | 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".