Influence of zirconium crosslinker chemical structure and polymer choice on the performance of crosslinked fracturing fluids
Bibliographic record
Abstract
Abstract Commonly used borate crosslinkers produce weak fracturing fluids at high temperature, high pressure, high salinity, and low pH conditions. Accordingly, zirconium crosslinkers were developed to address these shortcomings. Zirconium crosslinking chemistry is complicated and depends on many factors such as pH, ionic strength, ligand type, ligand order, and ligand to metal ratio. This work evaluated the rheological performance of four commercial zirconium crosslinkers with a polysaccharide and a polyacrylamide. The tested crosslinkers are manufactured with similar zirconium content but differ in ligand type and ligand order, producing different crosslinker chemical structures. The rheological performance was assessed using an HPHT rheometer at 93–204°C for 1.5 h. Shear tolerance performance was evaluated under shear rates of 40 s −1 –1000 s −1 . The results showed substantial variation in crosslinking performance due to the differences in the crosslinker chemical structure and type of polymer used. Zirconium lactate and propylene glycol crosslinker exhibited the greatest enhancement in shear and thermal stability with the polysaccharide‐based fracturing fluid. Remarkably, the same crosslinker performed the least with the polyacrylamide‐based fracturing fluid. However, Zirconium triethanolamine and lactate demonstrated considerable improvements in shear and thermal stability with the polyacrylamide‐based system. The work unravelled the influence of the zirconium crosslinker ligand type and ligand order on the rheological properties of both tested polymers. The performance evaluation showed that shear resistance, crosslinking delay, and thermal stability could be improved by utilizing the appropriate crosslinkers. The enhancements ultimately reduce additional additives required, prevent screenouts, and save cost during field treatments.
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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 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".