Aminated nano-silica reinforced slickwater fracturing fluids with enhanced drag reduction, proppant transport and thermal stability
Bibliographic record
Abstract
Conventional slickwater fracturing fluids undergo severe thermal degradation in hightemperature reservoirs, significantly impairing their drag reduction efficiency and proppant transport capability. To address this limitation, this study presents a novel temperatureresistant slickwater system by incorporating aminated nano-silica with an acrylamide-2acrylamido-2-methylpropane sulfonic acid copolymer and a flowback aid/clay stabilizer. Macroscopic experiments and molecular dynamics simulations reveal that the system achieves a drag reduction rate of 69.7% at 150 ◦C, a 10-percentage-point improvement over the non-reinforced system. It also reduces the proppant settling area by 21.2%, facilitating more uniform proppant distribution toward the fracture distal end, and retains 77.8% of its initial viscosity after thermal aging. Nanoparticles in the system exhibit a synergistic dualreinforcement mechanism: Their surface adsorption smooths wall roughness and thickens the elastic boundary layer, suppressing turbulence and mitigating energy dissipation; hydrogen bonding and electrostatic interactions between the amino groups of nanoparticles and the moieties of copolymer form an interfacial network, effectively restricting the segmental mobility of the copolymer. This method increases the glass transition temperature of the system by 57.5 ◦C, markedly enhancing its thermal stability. Molecular simulations confirm an 18.7% increase in hydrogen bond density and a 23.5% reduction in segmental mobility, collectively stabilizing the polymer against thermal degradation. This study provides valuable insights for developing high-performance fracturing fluids suitable for deep reservoirs. Document Type: Original article Cited as: Ding, F., Dai, C., You, Q., Yu, W., Sun, W., Song, X. Aminated nano-silica reinforced slickwater fracturing fluids with enhanced drag reduction, proppant transport and thermal stability. Advances in Geo-Energy Research, 2025, 18(2): 153-164. https://doi.org/10.46690/ager.2025.11.05
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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) | 0.000 | 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".