Performance Evaluation and Formation Mechanism of Low-Concentration Silicon Quantum Dot-Enhanced Viscoelastic Surfactant Fracturing Fluids
Bibliographic record
Abstract
Viscoelastic surfactant (VES) fracturing fluids gain significant attention in the hydraulic fracturing field with low reservoir damage. However, the harsh reservoir conditions seriously cause the structural instability and viscoelastic degradation of VES fracturing fluids. In this study, the silicon quantum dot (SiQD)-enhanced stearyl trimethylammonium bromide (STAB)/sodium salicylate VES fracturing fluids were constructed and evaluated. Then, the properties (proppant transport, gel-breaking, and permeability damage) of VES fracturing fluids containing different nanoparticles were systematically investigated. SiQDs were characterized by the Fourier transform infrared (FTIR), dynamic light scattering (DLS), transmission electron microscopy (TEM), and nitrogen adsorption–desorption experiments. Then, the rheological test and Cryo-TEM were employed to study the influences of various nanoparticles on the viscoelasticity of VES fracturing fluids. Compared to VES fracturing fluids enhanced by hydrophilic SiO 2 nanoparticles (SiNPs, 0.1 wt %), the relatively low-concentration SiQD (0.025 wt %)-reinforced VES fracturing fluids exhibited superior tolerance properties and application performance in harsh reservoir environments. The very intensive electrostatic interactions between STAB and SiQDs promoted the formation of more connection points, effectively extending contour length and improving viscoelasticity of VES fracturing fluids. In addition, the controlled experiments about SiQDs modified with propyltrimethoxysilane confirmed the dominant roles of the electrostatic interactions rather than hydrophobic interactions between SiQDs and STAB. To our knowledge, this work demonstrates the first successful application of SiQDs to improve the viscoelasticity of VES fracturing fluids for unconventional oil and gas development.
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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".