Performance Evaluation and Formation Mechanism of Viscoelastic Surfactant Fracturing Fluids with Moderate Interfacial Activity Enhanced by Janus-SiO<sub>2</sub> Nanoparticles
Bibliographic record
Abstract
Nanoparticles (NPs) exhibit great potential to improve various properties of viscoelastic surfactant (VES) fracturing fluids in the development of low-permeability reservoirs. In the present study, the amphiphilic Janus NPs (JANPs) were fabricated via the Pickering emulsion method and employed to construct the novel JA12C (JANPs with dodecyl hydrophobic carbon chains)-assisted VES fracturing fluid (JAVES). The successful fabrication of JANPs was confirmed via Fourier transform infrared spectroscopy (FTIR) measurements and water contact angle tests. The rheology behavior of the VES fracturing fluid incorporating various SiO 2 NPs including hydrophilic SiO 2 NPs (HLNPs), JA8C (JANPs with octyl hydrophobic carbon chains), and JA12C was systematically investigated. It was revealed that the additional JA12C significantly improved the tolerance and proppant suspension properties. To explore the subsequent oil recovery performance of various gel breaking liquids, the formation wettability and the oil–water interfacial tension (IFT) were studied after the evaluation of breaking properties and formation damage properties of various fracturing fluids. The results suggested that the JAVES gel breaking liquid showed remarkable wettability alternation capability and moderate oil–water IFT reduction ability, which can partially reduce the impact on reservoir permeability. Moreover, the formation mechanism of the JAVES was proposed by molecular dynamics simulations at the molecular level, which was further visually verified via the cryo-TEM images. The improved viscoelasticity of developed the JAVES with moderate interfacial activity is advantageous to enhance subsequent oil recovery.
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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".