Investigation of Novel Modified Nanoparticle-Enhanced Viscoelastic Surfactant Clean Fracturing Fluid System for Improving Oil Recovery in Low-Permeability Reservoirs
Bibliographic record
Abstract
Summary Nanoparticle-enhanced viscoelastic surfactant (VES) fracturing fluid systems have attracted significant attention for their exceptional rheological properties and superior resistance to temperature and shear. In this study, we introduce a novel modified nano-silica (nano-SiO2)-enhanced VES clean fracturing fluid system developed to optimize performance and enhance oil recovery. We achieved this by deeply investigating the fluid’s rheological characteristics, thermal and shear stability, and self-assembly behavior. From both fracturing and spontaneous imbibition perspectives, we formulated and tested two VES systems: VES-A (without nano-SiO2) and VES-B (incorporating modified nano-SiO2 materials). These systems were based on a synthesized zwitterionic surfactant, erucamidopropyl hydroxysultaine (EAPHS), along with sodium salicylate (NaSal) and potassium chloride (KCl). Our findings indicate that modified nano-SiO2 can effectively alter the surface charge of the VES system, promoting the formation of more uniform and stable wormlike micelles (WLMs). Moreover, nano-SiO2 particles act as effective crosslinking sites for micelles through electrostatic adsorption or hydrogen bonding, thereby constructing a longer, more robust 3D micellar network that significantly enhances the system’s viscoelasticity and shear resistance. The VES-A system maintained a viscosity of 32 mPa·s after shearing at 120°C and 170 s⁻¹ for 60 minutes, yielding an imbibition oil recovery rate of 35.21%. In stark contrast, the VES-B system’s viscosity remained at 61 mPa·s after shearing under more demanding conditions (140°C and 170 s⁻¹ for 60 minutes), and its imbibition oil recovery rate reached 47.35%. Throughout the entire imbibition process, the VES-B system demonstrated superior oil displacement efficiency compared with the VES-A system, which is of great significance for the development of low-permeability and tight oil reservoirs. Crucially, the developed VES-B fracturing fluid system has demonstrated significant engineering success through its successful field application in multiple wells within a low-permeability shale gas block in the Sichuan Basin, China. For instance, the SW-1 well achieved a daily gas production of 1.5×10⁶ m³/d, a remarkable 18.7% increase compared to adjacent wells stimulated using conventional polymer slickwater systems. Collectively, these comprehensive laboratory findings and successful field applications highlight the substantial potential of nano-SiO2 materials for enhancing reservoir use in low-permeability oil and gas formations, thereby promoting the efficient development of shale gas resources.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.001 |
| 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 teacher head, 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".