Analysis of the Influence Mechanism of Nanomaterials on Spontaneous Imbibition of Chang 7 Tight Reservoir Core
Bibliographic record
Abstract
This study investigates the impact of nanomaterials on different surfactant solutions. By measuring the parameters (including emulsification property, zeta potential, DLS, CA, IFT, etc.) of imbibition liquid system with nanoparticles and without nanoparticles, combining with imbibition experiments, the law and mechanism of improving the imbibition recovery of nanomaterials were obtained. The findings demonstrate that the nano-silica sol enhances the emulsification and dispersion of crude oil in the surfactant system, resulting in smaller and more uniform particle sizes for emulsified oil droplets. Non-ionic surfactant AEO-7 has the best effect under the synergistic action of nanomaterials. Zeta potential and DLS tests also showed that AEO-7 exhibits smaller particle sizes due to their insignificant electrostatic interaction with nanoparticles. Furthermore, the addition of nanomaterials enhances the hydrophilicity of core and reduces the interfacial tension. Under the synergistic action of nanoparticles, AEO-7 still showed the best enhanced core hydrophilicity (CA 0.61° after imbibition) and the lowest interfacial tension (0.1750 mN·m−1). In the imbibition experiment, the imbibition recovery of the system with nanomaterials is higher than that of the non-nanomaterials. The mixed system of AEO-7 and nano-silica sol ZZ-1 has the highest imbibition recovery (49.27%). Combined with the experiments above, it shows that nanomaterials have a good effect on enhancing the recovery rate of tight core, and the synergistic effect of non-ionic surfactant AEO-7 with nanomaterials is the best. Moreover, nanomaterials reduce adhesion work within the system while improving spontaneous imbibition recovery. These findings provide theoretical guidance for better understanding the mechanism behind nanomaterial-induced imbibition enhancement as well as improving tight oil’s imbibition recovery.
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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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.002 |
| 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".