Experimental Investigation of Heat and Oil Droplet Size Effects on Nanoemulsion Propagation in Porous Media
Notice bibliographique
Résumé
Abstract Hydraulic fracturing is a key technique for enhancing production from low-permeability, organic-rich shale oil and gas reservoirs by increasing rock permeability. Accurate characterization and imaging of hydraulically induced fractures are essential for predicting production performance and estimating the stimulated reservoir volume (SRV). Tracer concentrations measured during flowback and historical production data provide valuable insights into fracture and matrix properties, such as fracture geometry, hydraulic conductivity, and natural fracture density. However, the inherent complexity and uncertainty in fracture and reservoir characterization, combined with limited data availability, pose significant challenges to the accurate estimation of these properties. This study aims to address this challenge by introducing magnetic Pickering nanoemulsions as tracers. We investigate how heat and oil droplet size affect the transportation and retention behavior of these engineered nanoemulsions in porous media. Through tracer injection and flowback analysis, we provide insights into their performance and potential for improving subsurface characterization and reservoir management. Polymer-coated iron oxide (Fe3O4) nanoparticles were synthesized and utilized as stabilizers to produce stable oil-in-water (O/W) nanoemulsions. Four distinct nanoemulsions were formulated by applying varying emulsification energies (54, 59, 64, and 72 kJ) to achieve controlled oil droplet sizes. A series of core flooding experiments were performed in a sandpack at 70°C to evaluate the transport behavior of these nanoemulsions in porous media. To simulate reservoir conditions, an overburden pressure of 1000 psi was applied during nanoemulsion flooding. Subsequently, the overburden pressure was incrementally increased from 1000 psi to 2000 psi at a rate of 2.5 psi/min during chase water flooding. X-ray CT scanning was used to monitor nanoemulsion saturation profiles. Additionally, the oil droplet size distribution, effluent sample density and susceptibility, and pressure drop throughout the flooding process were measured to determine the most effective nanoemulsion formulation with minimal retention in porous media. The results demonstrated that the most stable nanoemulsion formulation, created by applying 64 kJ of emulsification energy, corresponding to a droplet size of 850 nm, exhibited efficient transport through the sandpack with minimal retention. Pressure-drop measurements revealed a steady increase during nanoemulsion flooding, which can be attributed to the higher viscosity and increased drag forces exerted by the nanoemulsion compared to water. This behavior highlights the significant influence of nanoemulsion properties on flow resistance within the porous medium. During nanoemulsion flooding across all experiments, the nanoemulsion exhibited piston-like displacement. However, during subsequent chase water flooding, bypassing of the nanoemulsion was observed, attributed to the lower viscosity of water compared to the nanoemulsion. This phase transition was marked by a noticeable reduction in pressure drop. The results indicate that the optimized emulsification energy effectively enhances the stability and mobility of the nanoemulsion, minimizing retention and ensuring efficient transport through the heated porous medium.
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Comment cette classification a été obtenuedéplier
Prédiction machine sur la base complète
Imitation des enseignantsNi prévalence calibrée, ni vérité terrain. Validation humaine à venir. Le volet Gemma est une étiquette directe du modèle pour chaque travail de la base, lue sur la notice réduite au titre. Le volet Codex est un classifieur appris des 10 348 étiquettes directes de Codex et calibré sur les taux pondérés de l'échantillon; les champs sans appui suffisant ne portent aucun appel Codex. Le mode candidate est l'union des deux volets; le consensus est leur intersection. Ces sorties portent le statut machine_predicted_unvalidated et ne sont pas des étiquettes humaines.
Scores du classifieur distillé par catégorie (deux têtes)
| Catégorie | Codex | Gemma |
|---|---|---|
| Métarecherche | 0,000 | 0,001 |
| Méta-épidémiologie (sens strict) | 0,000 | 0,000 |
| Méta-épidémiologie (sens large) | 0,000 | 0,000 |
| Bibliométrie | 0,000 | 0,000 |
| Études des sciences et des technologies | 0,000 | 0,000 |
| Communication savante | 0,000 | 0,000 |
| Science ouverte | 0,000 | 0,000 |
| Intégrité de la recherche | 0,000 | 0,000 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,001 | 0,000 |
Scores machine (provisoires)
Les deux têtes enseignantes du modèle étudiant, lues sur ce travail. Un score ordonne la base pour la relecture; il n'affirme jamais une catégorie, et le statut de validation accompagne chaque rangée tel quel.
Scores de référence d'un modèle non mature (critères de maturité non atteints, 7 itérations). Un score ordonne; il n'affirme jamais une catégorie.
score_only:v0-immature-baseline · tel quel depuis la passe de notation : score_only signifie que le nombre peut ordonner les travaux, et qu'aucune étiquette de catégorie n'en découleClassification
machine, non validéePrédiction automatique; un appel candidat d’une seule source (Gemma direct ou Codex distillé), pas un consensus.
Le détail, modèle par modèle et score par score, se trouve en fin de page sous « Comment cette classification a été obtenue ».