Enhanced Oil Recovery from Austin Chalk Carbonate Reservoirs Using Faujasite-Based Nanoparticles Combined with Low-Salinity Water Flooding
Notice bibliographique
Résumé
Recently, the application of nanoparticles for enhancing oil recovery (EOR) in carbonate reservoirs has received great attention from various researchers across the oil and gas industry. In contrast to sandstone reservoirs, carbonates are naturally neutral wet or preferentially oil wet and, therefore, the recovery of oil from these reservoirs by waterflooding techniques is relatively low and inefficient. Hence, the addition of chemical agents can modify rock wettability and increase the efficiency of the waterflooding process. The role of nanoparticles and their implementations in the field of oil recovery has been highlighted by many researchers in the past, due to their attractive features and characteristics. However, choosing the appropriate nanoparticles is not the only limiting factor to guarantee better performance in EOR but also depends on their stability and dispersion under aqueous conditions. Accordingly, many metal oxides or silicate-based nanomaterials have been subjected to surface modifications, following some complex and costly ineffective functionalization steps before their application. In this study, novel and stable nanomaterials of faujasite were synthesized at mild conditions without following any surface modification steps to alter the wettability of Austin Chalk carbonate rocks from oil wet to strongly water wet in the presence of low-salinity water (LSW). The synthesized nanoparticles were well characterized by scanning electron microscopy (SEM), transfer electron microscopy (TEM), X-ray diffraction (XRD), dynamic light scattering (DLS), and ζ potential to confirm their surface identity, functionality, morphology, and stability. The prepared nanofluids from the synthesized nanoparticles were tested in comparison to brine for their EOR efficiency in carbonate cores. The EOR performance was investigated by interfacial tension (IFT), contact angle, spontaneous imbibition, and displacement tests. The results showed that, compared to formation brine and LSW, the formulated nanofluid could notably alter the rock wettability from strong oil wet to strong water wet. To confirm this, a core-flooding test was performed, which further reiterated the capability of these nanofluids as effective EOR agents in hydrocarbon carbonate reservoirs by recovering an additional 9.6% of OOIP. Consequently, on the basis of the obtained findings, these faujasite-based nanofluids provide a prospect of being applied in EOR in carbonate formations.
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Prédiction distillée sur la base complète
Imitation des enseignantsNi prévalence calibrée, ni vérité terrain. Validation humaine à venir. Apprise à partir de 10 348 étiquettes directes de Codex et de 10 348 étiquettes directes de Gemma. Le mode candidate est l'union des têtes enseignantes seuillées; le consensus est leur intersection. Ces sorties portent le statut machine_predicted_unvalidated et ne sont ni des étiquettes humaines ni des étiquettes directes de modèles de pointe.
Scores Codex et Gemma par catégorie
| Catégorie | Codex | Gemma |
|---|---|---|
| Métarecherche | 0,000 | 0,000 |
| Méta-épidémiologie (sens strict) | 0,000 | 0,000 |
| Méta-épidémiologie (sens large) | 0,001 | 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) | 0,000 | 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 tête enseignante, 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 ».