Controlling Interactions of Colloidal Particles and Porous Media During Low Salinity Water Flooding and Alkaline Flooding By MgO Nanoparticles
Notice bibliographique
Résumé
Abstract Enhanced oil recovery has become a hot topic nowadays. Low-salinity water (LSW) and alkaline flooding are known as two efficient improved oil-recovery techniques to unlock residual oil. An enhanced oil recovery (EOR) project's success will be endangered when the production target is not achieved due to migration of fines, which affects reservoir permeability near the wellbore and leads to declining productivity. In this work, our experimental study aims to use nanoparticles (NPs) for the treatment of colloidal particles migration to improve the performance of the mentioned EOR methods. In LSW and alkaline flooding methods, one critical step is the precise selection of fluids, which increases the effectiveness on these techniques. Therefore, one should choose the optimum salinity rather than the lower one to enhance the efficiency of a typical LSW project and also the optimum pH for the injected slugs rather than the higher one to improve the efficiency of an alkaline flooding project. These limitations make the design of such flooding projects very difficult and challenging. The purpose of this study is to clarify how solution conditions (pH and ionic strength) act upon surface potentials and charge distributions close to solid surfaces. Also the effects of MgO NPs on the point of zero charge (PZC) and critical salt concentration (CSC) are inspected. Zeta potential and turbidity analyses have been utilized as useful tools to examine the effect of NPs on the interactions of colloidal particles with the medium surface. Our results illustrate that the magnitude of the repulsion forces compared to the attraction between fines particles and pore wall surfaces was considerably diminished when the surface of the glass beads was soaked with MgO NPs. The presence of MgO NPs on the bead surface significantly modifies the PZC, increasing it from 3 to around 9, which in turn justifies the retention of particles in a wide range of alkaline conditions. It was found that the MgO NP-treated medium tends to retain around 97% of the in situ fine particles under very alkaline conditions. A decrease in CSC for all divalent and monovalent salt solutions was also quantitative evidence of a striking improvement effect of these NPs. Therefore, pre-flushing of the medium with a slug of MgO nanofluid prior to alkaline flooding or LSW injection into the reservoir can serve as a promising remedy to counteract the subsequently induced migration of colloidal particles. This technique is of great interest for application in the field, where improved oil recovery is desired; however, fines migration and subsequent formation damage should be avoided. This method minimizes the creation of damage, prevents severe plugging in the near-wellbore area, and improves communication between the wellbore and the virgin formation.
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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) | 0,000 | 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) | 0,000 | 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 ».