Surfactant Partitioning and Adsorption in Chemical EOR: The Neglected Phenomenon in Porous Media
Notice bibliographique
Résumé
Abstract During chemical EOR, surfactants encounter significant losses when injected into porous media mainly due to retention. The key mechanisms of surfactant retention are adsorption onto the rock surface and partitioning into the oil phase. The significant losses due to adsorption and partitioning will not only result in poor displacement efficiency but also great financial increased costs. In this review, a comprehensive assessment on the importance of understanding and quantifying surfactant partitioning and adsorption data is presented. The study explains the surfactant flooding process and the related challenges at harsh reservoir conditions. The surfactant partitioning and adsorption mechanisms throughout the surfactant flooding process, as well as the most influential parameters affecting their behaviors in porous media are comprehensively addressed. Surfactant partitioning and adsorption studies at different operating conditions are then covered considering laboratory, modeling, and simulation studies. Lastly, the measurement procedure and the measurement techniques of surfactant partitioning and adsorption are comprehensively discussed. Laboratory and simulation studies have concluded that the misinterpretation of surfactant partitioning and adsorption data will affect the main function of surfactants (lowering oil–water interfacial tension). The reported studies have highlighted that surfactant partitioning and adsorption are affected by many factors such as surfactant concentration, pH, salinity, temperature, brine/oil ratio, and rock mineralogy. In contemporary research practice, there is no established method to quantify the surfactant losses due to partitioning in dynamic conditions owing to the occurrence of both adsorption and partitioning simultaneously. However, using static tests, adsorption and partitioning can be distinguished, quantified, and qualitatively verified with dynamic test results. The partitioning effect can be separated, since the test is performed with and without residual oil saturation (oil flood), and by comparing those tests, the effect of partitioning can be detected. The novelty of this review is based on the importance of understanding the mechanisms of surfactant partitioning and adsorption, which have not been fully covered in the literature. This paper gives more insight into the successful application of surfactant flooding and how it can be optimized with minimal surfactant losses. Findings elucidated in this paper can contribute to minimizing the experimental time and operating cost of future studies in the field of surfactant-based EOR.
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Comment cette classification a été obtenuedéplier
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 ».