Determination of Increase in Pressure Drop and Oil Recovery Associated with Alkaline Flooding for Heavy Oil Reservoirs
Notice bibliographique
Résumé
Abstract Alkaline flooding is a promising technique for enhancing heavy oil recovery, especially for thin reservoirs where other processes are not practicable. During alkaline flooding, oil recovery is increased by improving sweep efficiency as a result of in-situ formation of water-in-oil (W/O) emulsions. Even though flow of oil-in-water (O/W) emulsions in porous media has been extensively studied and well simulated, few attempts have been made for studying flow of W/O emulsions. In this study, techniques have been developed to determine increase in pressure drop and oil recovery associated with alkaline flooding for heavy oil reservoirs. Experimentally, both differential pressure and oil recovery are measured in an alkaline flooding process for heavy oils, while the associated emulsification is also studied. More specifically, the alkaline solutions are prepared with different concentrations of NaOH, while increase in pressure drop and oil recovery are measured and analyzed. Two different porous media are well prepared and accurately measured for their physical properties. Theoretically, a simulation technique is developed to model and match the experimental measurements for the alkaline flooding processes. Increase in differential pressure and oil recovery are found to be the two key parameters for determining the overall efficiency of the alkaline flooding for enhancing heavy oil recovery. The in-situ emulsification is found to be closely related to reduction of the injected water mobility so that increase in pressure drop is observed and the oil recovery is improved due to blocking the high permeability zone (or water channels) induced by the preceding waterflooding. Introduction Enhanced oil recovery (EOR) plays an increasingly important role in the petroleum industry for both light and heavy oil reservoirs. In general, after primary recovery and secondary recovery, it is found that the oil remaining in the light and medium oil reservoirs is generally in the range of 50–60% of the original oil in place (OOIP) and that the oil left in the heavy oil reservoirs is much higher. Among the EOR methods, alkaline flooding for the light and medium oil reservoirs have been studied extensively[1]. In spite of some technical successes in the oilfields, few economic successes have been documented because of the high cost of the injectants[2]. At present, conventional oil reserves are depleting, while there exists huge challenge to develop the heavy oil reservoirs. In practice, few attempts have been made to study the alkaline flooding for heavy oil reservoirs mainly due to the fact that the multiphase flow of heavy oil in reservoir formation is a more complicated process than that in conventional oil reservoirs. Therefore, it is of fundamental and practical importance to study the alkaline flooding process for heavy oil reservoirs. Alkaline flooding, also known as caustic flooding, is an EOR technique where an alkali, such as sodium hydroxide, sodium orthosilicate or sodium carbonate, is injected into hydrocarbon reservoirs during waterflooding stage[3]. Although dominant mechanisms for heavy oil production have not been well understood, emulsification mechanism is discovered to be one of the most important phenomena occurring in alkaline flooding process[4–6].
Récupéré en direct depuis OpenAlex et désinversé. Les résumés ne sont pas conservés dans cette base de données : les index inversés représentent 8,6 Go des 9,3 Go de texte de la base, et le serveur dispose de 13 Go libres.
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 ».