MétaCan
Menu
Retour à la cohorte
Enregistrement W1996243055 · doi:10.2118/01-02-04

Micellar Flooding and ASP-Chemical Methods for Enhanced Oil Recovery

2001· article· en· W1996243055 sur OpenAlexaff
Sara Thomas, S.M. Farouq Ali

Notice bibliographique

RevueJournal of Canadian Petroleum Technology · 2001
Typearticle
Langueen
DomaineEngineering
ThématiqueEnhanced Oil Recovery Techniques
Établissements canadiensPeraso Technologies (Canada)
Organismes subventionnairesnon disponible
Mots-clésResidual oilPetroleum engineeringFlooding (psychology)Pulmonary surfactantEnhanced oil recoveryPorous mediumEnvironmental scienceOil in placeOil fieldChemistryGeologyChemical engineeringPorosityPetroleumGeotechnical engineeringEngineeringOrganic chemistry

Résumé

récupéré en direct d'OpenAlex

Abstract Chemical flooding methods hold particular attraction for recovering the "residual oil" left in the reservoir after waterflooding. This paper describes and compares the results for two promising methods, viz. micellar flooding and alkaline-surfactant-polymer (ASP) flooding processes. Both of these methods have been tested successfully in the field, notably micellar flooding. Laboratory results are described for micellar floods in consolidated sandstone cores as well as in unconsolidated sand packs, including a three-dimensional model, equipped with horizontal or vertical wells. Floods were also carried out in unconsolidated cores using combinations of an alkali, surfactant and a polymer. Individual slugs were injected sequentially in some of the experiments, while the three components were mixed and injected as a single slug in other experiments. Oil recoveries in the two cases were similar. Results for the two processes are compared and contrasted, showing that, on the basis of oil volume recovered per unit mass of the chemical used, the two processes are similar, with micellar flooding having an edge. However, on the basis of total oil recovery, micellar flooding is the superior process, with oil recoveries ranging from 50 to 80﹪ of the oil left in the porous medium after a waterflood. Practical implications of the results are discussed. Introduction Among chemical flooding methods, micellar flooding and alkaline- surfactant-polymer (ASP) flooding processes are particularly effective for recovering a large fraction of the conventional oil (25 °CDATA[API, or higher) left in the reservoir after a waterflood, which could be as much as 60﹪ of the original oil in place. Many field tests of the micellar flooding process and several of ASP have established the effectiveness of these methods for mobilizing waterflood residual oil. The present laboratory study compares and contrasts the two processes, based on tertiary floods in sand packs and Berea sandstone cores. A number of investigators have noted the use of an alkali for reducing the divalent ion content and increasing the negative charge of the rock with a view to reducing chemical loss(1,2). Surkalo(3) reported the alkaline-surfactant-polymer (ASP) process as an alternative to micellar flooding. Several field tests have also been reported. Another function of alkali, if the Acid No. of the crude oil is large enough (> 0.5 mg KOH/g crude oil) is that the alkali may react with the acid components to form a surfactant in situ. Other factors, such as gravity segregation of alkali solutions, rate of diffusional and mechanical mixing, and subsequent mixing of surfactant formed would limit the effectiveness of this mechanism. The Process Chemical flooding methods are based on improving the mobility ratio, i.e., making the mobility of the displacing flood less than or equal to the mobility of the displaced fluid, and increasing the capillary number, mainly by making the interfacial tension (IFT) between the displacing and the displaced phases small, usually by about 1,000 fold. Other effects are also present, such as formation of macro- and microemulsions, formation of precipitates, wettability changes, relative permeability shifts, etc. Macroemulsions may improve the mobility ratio through drop entrainment and entrapment. At the same time, surfactant adsorption occurs on the rock surface.

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 enseignants

Ni 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.

score de la tête « metaresearch » (Codex)0,000
score de la tête « metaresearch » (Gemma)0,000
Version: codex-gemma-dda1882f352aStatut de validation: machine_predicted_unvalidated
Catégories candidatesaucune
Catégories consensuellesaucune
DomaineSignal candidat: aucune · Signal consensuel: aucune
Devis d'étudeSignal candidat: Expérimental (laboratoire) · Signal consensuel: Expérimental (laboratoire)
GenreSignal candidat: Empirique · Signal consensuel: Empirique
Score de désaccord entre enseignants0,374
Score d'incertitude au seuil0,837

Scores Codex et Gemma par catégorie

CatégorieCodexGemma
Métarecherche0,0000,000
Méta-épidémiologie (sens strict)0,0000,000
Méta-épidémiologie (sens large)0,0000,000
Bibliométrie0,0020,001
Études des sciences et des technologies0,0000,000
Communication savante0,0000,000
Science ouverte0,0000,000
Intégrité de la recherche0,0000,001
Charge utile insuffisante (le modèle a refusé de juger)0,0000,000

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.

Tête enseignante Opus0,008
Tête enseignante GPT0,255
Écart entre enseignants0,247 · la distance entre les deux têtes enseignantes sur ce seul travail
Statut de validationscore_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 découle

Classification

machine, non validée

Prédiction automatique; un appel candidat d’une seule tête enseignante, pas un consensus.

Les modèles n’ont appliqué aucune catégorie : rien dans la taxonomie ne correspondait à ce travail.
Devis d'étudeExpérimental (laboratoire)
Domainenon disponible
GenreEmpirique

Le détail, modèle par modèle et score par score, se trouve en fin de page sous « Comment cette classification a été obtenue ».

En bref

Citations37
Publié2001
Routes d'admission1
Résumé présentoui

Explorer davantage

Même revueJournal of Canadian Petroleum TechnologyMême sujetEnhanced Oil Recovery TechniquesTravaux en français237 207