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Enregistrement W4234290571 · doi:10.2118/2008-156

Mechanisms of Heavy Oil Recovery by Low Rate Waterflooding

2008· article· en· W4234290571 sur OpenAlex

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Notice bibliographique

RevueCanadian International Petroleum Conference · 2008
Typearticle
Langueen
DomaineEngineering
ThématiqueReservoir Engineering and Simulation Methods
Établissements canadiensLaricina Energy (Canada)
Organismes subventionnairesNatural Sciences and Engineering Research Council of CanadaCanada Research Chairs
Mots-clésPetroleum engineeringEnvironmental scienceGeology

Résumé

récupéré en direct d'OpenAlex

Abstract At the conclusion of primary heavy oil production, significant volumes of oil still remain in the reservoir under depleted reservoir pressure. Waterfloods are often consideredfor additional oil recovery. It is accepted that conventional oil waterflooding theory is not applicable for heavy oil. However, there is a lack of understanding of how waterfloods should perform in these reservoirs, particularly after water breakthrough. In this study, waterfloods were performed at multiple rates in cores containing heavy oil and connate water. In some cores oil was initially free of solution gas, and waterfloods were a primary recovery process. In other cores, waterfloods were performed after primary production. Experiments were performed in linear systems for a high viscosity oil (11,500 mPa?s), at different injection rates. The influence of viscous and capillary forces is studied in primary vs. secondary recovery systems. A common misconception is that capillary forces are negligible in heavy oil; however this work shows that these forces are significant, and that water imbibition after water breakthrough can lead to improved oil recovery in both primary and secondary waterfloods. Introduction The Canadian deposits of heavy oil and bitumen are some of the largest in the world. Recent estimates by the AEUB1 suggest that this resource could exceed 270 billion m3 in Alberta alone, with a significant portion of this oil located in reservoirs where expensive thermal operations will not be economic for recovering the oil. Heavy oil is a special class of this unconventional oil, which has viscosity ranging from 50 – 50,000 mPa?s (cP), and low API gravity. Heavy oil reservoirs are often found in high porosity, high permeability, unconsolidated sand deposits. Permeability of the sand averages in the range of 3 D2, but oil does not flow easily due to its high viscosity. At the initial reservoir temperature andressure, the oil may contain dissolved solution gas, thus a fraction of the oil can be recovered using the energy from heavy oil solution gas drive. Primary production can recover around 5% of the oil in place1, leaving significant oil volumes in the reservoir for potential secondary recovery. Waterflooding is a common technique for secondary oil recovery in conventional oil reservoirs. In heavy oil systems, the extremely high oil viscosities lead to adverse mobility ratio conditions, thus water will tend to "finger" through the oil, and recoveries are expected to be extremely low3,4. Despite the poor recoveries predicted theoretically, there have been numerous reports of heavy oil waterfloods performed in the literature5-8. All of these studies reported poor sweep efficiencies and overall recovery. However it is significant that in all cases some oil was recovered despite the highly adverse mobility ratios in the waterfloods. Laboratory studies of waterflooding in heavy oil systems also demonstrate that some oil can be recovered by controlled injection of water. Although heavy oil waterflood responses cannot be readily predicted through theory5, waterfloods have still been carried out in heavy oil reservoirs in Alberta and Saskatchewan for the past 50 years8,9.

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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: Simulation ou modélisation · Signal consensuel: Simulation ou modélisation
GenreSignal candidat: Empirique · Signal consensuel: Empirique
Score de désaccord entre enseignants0,099
Score d'incertitude au seuil0,618

Scores Codex et Gemma par catégorie

CatégorieCodexGemma
Métarecherche0,0000,000
Méta-épidémiologie (sens strict)0,0000,000
Méta-épidémiologie (sens large)0,0000,000
Bibliométrie0,0000,000
Études des sciences et des technologies0,0000,000
Communication savante0,0000,000
Science ouverte0,0000,000
Intégrité de la recherche0,0000,000
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 Opus0,018
Tête enseignante GPT0,223
Écart entre enseignants0,205 · 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