Processes Responsible for Heavy-Oil Recovery by Alkali/Surfactant Flooding
Notice bibliographique
Résumé
This article, written by Senior Technology Editor Dennis Denney, contains highlights of paper SPE 113993, "Investigation into the Processes Responsible for Heavy-Oil Recovery by Alkali/Surfactant Flooding," by J. Bryan, SPE, University of Calgary and TIPM Laboratory; A. Mai, SPE, University of Calgary and TIPM Laboratory and Laricina Energy; A. Kantzas, SPE, University of Calgary and TIPM Laboratory, prepared for the 2008 SPE Improved Oil Recovery Symposium, Tulsa, Oklahoma, 19-23 April. The paper has not been peer reviewed. This paper describes a suite of alkali/surfactant (AS) floods that were performed in systems containing viscous heavy oil (11 500 mPa⋅s). The study investigates how AS injection can be used to generate oil-in-water (OIW) emulsions, which can in turn improve sweep efficiencies and oil recovery. Data were obtained from coreflooding, with in-situ saturation measurements made using low-field nuclear-magnetic-resonance (NMR) analysis. The corefloods in this study indicate that emulsification is most efficient when used to block preformed water channels and improve the sweep efficiency of the flood. Introduction Several countries, including Canada and Venezuela, contain significant deposits of heavy oil and bitumen. These oil-sands are high-porosity and -permeability unconsolidated reservoirs. The viscosity of the oil in place may range from tens to millions of mPa·s at reservoir conditions, and the oil densities approach or are higher than that of water. Recent estimates put the primary recovery of heavy oil at an average of approximately 5% of the original oil in place (OOIP), with significant oil resources remaining as potential for secondary and tertiary recovery. However, many of the reservoirs in Canada are relatively small or thin, and were possibly disturbed during primary production. As a result, these reservoirs are not prime candidates for expensive thermal or hydrocarbon-solvent enhanced-oil-recovery technologies. Therefore, less-expensive (nonthermal) methods of recovering the oil must be considered. Previous research focused on improved heavy-oil recovery by application of waterflooding and AS flooding. This work found that during a heavy-oil waterflood, water will break through very early in the life of the flood because of viscous instabilities, resulting from the adverse water/oil mobility ratio. After water breakthrough, continuous channels of water existed throughout the reservoir. At later stages in a heavy-oil waterflood, capillary forces and water imbibition were the dominant recovery mechanism. At low injection rates, significant volumes of heavy oil can be recovered after water breakthrough, though at a high water cut. AS injection could be considered either as a primary- or secondary-recovery process. Overall, a combination of waterflooding and AS flooding can lead to significantly improved oil recovery beyond that of primary production. Pressure and recovery data were analyzed to infer how the chemical flood worked, and NMR spectra of the fluids in the sand pack were monitored to understand the wettability of the core as the flood progresses.
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Comment cette classification a été obtenuedéplier
Prédiction machine sur la base complète
Imitation des enseignantsNi prévalence calibrée, ni vérité terrain. Validation humaine à venir. Le volet Gemma est une étiquette directe du modèle pour chaque travail de la base, lue sur la notice réduite au titre. Le volet Codex est un classifieur appris des 10 348 étiquettes directes de Codex et calibré sur les taux pondérés de l'échantillon; les champs sans appui suffisant ne portent aucun appel Codex. Le mode candidate est l'union des deux volets; le consensus est leur intersection. Ces sorties portent le statut machine_predicted_unvalidated et ne sont pas des étiquettes humaines.
Scores du classifieur distillé par catégorie (deux têtes)
| 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 | 0,001 |
| Science ouverte | 0,000 | 0,000 |
| Intégrité de la recherche | 0,000 | 0,000 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,001 | 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 source (Gemma direct ou Codex distillé), 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 ».