Experimental and Numerical Modeling of Heavy-Oil Recovery by Electrical Heating
Notice bibliographique
Résumé
Abstract Electrical heating for heavy-oil recovery is not a new idea but commercialization and wider application of this technique require detailed analyses for determination of optimal application conditions. In this study, applicability of electrical heating for heavy-oil recovery from two heavy-oil fields in Turkey (Bati Raman and Camurlu) was tested experimentally and numerically. The physical and chemical properties of the oil samples for the two fields were compiled and measured. Then, core samples were exposed to electrical heating and oil recovery performances by the retort technique were determined for different conditions. Experiments with and without using iron powder were analyzed and in-situ viscosity reduction during the heating process was determined through a history matching process using the simulation of the laboratory experiments. Experimentally obtained oil recovery and temperature distributions were used in this history matching exercise. Iron powder addition to oil samples causes a decrease in the polar components of oil and the viscosity of oil can strongly be influenced by the magnetic fields created by iron powders. Therefore, three different iron powder types at three different doses were tested to observe their impact on oil recovery. Experimental observations showed that viscosity reductions were accomplished as 88% and 63% for Bati Raman and Camurlu crude oils, respectively, after 0.5% Fe addition, which was determined as the optimum type and dose for both crude oil samples. Different parameters (thermal diffusion coefficients, oil viscosity, and relative permeabilities) that are needed in numerical modeling as data were determined through experimentally validated numerical modeling study. Furthermore, field scale recovery was tested numerically using the parameters obtained from laboratory scale experimental and numerical modeling results. The power of the system, operation period and the number of heaters were optimized. Economic evaluation done using the field scale numerical modeling study showed that the production of one barrel petroleum costs about 5 USD and at the end of 70 days, 320 barrels petroleum can be produced. When 0.5% Fe is added, oil production increased to 440 barrels for the same operational time period.
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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 ».