Evaluation of Heavy Oil/Bitumen-Solvent Mixture Viscosity Models
Notice bibliographique
Résumé
Abstract High viscosity is a major concern in the recovery of heavy oil and bitumen. Viscosity reduction could be achieved by mixing bitumen with solvents. Cragoe(1) and Shu(2) have developed widely used methods for liquid mixture viscosity predictions. However, in these two models, the viscosities or densities of the heavy oil/ bitumen and solvents have to be known at some reference condition. Low field nuclear magnetic resonance (NMR) relaxometry is an effective, non-destructive alternative for determining the petrophysical properties of oil reservoirs, and has also shown to successfully predict the viscosity of conventional oils, heavy oils and mixtures of oils with solvents. Specially, NMR could be a potential tool for in-situ viscosity measurements, which could be implemented on a logging tool allowing viscosity to be estimated without having to extract oil samples in the lab. In this paper, a regression model of experimental data, Cragoe, Shu and NMR models are compared with experimental data, which were obtained from four heavy oil/bitumen samples mixed with six solvents in different ratios. NMR based predictions are found to be similar to those of the Shu(2) model and superior to the predictions of the Gragoe(1) model. Introduction Viscosity and density reduction could be achieved by mixing with a solvent. The information of viscosity of the heavy oil/bitumen-solvent mixture is vital for designing solvent flooding and as input to reservoir simulators both for recovery processes and reserves assessment. Several correlations have been proposed for estimating the viscosity of a mixture of liquids. Cragoe(1) and Shu(2) have developed two widely used methods for mixture viscosity predictions. In both of the models, viscosities of the heavy oil/ bitumen and solvents have to be known for prediction. Sometimes, it is hard to measure the viscosity accurately when it is too high or too low using conventional viscometers and it is not a convenient method for in-situ measurements. Low field nuclear magnetic resonance (NMR) relaxometry is an effective, non-destructive alternative for determining the petrophysical properties of an oil reservoir. It was also shown to successfully predict the viscosity of conventional oils(3) and heavy oils(4). The greatest advantage of NMR is its potential to translate these density and viscosity measurements to in-situ measurements, which could be implemented on a logging tool allowing density and viscosity to be estimated without having to extract oil samples in the lab. The NMR viscosity model is especially significant for use in designing solvent injection process for heavy-oil recovery Experimental Procedure Four oils were used in the solvent experiments(5). They were from Peace River, Cold Lake, Edam and Atlee Buffalo, and have viscosities of 670,000 mPas, 130,000 mPas, 14,000 mPas and 6,000 mPas respectively, at 25 °. Kerosene, toluene, naphtha, heptane, hexane and pentane were added to the oils in several pre-defined mass fractions: 100% oil, 99%, 96%, 93%, 90%, 85%, 80%, 70%, 50%, 30% and 0% (100% solvent). The samples were slightly heated and mixed by stirring, and the resulting solvent-oil mixtures were cooled. NMR spectra were measured at °25 using an Ecotek FTB bench top relaxometer.
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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) | 0,002 | 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 ».