PVT and Viscosity Measurements for Lloydminster-Aberfeldy and Cold Lake Blended Oil Systems
Notice bibliographique
Résumé
Abstract In the Solvent-Assisted Processes Project of the AACI Research Program, many experiments were done to evaluate the feasibility of using light hydrocarbon and other solvents as agents for recovery of heavy oil and bitumen. In order to have a rational basis for designing these experiments, measurements of gas solubility in the oil at operating conditions are needed. To predict the behaviour of the process by numerical simulation, a set of k-values for the relevant gas-liquid systems is needed. Simple analog models of the Vapex process require the viscosity of the oil-solvent blends at equilibrium conditions. A data bank of oil-solvent mixture viscosity and solubility is useful for reference purposes or for developing correlations. Measurements were done on a blended Cold Lake/Lloydminster, and on Lloydminster Aberfeldy oil. The gasses used were CH4, C2H8, C3H8 and CO2. Measurements were done at reservoir temperature. The data were regressed using the Peng-Robinson Equation of State. The equation was then used to generate k-values for the gas-oil systems. Regression was by varying the interaction coefficienst for the various gas-oil systems. These coefficients enabled use of the equation to generate k-value tables for other conditions. Measured viscosity data were used to confirm the usefulness of the Puttagunta equation for calculating the viscosity of oil-solvent mixtures. The work also confirmed the formation of 2 liquid phases in the oil-propane system at high solvent loading. An anomaly in the viscosity curve at high solvent loading indicated possible asphaltene precipitation/deposition in the viscometer tube for propane-oil systems. Measurements confirmed the high viscosity reduction possible (100:1 - 200:1) by saturating light oil with hydrocarbons. The observations confirmed the need for an integrated PVT/viscosity/asphaltene study for oil/solvent systems intended for a Vapex process. The data have been applied to numerical simulations of these experiments and proposed field processes. Introduction Thermal recovery processes have been used successfully on many Alberta bitumen and heavy oil reservoirs. Some reservoirs, however, are not suited to thermal processes. This may be due to depth, unfavourable mineralogy, bottom water, thin pay sections, or a combination of these factors. For these reservoirs, a non-thermal process may be more suitable. The most likely candidate is a Vapex-type process, where oil is contacted by solvent vapour. The vapour dissolves in the oil, and diluted oil drains to a production well. The application of this technology to heavy oil recovery requires confident prediction of the process performance for a field-scale operation. This in turn requires knowledge of the mechanisms active in the process and the magnitude of each of these mechanisms. Mechanisms identified to date include solubilization of the solvent in oil, mass transfer from vapour to liquid phases by diffusion, mixing of diluted and undiluted oil by diffusion and dispersion, reduction of the oil viscosity by solvent dilution, and upgrading of the oil by asphaltene precipitation and deposition. This work measured solubility and viscosity of several oil-solvent systems. DESIGN OF EXPERIMENT The experiment was performed in a PVT apparatus constructed from standard components. Figure 1 illustrates the PVT system and its associated hardware.
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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 | 0,001 |
| Méta-épidémiologie (sens strict) | 0,000 | 0,000 |
| Méta-épidémiologie (sens large) | 0,000 | 0,000 |
| Bibliométrie | 0,001 | 0,001 |
| 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 | 0,001 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,003 | 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 ».