An Investigation Into Optimal Solvent Use and the Nature of Vapor/Liquid Interface in Solvent-Aided SAGD Process With a Semianalytical Approach
Notice bibliographique
Résumé
Summary The steam-assisted gravity-drainage (SAGD) process is used widely to recover heavy oil and bitumen from formations in which no other recovery method has proved to be economical. It is an energy-intensive process, and because of economic and environmental reasons, solvents as additives to the injected steam are being explored currently to reduce the energy and emissions intensity of SAGD. The solvent-aided process (SAP), tested in the field and described in the literature, is one such attempt. In the SAP, a small amount of hydrocarbon solvent is introduced as an additive to the injected steam. Thus, the viscosity of the oil is also reduced because of solvent dilution in addition to heating. The SAP can improve the energy efficiency of SAGD significantly, thus reducing the heat requirement, as shown in field trials discussed elsewhere. However, on the use of the right amount of solvent that can result in best overall performance, there is very little discussion in the literature. Because of the high cost of such solvents, there is incentive to optimize their use in SAGD. Recently, various authors have attempted to address the subject with, for example, arbitrary time-dependent schemes of solvent injections, assessing their impact on results or by treating the internal reservoir dynamics as a black box and using optimization methods, such as genetic algorithms (GAs), to estimate the optimal amount of solvent. While these approaches orient us to the problem in a context-specific manner, it is believed a generalized treatment to estimate optimal use of solvent requires a mechanism-based understanding. The approach presented in this paper is aimed at estimating the optimal solvent in the context of SAGD. It combines the existing Butler's oil-drainage analytical models (Butler 1985, 1988, 1994) for SAGD and vapor extraction (VAPEX), which deal with heating effect and solvent-dilution effect one at a time, into one. Then, it calculates the time-dependent steam rates to maintain the predicted oil rates in conjuction with solvent rates and, thus, estimates the solvent/steam ratio (SSR) and the steam/oil ratio (SOR). The results are discussed for a few light-alkane solvents. In the process of this exercise, it is discovered that to obtain reasonable SSR and SOR, a significant amount of oil has to drain from a diffuse layer, which has a varying temperature, solvent concentration, and gas saturation (from maximaum gas saturation at the injection end to zero at the vapor/liquid interface).
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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 | 0,001 | 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 | 0,001 |
| 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.
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