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Enregistrement W2018192541 · doi:10.2118/2005-180

A Numerical Approach to Simulateand Design VAPEX Experiments

2005· article· en· W2018192541 sur OpenAlexaff
X. Wu, M. Polikar, Luciane B. Cunha

Notice bibliographique

RevueCanadian International Petroleum Conference · 2005
Typearticle
Langueen
DomaineDecision Sciences
ThématiqueProbabilistic and Robust Engineering Design
Établissements canadiensUniversity of Alberta
Organismes subventionnairesnon disponible
Mots-clésComputer scienceEngineering drawingEngineering

Résumé

récupéré en direct d'OpenAlex

Abstract The Vapour Extraction (VAPEX) process is a promising technique directed towards heavy oil reservoirs that are typically thin and underlain with water, and cannot be exploited economically or technically by conventional thermal recovery methods. The VAPEX technique was developed by Butler and Mokrys in the 1990s as an alternative to Steam-Assisted Gravity Drainage. This process is mechanistically complex and some questions regarding its expected performance are still pending. A numerical model can play a critical role in addressing important questions about the process. Specifically, a numerical model can predict the performance of the process, especially the occurrence and effect of asphaltenes precipitation during the ‘upgrading’ process. This research proposes an alternative approach to simulate numerically the asphaltene precipitation effect of the VAPEX process. The model was constructed using a commercial thermal reservoir simulator. It was then validated using published experimental data. The effect of relative permeability curves, reaction frequency factor, selection of reactant, apparent dispersion coefficient, and operating parameters on performance were investigated. In addition, the model was used to design a physical experiment. The operating conditions of the experiment were optimized to represent the main mechanisms of the VAPEX process. The results of the study indicate that the numerical model can reproduce the process with acceptable accuracy. Moreover, despite the significant viscosity reduction, it wasfound that there was no significant evidence to demonstrate blockage of fluid flow through the porous medium due to asphaltene precipitation. Further experiments would be required to confirm these findings. Introduction The vapour extraction (VAPEX) process1 (Figure 1) has drawn the increasing attention of the heavy oil/bitumen industry since it was developed by Butler and Mokrys in 1991. In this process, the same well configuration, as well as the same counter-current drainage concept as the popular steam-assisted gravity drainage (SAGD)2 process (Figure 2), are used. However, solvents, such as butane and propane3,4 are injected, rather than steam, at or near their dew point. The mobility of the oil is improved through the mass transfer (diffusion and dispersion) effect between the solvent vapour chamber and the oil. The VAPEX process is a non-thermal recovery method. It is more energy efficient and environmentally friendly than thermal processes. It is expected to improve recovery in problematic reservoirs, such as thin reservoirs and reservoirs with bottom water3,5, where thermal recovery methods are not economically or technically feasible. Another benefit of the VAPEX process is the potential to upgrade the oil in-situ3 resulting from asphaltenes deposition. However, the initiation and effect of asphaltenes precipitation on fluid flow during the VAPEX process has not been resolved. Asphaltenes deposition was observed in almost all the physical experiments that Butler and his colleagues performed3–9. However, other researchers10–13 observed less or no asphaltenes deposition in their experiments. It is also plausible that asphaltenes precipitation during the VAPEX process might plug the pore throats under reservoir conditions. Investigations on scaled physical models with significant permeability suggest that such is not the case. However, the performance of reservoirs with a range of the reservoir permeabilities is of concern.

Récupéré en direct depuis OpenAlex et désinversé. Les résumés ne sont pas conservés dans cette base de données : les index inversés représentent 8,6 Go des 9,3 Go de texte de la base, et le serveur dispose de 13 Go libres.

Comment cette classification a été obtenuedéplier

Prédiction distillée sur la base complète

Imitation des enseignants

Ni 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.

score de la tête « metaresearch » (Codex)0,001
score de la tête « metaresearch » (Gemma)0,001
Version: codex-gemma-dda1882f352aStatut de validation: machine_predicted_unvalidated
Catégories candidatesCharge utile insuffisante (le modèle a refusé de juger)
Catégories consensuellesCharge utile insuffisante (le modèle a refusé de juger)
DomaineSignal candidat: aucune · Signal consensuel: aucune
Devis d'étudeSignal candidat: Simulation ou modélisation · Signal consensuel: Simulation ou modélisation
GenreSignal candidat: Empirique · Signal consensuel: aucune
Score de désaccord entre enseignants0,952
Score d'incertitude au seuil1,000

Scores Codex et Gemma par catégorie

CatégorieCodexGemma
Métarecherche0,0010,001
Méta-épidémiologie (sens strict)0,0000,000
Méta-épidémiologie (sens large)0,0000,000
Bibliométrie0,0010,000
Études des sciences et des technologies0,0000,000
Communication savante0,0000,000
Science ouverte0,0010,000
Intégrité de la recherche0,0000,000
Charge utile insuffisante (le modèle a refusé de juger)0,0010,001

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.

Tête enseignante Opus0,125
Tête enseignante GPT0,329
Écart entre enseignants0,204 · la distance entre les deux têtes enseignantes sur ce seul travail
Statut de validationscore_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 découle

Classification

machine, non validée

Prédiction automatique; les deux têtes enseignantes s’accordent sur ce qui est montré ici.

Devis d'étudeSimulation ou modélisation
Domainenon disponible
GenreEmpirique

Le détail, modèle par modèle et score par score, se trouve en fin de page sous « Comment cette classification a été obtenue ».

En bref

Citations11
Publié2005
Routes d'admission1
Résumé présentoui

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