Computed Tomography Study of VAPEX Process in Laboratory 3-D Model
Notice bibliographique
Résumé
Abstract Vapour Extraction (VAPEX) process has been an intense research topic in recent years as an alternative technology to thermal recovery method for heavy oil and bitumen resources. In the past, most two-dimensional (2-D) transparent modelsstudies have only simulated the vapour chamber evolution behavior of a vertical slice of the reservoir; however, the longitudinal vapour chamber evolution characteristic in three dimensional reservoirs could not be detected. This paper presents the results of three-dimensional (3-D) monitoring of the VAPEX process in a small laboratory model, using computed tomography (CT) technology to investigate the vapour chamber expansion behavior in both radial and longitudinal directions. The model is an aluminum cylinder with 140 mm inside diameter and 600 mm length. Two slottedaluminum tubes were installed inside to act as the injection and production well, respectively. Solvent by-passing concern was considered for the model designing and sand packing. Solvent was commercial propane, oil was Lloydminster type heavy oil, and all experiments were conducted under room temperature. The experiments showed that in 3-D geometry, the claimed "V" shape vapour chamber expansion by the previous 2-D model was only a localized phenomenon. In longitudinal direction the dominant characteristic was overriding of the injected solvent at the top of the model promoted by gravity segregation, solvent gas longitudinally expanding was more significant than upward expanding at the early stage of VAPEX process. This is enhanced by the confinement provided by the cylindrical geometry of the core holder. In addition, the further residual oil recovery potential after VAPEX process was investigated by "solvent soaking" method. The oil production performance under different solvent injection rates was compared. By numerical analysis of the CT images, the in-situ model porosity, density and oil saturation profiles were obtained. The results indicate some new ideas about VAPEX process mechanism. Introduction From the more than 400 billion m3 heavy oil and bitumen deposits in Canada, only 10% is surface minable. The major part of the deposits has to be relied on in-situ recovery process.1 However, due to their high viscosities and low degree API gravities in native state, 2 these reservoirs can only be recovered with low recovery efficiency by conventional methods. For example, primary recovery in the best of these heavy oil reservoirs is about 6% of the original-oil-in-place (OOIP). Subsequent water flooding can improve the recovery to an extent of 1% ∼2% incremental of OOIP.3 In order to more effectively recover these reserves, tertiary recovery methods have to be directly applied.4 The main technology challenge is to reduce the heavy oil viscosity in-situ.5 As the oil viscosity is very sensitive to temperature, thermal recovery methods seem to be very effective and have been widely researched and pilot tested, 6 including as Cyclic Steam Simulation (CSS), In-Situ Combustion (ISC), Steam Assisted Gravity Drainage (SAGD) and Steam Flooding.7 The SAGD process has been commercially used by several oil companies.
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 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 | 0,000 |
| 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 | 0,001 | 0,000 |
| Science ouverte | 0,000 | 0,000 |
| Intégrité de la recherche | 0,001 | 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 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 ».