Steam Alternating Solvent Process: Lab Test and Simulation
Notice bibliographique
Résumé
Abstract A new heavy oil recovery process, the Steam Alternating Solvent (SAS) process, is studied by lab experiments and corresponding numerical simulation. The SAS process involves injecting steam and solvent alternately, using well configurations similar to those in the SAGD process. This process is designed to combine the advantages of the SAGD and VAPEX processes to minimize the energy input in heavy oil and bitumen recovery. Lab experiments were conducted using a 2D high-pressure/ high-temperature model. One baseline SAGD test and one SAS test were performed using an oil sample from Burnt Lake. A mixture of propane and methane was used as the solvent in the SAS test. The results showed that the energy input in the SAS process was 47% lower than that of the SAGD process for recovering the same amount of oil. The post-run analysis revealed that asphaltene precipitation occurred in the porous medium. Numerical history matching of the test data using Computer Modelling Group's STARS reservoir simulator captured the main features of the process. Introduction The main obstacle to producing oil from the large deposits of heavy oil and bitumen resources in northern Alberta is the high viscosity of these oils, usually over 10,000 mPa's at reservoir conditions. There are generally two types of methods for the reduction of oil viscosity. The first is to increase oil temperature by injecting a hot fluid, such as steam, into the reservoir, or by in situ combustion through the injection of oxygen-containing gas. The second method is to dilute the viscous oil by injecting low viscosity hydrocarbons (solvent). As the solvent dissolves and mixes with the viscous oil, the lower viscosity solvent-diluted oil can then be recovered. Two processes, Steam Assisted Gravity Drainage (SAGD) and Vapour Extraction (VAPEX), have been developed for the recovery of heavy oil and bitumen resources(1–3) based on the combination of the above viscosity reduction methods and horizontal well technology. The first has been successfully tested in the field and is moving to commercial scale application(4, 5). The second is presently in the initial field-testing stage(6). The advantage of the SAGD process is its relatively higher oil production rate. However, the higher production rate is associated with significant energy requirements, CO2 generation, and costly water treatment. The VAPEX process has the advantage of lower energy consumption, therefore, less CO2 generation. The major predicted drawback of the VAPEX process, however, is its relatively lower oil production rate. In the past several years, modifications, such as ES-SAGD(7) and SAGP(8), have been proposed to improve SAGD's energy efficiency. In the ES-SAGD process, a small amount of solvent with a boiling temperature matching the steam temperature is coinjected with steam to increase the oil production rate. In the SAGP process, non-condensable gas is co-injected with steam for the purpose of reducing heat loss to the overburden. Recent studies(9, 10) have indicated that the economics of a SAGD project are more sensitive to the energy consumption per unit of oil production than to the oil production rate.
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 | 0,001 |
| Méta-épidémiologie (sens strict) | 0,000 | 0,000 |
| Méta-épidémiologie (sens large) | 0,001 | 0,001 |
| Bibliométrie | 0,000 | 0,000 |
| Études des sciences et des technologies | 0,000 | 0,000 |
| Communication savante | 0,000 | 0,000 |
| Science ouverte | 0,001 | 0,000 |
| Intégrité de la recherche | 0,001 | 0,000 |
| 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 ».