Effect of Solvent Sequencing and Other Enhancements on Solvent Aided Process
Notice bibliographique
Résumé
Abstract Alberta's oilsands clearly present an economical solution to the world's dwindling conventional oil supply. SAGD technology, which can potentially be applied to recover over 80% of the bitumen in prospective oilsands, is an energy intensive process. The authors have previously described an improvement to SAGD - Solvent Aided Process (SAP) that aims to combine the benefits of using steam with solvents. In SAP, a small amount of hydrocarbon solvent is introduced as an additive to the injected steam during SAGD. SAP holds the promise to significantly improve the energy efficiency of SAGD thus reducing the heat requirement. Previously discussed results from Encana's field trials of SAP have shown the practical upside of this process. In theory, a variety of solvents can be employed with steam to tap the benefit of solvent dilution in combination with (in situ) heating the oil. However, due to their commercial availability, light alkanes present in the natural gas condensate are the practical choice for the purpose. These different solvents can be used individually or as a mixture, together or one after the other, with varying degree of benefit. The economics of a SAP project depends on the enhancement of oil recovery and rates as well as solvent recovery. This paper, based on modeling work, investigates the effect of solvent sequencing and other potential enhancements on the performance of SAP. Introduction As previously described, in SAGD, oil viscosity is reduced by heating with steam1,2. In SAP3,4,5, solvent dilution is also taken advantage of to aid this viscosity reduction. The result is an enhanced rate of oil production and recovery leading to superior economics with lower energy intensity and impact on environment. VAPEX, a process similar to SAGD but employing only hydrocarbon vapor instead of steam has been described in literature6,7,8,9 and can do away with the expensive heating requirement of SAGD. However, its development is awaiting a successful field trial. Use of solvent with steam for oil recovery is also discussed in literature10,11,12,13 with a focus on enhancement of steam displacement or steam stimulation. Using solvent with steam in a gravity drainage context offers some practical advantages. The pressure in the vapor chamber does not need to be supported by a non-condensable as required in some versions of VAPEX. This means that the progression of the vapor chamber in SAP does not get overwhelmed by the heat/mass transfer resistance at the vapor/oil interface. Encana has been developing SAP since 1996 and piloted the process first at its Senlac Thermal Project in 2002. Encouraged by the results, it is presently testing SAP for in situ bitumen extraction at its Christina Lake Thermal Project5,14. Although the field testing is focused on proving the economics with a basic configuration, this paper explores some new aspects of the process with the help of numerical modeling that can enhance the performance and hence the economics of oil recovery. Measure of SAP Performance The aim of SAP is to improve the economics of SAGD. This improvement, apart from market behavior, depends on reduced capital, energy intensity and solvent requirement.
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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) | 0,001 | 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 | 0,001 |
| Science ouverte | 0,000 | 0,000 |
| Intégrité de la recherche | 0,000 | 0,001 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,004 | 0,001 |
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.
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 ».