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Enregistrement W4249314453 · doi:10.2118/07-09-06

Effect of Solvent Sequencing and Other Enhancements on Solvent Aided Process

2007· article· en· W4249314453 sur OpenAlex

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Notice bibliographique

RevueJournal of Canadian Petroleum Technology · 2007
Typearticle
Langueen
DomaineEngineering
ThématiqueReservoir Engineering and Simulation Methods
Établissements canadiensEncana (Canada)
Organismes subventionnairesnon disponible
Mots-clésSolventPetroleum engineeringDilutionOil sandsProcess engineeringWork (physics)Process (computing)Oil fieldSteam injectionHydrocarbonEnhanced oil recoveryChemistryEnvironmental scienceWaste managementChemical engineeringMaterials scienceEngineeringMechanical engineeringOrganic chemistryComputer scienceThermodynamics

Résumé

récupéré en direct d'OpenAlex

Abstract Alberta 's oil sands clearly present an economical solution to the world 's dwindling conventional oil supply. Over 80% of these oil sands can only be recovered by in situ methods, such as SAGD technology. However SAGD, in its current commercial form, 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 this purpose. These different solvents can be used individually or as a mixture, together or sequentially, with varying degrees 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, the impact of cross-flow of gas on solvent recovery, the effect of low pressure operation on SAP performance and the operation of SAP in bottom-up geometry. Based on this work, the performance of SAP can be improved by employing proper sequence of solvent, cross-flow solvent recovery and low pressure operation. Bottom-up SAP takes the bitumen recovery to new levels of energy efficiency with steam-oil ratios below 0.5. Introduction As previously described, in SAGD, oil viscosity is reduced by heating with steam(1,2). In SAP(3–5), 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 the environment. VAPEX, a process similar to SAGD, employs only hydrocarbon vapour instead of steam as described in the literature(6–9) and can eliminate 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 the literature(10–13) with a focus on the enhancement of steam displacement or steam stimulation. Using solvent with steam in a gravity drainage context offers some practical advantages. The pressure in the vapour chamber does not need to be supported by a non-condensable gas as required in some embodiments of VAPEX. This means that the progression of the vapour chamber in SAP does not get overwhelmed by the heat/mass transfer resistance at the vapour/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 Project(5,14).

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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)0,000
Version: codex-gemma-dda1882f352aStatut de validation: machine_predicted_unvalidated
Catégories candidatesaucune
Catégories consensuellesaucune
DomaineSignal candidat: aucune · Signal consensuel: aucune
Devis d'étudeSignal candidat: Simulation ou modélisation · Signal consensuel: aucune
GenreSignal candidat: Empirique · Signal consensuel: Empirique
Score de désaccord entre enseignants0,838
Score d'incertitude au seuil0,444

Scores Codex et Gemma par catégorie

CatégorieCodexGemma
Métarecherche0,0010,000
Méta-épidémiologie (sens strict)0,0000,000
Méta-épidémiologie (sens large)0,0000,000
Bibliométrie0,0020,000
Études des sciences et des technologies0,0000,000
Communication savante0,0000,000
Science ouverte0,0000,000
Intégrité de la recherche0,0000,000
Charge utile insuffisante (le modèle a refusé de juger)0,0000,000

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.

Tête enseignante Opus0,010
Tête enseignante GPT0,272
Écart entre enseignants0,262 · 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