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Enregistrement W1981751659 · doi:10.2118/05-11-tn1

Field Implementation of Solvent Aided Process

2005· article· en· W1981751659 sur OpenAlex

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Notice bibliographique

RevueJournal of Canadian Petroleum Technology · 2005
Typearticle
Langueen
DomaineEngineering
ThématiqueEnhanced Oil Recovery Techniques
Établissements canadiensEncana (Canada)
Organismes subventionnairesnon disponible
Mots-clésBoiler (water heating)Petroleum engineeringSolventProcess engineeringSteam injectionOil fieldViscosityThermalPilot plantProcess (computing)Oil viscosityWaste managementEnvironmental scienceEngineeringChemistryMaterials scienceComputer scienceThermodynamicsOrganic chemistry

Résumé

récupéré en direct d'OpenAlex

Abstract The authors have previously described a Solvent Aided Process (SAP) that aims to combine the benefits of SAGD and VAPEX. In SAP, a small amount of hydrocarbon solvent is introduced as an additive to the injected steam during SAGD. While steam is intended to be the main heat-carrying agent, the solvent will dilute the oil to reduce its viscosity over and above what is accomplished by heating alone. The overall effect should be an improved oil to steam ratio (or reduced energy intensity). Although promising based on the authors' calculations, the process has not been previously applied or tested on a field scale. This paper describes the implementation of a SAP pilot at Encana's Senlac Thermal Facility. In addition to dwelling on some of the important parameters of a SAP test, it discusses the design considerations for the field pilot and the necessary modifications to an existing SAGD plant, specifically in the area of boiler operations controls. Although the design calls for an assessment of reservoir performance results on a longer-term basis, initial results from this pilot look very encouraging. The oil rates have shown a substantial increase, and the steam-oil ratio has shown a corresponding decrease. This paper also discusses directional economics with SAP and its beneficial impact on the environment. Introduction Just as steam tackles the viscosity reduction of in situ oil in SAGD(1,2) by heating it, solvents(3–7) do this by diluting the oil. Although employment of both steam and solvent together has been discussed in the literature(8–15), these discussions have mostly focused on enhancement of steam-flood or steam-stimulation. In their discussion on the subject, Gupta et al.(16) described SAP as a process enhancement to SAGD where a small amount of a light alkane solvent, namely propane, butane, pentane, etc., or a mixture thereof, is added to the injected steam. They also suggested, with the help of lab experiments and numerical modelling, that SAP has the potential to substantially improve the performance of SAGD. Expected SAP Advantages Figure 1 shows a comparison of an expected numerically obtained oil rate profile from a SAGD application vs. one obtained similarly with the application of SAP in the same reservoir. It is assumed that SAP would start after the expiry of a certain initial period in the life cycle of SAGD to allow for the initial development of the chamber with steam. The units from the rate and time axes are omitted to emphasize the general nature of these profiles. The comparison of the rate profiles is provided in order to suggest that the bulk of the oil that would have been produced in the later period with SAGD can be produced sooner with SAP. The acceleration of production and the corresponding cash flow could lead to improved economics for the project. Apart from the improved economics as a consequence of production rate acceleration, the other expected advantages of SAP include reduced environmental impact, possible down-hole upgrading of the heavy oil, and a small increase in the ultimate recovery.

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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)0,000
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: Expérimental (laboratoire) · Signal consensuel: aucune
GenreSignal candidat: Empirique · Signal consensuel: Empirique
Score de désaccord entre enseignants0,812
Score d'incertitude au seuil0,927

Scores Codex et Gemma par catégorie

CatégorieCodexGemma
Métarecherche0,0000,000
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 Opus0,004
Tête enseignante GPT0,246
Écart entre enseignants0,241 · 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