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Enregistrement W2091988989 · doi:10.2118/2005-075

Chamber Volume/Size Estimation for SAGD Process From Horizontal Well Testing

2005· article· en· W2091988989 sur OpenAlexafffund
Abdulftah Shamila, Ezeddin Shirif, M Dong, A. Henniz

Notice bibliographique

RevueCanadian International Petroleum Conference · 2005
Typearticle
Langueen
DomaineEngineering
ThématiqueHydraulic Fracturing and Reservoir Analysis
Établissements canadiensUniversity of Regina
Organismes subventionnairesPetroleum Technology Research Centre
Mots-clésVolume (thermodynamics)Process (computing)Computer sciencePetroleum engineeringMechanicsGeologyPhysicsThermodynamics

Résumé

récupéré en direct d'OpenAlex

Abstract Steam-assisted gravity drainage, SAGD, is one of the most recent and the most promising technique in enhanced oil recovery processes. The determination of the swept volume in a steam chamber process provides an early means in which to evaluate the project's progress. The pseudosteady state method has been used to estimate the swept volume, from pressure falloff testing, of horizontal wells. This method is easy to use and is similar in form to the well-known productivity equation for a vertical well. The applicability of the pseudosteady state method in estimating the swept volume for steam injection through a horizontal well was thoroughly investigated. A 3-D thermal numerical simulator was used to investigate the accuracy of the numerically calculated swept volume with the analytically estimated swept volume in SAGD process. In addition, a number of parameters, such as grid blocks number, duration of njection time, permeability ratio(kv kh), steam quality, and the location of the producer, were also studied. Results of this studies show that the pseudosteady state method is valid to use to estimate the swept volume for steam injection through a horizontal well. Within the norms of the independent parameters, the analytically (PSS) estimated swept volume was in good agreement with the numerically simulated swept volume. Introduction In the past, heavy oil or bitumen production has been only marginally economic. However, in recent years, tremendous advances have been achieved in the technology for the recovery of heavy oil or bitumen in both surface mining and in-situ operations. Syncrude and Suncor, the two current mining operations, have been increasing their production and profits consistently, even in the face of varying oil prices. Surface mining currently produces more than 500,000 barrels a day of bitumen. For in-situ recovery, horizontal well technologies have been widely used in both the thermal and non-thermal recovery of heavy oil and bitumen. Primary recovery by conventional production usually yields a low oil recovery in heavy oil reservoirs. Economically feasible technologies, such as steamflooding, steam-assisted gravity drainage (SAGD), and vapour extraction (Vapex) have been developed in order to upgrade large volumes of heavy oil underground (~10 ° to 25 °) API oil1–3. Nowadays, over 80 percent of the total oil produced from all enhanced oil recovery projects in the world is produced by the thermal method. The most economically feasible method of transporting heat to a reservoir, and the flux there of, is by the use of steam. This medium is fully utilized within the areas of steamflooding and SAGD. The Steam-Assisted Gravity Drainage (SAGD) process was successfully tested in AOSTRA's UTF project and has been applied commercially to the Athabasca oil sands and to the Tangleflags' North field. In the usual form of this process, two parallel horizontal wells are placed in the reservoir with a relatively small vertical spacing between them. The top one is used as an injector and the lower one as a producer. As injected steam rises and spreads in the reservoir to form a steam chamber above the injector, heated oil with condensed steam flows continuously downward to the production well byravity4.

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 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 candidatesCharge utile insuffisante (le modèle a refusé de juger)
Catégories consensuellesaucune
DomaineSignal candidat: aucune · Signal consensuel: aucune
Devis d'étudeSignal candidat: Simulation ou modélisation · Signal consensuel: Simulation ou modélisation
GenreSignal candidat: Empirique · Signal consensuel: Empirique
Score de désaccord entre enseignants0,095
Score d'incertitude au seuil1,000

Scores Codex et Gemma par catégorie

CatégorieCodexGemma
Métarecherche0,0000,000
Méta-épidémiologie (sens strict)0,0000,000
Méta-épidémiologie (sens large)0,0000,000
Bibliométrie0,0000,000
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)0,0010,000

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 Opus0,012
Tête enseignante GPT0,225
Écart entre enseignants0,214 · 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

Classification

machine, non validée

Prédiction automatique; un appel candidat d’une seule tête enseignante, pas un consensus.

Devis d'étudeSimulation ou modélisation
Domainenon disponible
GenreEmpirique

Le détail, modèle par modèle et score par score, se trouve en fin de page sous « Comment cette classification a été obtenue ».

En bref

Citations8
Publié2005
Routes d'admission2
Résumé présentoui

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