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Enregistrement W1972794471 · doi:10.2118/08-09-40

The Impact of Oil Viscosity Heterogeneity on the Production Characteristics of Tar Sand and Heavy Oil Reservoirs. Part II: Intelligent, Geotailored Recovery Processes in Compositionally Graded Reservoirs

2008· article· en· W1972794471 sur OpenAlex

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Notice bibliographique

RevueJournal of Canadian Petroleum Technology · 2008
Typearticle
Langueen
DomaineEngineering
ThématiqueReservoir Engineering and Simulation Methods
Établissements canadiensUniversity of Calgary
Organismes subventionnairesNatural Sciences and Engineering Research Council of Canada
Mots-clésOil sandsPetroleum engineeringEnhanced oil recoverySteam injectionAsphaltSteam-assisted gravity drainageEnvironmental scienceThermalGeologyViscosityMaterials science

Résumé

récupéré en direct d'OpenAlex

Abstract Compositional and fluid property gradients are common and documented in conventional heavy oilfields and in super heavy oil occurrences such as oil sand reservoirs. In the severely biodegraded oils of both Athabasca and Peace River oil sand reservoirs, highly non-linear vertical and lateral chemical compositional and fluid viscosity gradients are common and have been shown to dramatically impact existing generation recovery processes such as SAGD and CSS. The fluid and geological heterogeneities at a variety of spatial scales in heavy oil and bitumen reservoirs, combined with the dynamic evolution of produced fluids during solvent or thermal recovery, should be integrated into recovery methods tailored to each reservoir which are operated using time-variant production strategies informed by produced fluid composition, flow rates and detailed history matching. We describe here, two new approaches of a new generation of transitional and initial thermal recovery processes that take advantage of mobility gradients: JAGD (J-well and Gravity Drainage) and gSAGD (mobility ratio optimized SAGD), which demonstrate significant improvements in recovery, economics and, thus, carbon dioxide emissions over existing thermal methods. Well configurations tailored to specific reservoir geometries and properties as well as fluid property distributions for primary thermal recovery increase initial production by 50 to 100%. Substantial cost savings are achieved in transitional cold primary to thermal secondary recovery methods (JAGD) by using a production J-well placed below what is initially a CHOPS production well, which is then later used to inject steam, as in SAGD. Three-dimensional reservoir simulations predict 25% more oil recovery with up to a 50% decrease in cumulative steam-oil ratio compared to standard SAGD in an identical reservoir. The JAGD process has many similarities to SAGD, such as steam trap control and potential for low pressure and solvent-assisted operation. Such geotailored processes (processes tailored, operated and optimized to reservoir fluid and geological heterogeneities) are expected to outperform conventional 'off the shelf' well placement designs and operating strategies. Introduction The bulk of the world's petroleum resources are stored in heavy oil and oil sand reservoirs. While some of this resource can be recovered by geotolerant (tolerant of unfavourable geology) recovery processes such as mining, these procedures are only suitable for shallow resources, are very costly and have high carbon dioxide emissions and other environmental penalties. Most of the world's heavy oil and bitumen resources are too deep to mine and so in situ recovery methods predominate. In situ recovery of viscous and poor quality oils currently relies on either high pressure primary production, as in cold heavy oil production, or thermal and/or solvent-based methods to mobilize the oil by reducing its viscosity. Average recoveries from heavy oil and oil sand reservoirs are typically low ranging from 5 to 15% for cold heavy oil production and from 30 to 85% for steam-based in situ processes. However, such processes are not very geotolerant. Also, profit margins are small because of high capital and operational costs.

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.

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)0,001
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 enseignants0,857
Score d'incertitude au seuil0,469

Scores Codex et Gemma par catégorie

CatégorieCodexGemma
Métarecherche0,0010,001
Méta-épidémiologie (sens strict)0,0000,000
Méta-épidémiologie (sens large)0,0000,000
Bibliométrie0,0010,001
Études des sciences et des technologies0,0000,000
Communication savante0,0000,000
Science ouverte0,0000,000
Intégrité de la recherche0,0000,001
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 Opus0,023
Tête enseignante GPT0,250
Écart entre enseignants0,227 · 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