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Enregistrement W4245829815 · doi:10.2118/2006-134

The Origin, Prediction and Impact of Oil Viscosity Heterogeneity on the Production Characteristics of Tar Sand and Heavy Oil Reservoirs

2006· article· en· W4245829815 sur OpenAlex
S.R. Larter, J. Adams, I.D. Gates, B. Bennett, H. Huang

Pourquoi ce travail est dans la base

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affAu moins un auteur déclare une institution canadienne dans l'instantané OpenAlex épinglé.
fundUn bailleur canadien est enregistré sur le travail.

Notice bibliographique

RevueCanadian International Petroleum Conference · 2006
Typearticle
Langueen
DomaineEngineering
ThématiqueHydraulic Fracturing and Reservoir Analysis
Établissements canadiensUniversity of Calgary
Organismes subventionnairesNatural Sciences and Engineering Research Council of Canada
Mots-clésPetroleum engineeringOil productiontar (computing)Oil sandsViscosityOil viscosityProduction (economics)Environmental scienceGeologyComputer scienceMaterials scienceAsphalt

Résumé

récupéré en direct d'OpenAlex

Abstract The defining characteristic of heavy and super heavy oilfields is the large spatial variation in fluid properties, such as oil viscosity, commonly seen within the reservoirs. Traditional heavy oil and tar sand exploration and production strategies rely significantly on characterization of key reservoir heterogeneities and assessments of fluid saturations. While it is important to understand how these properties vary, the spatial distribution of fluid properties can often dominate production behavior but surprisingly, are usually ignored! Heavy oil and tar sands are formed by microbial degradation of conventional crude oils over geological timescales. Constraints such as oil charge mixing, reservoirtemperature dependant biodegradation rate and supply of water and nutrients to the organisms ultimately dictate the final distribution of API gravity and viscosity found in heavy oil fields. Large-scale lateral and small-scale vertical variations in fluid properties due to interaction of biodegradation and charge mixing are common, with up to orders of magnitude variation in viscosity over the thickness of a reservoir. These variations are often predictable and can be integrated into reservoir simulation models in a manner similar to specifying geological heterogeneity. In this work, we describe and illustrate quantitative geological controls on fluid property variations and show how petroleum geochemistry can be used to rapidly produce high resolution fluid property images of tar sand and heavy oil reservoirs. The impact of viscosity variations in a heavy oil reservoir on production depends on recovery method. Numerical thermal reservoir simulations reveal that oil viscosity heterogeneity (i.e., a vertical viscosity profile in the reservoir) lowers the oil production volumes from Steam- Assisted Gravity Drainage in geologically realistic reservoirs compared to results from equivalent models run with uniform average viscosity profiles. Similar results are found for the Cyclic Steam Stimulation process. In cases with viscosity profiles, the relatively high viscosity at the base of the reservoir slows the growth of steam chambers relative to that in uniform viscosity reservoirs. We also describe how the chemical fluid heterogeneities can be used to predict oil viscosity from well cuttings and/or core or to de-mix produced oils into zonal contributions from different parts of the production well. Introduction There are over 6 trillion barrels of heavy oil and tar reserves on Earth but average recoveries remain tantalizingly low (5 to 15% for cold heavy oil production and 40 to 85% for steam assisted gravity drainage operations.1) The defining characteristic of heavy and super heavy oilfields is the large spatial variation in fluid properties, such as oil viscosity, commonly seen within the reservoirs. Traditional heavy oil and tar sand exploration and production strategies rely significantly on characterization of key reservoir heterogeneities and distributions of porosity and permeability and assessments of fluid saturations. While these reservoir characteristics are controlling factors, variations in fluid properties can often dominate production behavior, but are usually ignored. Figure 1 reminds us that according to Darcy's law, reservoir permeability and fluid viscosity contribute equally to controlling the net flow rate of oil during production under a given fluid potential gradient.

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)0,000
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: Observationnel · Signal consensuel: aucune
GenreSignal candidat: Empirique · Signal consensuel: Empirique
Score de désaccord entre enseignants0,439
Score d'incertitude au seuil0,983

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)0,0000,000

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 Opus0,011
Tête enseignante GPT0,221
Écart entre enseignants0,210 · 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