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Enregistrement W2092175005 · doi:10.2118/2006-045

Geological Controls on the Origin of Heavy Oil and Tar Sands and Their Impacts on In Situ Recovery

2006· article· en· W2092175005 sur OpenAlex
Haiping Huang, Barry Bennett, Thomas B. P. Oldenburg, Jennifer Adams, Steve Larter

Pourquoi ce travail est dans la base

Une base qui oublie comment elle a trouvé un travail ne peut pas être vérifiée. Voici les voies qui ont admis celui-ci.

affAu moins un auteur déclare une institution canadienne dans l'instantané OpenAlex épinglé.

Notice bibliographique

RevueCanadian International Petroleum Conference · 2006
Typearticle
Langueen
DomaineEngineering
ThématiqueHydrocarbon exploration and reservoir analysis
Établissements canadiensUniversity of Calgary
Organismes subventionnairesnon disponible
Mots-clésOil sandsIn situGeologytar (computing)Petroleum engineeringEnvironmental scienceChemistryMaterials scienceAsphaltComputer scienceComposite material

Résumé

récupéré en direct d'OpenAlex

Abstract Biodegradation of crude oil in subsurface petroleum reservoirs is an important alteration process affecting most of the world's oil deposits. The process preferentially removes light components from conventional oil to form heavy oil and tar sands, which are more difficult to produce and are more costly to refine. Although reservoir temperature is a key control on biodegradation, large variations in oil properties have been documented in accumulations from similar depths within a play area. Data from the Liaohe Basin, NE China and other basins in China and elsewhere, indicate that biodegradation is most active in a narrow zone at or near the base of the oil column in contact with the water leg. The availability of nutrients from mineral dissolution within the water leg is also thought to have a significant impact upon the degree of biodegradation. Thus the level of biodegradation increases with water leg thickness. Charge history and in-reservoir mixing, of continuously charged oil with residual biodegraded oil also have a significant impact on oil physical properties. The conceptual biodegradation model proposed combines geochemical and geological factors to provide a coherent approach to estimate the impact of degradation on petroleum and to help reliably predict biodegradation risk at the prospect level. Our geochemical approach can be used to locate sweet-spots (areas of less degraded oil), optimize the placement of new wells and completion intervals and help with production allocation from long production wells. Introduction Biodegradation has a large influence on oil physical properties, which typically reduces oil producibility by increasing oil viscosity. Viscosity and density are key properties for the evaluation, simulation, and development of petroleum reservoirs. In order to develop and manage heavy oil fields cost effectively, it is essential to understand the variation in petroleum fluid properties, especially viscosity throughout each reservoir within a field. A variety of studies demonstrated how oil properties in biodegraded oil accumulations can be predicted from core and cutting extracts prior to well testing using geochemical parameters sensitive to biodegradation1–5. McCaffrey et al. 2 identified geochemical parameters that are sensitive to the degree of oil biodegradation and to the quantity of the secondary charge and then developed transforms that related those geochemical parameters to oil quality. Those transforms were used to predict oil quality from geochemical analysis of sidewall cores. Smalley et al. 3 used a similar approach to predict oil viscosity in a biodegraded heavy oil accumulation. Guthrie et al. 4 developed a predictive model of oil quality based on a sample set of produced oils from Venezuela for predicting viscosity, API gravity, and sulphur content in oil-stained sidewall cores where these properties cannot be measured directly. Koopmans et al. 5 analyzed oils from a single oilfield in the Liaohe basin, NE China. They found the large variations in viscosity across the field can be explained by mixing, to various extents, of heavy biodegraded oils with less degraded oils. They established a simple binary mixing model, which may assist in predicting the viscosity of reservoired oils before production.

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: Simulation ou modélisation · Signal consensuel: Simulation ou modélisation
GenreSignal candidat: Empirique · Signal consensuel: Empirique
Score de désaccord entre enseignants0,261
Score d'incertitude au seuil0,996

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 Opus0,015
Tête enseignante GPT0,212
Écart entre enseignants0,197 · 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