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Enregistrement W2006741738 · doi:10.2118/2006-116

In-situ Viscosity of Heavy Oil: Core and Log Calibrations

2006· article· en· W2006741738 sur OpenAlex
J. Bryan, Apostolos Kantzas, R. Badry, J.G. Emmerson, T. Hancsicsak

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Notice bibliographique

RevueCanadian International Petroleum Conference · 2006
Typearticle
Langueen
DomainePhysics and Astronomy
ThématiqueNMR spectroscopy and applications
Établissements canadiensUniversity of Calgary
Organismes subventionnairesnon disponible
Mots-clésViscosityIn situCore (optical fiber)Petroleum engineeringWell loggingLoggingNMR spectra databaseOil fieldEnvironmental scienceSpectral lineOil viscosityAnalytical Chemistry (journal)Materials scienceSoil scienceGeologyChemistryPhysicsComposite materialChromatographyOrganic chemistry

Résumé

récupéré en direct d'OpenAlex

Abstract Having knowledge of oil viscosity variation within reservoirs would be of considerable benefit when producing from heavy oil fields. Previous work has demonstrated that low field NMR bench top instruments can be used to perform measurements of in-situ viscosity. Ideally, if these measurements could be performed on NMR logging tools, viscosity characterization studies could be carried out with using fewer core samples. In this paper, data is presented for a heavy oil reservoir in northern Alberta. A methodology is presented for tuning NMR viscosity estimates to the field in question, and core analysis results are collected, showing that in-situ viscosity predictions are possible in the laboratory. NMR spectra measured in the laboratory are compared to NMR logging tool spectra, in order to determine if results obtained using bench top instruments can be extrapolated to logging tool data. Introduction Canada has significant proven reserves from our oil sands in Saskatchewan and northern Alberta, which constitute some of the largest resource bases in the world. With the decline of conventional oil reserves in Canada, interest is shifting rapidly to the production of this heavy oil. Heavy oil and bitumen are characterized by high fluid viscosity, and density values similar to that of water. The high oil viscosity is the single greatest impediment to the successful recovery of this resource, and the viscosity is directly related to both the technical success of any chosen recovery scheme and the economic value of the oil. As a result, oil viscosity information is key when estimating reserves and developing recovery options from heavy oil and bitumen formations. Viscosity is conventionally measured in two different Methods1. Samples are either taken from the produced fluid from the wellhead, or oil sand samples are taken into the laboratory, and oil is extracted in order to measure its viscosity. The difficulty in making measurements on wellhead samples is that the oil may have been contaminated by diluents or drilling fluid1, or may contain significant emulsified water from thermal operations. This means that viscosity values obtained from wellhead oil samples must be used carefully and should be analyzed in order to ensure that they are truly representative of the oil viscosity in the formation. Measurements on bitumen that has been extracted from core samples are generally more accurate, but also tend to be more expensive. Care must be taken to ensure that enough samples are taken to properly characterize the fluid viscosity in the reservoir. Variations may also be observed between different laboratory results, and in repeat measurements of crude oil samples2. Therefore, if measurements of oil viscosity could be made in-situ, during the initial logging of the reservoir, this could be of considerable benefit to geologists and reservoir engineers seeking to understand their reservoirs. In the past, low field nuclear magnetic resonance (NMR) has been shown to have great potential as a tool for making viscosity measurements. The uses of NMR are many and varied, and the theory has been well explored in the literature3–5.

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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: Théorique ou conceptuel · Signal consensuel: aucune
GenreSignal candidat: Empirique · Signal consensuel: Empirique
Score de désaccord entre enseignants0,679
Score d'incertitude au seuil0,861

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 Opus0,013
Tête enseignante GPT0,278
Écart entre enseignants0,265 · 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