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Enregistrement W4255660401 · doi:10.2523/77329-ms

Viscosity Predictions for Crude Oils and Crude Oil Emulsions Using Low Field NMR

2002· article· en· W4255660401 sur OpenAlexafffundabout
J. Bryan, A. Kantzas, C. Bellehumeur

Notice bibliographique

RevueProceedings of SPE Annual Technical Conference and Exhibition · 2002
Typearticle
Langueen
DomainePhysics and Astronomy
ThématiqueNMR spectroscopy and applications
Établissements canadiensUniversity of Calgary
Organismes subventionnairesNatural Sciences and Engineering Research Council of Canada
Mots-clésCitationExhibitionCrude oilViscosityComputer scienceLibrary scienceEnvironmental scienceMaterials scienceEngineeringPetroleum engineeringArchaeologyGeography

Résumé

récupéré en direct d'OpenAlex

Viscosity Predictions for Crude Oils and Crude Oil Emulsions Using Low Field NMR J. Bryan; J. Bryan University of Calgary Search for other works by this author on: This Site Google Scholar A. Kantzas; A. Kantzas University of Calgary Search for other works by this author on: This Site Google Scholar C. Bellehumeur C. Bellehumeur University of Calgary Search for other works by this author on: This Site Google Scholar Paper presented at the SPE Annual Technical Conference and Exhibition, San Antonio, Texas, September 2002. Paper Number: SPE-77329-MS https://doi.org/10.2118/77329-MS Published: September 29 2002 Cite View This Citation Add to Citation Manager Share Icon Share Twitter LinkedIn Get Permissions Search Site Citation Bryan, J., Kantzas, A., and C. Bellehumeur. "Viscosity Predictions for Crude Oils and Crude Oil Emulsions Using Low Field NMR." Paper presented at the SPE Annual Technical Conference and Exhibition, San Antonio, Texas, September 2002. doi: https://doi.org/10.2118/77329-MS Download citation file: Ris (Zotero) Reference Manager EasyBib Bookends Mendeley Papers EndNote RefWorks BibTex Search Dropdown Menu toolbar search search input Search input auto suggest filter your search All ContentAll ProceedingsSociety of Petroleum Engineers (SPE)SPE Annual Technical Conference and Exhibition Search Advanced Search AbstractKnowledge of oil viscosity is vital to the petroleum industry, and is especially important when considering production of heavy oil and bitumen. As viscosity increases, conventional measurements become progressively less accurate and more difficult to obtain. Oil viscosities measured in the lab may also be not indicative of true in-situ viscosities. An alternate method is required for predicting oil viscosity, especially if this method can be applied in-situ. Stable crude oil emulsions are prevalent in many stages of the production and transport of heavy oil and bitumen. Knowledge of emulsion viscosity is necessary for determining energy requirements for transport and upgrading of the produced crude.Low field nuclear magnetic resonance is examined in this work for its potential to predict viscosity of crude oil and crude oil emulsions. NMR is an attractive alternative to conventional viscosity measurements, because it can provide fast, unbiased and non-destructive data. A correlation is presented that predicts fluid viscosities from under 1 cP to over 3 000 000 cP over 25–80°C, making it valid over a wider range of viscosities and temperatures than any other published NMR viscosity correlation. With tuning, this model can predict very accurate changes in viscosity with temperature for a single oil. An NMR emulsion viscosity model is also presented that uses the oil viscosity and water fraction, both determined from NMR, to predict emulsion viscosity. This correlation is able to provide order of magnitude emulsion viscosity predictions for a wide range of emulsion water cuts and viscosities. Work has also been done to extend the viscosity predictions to in-situ viscosity measurements, which can then be extracted from logs. Preliminary findings on in-situ oil viscosity are encouraging, and indicate that NMR has great potential as a tool for in-situ viscosity determination.IntroductionKnowledge of oil viscosity is essential to many areas of the petroleum industry, from reservoir engineering and enhanced oil recovery to upgrading and transport of produced fluids. When producing heavy oil and bitumen, the high viscosities are one of the major impediments to recovering these oils. Oil viscosity is often correlated directly to the reserves estimate in heavy oil and bitumen formations1, and can determine the success or failure of a given EOR scheme. As a result, viscosity is an important parameter for doing numerical simulation and determining the economics of a project.Water-in-oil emulsions, also known as crude oil emulsions, are also prevalent in the industry. All oil is produced along with some water, and for heavier crudes, which are usually produced using injected steam, the water cut can be significant. Knowledge of emulsion viscosity can aid in determining energy requirements for transport and upgrading of these fluids2.As viscosity increases, conventional measurements become progressively less accurate and more difficult to obtain. Oil samples and emulsions extracted and measured in the lab may also no longer be representative of in-situ or on site conditions1. An alternate method of measuring the viscosity of crude oils and crude oil emulsions would therefore be of great value to the petroleum industry. Low field nuclear magnetic resonance (NMR) is an attractive alternative to conventional viscosity measurements, as its measurements are fast, non-destructive and insensitive to technician error. Low field NMR is an accepted tool in conventional oil sandstone reservoirs, but has so far found only limited use in heavy oil and bitumen analysis. This work demonstrates that NMR can in fact be a valuable technology for heavy oil and bitumen formations like those in Alberta. Keywords: nmr, spectrum, viscosity prediction, viscosity model, relaxation, viscosity, droplet size, bitumen, spe 77329, emulsion Subjects: Formation Evaluation & Management, Open hole/cased hole log analysis This content is only available via PDF. 2002. Society of Petroleum Engineers You can access this article if you purchase or spend a download.

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 candidatesaucune
Catégories consensuellesaucune
DomaineSignal candidat: aucune · Signal consensuel: aucune
Devis d'étudeSignal candidat: Expérimental (laboratoire) · Signal consensuel: Expérimental (laboratoire)
GenreSignal candidat: Empirique · Signal consensuel: Empirique
Score de désaccord entre enseignants0,079
Score d'incertitude au seuil0,495

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 Opus0,023
Tête enseignante GPT0,308
Écart entre enseignants0,285 · 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.

Les modèles n’ont appliqué aucune catégorie : rien dans la taxonomie ne correspondait à ce travail.
Devis d'étudeExpérimental (laboratoire)
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

Citations1
Publié2002
Routes d'admission3
Résumé présentoui

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