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Enregistrement W4249916743 · doi:10.2523/63257-ms

Combining NMR and Conventional Logs to Determine Fluid Volumes and Oil Viscosity in Heavy-Oil Reservoirs

2000· article· en· W4249916743 sur OpenAlexaboutno aff
J.E. Galford, D.M. Marschall

Notice bibliographique

RevueProceedings of SPE Annual Technical Conference and Exhibition · 2000
Typearticle
Langueen
DomainePhysics and Astronomy
ThématiqueNMR spectroscopy and applications
Établissements canadiensnon disponible
Organismes subventionnairesnon disponible
Mots-clésCitationExhibitionOil viscosityViscosityComputer sciencePetroleum engineeringEnvironmental scienceDatabaseInformation retrievalLibrary scienceGeologyArchaeologyPhysicsGeographyThermodynamics

Résumé

récupéré en direct d'OpenAlex

Combining NMR and Conventional Logs to Determine Fluid Volumes and Oil Viscosity in Heavy-Oil Reservoirs J.E. Galford; J.E. Galford Halliburton Energy Services Search for other works by this author on: This Site Google Scholar D.M. Marschall D.M. Marschall Halliburton Energy Services Search for other works by this author on: This Site Google Scholar Paper presented at the SPE Annual Technical Conference and Exhibition, Dallas, Texas, October 2000. Paper Number: SPE-63257-MS https://doi.org/10.2118/63257-MS Published: October 01 2000 Cite View This Citation Add to Citation Manager Share Icon Share Twitter LinkedIn Get Permissions Search Site Citation Galford, J.E., and D.M. Marschall. "Combining NMR and Conventional Logs to Determine Fluid Volumes and Oil Viscosity in Heavy-Oil Reservoirs." Paper presented at the SPE Annual Technical Conference and Exhibition, Dallas, Texas, October 2000. doi: https://doi.org/10.2118/63257-MS Download citation file: Ris (Zotero) Reference Manager EasyBib Bookends Mendeley Papers EndNote RefWorks BibTex Search Dropdown Menu nav search search input Search input auto suggest search filter All ContentAll ProceedingsSociety of Petroleum Engineers (SPE)SPE Annual Technical Conference and Exhibition Search Advanced Search AbstractThe presence of viscous oil in a reservoir greatly complicates the interpretation of NMR log data. Because the NMR signal from an oil with a viscosity greater than 1,000 cp in the sub-surface typically decays with a T2 time-constant that is comparable to that of capillary-bound or clay-bound water, it is impossible to distinguish the signal from the oil phase from that of the bound-water phases. Various petrophysical quantities, such as permeability and fluid volumes, that are normally derived relatively directly from NMR measurements, thus require a significant additional interpretation effort if they are to be determined in reservoirs containing viscous oils.Work published by other authors shows that signal (porosity) loss in viscous oils is a predictable function of the viscosity and the interecho spacing used in the NMR CPMG acquisition sequence. Therefore, a reasonable estimate of viscosity can be obtained by combining the NMR logs with conventional logs to estimate the NMR signal loss at a specific interecho spacing. When this method is combined with NMR diffusion measurements, the volume of movable water can be estimated. Further combinations of conventional and NMR log data provide the quantity of capillary-bound water and a good estimation of permeability. Log examples are available from areas where viscous oils are problematic to users of both conventional and NMR data. These examples are presented to introduce and demonstrate these new methods.IntroductionHeavy oil and bitumen account for roughly 6 trillion barrels of the world's known oil reserves, the vast majority of which is found in Venezuela, Canada, and the Former Soviet Union.1 Most of the heavy oil produced in the United States comes from fields in California, Wyoming, Utah, Texas, Kentucky, and Mississippi.The term heavy oil in the context of this paper refers to oils having API gravity lower than 20° and viscosity at formation conditions above 100 cp. These hydrocarbons generally pose challenging production problems and are marketed at a discounted price, however, improved technology and higher oil prices have combined to make exploitation of heavy oil deposits more economically attractive.In addition to the multitude of production and processing problems associated with heavy oils, evaluation of petro-physical properties from NMR logging measurements can be complicated for a number of reasons. Heavy oil NMR responses are similar to signals from capillary-bound water. Furthermore, heavy-oil chemistry may be conducive to a wettability alteration2 that may contribute to a misinterpretation of water content from conventional and NMR logs. These phenomena make it difficult to quantify fluid volumes in heavy-oil reservoirs from NMR measurements alone.Present-day NMR logging instruments do not fully capture heavy-oil signals because they operate at interecho spacings that make them unable to adequately sample important rapid-decay components when viscosity exceeds ~ 1000 cp. This situation causes the indicated NMR porosity to be too small, as though the reservoir fluid had a hydrogen index (HI) smaller than one. LaTorraca, et al., have shown how the NMR signal loss in these situations is related to oil viscosity and interecho spacing.3These factors make it necessary to apply additional interpretation methods to obtain indications of altered wettability and evaluate petrophysical quantities such as fluid volumes, permeability, and apparent in-situ oil viscosity. The methods outlined in this paper rely on combinations of NMR and conventional wireline logs to determine the signal loss and estimate the viscosity of oils whose in-situ viscosity is larger than a few hundred centipoise. Additional combinations with conventional logs can be formed with NMR diffusion measurements to infer movable and capillary-bound water volumes which can be used to refine interpretations of resistivity logs, indicate altered wettability, and provide an improved estimate of permeability in heavy-oil reservoirs. Keywords: saturation, log analysis, porosity, upstream oil & gas, heavy-oil reservoir, relaxation time, irreducible water, oil viscosity, conventional log, movable water Subjects: Formation Evaluation & Management, Open hole/cased hole log analysis This content is only available via PDF. 2000. 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 machine sur la base complète

Imitation des enseignants

Ni prévalence calibrée, ni vérité terrain. Validation humaine à venir. Le volet Gemma est une étiquette directe du modèle pour chaque travail de la base, lue sur la notice réduite au titre. Le volet Codex est un classifieur appris des 10 348 étiquettes directes de Codex et calibré sur les taux pondérés de l'échantillon; les champs sans appui suffisant ne portent aucun appel Codex. Le mode candidate est l'union des deux volets; le consensus est leur intersection. Ces sorties portent le statut machine_predicted_unvalidated et ne sont pas des étiquettes humaines.

score de la tête « metaresearch » (Codex)0,002
score de la tête « metaresearch » (Gemma)0,004
Version: metacan-v3-hybrid-931329e0061cStatut 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 enseignants0,004
Score d'incertitude au seuil0,008

Scores du classifieur distillé par catégorie (deux têtes)

CatégorieCodexGemma
Métarecherche0,0020,004
Méta-épidémiologie (sens strict)0,0010,001
Méta-épidémiologie (sens large)0,0010,000
Bibliométrie0,0040,002
Études des sciences et des technologies0,0000,001
Communication savante0,0010,002
Science ouverte0,0010,001
Intégrité de la recherche0,0010,001
Charge utile insuffisante (le modèle a refusé de juger)0,0020,001

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 Opus0,016
Tête enseignante GPT0,291
Écart entre enseignants0,275 · 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 source (Gemma direct ou Codex distillé), 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

Citations3
Publié2000
Routes d'admission1
Résumé présentoui

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Même revueProceedings of SPE Annual Technical Conference and ExhibitionMême sujetNMR spectroscopy and applicationsTravaux en français237 207