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Enregistrement W4240797479 · doi:10.2523/75687-ms

On the Characterization of Carbonate Reservoirs Using Low Field NMR Tools

2002· article· en· W4240797479 sur OpenAlexafffundabout
A. Mai, A. Kantzas

Notice bibliographique

RevueProceedings of SPE Gas Technology Symposium · 2002
Typearticle
Langueen
DomainePhysics and Astronomy
ThématiqueNMR spectroscopy and applications
Établissements canadiensUniversity of Calgary
Organismes subventionnairesNatural Sciences and Engineering Research Council of Canada
Mots-clésCarbonateCitationComputer scienceCharacterization (materials science)Library scienceGeologyMaterials scienceNanotechnology

Résumé

récupéré en direct d'OpenAlex

On the Characterization of Carbonate Reservoirs Using Low Field NMR Tools A. Mai; A. Mai University of Calgary and TIPM Laboratory Search for other works by this author on: This Site Google Scholar A. Kantzas A. Kantzas University of Calgary and TIPM Laboratory Search for other works by this author on: This Site Google Scholar Paper presented at the SPE Gas Technology Symposium, Calgary, Alberta, Canada, April 2002. Paper Number: SPE-75687-MS https://doi.org/10.2118/75687-MS Published: April 30 2002 Cite View This Citation Add to Citation Manager Share Icon Share Twitter LinkedIn Get Permissions Search Site Citation Mai, A., and A. Kantzas. "On the Characterization of Carbonate Reservoirs Using Low Field NMR Tools." Paper presented at the SPE Gas Technology Symposium, Calgary, Alberta, Canada, April 2002. doi: https://doi.org/10.2118/75687-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 Unconventional Resources Conference / Gas Technology Symposium Search Advanced Search AbstractAlberta contains significant deposits of oil and gas in carbonate formations. Carbonates tend to have fairly tight matrix structures, resulting in low primary porosity and permeability. As a result, laboratory characterization of carbonate properties is a slow and tedious process. Low field NMR is an emerging technology shows great promise in rock characterization. In a single NMR experiment, rock properties like porosity, permeability and Swi can in theory be measured.Experiments were performed on approximately 80 core plugs from six carbonate formations. Porosity measurements were performed through gas expansion and brine saturation (Archimedes' principle). Air permeabilities were also measured. Cores were also saturated with brine and spun to irreducible water saturation. NMR measurements were taken at both saturation stages (Sw = 1.0 and Sw = Swi). NMR data were interpreted using the conventional core analysis results as guides. Observations were made regarding NMR trends and corresponding rock properties.Preliminary analysis of the data shows that NMR can successfully predict the content and distribution of the fluids in the porous media. Also, the NMR spectra of carbonate samples seem to suggest that NMR can be used as a tool for classifying cores into different pore systems.Using T2cutoff values as a tool, the cores can be divided into groups. Group 1 has T2cutoff< 80 ms, while group 2 has T2cutoff in the range of 80 to 200 ms and group 3 has T2cutoff values > 200 ms. Group 1 cores generally have higher porosity values than groups 2 and 3. Group 1 also has low values of T2gm, compared to the other two groups. Air permeability was compared to the geometric means and T2gm cutoff values of the three groups and some general trends were observed, but more analysis is required before these trends can be quantified.IntroductionReservoir rocks can be divided into two groups: clastics and carbonates. Clastics include silts, sands and gravels, while carbonates encompass limestones and dolomites. Both of these two groups have intergranular porosity, which is the pore space between sand grains or carbonate crystals originally deposited1. However, carbonates also have vugular porosity, formed by the leaching of carbonate grains or other soluble materials.Vugs are usually defined as pores that are larger than adjacent grains. The presence of vugs in carbonate reservoirs is very common. However, conventional wireline logs cannot always detect such pores due to the limited vertical resolution of the tools2. Unfortunately, vugs have a significant effect on productivity, porosity and permeability. Along with vugs, another problem with carbonate rocks is that their porosity is very difficult to determine. This is due to the fact that the rock matrix or specifically the amount of limestone and dolomite varies, making it difficult to analyze using conventional logs3,4. In complex carbonates estimations from logs are deemed unreliable and it is normally required to calibrate logs against core analysis. However, core analysis is a time consuming and costly process, therefore it is not always practical.Nuclear Magnetic Resonance (NMR) is a new and promising technology that is fast, nondestructive and able to yield a vast amount of information about the reservoir formation5. In theory, a single NMR measurement can be used to determine porosity, permeability and irreducible water saturation. However, most of the earlier work on NMR usually assumes a simple formation lithology (such as sandstone) or simply ignores the effect of rocks on NMR interpretation and data analysis. Thus the methods originally developed might not be able to correctly predict the properties of complex systems such as carbonates 6,7.This paper investigates porosity and irreducible water saturation of carbonate samples from many fields in Western Canada through the analysis of T2cutoff values. Keywords: nmr, well logging, amplitude, carbonate reservoir, pore, permeability, group 1, nmr response, upstream oil & gas, characterization 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 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)0,001
score de la tête « metaresearch » (Gemma)0,001
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: aucune
Score de désaccord entre enseignants0,006
Score d'incertitude au seuil0,021

Scores du classifieur distillé par catégorie (deux têtes)

CatégorieCodexGemma
Métarecherche0,0010,001
Méta-épidémiologie (sens strict)0,0000,000
Méta-épidémiologie (sens large)0,0000,000
Bibliométrie0,0020,001
Études des sciences et des technologies0,0000,000
Communication savante0,0010,001
Science ouverte0,0010,001
Intégrité de la recherche0,0010,001
Charge utile insuffisante (le modèle a refusé de juger)0,0060,002

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 GPT0,258
Écart entre enseignants0,245 · 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

Citations2
Publié2002
Routes d'admission3
Résumé présentoui

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