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Enregistrement W4254761407 · doi:10.2523/77401-ms

An Evaluation of the Application of Low Field NMR in the Characterization of Carbonate Reservoirs

2002· article· en· W4254761407 sur OpenAlex
A. Mai, Apostolos Kantzas

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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-clésCitationExhibitionComputer scienceLibrary scienceDownloadInformation retrievalField (mathematics)DatabaseWorld Wide WebArchaeologyHistoryMathematics

Résumé

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An Evaluation of the Application of Low Field NMR in the Characterization of Carbonate Reservoirs An Mai; An Mai University of Calgary Search for other works by this author on: This Site Google Scholar Apostolos Kantzas Apostolos Kantzas 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-77401-MS https://doi.org/10.2118/77401-MS Published: September 29 2002 Cite View This Citation Add to Citation Manager Share Icon Share Twitter LinkedIn Get Permissions Search Site Citation Mai, An, and Apostolos Kantzas. "An Evaluation of the Application of Low Field NMR in the Characterization of Carbonate Reservoirs." Paper presented at the SPE Annual Technical Conference and Exhibition, San Antonio, Texas, September 2002. doi: https://doi.org/10.2118/77401-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 AbstractConventional reservoir analysis has always been an extensive process. In order to properly characterize a reservoir, cores and/or logs have to be obtained. Both core and log analysis is expensive and time consuming. NMR is an attractive alternative to these tools due to the fact that in theory, only one measurement is required. However, the conventional methods of interpreting NMR data only seem to work for simple sandstones. A new method of interpreting NMR data is required for complex porous structure such as carbonates.It was found that NMR can predict porosity that is similar to the values obtained by gas expansion. By using the NMR data at fully saturated and irreducible water saturation (Swi), a T2cutoff value was obtained for each sample that separates the bound and movable fluid signals. It was found that T2cutoff for carbonates is not 100 ms as is widely believed by many people who have analyze NMR in carbonates. A correlation for T2cutoff was found as a function of the size of the last peak and its geometric mean. A correlation was also found for Swi, which was a function of the size of the first and last peak.The Free Fluid and the mean T2 permeability models were evaluated. It was seen that the predictions from these models were not adequate. Another permeability model was developed, which is expressed in terms of the size of the first and last peak of the NMR spectrum obtained from the fully saturated sample. It was found that the correlation did a better job of predicting the permeability values. The new model has its own limitations, a method is currently being investigated to overcome these limitations. Despite these limitations, however, the new NMR permeability model provides better estimates of carbonate permeability than any other established methods.IntroductionConventional methods of analyzing the characteristics of carbonate reservoirs usually involve physically analyzing the core samples and/or analyzing the various logs collected from the wells. The important reservoir parameters that are usually investigated are porosity, permeability, and irreducible water saturation. These parameters will give an indication of the amount of hydrocarbons existing in the reservoir and how easy it is to recover them. In order to find these parameters using conventional core analysis, the cores samples taken from the wells first have to be cut and cleaned. The samples are then measured for porosity using one of many options available, and permeability is measured at the dry state. To find Swi, the core samples have to be saturated with brine and spun to the irreducible water condition. Carbonates generally have very tight pore structures, so the process of finding these parameters through core analysis is expensive and time consuming.To determine these reservoir parameters through log analysis, various logs have to be run. Due to limited vertical resolution, the presence of vuggy porosity might not be detected at all1,2. Also, to estimate porosity from logs, lithology components are required3. This causes difficulties in analyzing carbonate reservoirs in which the lithology is quite complex.Nuclear Magnetic Resonance (NMR) is a fairly recent application in reservoir study and it has garnered major successes in characterizing sandstone reservoirs4. From a single NMR spectrum at the fully saturated conditions, porosity, irreducible water saturation and permeability of these reservoirs could be estimated. However, NMR application in carbonates has not been very successful.This is due to the fact that most earlier works assumed simple lithology and attempted to use the same models as for the sandstone reservoir. Thus the traditional method of interpreting NMR data can often lead to erroneous estimations in complicated porous media such as carbonates4.This paper details an attempt to investigate porosity, permeability and irreducible water saturation by using NMR and Computed Tomography (CT) method to provide details on the pore structure of the carbonate samples. Keywords: porosity, upstream oil & gas, permeability model, permeability, amplitude fraction, pore, secondary porosity fraction, relaxation, application, spe 77401 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.

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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 enseignants0,077
Score d'incertitude au seuil0,175

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 Opus0,022
Tête enseignante GPT0,311
Écart entre enseignants0,289 · 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