Advances in Carbonate Characterization Using Low Field NMR
Notice bibliographique
Résumé
Abstract Western Canada contains significant deposits of oil and gas in carbonate formations. Carbonates have fairly complicated pore structures with various types of porosity, thus the characterization of carbonates still remains a daunting task. Conventional log analysis of carbonates often leads to incorrect descriptions of the reservoir properties. Low field nuclear magnetic resonance (NMR) is an emerging technology that shows great promise in rock characterization. Previous results in the literature give disparaging accounts of the applicability of NMR in carbonate rock characterization, but this work demonstrates that low field NMR can be a valuable tool even in these reservoirs. The data set for this experimental work consists of a large collection of core samples from many different fields in Canada. NMR spectra interpretations have been compared to other core analysis methods. Definite correlations have been observed between the NMR spectra properties and the results from conventional core analysis, which verifies that NMR spectra can be used to characterize even complex pore structures. Unfortunately, there is too much scatter in these correlations for them to be accurate to within less than an order of magnitude. The trends observed were developed using all the data from different formations. In this work, the data were divided into their respective formations. Within a formation, the properties of the NMR spectra are compared to the conventional data to develop correlations to predict T2cutoff, irreducible water saturation (Swi), and permeability. These results show that if the general NMR correlations developed can be tuned to specific formations, NMR can become a very useful tool for characterizing carbonate reservoirs. Introduction Traditionally reservoir characteristics are studied through core and/or log analysis. The important reservoir parameters being investigated are porosity, irreducible water saturation, and permeability. These parameters give insight into the amount of existing hydrocarbon reserves, and the ease with which these reserves can be produced. These parameters can be found through core analysis, but this is a costly and time consuming process. Log analysis as an alternative has many inherent problems as well. Nuclear magnetic resonance in reservoir characterization shows promise in predicting porosity, irreducible water saturation and permeability of sandstone reservoirs. For carbonates reservoirs, however, NMR performance in the literature has not very encouraging. This is due to the direct application of the interpretation models, which were developed for sandstone reservoirs, in carbonate reservoirs. In order to predict the properties of the carbonate reservoirs through NMR data, it is important to develop a different interpretation method. Attempts have been made by many researchers to extract important reservoir information from NMR data collected for carbonate samples. Correlations were found to estimate T2cutoff, Swi and permeability. Mai and Kantzas1–4, have presented a series of experimental procedures aiming at the development of NMR-based carbonate characterization methods. Plugs from several formations were used in an attempt to provide predictive correlations for porosity, movable fluids, Swi and permeability. While porosity, movable fluids and Swi showed promise, the permeability predictions were poor. In this paper, a subset of the data investigated previously was further analyzed on a formation basis.
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 enseignantsNi 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.
Scores Codex et Gemma par catégorie
| Catégorie | Codex | Gemma |
|---|---|---|
| Métarecherche | 0,000 | 0,000 |
| Méta-épidémiologie (sens strict) | 0,000 | 0,000 |
| Méta-épidémiologie (sens large) | 0,000 | 0,000 |
| Bibliométrie | 0,000 | 0,000 |
| Études des sciences et des technologies | 0,000 | 0,000 |
| Communication savante | 0,000 | 0,000 |
| Science ouverte | 0,000 | 0,000 |
| Intégrité de la recherche | 0,000 | 0,000 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,002 | 0,000 |
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.
score_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 découleClassification
machine, non validéePrédiction automatique; un appel candidat d’une seule tête enseignante, pas un consensus.
Le détail, modèle par modèle et score par score, se trouve en fin de page sous « Comment cette classification a été obtenue ».