Bituminous Ore Characterization by Integrating Low-Field NMR With Density and Particle Size Distribution Measurements
Notice bibliographique
Résumé
Abstract Low field NMR was demonstrated as a useful tool for a fast, non-destructive method to calculate the bitumen, water and solids content of oil sands ore. However, on occasion it was found that the presence of clay bound water ends upoverlapping the bitumen NMR signals. Thus in such cases it is difficult to accurately determine fluid content from T2 relaxation only. This paper proposes a somewhat more complex method for the determination of fluid and solids content of oil sands byintegrating a density measurement to the NMR algorithm. By a fast measurement of both the weight and the volume of the sample and subsequently its bulk density, an independent solid content estimate is provided, which in turn helps in tightening up the fluid content estimates. Preliminary work to date indicates that the combined density-NMR method matches much better the results of Dean-Stark extraction than NMR alone. Particle size distribution analysis of the solids after Dean-Stark extraction is also shown to correlate with the fast relaxation components of the water spectra in both oil sands ore and in water saturated sand extracted from the ore. The latter is obtained through a vacuum saturation step of the extracted sand by brine, followed by a centrifugal desaturation of the sand to irreducible saturation. The methodology was testedusing samples from four wells from Athabasca oil sands. Methodology and results are presented in the paper. Introduction With the declining production of conventional oil and gas, exploration and development of the unconventional resources becomes crucial for the future energy supply. Many projects have been invested and expanded into the massive oil sands deposits in Alberta, Canada. One of the big challenges in the oil sands development is how to predict and evaluate the bitumen reserves accurately and exploit them as economically as possible. This paper discusses how low-field NMR technology can be applied to determine the amount of bitumen, water and solids in oil sands. Previous works1,2,3,4,5 have shown that for water and bitumen content, there was a correlation between NMR-basedalgorithm and Dean-Stark extraction. For the time being, Dean-Stark method is an accepted industry standard for core analysis. However, the advantage of low-field NMR technology is that the NMR measurement on rocks directly correlates to the hydrogen nuclei only from the fluid6,7, it can provide quick solutions, is simple to operate and is non-destructive to core samples. Because of the unique characteristics of oil sands such as extremely high viscosity and potentially high amount of clay, it is not easy for the current low-field NMR spectrometer to differentiate bitumen spectra and clay-bound water signals. In order to fully use this advanced technology to calculate the bitumen content in oil sands accurately and try to replace the tedious Dean-Stark procedures, an extra experiment - density measurement - has been integrated. To further investigate the behavior of the bound water in NMR spectra, particle size analysis has also been incorporated. The study between particle size distribution and NMR spectra for oil sands is still in very initial stage.
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Comment cette classification a été obtenuedéplier
Prédiction machine sur la base complète
Imitation des enseignantsNi 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.
Scores du classifieur distillé par catégorie (deux têtes)
| Catégorie | Codex | Gemma |
|---|---|---|
| Métarecherche | 0,000 | 0,001 |
| Méta-épidémiologie (sens strict) | 0,000 | 0,000 |
| Méta-épidémiologie (sens large) | 0,000 | 0,000 |
| Bibliométrie | 0,001 | 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) | 0,001 | 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 source (Gemma direct ou Codex distillé), 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 ».