Determining Bitumen, Water and Solids in Oil Sands Ore by Using Low-Field NMR
Notice bibliographique
Résumé
Abstract In previous work, low-field nuclear magnetic resonance (NMR) has been considered as a fast and non-destructive method to characterize oil and water. In this work, we continue to use the low-field NMR technique to determine the amount of bitumen, water and solids for unconsolidated oil sand ores from two different depositional environments. Simple T2 cut-off and signal deconvolution are applied to the NMR spectra to estimate bitumen and water content. Comparison results are given. It has been found previously that in most cases, the signals from clay bound water and bitumen overlap, thus the estimation of fluid content needs correction. To replace the well-known Dean-Stark extraction method, it is necessary to seek a fast, simple, non-destructive and inexpensive method. A densitometry technique, with simultaneous pore volume measurement, is developed to provide the volume of the ore sample and complement the NMR results. A density algorithm is introduced to determine fluid and solid content. Results from pore volume measurement are comparable with those from Dean-Stark extraction and low-field NMR. A combined NMRpore volume technique appears to minimize errors compared to Dean-Stark extraction. Introduction It is well known that the global oil demand has continued to accelerate over the years and conventional oil supplies tend to decline. Therefore, more attention turns to the unconventional resources such as oil sands. The Alberta oil sands, the largest source of bitumen in the world, are relatively under-exploited, with estimated recoverable bitumen reserves of roughly 39 billion m3 and established reserves of approximately 376 billion m3(1). However, the varying depositional history and complex sedimentary sequences (2) result in the formations hosting oil sands deposits that are not homogeneous, with significant difference in pay thickness, permeability, as well as bitumen and water saturation. Moreover, most of the higher-quality deposits are already producing or under development. In order to evaluate the oil sands potential resource and generate maximum economic returns, it is necessary to determine the content of bitumen, water and solids properly. Low-field nuclear magnetic resonance logging has become popular since the 1990s for analyzing reservoir fluids and fluid/rock interaction. Recently, considerable effort has been put on the applications of NMR technology in oil sands. Such works include bitumen viscosity determination (3, 4, 5, 6), in-situ viscosity measurement (7, 8), the clay content estimation in unconsolidated samples (9, 10, 11) and in-situ fluid saturation assessment for ore and froth (12, 13). In oil sands mining operations, a quick and accurate way to determine the bitumen, water and solids content would improve the operating costs efficiently. Dean-Stark extraction has been considered to be an industry standard to measure the bitumen, water and solids (14), but such an extraction procedure is highly time-consuming and requires significant volumes of very expensive and toxic solvents. Also, the sample itself is totally destroyed after the extraction. Our research proposes a fast and non-invasive method. A pore-volume experiment is run to determine the amount of solids using a density algorithm. The result is integrated with low-field NMR relaxometry to obtain the amount of bitumen and water.
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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) | 0,001 | 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 tête enseignante, pas un consensus.
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