Estimation of Bitumen and Clay Content in Fine Tailings
Notice bibliographique
Résumé
Abstract Fine tailings are mixtures consisting primarily of water with small amounts of bitumen, sand, silts and clays. They are the components of the tailings ponds and the by-product of the oil sand extraction process. There are concerns regarding the possible environmental implications of the tailings when they will be reclaimed. For this reason, it is important to understand not only the tailings content, but also their stability and settling properties. In this study, low field Nuclear Magnetic Resonance (NMR) has been used to estimate the bitumen, clay and water content of synthetic tailings samples. Numerous samples with variable water, bitumen, sand and clay concentrations were prepared and tested in the NMR tool under ambient conditions. The amplitude and relaxation properties of water were correlated to the clay content. The experimental measurements were successful and were conducted in a matter of a few minutes. The results confirm and expand previous knowledge generated by the group and identify potential applications for on-line determination of tailings streams composition. Introduction Oil production from the oil sands in Alberta generates large volumes of solid and liquid non-usable materials that must be reclaimed. For each barrel of synthetic crude produced, about 2 tonnes of ore are processed, with a by-product of 2 m3 of processed water and 1.8 tonnes of solid tailings(1). The tailings stream is a mixture composed primarily of water, sand, silt, clays and a small amount of unrecovered bitumen. Conventionally, tailings are discharged of into ponds where the segregation between coarse sand and fine clay solids occurs. Fines are defined as minerals passing through a 325 U.S. (<44 µm) mesh sieve, and subdivided into silt-sized (2 µm< particle size <44 µm) and clay-sized (<2 µm) particles. The claysized fraction in tailings includes principally illite, kaolinite and a small fraction of sodium montmorillonite(2). In the tailings ponds, coarse sand settles rapidly while fine clay solids settle slowly. As a result, disposal of these fine tailings is a serious problem because full consolidation is estimated to take thousands of years, and the accumulated volume of mature fine tailings is expected to increase to over one billion cubic meters by the year 2020(3). Due to the large volumes of tailings, the reduction of the remaining bitumen in the tailings stream is an important environmental and production process issue. The present study focuses on the use of Nuclear Magnetic Resonance (NMR) as a tool to estimate the bitumen, water and clay content in fine tailings. NMR is a non-destructive technique that is currently used to establish compositions of oil / brine emulsions and the viscosity of heavy oil and bitumen(4, 5). In addition, the NMR logging tool is used in reservoir characterization, measuring properties such as permeability(6–9), porosity(8, 9), mobile and immobile fluids(10–13), and fluid saturations(14). In comparison to other techniques such as Dean- Stark extraction, NMR provides a faster composition measurement and requires a smaller sample. Another advantage is that the NMR technique can provide compositional results on-site or may even be implemented on-line in a process, such as oil sand processing.
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) | 0,000 | 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 ».