Evaluation of Bitumen-Solvent Properties Using Low Field NMR
Notice bibliographique
Résumé
Abstract The VAPEX (vapor extraction) process is a new technique for the recovery of highly viscous heavy oil and bitumen. This process involves injection of vaporized hydrocarbon solvent into heavy oil and bitumen reservoirs and production of the resulting solvent-diluted oil that drains by gravity in a horizontal well. Research has shown that this process is highly efficient and that different solvents give different results. In this paper, six different solvents were added to several oils of different viscosities and densities. The solvents were added in different ratios to each of the oils and NMR spectra were obtained. The mixture of solvent and heavy oil or bitumen produces a spectrum that is distinctly different than that of the solvent or oil alone. From the shape and amplitude of the NMR spectra we can calculate the amount of solvent prevent. Furthermore, we can predict the viscosity of the mixture without any additional viscosity measurements. As asphaltenes precipitate with the addition of solvent we can correlate the amount of asphaltene reduction to changes in the NMR spectra. In this manner, NMR can possibly be used to show the asphaltene precipitation of different oils in the presence of solvent. By measuring the amount of asphaltene precipitation, NMR can also provide an indication of in-situ upgrading of the oil that occurs with the addition of solvent. Using NMR as an analysis tool, the effect of the different solvents on viscosity reduction and asphaltene precipitation is quantified. Introduction The VAPEX process was proposed by Butler and Mokrys(1) for the first time as an alternative to Steam Assisted Gravity Drainage (SAGD) for thin reservoirs where heat lost in the formation would make the process uneconomic. In the VAPEX process vapour solvents, instead of steam, are injected in the reservoir. The solvents dissolve into the bitumen and dramatically reduce its viscosity. The diluted bitumen can drain down to the producer by gravity. Since the original paper, many valuable experimental studies were published using different systems, including Hele-Shaw cells (2)(3)(4), pore network glass micromodels (5), Magnetic Resonance Imaging (MRI) (6) and PVT experiments (7). In this paper, low field Nuclear Magnetic Resonance (NMR) was used to measure the physical properties of heavy oil and bitumen samples with kerosene, hexane, naphtha, haptane, pentane, toluene in several ratios at room temperature and pressure. Low field nuclear resonance (NMR) has vast potential as a tool for measuring properties of a reservoir fluid (8). NMR measurements are simple and non-destructive, but capable of yielding an incredible wealth of information about the reservoir fluid under investigation in a particular sample (9)(10). The mixture viscosity decreases dramatically as the ratio of the injected solvent to heavy oil or bitumen increases, and different solvents show different dilution capacity. The bulk relaxation time of a hydrocarbon fluid is inversely proportional to its viscosity (10). NMR spectra show changes in the response from the mixture after solvent has been added. One goal of this work is to see if one form of viscosity model works for oil-solvent mixtures for different oils mixed with different solvents over a wide range of mixture composition.
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) | 0,004 | 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 ».