Viscosity Reduction in Extra Heavy Crude Oils
Notice bibliographique
Résumé
Latin American sources are expected to increase production to around 4.2 million bbl/day by 2005. Of this 1 million bbVday is expected to be extra-heavy Orinoco oil from Venezuela. It has been projected that imports to the US from Latin America will increase by around 2.7 million bbVday during the same period. As well as this, a big increase in crude oil production will come heavy crude oil reserves in Western Canada. These reserves are intended to be produced a diluted heavy crudes and as synthetic oils, with vaqing degrees of upgrading. It is expected that by 2005 another 700,OO bbVday will be coming from Canadian heavy oil fields. Most of this will end up in the US Mid-West Market. This supply will replace the US supplies at lighter crude. With such increases of heavy crude oil sources making there way into the US market in the near future now seems a good time to exploit new avenues of research into the reduction at their viscosity. Some of these crude oils can have viscosities in excess of 15,000 centistokes at 100°F (2). In order for these crude oils to be transported via pipeline from the source, the viscosity must be lower than 150 centistokes at 100°F. The crude oils being very viscous on extraction have to go through some processing e.g. gas plasticization, thermal cracking or blending with lighter distillate fractions, to enable them to be transported by pipeline. Taking blending for an example, typically the crude oils are blended with a kerosene distillate fraction. This process has its disadvantages. In some cases up to 30 wt.% of kerosene must be added to sufficiently reduce the viscosity, this uses up a great quantity of a valuable commercial product. Also the added kerosene must be processed again through the refinery along with heavy crude oil. It is widely assumed that the asphaltene molecules in oils agglomerate to form micelle-like clusters. Interactions between these clusters contribute towards the viscosity of the oils. By breaking these agglomerates apart viscosity will be reduced. The kerosene used to cut the oils and reduce viscosity only does so by being an effective diluent. It does not break down the agglomerates by any significant amount. The following work is from a scoping study, the objective of which was to see whether the viscosity of extra heavy crude oils could be reduced by the addition of other chemical compounds and/or distillate fractions, which may break down the asphaltene agglomerates, and thus achieve greater viscosity reductions, when compared with kerosene. The intention is to then augment the better of these additive compounds with kerosene, thus initiating viscosity reduction for less volume of diluent. In this period of initial investigation several dispersants with differing physical and chemical properties have been assessed for their effect on viscosity reduction. Figure 1 shows the chemical structure and the polarity of the compounds, in Debye units (DU). The compounds were chosen for their ring type structures. The presence of x electrons in the ring may play a role in the interaction between the compounds added and the n electrons in the polyaromatic systems of the asphaltene agglomerates. The compounds were kept to one ring to keep the size of the molecules small and allow for a greater diffusion through the crude oil matrix, and penetration into the asphaltene agglomerates.
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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 | 0,000 |
| Méta-épidémiologie (sens strict) | 0,001 | 0,000 |
| Méta-épidémiologie (sens large) | 0,000 | 0,000 |
| Bibliométrie | 0,001 | 0,001 |
| Études des sciences et des technologies | 0,000 | 0,000 |
| Communication savante | 0,001 | 0,001 |
| Science ouverte | 0,000 | 0,001 |
| Intégrité de la recherche | 0,000 | 0,001 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,005 | 0,001 |
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 ».