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Enregistrement W4251699980 · doi:10.2118/08-01-38

Heats of Combustion of Selected Crude Oils and Their SARA Fractions

2008· article· en· W4251699980 sur OpenAlexafffund
G.J. Mendez Kuppe, S. A. Mehta, R.G. Moore, M.G. Ursenbach, E. Zalewski

Notice bibliographique

RevueJournal of Canadian Petroleum Technology · 2008
Typearticle
Langueen
DomaineChemistry
ThématiquePetroleum Processing and Analysis
Établissements canadiensUniversity of Calgary
Organismes subventionnairesArmy Research OfficeNatural Sciences and Engineering Research Council of CanadaUniversity of Calgary
Mots-clésAsphalteneCombustionLight crude oilCrude oilChemistryHeat of combustionMixing (physics)Petroleum engineeringChemical engineeringOrganic chemistryGeology

Résumé

récupéré en direct d'OpenAlex

Abstract In situ combustion and high-pressure air injection are enhanced oil recovery (EOR) processes used to recover oil from both heavy and light oil reservoirs. These processes are quite complex and involve consideration of heat and mass transfer, phase behaviour of oil, water and gas, as well as relative permeability effects. This paper outlines a study that was conducted in order to develop a better understanding of the heats of combustion (HOC) for three different types of crude oils and their respective saturate, aromatic, resin and asphaltene (SARA) fractions. One outcome of the study indicated that saturates and aromatics have higher heating values than resins and asphaltenes, where this value in both saturates and aromatics (in any given crude oil) is close. Resins and asphaltenes also displayed heating values that were almost the same, however, were consistent in having a lower heating value than saturates and aromatics. The linear mixing rule was applied to predict the heat of combustion for the three crude oils studied. The HOCs for the maltene and asphaltene fractions were mathematically combined (per the mixing rule) to predict the actual observed HOC of the combined maltene/asphaltene crude. This rule did not hold true for all the crude oils studied, however, which suggests that the heat of combustion is not necessarily independent of the presence of other fractions. Introduction In situ combustion and high pressure air injection are technologies used for the recovery of both heavy and light crude oils. These technologies involve the creation of an oxidation front in the reservoir with subsequent propagation by air injection. Generally, air is injected in the reservoir and the oxygen contained in the air reacts with the oil through various oxidation reactions. The burning front is formed and the combustion gases produced from these reactions are available to help displace the oil. This process offers economic and technical opportunities for improved oil recovery in many reservoirs. Many thermal analysis studies on both light and heavy crude oils have been conducted and several oxidation tests for modelling the process have been performed. Verkoczy and Freitag(1) applied the relevance of various oxidation reactions to the modelling of in situ combustion in heavy oils, through three different sets of experiments. They performed thermogravimetric scans and autoclave tests on three heavy oils and their SARA fractions. They found that low temperature oxidation had significant and sometimes dramatic effects on the amount of coke formation. They also found that asphaltenes apparently underwent low temperature oxidation more rapidly than other crude fractions. K?k et al.(2) used thermogravimetric analysis under an air atmosphere at a 10 °C/min heating rate. Two oils (medium and heavy) were separated into their SARA fractions. Then a quantitative investigation was performed in order to determine the temperature intervals at which evaporation, oxidation and combustion effects operated for each fraction. Kinetic parameters of SARA fractions according to the Coat and Redfern technique were also established. K?k and Karacan(3) studied the behaviour and effect of SARA fractions of two different oils during combustion using a thermogravimetric analyzer and a differential scanning calorimeter.

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 enseignants

Ni 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.

score de la tête « metaresearch » (Codex)0,000
score de la tête « metaresearch » (Gemma)0,000
Version: codex-gemma-dda1882f352aStatut de validation: machine_predicted_unvalidated
Catégories candidatesaucune
Catégories consensuellesaucune
DomaineSignal candidat: aucune · Signal consensuel: aucune
Devis d'étudeSignal candidat: Expérimental (laboratoire) · Signal consensuel: Expérimental (laboratoire)
GenreSignal candidat: Empirique · Signal consensuel: Empirique
Score de désaccord entre enseignants0,031
Score d'incertitude au seuil0,690

Scores Codex et Gemma par catégorie

CatégorieCodexGemma
Métarecherche0,0000,000
Méta-épidémiologie (sens strict)0,0000,000
Méta-épidémiologie (sens large)0,0000,000
Bibliométrie0,0020,001
Études des sciences et des technologies0,0000,000
Communication savante0,0000,000
Science ouverte0,0000,000
Intégrité de la recherche0,0000,000
Charge utile insuffisante (le modèle a refusé de juger)0,0000,000

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.

Tête enseignante Opus0,010
Tête enseignante GPT0,209
Écart entre enseignants0,199 · la distance entre les deux têtes enseignantes sur ce seul travail
Statut de validationscore_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 découle

Classification

machine, non validée

Prédiction automatique; un appel candidat d’une seule tête enseignante, pas un consensus.

Les modèles n’ont appliqué aucune catégorie : rien dans la taxonomie ne correspondait à ce travail.
Devis d'étudeExpérimental (laboratoire)
Domainenon disponible
GenreEmpirique

Le détail, modèle par modèle et score par score, se trouve en fin de page sous « Comment cette classification a été obtenue ».

En bref

Citations22
Publié2008
Routes d'admission2
Résumé présentoui

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