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Enregistrement W4255918171 · doi:10.2118/2003-176

Low-Temperature Oxidation of Oils in Terms of SARA Fractions: Why Simple Reaction Models Don't Work

2003· article· en· W4255918171 sur OpenAlexaff
N.P. Freitag, B. Verkoczy

Notice bibliographique

RevueCanadian International Petroleum Conference · 2003
Typearticle
Langueen
DomaineChemistry
ThématiquePetroleum Processing and Analysis
Établissements canadiensSaskatchewan Research Council (Canada)
Organismes subventionnairesnon disponible
Mots-clésWork (physics)Simple (philosophy)ChemistryThermodynamicsPhysicsPhilosophy

Résumé

récupéré en direct d'OpenAlex

Abstract The low-temperature oxidation (LTO) reactions of the SARA fractions separated from two crude oils were studied in the presence of their reservoir sands at temperatures between 125 and 230 °C. The results indicated that the usual approach to modelling LTO ? the use of a very few single-step Arrhenius-rate equations ??could not be made to reflect the observed reaction kinetics. Instead, this investigation found that the following reaction characteristics were needed for accurate reaction modelling:a change in the order of reaction with respect to oxygen concentration from 1/2 to 1 as temperature rises,the repression of a saturates oxidation reaction by other fractions, anda prominent induction period exhibited by the saturates fraction. The compositions and yields of the ultimate LTO reaction products were measured, and these included relatively stable residues with high oxygen contents. Because the LTO reactions play an important role in enhanced oil recovery by air injection methods, the above information is valuable for the simulation and prediction of these processes. Introduction Enhanced oil recovery processes need to be predictable before they can be seriously considered for widespread field application. One of the main problems limiting the development and application of new process variations for air injection or in-situ combustion is that their field performance and consequently their technical success or failure can simply not be predicted with any reliability. The most serious questions frequently hinge upon the nature of stability of the combustion/oxidation zones. Many studies have provided valuable knowledge as to the nature of the related chemical reactions, but the usefulness of proposed reaction models for numerical simulation prediction is still limited. hree main types of reaction have been found to govern air-injection EOR processes: pyrolysis/coking, low-temperature oxidation, and high-temperature oxidation (combustion). This study was concentrated on the second category of reaction, low-temperature oxidation (LTO). The free-radical nature of low-temperature oxidation of hydrocarbons has long been known. In a 1958 review, Morton and Bell1 confirmed that LTO occurs through a free-radical mechanism in which the production of hydroperoxides is an important first step. They also mentioned the role of inhibitors, discussed catalysis by metal surfaces and metallic salts, and described why long induction periods could occur before the onset of significant oxygen consumption was observed. Initially, most studies of the chemical mechanisms conducted both before and after Morton and Bell's review used pure compounds. The results varied between compounds, and could not be used directly to describe the oxidation of complex mixtures like crude oils in a petroleum reservoir. In 1968, Bousaid and Ramey,2 while investigating high-temperature oxidations, carried out three low-temperature oxidation tests on a heavy oil between 23 and 52 °C. They reported very low values of oxygen consumption, with rates that were correlated with an activation energy of 53,200 J/gmol. Later, Dabbous and Fulton3 published much more extensive results for LTO of two whole oils on crushed Berea sand over the temperature range of 121 to 246 °C. They observed the activation energy to be about 72,000 J/gmol, and noted that the orders of reaction with respect to oxygen were 0.5 and 0.75 for the two oils.

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 candidatesCharge utile insuffisante (le modèle a refusé de juger)
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 enseignants0,068
Score d'incertitude au seuil1,000

Scores Codex et Gemma par catégorie

CatégorieCodexGemma
Métarecherche0,0000,000
Méta-épidémiologie (sens strict)0,0000,000
Méta-épidémiologie (sens large)0,0000,000
Bibliométrie0,0000,000
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)0,0010,000

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 Opus0,015
Tête enseignante GPT0,238
Écart entre enseignants0,223 · 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.

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

Citations6
Publié2003
Routes d'admission1
Résumé présentoui

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