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Enregistrement W1974847648 · doi:10.2118/1207-0051-jpt

A Heavy- to Light-Crude-Oil Upgrading Process

2007· article· en· W1974847648 sur OpenAlexaboutno aff
Karen Bybee

Notice bibliographique

RevueJournal of Petroleum Technology · 2007
Typearticle
Langueen
DomaineChemistry
ThématiquePetroleum Processing and Analysis
Établissements canadiensnon disponible
Organismes subventionnairesnon disponible
Mots-clésAsphaltCrude oilEnvironmental scienceWaste managementOil fieldLight crude oilPetroleumSynthetic crudeFossil fuelEngineeringPetroleum engineeringShale oilGeologyArchaeologyGeography

Résumé

récupéré en direct d'OpenAlex

This article, written by Assistant Technology Editor Karen Bybee, contains highlights of paper SPE 108678, "Performance of a Heavy- to Light-Crude-Oil Upgrading Process," by E.J. Veith, Ivanhoe Energy, prepared for the 2007 SPE International Oil Conference and Exhibition in Mexico, Veracruz, Mexico, 27–30 June. A proprietary heavy- to light-oil (HTL) upgrading technology is designed to process heavy oil cost effectively in the field and provide a stable, significantly upgraded synthetic-oil product along with byproduct energy that can be used to generate steam or electricity. Since the commissioning of a commercial demonstration facility (CDF) for upgrading heavy oil in 2005, a number of crude oils and vacuum-tower-bottoms (VTBs) feedstocks have been tested. Analysis of CDF performance shows that the HTL process is capable of delivering high yields of significantly upgraded product. Introduction In mid-2005, Ivanhoe Energy acquired a new patented process, called rapid thermal (RT) processing, for the field-located upgrading of heavy oil and bitumen. Included in the acquisition was a new CDF in the San Joaquin Valley in southern California that demonstrates a processing capacity of approximately 1,000 B/D of heavy crude oil. Fig. 1 shows the CDF in the Belridge oil field. There are significant accumulations of heavy crude and bitumen throughout the world that can be targeted by this technology. Both Canada and Venezuela have extensive heavy-oil reserves that compare in size to current reserves in the Middle East. As conventional lighter-crude-oil supplies decline, they will need to be replaced by heavier crudes. New residue-processing capacity could be added to existing refineries, or it could be built in separate, standalone upgrading facilities. If the oil is too heavy to transport by pipeline, and/or there is the need for heat or energy at the production site, heavy-oil upgrading in the field is attractive and may avoid extensive modifications of existing refineries. Traditional residue processing such as coking or hydrocracking are very expensive processes and require a large scale to be viable. The HTL technology would provide a lower-cost, simpler residue-processing option compatible with field development. HTL-Technology Development The development of the RT processing technology began in the early 1980s when it was discovered that a broad array of carbonaceous feedstocks (e.g., wood and heavy oil) could be thermally cracked to obtain valuable products at residence times of a few seconds. The initial commercial focus of the technology, beginning in 1989, was aimed at conversion of wood and wood residues to value-added fuels and chemicals. Seven commercial biomass plants based on this technology have been in operation for many years. As the biomass side of the business grew and operational and design parameters were optimized, the focus turned toward petroleum feedstocks. The petroleum application of the technology was demonstrated in a pilot plant in Ottawa, Canada, on more than 90 experimental runs using a number of different crude oils and bitumen between 1999 and 2002. Because it was believed that the technology had relatively low capital and operating costs compared to conventional carbon rejection technologies, such as delayed coking, commercialization of the HTL process was initiated.

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)0,001
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 enseignants0,146
Score d'incertitude au seuil0,916

Scores Codex et Gemma par catégorie

CatégorieCodexGemma
Métarecherche0,0010,000
Méta-épidémiologie (sens strict)0,0000,000
Méta-épidémiologie (sens large)0,0010,000
Bibliométrie0,0020,001
Études des sciences et des technologies0,0000,000
Communication savante0,0000,000
Science ouverte0,0010,000
Intégrité de la recherche0,0000,001
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 Opus0,007
Tête enseignante GPT0,267
Écart entre enseignants0,261 · 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

Citations1
Publié2007
Routes d'admission1
Résumé présentoui

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