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Enregistrement W6922177684 · doi:10.11575/sppp.v11i0.53039.g40539

The North West Redwater Sturgeon Refinery: What are the Numbers for Alberta’s Investment?

2018· article· en· W6922177684 sur OpenAlexaboutno aff

Notice bibliographique

RevueUniversity of Calgary · 2018
Typearticle
Langueen
DomaineSocial Sciences
ThématiqueCanadian Policy and Governance
Établissements canadiensnon disponible
Organismes subventionnairesnon disponible
Mots-clésRefineryOil refineryPetroleumAsphaltRaw materialPetroleum productCrude oilSturgeon

Résumé

récupéré en direct d'OpenAlex

Since 2006, the government of Alberta has tried to increase the volume of raw bitumen upgraded and refined in the province. More specifically, the Alberta Petroleum Marketing Commission (APMC) and Canadian Natural Resources Ltd. (CNRL) have entered into agreements with a facility northeast of Edmonton called the North West Redwater (NWR) Sturgeon Refinery. The NWR Sturgeon Refinery is designed to process 79,000 barrels per day (bpd) of feedstock, consisting of 50,000 bpd of bitumen and 29,000 bpd of diluent (referred to as dilbit). The refinery will produce petroleum products consisting of approximately 40,000 bpd of low sulphur diesel, 28,000 bpd of diluent and 13,000 bpd of other lighter petroleum products. It will also be able to capture 1.2 million tonnes per year of carbon dioxide emitted from the refinery’s operations. This captured carbon dioxide will be compressed, put into a pipeline and then injected into an existing oil field in order to achieve increased production of crude oil (referred to as enhanced oil recovery or EOR). It is the first refinery built in Canada since 1984, and the first one in Canada to refine bitumen into petroleum products such as diesel fuel. It differs from the upgrader built in Lloydminster which only upgrades bitumen into synthetic crude oil that requires further refining at a conventional refinery in order to produce petroleum products. This paper gives a description of the structure of this support by APMC and CNRL using a mechanism whereby those two parties agree to enter into tolling agreements to process the diluted bitumen feedstock into refined petroleum products for sale. Under the tolling agreement, APMC and CNRL retain ownership of the diluted bitumen as it is refined into petroleum products. APMC and CNRL then sell such petroleum products, and pay a tolling fee to the NWR Sturgeon Refinery for the refining service provided. The paper also uses an economic model in Excel to give a projection of the economics of this facility for the first full year of operation. The objective is to put numbers to a project that has been the subject of much qualitative discussion. The Excel economic model contains base case assumptions for a number of variables such as the capital cost of the refinery, the financing costs associated with such capital costs, the operating costs of the refinery, the cost of the diluted bitumen feedstock and the price of the petroleum products produced by and sold from the refinery. Based on these base case assumptions, the Excel economic model shows that in the first full year of operation of the NWR Sturgeon Refinery in 2019, the two toll-paying entities, APMC and CNRL, are projected to lose a cash amount of about $24 million (about $1 per barrel of diluted bitumen supplied). This loss is based on a comparison to the toll payers’ alternative of just selling the diluted bitumen feedstock at market prices. The paper then uses the economic model to do a sensitivity analysis to show the effect of a lower or higher price of diluted bitumen feedstock, as well as the effect of a lower or higher price of the produced petroleum products. If the cost of the diluted bitumen feedstock were lower, or the price of the petroleum products were higher, then APMC and CNRL would earn a profit. If the converse occurred (feedstock costs higher or petroleum product price lower), then the loss to APMC and CNRL would be greater than $24 million. Finally, the paper attempts to create a template for governments to use when they consider whether or not to provide financial assistance to various projects.

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: Sans objet · Signal consensuel: Sans objet
GenreSignal candidat: Empirique · Signal consensuel: Empirique
Score de désaccord entre enseignants0,604
Score d'incertitude au seuil0,890

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 technologies0,0010,001
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 Opus0,013
Tête enseignante GPT0,222
Écart entre enseignants0,209 · 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'étudeSans objet
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

Citations0
Publié2018
Routes d'admission1
Résumé présentoui

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