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

2018· article· en· W6922177684 on OpenAlexaboutno aff

Bibliographic record

VenueUniversity of Calgary · 2018
Typearticle
Languageen
FieldSocial Sciences
TopicCanadian Policy and Governance
Canadian institutionsnot available
Fundersnot available
KeywordsRefineryOil refineryPetroleumAsphaltRaw materialPetroleum productCrude oilSturgeon

Abstract

fetched live from 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.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: Not applicable
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.604
Threshold uncertainty score0.890

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0010.001
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

Machine scores (provisional)

The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.

Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.

Opus teacher head0.013
GPT teacher head0.222
Teacher spread0.209 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designNot applicable
Domainnot available
GenreEmpirical

How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".

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Citations0
Published2018
Admission routes1
Has abstractyes

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