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Record W4251528109 · doi:10.1115/htr2008-58239

Integration of Nuclear Energy Into Oil Sands Projects

2008· article· en· W4251528109 on OpenAlexaboutno aff
Ashley Finan, Andrew C. Kadak

Bibliographic record

Venuenot available
Typearticle
Languageen
FieldEngineering
TopicHydraulic Fracturing and Reservoir Analysis
Canadian institutionsnot available
Fundersnot available
KeywordsOil sandsNatural gasNuclear powerGreenhouse gasEnvironmental scienceUnconventional oilElectricity generationWaste managementCoalFossil fuelElectricityRenewable energyNatural resource economicsEngineeringAsphaltGeologyEconomicsPower (physics)

Abstract

fetched live from OpenAlex

Energy security and greenhouse gas reductions are thought to be two of the most urgent priorities for sustaining and improving the human condition in the future. Few places pit the two goals so directly in opposition to one another as the Alberta oil sands. Here, Canadian natural gas is burned in massive quantities to extract oil from one of North America’s largest native sources of carbon-intensive heavy oil. This conflict need not continue, however; non-emitting nuclear energy can replace natural gas as a fuel source in an economical and more environmentally sound way. This would allow for the continued extraction of transportation fuels without greenhouse gas emissions, while freeing up the natural gas supply for hydrogen feedstock and other valuable applications. Bitumen production in Alberta has expanded dramatically in the past five years as the price of oil has risen to record levels. This paper explores the feasibility and economics of using nuclear energy to power future oil sands production and upgrading activities, and puts forth several nuclear energy application scenarios for providing steam and electricity to in-situ and surface mining operations. This review includes the Enhanced CANDU 6, the Advanced CANDU Reactor (ACR) and the Pebble Bed Modular Reactor (PBMR). Based on reasonable projections of available cost information, nuclear energy used for steam production is expected to be less expensive than steam produced by natural gas at current natural gas prices and under $7/MMBtu (CAD). For electricity production, nuclear becomes competitive with natural gas plants at natural gas prices of $10–13/MMBtu (CAD). Costs of constructing nuclear plants in Alberta are affected by higher local labor costs, which this paper took into account in making these estimates. Although more definitive analysis of construction costs and project economics will be required to confirm these findings, there appears to be sufficient merit in the potential economics to support further study. A single 500MWth PBMR reactor is able to supply high-pressure steam for a 40,000 to 60,000 bpd Steam Assisted Gravity Drainage (SAGD) plant, whereas the CANDU and ACR reactors are unable to produce sufficient steam pressures to be practical in that application. The CANDU, ACR and PBMR reactors have potential for supplying heat and electricity for surface mining operations. The primary environmental benefit of nuclear energy in this application is to reduce CO2 emissions by up to 3.1 million metric tons per year for each 100,000 barrel per day (bpd) bitumen production SAGD facility, or 2.0 million metric tons per year for the replacement of 700MWe of grid electricity with a nuclear power plant. Should carbon emissions be priced, the economic advantages of nuclear energy would be dramatically improved such that with a $50/ton CO2e at the releases expected for typical projects using natural gas, breakeven gas prices for nuclear drop to less than $3.50/MMBtu, well below the current natural gas price of $10/MMBtu for SADG steam production.

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 machine prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.

metaresearch head score (Codex)0.001
metaresearch head score (Gemma)0.001
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Theoretical or conceptual · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.959
Threshold uncertainty score0.081

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0010.000
Scholarly communication0.0010.001
Open science0.0010.001
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0060.001

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 head0.009
GPT teacher head0.195
Teacher spread0.186 · 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 source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designTheoretical or conceptual
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".

Quick stats

Citations2
Published2008
Admission routes1
Has abstractyes

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