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Record W2610637117 · doi:10.3390/en10050614

Energy Return on Investment of Canadian Oil Sands Extraction from 2009 to 2015

2017· article· en· W2610637117 on OpenAlexafffundabout
Ke Wang, Harrie Vredenburg, Jianliang Wang, Xiong Yi, Lianyong Feng

Bibliographic record

VenueEnergies · 2017
Typearticle
Languageen
FieldEnergy
TopicGlobal Energy and Sustainability Research
Canadian institutionsUniversity of Calgary
FundersChina University of Petroleum, BeijingNational Natural Science Foundation of ChinaUniversity of Calgary
KeywordsOil sandsExtraction (chemistry)Environmental scienceFossil fuelPetroleum engineeringRange (aeronautics)Investment (military)Unconventional oilMining engineeringGeologyEngineeringWaste managementAsphaltGeographyArchaeologyChemistry

Abstract

fetched live from OpenAlex

Oil sands, as unconventional oil, are so essential to both Canada and the world that special attention should be paid to their extraction status, especially their energy efficiency. One of the most commonly used methods to evaluate energy efficiency is the Energy Return on Investment (EROI) analysis. This paper focuses on EROI analysis for both in situ oil sands and mining oil sands over the period of 2009 to 2015. This time period represents an extension to periods previously considered by other analyses. An extended Input-Output model is used to quantify indirect energy input, which has been ignored by previous analyses of oil sands extraction. Results of this paper show that EROI of both mining oil sands (range of value: 3.9–8) and in situ oil sands (range of value: 3.2–5.4) display an upward trend over the past 7 years; EROI of mining oil sands is generally higher, but is more fluctuating than the EROI of in situ oil sands. Compared with EROI of other hydrocarbons, the EROI of oil sands is still quite low, despite the fact that it is increasing gradually.

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: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.840
Threshold uncertainty score0.689

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 studies0.0000.000
Scholarly communication0.0000.000
Open science0.0010.000
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 head0.023
GPT teacher head0.294
Teacher spread0.272 · 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".

Quick stats

Citations18
Published2017
Admission routes3
Has abstractyes

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