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Record W2019992201 · doi:10.2118/169373-ms

The Effect of Oil Price on Oil Consumption and Reserves

2014· article· en· W2019992201 on OpenAlexafffund
Roberto F. Aguilera

Bibliographic record

VenueSPE Latin America and Caribbean Petroleum Engineering Conference · 2014
Typearticle
Languageen
FieldEconomics, Econometrics and Finance
TopicMarket Dynamics and Volatility
Canadian institutionsUniversity of Calgary
FundersAlberta InnovatesCurtin University of TechnologyUniversity of Calgary
KeywordsVolatility (finance)Consumption (sociology)EconomicsOil reservesOil priceCrude oilOil consumptionEconometricsPetroleumEnvironmental scienceNatural resource economicsMonetary economicsPetroleum engineeringChemistryEngineering

Abstract

fetched live from OpenAlex

Abstract There has been considerable price volatility in the crude oil market since 1861. At times the yearly variations, as reported by the BP Statistical Review, have been minor but on occasion they have been quite significant. The small and large variations have been matched successfully with a Variable Shape Distribution (VSD) model. Remarkably, the comparison between actual price variations and those calculated by the VSD leads to a coefficient of determination (R2) larger than 0.98. Given this validation, we integrate the results with a Global Energy Market (GEM) model developed in 2007 that presented an oil consumption forecast to 2030. Thus far, the forecast has been in line with actual oil consumption to 2012. Contrary to statements made by various oil commentators, the integration of results suggests that the possibilities of seeing sudden, large and permanent increases in future oil prices are very low. Given the huge quantities of conventional and unconventional oil available at or below current market prices, society should be able to substitute between alternative sources long before depletion causes oil to become unduly expensive. Further, a case can be made that with the vast global oil resource base and the significant technological advances being implemented by the industry, oil prices could decrease in the future.

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)0.001
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: Simulation or modeling · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.833
Threshold uncertainty score0.554

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
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 science0.0000.000
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 head0.009
GPT teacher head0.194
Teacher spread0.185 · 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 designSimulation or modeling
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

Citations3
Published2014
Admission routes2
Has abstractyes

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