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Current Development of Venezuela Extra Heavy Crude and Canadian Oil Sands Processing

2012· article· en· W2380472741 on OpenAlexaboutno aff
Zhenyu Li, Qiao Ming, Ren Wen-Po

Bibliographic record

VenueActa Petrolei Sinica(Petroleum Processing Section) · 2012
Typearticle
Languageen
FieldChemistry
TopicPetroleum Processing and Analysis
Canadian institutionsnot available
Fundersnot available
KeywordsSynthetic crudeCrude oilOil refineryAsphalteneRaw materialRefining (metallurgy)Waste managementPetroleumEnvironmental scienceOil sandsRefineryUnconventional oilFossil fuelPetroleum engineeringEngineeringChemistryAsphaltMetallurgyMaterials science

Abstract

fetched live from OpenAlex

Venezuela extra heavy crude and Canadian oil sands are two main kinds of unconventional crude,which can't be directly processed in conventional refineries due to their special properties of low API,high viscosity,high sulfur content,high carbon residue,high metal and high asphaltene contents.At present,the two unconventional crudes are handled mainly in upgrading facilities near production sites,producing synthetic crude suitable for conventional refineries to produce transportation fuels and other oil products.There are currently four upgrading facilities in Venezuela for extra heavy crude,three of which are designed to produce heavy,high sulfur synthetic crude and the other can provide light,low sulfur synthetic crude.Coking is chosen by all of the four plants as the main upgrading technology.Six main upgrading facilities are located in Alberta and Saskatchewan in Canada to produce synthetic crude with different properties by using coking and/or hydrocracking technologies.The two unconventional crudes are seen as important feedstock resources of refineries in future.Chinese oil companies should pay more attention to the development and application of our own upgrading technologies,which will strengthen our competitiveness and lead to cooperation opportunities.

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 categoriesMeta-epidemiology (narrow), Science and technology studies
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.844
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0010.001
Science and technology studies0.0010.000
Scholarly communication0.0000.001
Open science0.0010.000
Research integrity0.0000.001
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 head0.019
GPT teacher head0.266
Teacher spread0.247 · 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.

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

Citations1
Published2012
Admission routes1
Has abstractyes

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