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The Research Progress of Oil Sand Separation Technology in China

2012· article· en· W2132501929 on OpenAlexvenueno aff
Chaohe Fang, Dewen Zheng, Xiaolong Li, Zhilong Huang

Bibliographic record

VenueAdvances in petroleum exploration and development · 2012
Typearticle
Languageen
FieldEngineering
TopicEnhanced Oil Recovery Techniques
Canadian institutionsnot available
Fundersnot available
KeywordsOil sandsPetroleum engineeringPetroleumSeparation (statistics)Environmental sciencePetroleum explorationStructural basinGeologyAsphaltArchaeologyGeography

Abstract

fetched live from OpenAlex

From 2007 to 2008, Research Institute of Petroleum Exploration & Development, Langfang Branch launched oil sand resource exploration and the study of hot water separation technology in Fengcheng area, Northwest of Junggar Basin, and the recoverable oil-sand oil resource is 54.98 million tons with the oil content in 7.1-10%, which is distributed in Cretaceous and Jurassic with the thickness of 80-140 meters, the cover depth of oil sand is 50-90 meters. Combining with the characteristics of the oil sand in this area and based on the research of hot water separation mechanism in oil sand, the hot water separation reagent for the oil sand in this area has been successfully developed, and its separation rate reaches 90%, provided that the concentrations of the agent is 4% and the separation temperature is 85 °C. Based on series of study, the construction of testing site, which is capable of processing 10,000 tons oil sand in this area, is completed, and the on-site separation tests of oil sand are launched with the recovery rate of 90% in normal operation, and the hot water separation technology and equipment research & development are successful. Key words : Oil sand; Hot water separation technology; Separation reagent; Tests

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: Other design · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.943
Threshold uncertainty score0.245

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 communication0.0000.001
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 head0.022
GPT teacher head0.334
Teacher spread0.312 · 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 designOther design
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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