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Research of Improving Super Heavy Oil Properties Through Flue Gas

2013· article· en· W1910716197 on OpenAlexvenueno aff
Bin Li

Bibliographic record

VenueAdvances in petroleum exploration and development · 2013
Typearticle
Languageen
FieldEngineering
TopicEnhanced Oil Recovery Techniques
Canadian institutionsnot available
Fundersnot available
KeywordsFlue gasSolubilityViscosityPetroleum engineeringVolume (thermodynamics)ChemistryWaste managementOil viscosityFlueEnvironmental scienceMaterials scienceEngineeringThermodynamicsOrganic chemistryComposite material

Abstract

fetched live from OpenAlex

Flue gas capacity of improving super heavy oil properties was investigated by laboratory experiment, and the experimental results showed that flue gas could play a role of swelling and reducing viscosity after being dissolved in super heavy oil. Within the experiment temperature range, flue gas maintained smaller solubility and volume factor in super heavy oil, and the viscosity of oil and gas mixture appeared a sharp fall after flue gas being dissolved in super heavy oil, meanwhile the viscosity reduction rate fundamentally appeared a linear increase. Flue gas assisting SAGD feasibility was analyzed, and the results showed that flue gas could obviously enlarge the steam chamber extension in the transverse direction and slow down the rate of increase in the longitudinal direction after being injected with steam. As a result, it could play a very good effect of adjusting steam chamber shape and temperature distribution. Therefore flue gas assisting SAGD is feasible, which is also an important means of improving development effect and enhancing oil recovery for super heavy oil reservoir SAGD in the middle and later periods. Key words: Super heavy oil; Flue gas; Solubility; Volume factor; Viscosity

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: Bench or experimental · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.892
Threshold uncertainty score0.436

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 communication0.0000.002
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 head0.041
GPT teacher head0.284
Teacher spread0.243 · 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 designBench or experimental
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

Citations0
Published2013
Admission routes1
Has abstractyes

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