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Record W2009948113 · doi:10.1115/imece2012-88151

Microfluidic Characterization of Steam-Bitumen Interactions During Steam Assisted Gravity Drainage Operations

2012· article· en· W2009948113 on OpenAlexafffund
Thomas de Haas, Hossein Fadeai, David Sinton

Bibliographic record

VenueVolume 9: Micro- and Nano-Systems Engineering and Packaging, Parts A and B · 2012
Typearticle
Languageen
FieldEngineering
TopicEnhanced Oil Recovery Techniques
Canadian institutionsUniversity of Toronto
FundersNatural Sciences and Engineering Research Council of CanadaSuncor Energy Incorporated
KeywordsSteam-assisted gravity drainagePetroleum engineeringAsphaltOil sandsSteam injectionEnvironmental scienceSteam drumGeologyWaste managementSuperheated steamBoiler (water heating)Materials scienceEngineering

Abstract

fetched live from OpenAlex

In-situ recovery of heavy-oil and bitumen is used when reserves are too deep underground for conventional surface mining technologies. Steam assisted gravity drainage (SAGD) is process in which two horizontal wells, one vertically 5m above the other, are drilled into an oil-rich region. Steam is injected into the reservoir from the top well, and an oil steam-condensate mixture is pumped out the production well. The aim of this research is to physically model a section of oil sand in a SAGD operation. An array of micropillars fabricated into a glass microfluidic chip is used to represent the grains of sand. The chip was positioned vertically so that gravity plays a dominate role in drainage. Steam was pumped into the chip, reducing the viscosity of the oil and allowing oil and steam to flow under gravity to the outlet. The position of the steam front and the micro-scale interactions of the steam and oil were recorded over time.

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 categoriesMeta-epidemiology (narrow)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Bench or experimental · Consensus signal: Bench or experimental
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.315
Threshold uncertainty score1.000

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 communication0.0000.001
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 head0.006
GPT teacher head0.197
Teacher spread0.190 · 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 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

Citations1
Published2012
Admission routes2
Has abstractyes

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Same venueVolume 9: Micro- and Nano-Systems Engineering and Packaging, Parts A and BSame topicEnhanced Oil Recovery TechniquesFrench-language works237,207