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Record W2022642550 · doi:10.2118/157904-ms

Experimental Evaluation of Dispersion and Diffusion in a UTF BItumen/n-Butane System

2012· article· en· W2022642550 on OpenAlexaffabout
T. Frauenfeld, C. Jossy, Eddie Jossy, Brad Wasylyk, Brigida Meza Diaz

Bibliographic record

VenueSPE Heavy Oil Conference Canada · 2012
Typearticle
Languageen
FieldEngineering
TopicHydraulic Fracturing and Reservoir Analysis
Canadian institutionsAlberta Innovates
Fundersnot available
KeywordsAsphaltSteam injectionButanePetroleum engineeringSolventIsothermal processOil sandsOil fieldEnvironmental scienceHydrocarbonSteam-assisted gravity drainageMaterials scienceChemistryThermodynamicsGeologyComposite materialOrganic chemistryPhysicsCatalysis

Abstract

fetched live from OpenAlex

Abstract Laboratory experiments at Alberta Research Council (now Alberta Innovates Technology Futures) have indicated the potential for improving the recovery of bitumen and heavy oil, and a substantial reduction of SOR relative to SAGD, by the addition of substantial volumes of light hydrocarbon to steam as an enhancement of SAGD. Many scaled lab model experiments have been completed, some of which have identified solvent/steam ratios that outperformed low pressure SAGD. In order to more reliably scale the results of these experiments, it was desired to measure the rate of oil production and hence solvent front advance rate in field permeability sand. The experiments were isothermal to simplify the numerical simulations. Three experiments were completed, one at 68°C one at 80°C and one at 100°C. The respective butane pressures were 700 kPa, 1020 kPa and 1450 kPa. The respective oil rates were 52.5 g/h, 62.3 g/h and 63.2 g/h. Solvent front advance rates were 7.81 cm/d, 9.0 cm/d and 9.42 cm/d. These rates translate into SAGD-like rates when extrapolated to a field size project.

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: Simulation or modeling · Consensus signal: Simulation or modeling
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.551
Threshold uncertainty score0.831

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 communication0.0000.000
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 head0.016
GPT teacher head0.230
Teacher spread0.214 · 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

Citations2
Published2012
Admission routes2
Has abstractyes

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