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Record W2792688437 · doi:10.2118/190469-ms

Thermodynamics of Hydrocarbon Solvents at the Pore Scale During Hybrid Solvent-Thermal Application for Heavy-Oil Recovery

2018· article· en· W2792688437 on OpenAlexafffund
Ilyas Al-Kindi, Tayfun Babadagli

Bibliographic record

VenueSPE EOR Conference at Oil and Gas West Asia · 2018
Typearticle
Languageen
FieldEngineering
TopicEnhanced Oil Recovery Techniques
Canadian institutionsUniversity of Alberta
FundersNatural Sciences and Engineering Research Council of Canada
KeywordsBoilingHeptaneBoiling pointCapillary actionPorous mediumThermodynamicsMicromodelWettingDecaneEnhanced oil recoveryHydrocarbon mixturesChemistryMaterials sciencePhase (matter)PorosityHydrocarbonChemical engineeringOrganic chemistry

Abstract

fetched live from OpenAlex

Abstract Phase behavior of fluids at capillary conditions differs from that in bulk media. Therefore, understanding the thermodynamics of solvents in confined media is essential for modeling thermal EOR applications. The Thomson equation states that pore sizes have a control on boiling points of liquids in capillary channels. As pore spaces become smaller, boiling points become lower than normal boiling temperatures of the same liquids. The target of this paper is to inspect this phenomenon by physically measuring the boiling points of several solvents and compare them with the calculated boiling temperatures for different capillary structures. Furthermore, the feasibility and accuracy of the Thomson equation is investigated to check its applicability in heavy-oil recovery modelling. To do so, Hele-Shaw cells with several gap thicknesses (0.04, 0.45, 1.02, and 12 mm) are used to measure the boiling points of heptane, heptane-decane mixture, and naphtha. Experiments are repeated for the same solvents on homogeneous and heterogeneous micromodels to observe the phase behavior in a more realistic porous medium. Finally, the effect of surface wettability on boiling temperatures is examined in Hele-Shaw and micromodel experiments.

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: Bench or experimental
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.286
Threshold uncertainty score0.814

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 head0.007
GPT teacher head0.216
Teacher spread0.209 · 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

Citations10
Published2018
Admission routes2
Has abstractyes

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