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Record W2617394107 · doi:10.3997/2214-4609.201701345

Coupling Heat and Mass Transfer at Interface for ES-SAGD with Multicomponent Solvent Injection

2017· article· en· W2617394107 on OpenAlexaff
H. Liu, Lin Cheng, Peiying Jia

Bibliographic record

VenueProceedings · 2017
Typearticle
Languageen
FieldEngineering
TopicMicrofluidic and Capillary Electrophoresis Applications
Canadian institutionsUniversity of Calgary
Fundersnot available
KeywordsMass transferSolventDilutionSteam injectionPetroleum engineeringHeat transferMaterials scienceCoupling (piping)ChemistryChemical engineeringMechanicsThermodynamicsChromatographyOrganic chemistryComposite materialGeologyEngineering

Abstract

fetched live from OpenAlex

Summary Co-injection of solvent and steam in Expanding Solvent-Steam Assisted Gravity Drainage (ES-SAGD) increases the mobility of highly viscous oil relative to conventional SAGD wherein only steam is injected. This consequently results in higher oil production rates and lower energy consumption relative to SAGD due to the combined benefits of heating and dilution. Current efforts have been made to study mass transfer from a single-component-solvent/steam mixture into the heavy oil at the steam front, while the low-cost multicomponent solvent is actually co-injected with steam on the field, which presents a much more complicated interfacial condition.A generalized methodology has been developed to couple heat and mass transfer of this solventssteamheavy oil system at the interface.Results show that there exists a noticeable transient interface in the experiments and the mathematical model can reproduce the range of transit zone shown in the 2D glass-bead model with different multicomponent solvents.With the new methodology, an optimum multicomponent solvent can be screened from the ocean of solvent candidates for a best heat and mass transfer performance in an ES-SAGD process and hence the oil flux at the chamber edge can be maximized with a minimized solvent cost.

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 score0.101
Threshold uncertainty score0.424

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 head0.011
GPT teacher head0.221
Teacher spread0.210 · 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
Published2017
Admission routes1
Has abstractyes

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