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Record W3089024916 · doi:10.3390/en13184986

Thermal Viscous Fingering in Thermal Recovery Processes

2020· article· en· W3089024916 on OpenAlexafffund
Maureen Austin-Adigio, Ian D. Gates

Bibliographic record

VenueEnergies · 2020
Typearticle
Languageen
FieldEngineering
TopicEnhanced Oil Recovery Techniques
Canadian institutionsUniversity of Calgary
FundersNatural Sciences and Engineering Research Council of CanadaCanada First Research Excellence Fund
KeywordsHeat transferThermal conductionMechanicsSteam injectionThermalViscosityFluid dynamicsAsphaltGeologyMaterials scienceFlow (mathematics)Petroleum engineeringThermodynamicsComposite materialPhysics

Abstract

fetched live from OpenAlex

Heat and fluid flow at the edge of steam chambers found in thermal recovery processes such as steam-assisted gravity drainage and cyclic steam stimulation remain unresolved. Given the multiple phases present and contrast of thermophysical properties, it remains unclear where instabilities occur within this thin, yet critical, zone of the process. In the research reported here, heat and fluid flow are examined in vertical and horizontal sections of a steam chamber to understand the differences between the two orientations by using detailed and fine-gridded thermal reservoir simulation models. The results show that heat transfer in vertical and horizontal directions are different with greater heat transfer found in the vertical orientation. In the vertical direction, heat transfer occurs with mobilized bitumen draining with subsequent steam moving into the emptied pore space. Conduction beyond the edge of the chamber dominates and heated, low viscosity bitumen fingers into cold, higher viscosity bitumen at the edge of the chamber. Relative permeability effects are observed which can interfere with enhanced oil mobility.

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 machine prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.001
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Simulation or modeling · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.002
Threshold uncertainty score0.005

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.001
Scholarly communication0.0010.001
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0020.000

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 head0.010
GPT teacher head0.202
Teacher spread0.192 · 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 source (direct Gemma or distilled Codex), 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

Citations10
Published2020
Admission routes2
Has abstractyes

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