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Investigating vertical and lateral gas migration during thermal conduction heating in heterogeneous porous media

2025· article· en· W4409351847 on OpenAlexafffund
Kevin G. Mumford

Bibliographic record

VenueAdvances in Water Resources · 2025
Typearticle
Languageen
FieldEngineering
TopicHydraulic Fracturing and Reservoir Analysis
Canadian institutionsQueen's University
FundersNatural Sciences and Engineering Research Council of Canada
KeywordsPorous mediumThermal conductionMaterials scienceThermalPorosityGeotechnical engineeringGeologyPetroleum engineeringEnvironmental scienceMechanicsPetrologyComposite materialThermodynamicsPhysics

Abstract

fetched live from OpenAlex

• Tracked evolution of gas saturation adjacent to a heater using light transmission. • Wider two-phase zones and a dry zone observed in finer sand. • Steam accumulated underneath capillary barriers despite a connected gas pathway. • Steam and contaminant vapour may migrate laterally away from heaters in the field. • New conceptual model of gas migration during thermal conduction heating. The successful treatment of contaminated soil and groundwater using thermal remediation technologies relies on the capture and treatment of contaminant vapour produced during heating. The migration of that vapour is affected by subsurface heterogeneity, which must be understood to ensure capture and to prevent condensation outside of a target heating zone. Bench-scale thermal conduction heating experiments were conducted to investigate the migration of steam through homogeneous and heterogeneous porous media. The steam pattern was parabolic adjacent to the heater during homogeneous experiments. In heterogeneous experiments, gas accumulation and migration were observed underneath the capillary barrier. Despite a connected gas pathway to the atmosphere, the transmissivity of the capillary barrier was not sufficient to prevent this migration. This has implications for the transport of heat and contaminant vapour outside of the heated zone, emphasizing the need for effective vapour capture.

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: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.570
Threshold uncertainty score0.385

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 head0.005
GPT teacher head0.217
Teacher spread0.212 · 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

Citations1
Published2025
Admission routes2
Has abstractyes

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