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Record W2058659688 · doi:10.2118/91957-ms

Steady-State Propagation of In-situ Combustion Fronts with Sequential Reactions

2004· article· en· W2058659688 on OpenAlexaff
I. Yücel Akkutlu, Yannis C. Yortsos

Bibliographic record

Venuenot available
Typearticle
Languageen
FieldChemistry
TopicPetroleum Processing and Analysis
Canadian institutionsUniversity of Alberta
Fundersnot available
KeywordsCombustionClassification of discontinuitiesJumpMechanicsChemical reactionWork (physics)Perturbation (astronomy)In situMaterials scienceThermodynamicsChemistryPhysicsPhysical chemistryMathematics

Abstract

fetched live from OpenAlex

Summary The sustained propagation of a combustion front is necessary for the improved recovery of oil during an in situ combustion process. In situ combustion involves the added complexity of chemical reactions. In this paper, combustion will involve two sequential oxidation reactions. High-temperature oxidation represents proper combustion, its fuel generated by the preceding low-temperature oxidation. The interaction between the two reactions in the presence of reservoir heat losses and their overall influence on front propagation are investigated using a perturbation, analytical approach, based on the assumption of large activation energies. The places where the reactions occur are treated as spatial discontinuities for heat and mass fluxes, across which appropriate jump conditions are developed. Under certain conditions, the two reaction regions are coupled and travel coherently with the same velocity. The corresponding parameter space is delineated. The resulting common velocity is investigated as a function of the various parameters, including heat losses. The work finds application to our understanding of in-situ combustion processes and their application to oil and bitumen recovery.

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: Simulation or modeling
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.002
Threshold uncertainty score0.008

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 science0.0000.001
Research integrity0.0010.001
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 head0.012
GPT teacher head0.239
Teacher spread0.226 · 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

Citations11
Published2004
Admission routes1
Has abstractyes

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