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Record W2969214379 · doi:10.1002/cjce.23627

Phase behaviour modelling of Athabasca bitumen for in situ combustion applications

2019· article· en· W2969214379 on OpenAlexaffvenue
Ahmad Alizadeh, Robert Moore, Raj Mehta, Hossein Nourozieh

Bibliographic record

VenueThe Canadian Journal of Chemical Engineering · 2019
Typearticle
Languageen
FieldChemistry
TopicPetroleum Processing and Analysis
Canadian institutionsVirtual Materials Group (Canada)University of Calgary
Fundersnot available
KeywordsAsphaltConsistency (knowledge bases)Reliability (semiconductor)Process (computing)Oil sandsPetroleum engineeringEnhanced oil recoveryWork (physics)Process engineeringCombustionPhase (matter)Computer scienceEngineeringMechanical engineeringMaterials scienceChemistryThermodynamics

Abstract

fetched live from OpenAlex

Abstract Phase behaviour modelling of reservoir fluid is a fundamental step for reservoir simulation. Furthermore, as the complexity of the recovery process increases, the fluid model plays a more important role in the reliability of the simulation outputs. Although the in situ combustion enhanced oil recovery method (ISC) is one of the most complex recovery techniques available in the petroleum engineering literature, for most of the simulation jobs related to this elaborate process only simple and rudimentary fluid characterization layouts are considered. In this work, the principal fluid properties of Athabasca bitumen with regard to the ISC process are recognized, extracted from the literature, validated for consistency, and used for the development of an inclusive and accurate fluid model. Then the fluid model is fully developed while considering the ISC reaction kinetics so that the model has both accuracy, indispensable for phase behaviour modelling, and consistency, essential for the reactions definitions.

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)0.000
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 score0.989
Threshold uncertainty score0.023

Distilled classifier scores by category (both heads)

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 communication0.0010.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0010.000

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 head0.222
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 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

Citations3
Published2019
Admission routes2
Has abstractyes

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