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Record W4233786552 · doi:10.1504/ijogct.2020.109448

Mechanistic study and performance evaluation of steam assisted gravity drainage using direct visualisation of pore-level experiments

2020· article· en· W4233786552 on OpenAlexaff
Omid Mohammadzadeh, Nima Rezaei, Ioannis Chatzis

Bibliographic record

VenueInternational Journal of Oil Gas and Coal Technology · 2020
Typearticle
Languageen
FieldEngineering
TopicEnhanced Oil Recovery Techniques
Canadian institutionsMemorial University of Newfoundland
Fundersnot available
KeywordsSteam-assisted gravity drainageSteam injectionCapillary actionPetroleum engineeringViscous fingeringMaterials scienceGeologyMechanicsEnvironmental scienceGeotechnical engineeringOil sandsPorous mediumComposite materialPorosityPhysics

Abstract

fetched live from OpenAlex

In this paper, insights into the pore-scale physics of SAGD process are presented through analysis of a systematic series of SAGD visualisation experiments, conducted using optical imaging technique and glass-etched micromodels of capillary networks. The steam chamber propagation was found to be influenced by the randomly-distributed fingering of the invading steam front near the mobile oil-steam chamber interface. The steam fingering was extensive during the vertical growth of the steam chamber where the steam buoyant front was protruding through the pores containing a continuum of oil. The steam fingering phenomenon was also observed during the outward propagation of the steam chamber near its lateral wings, but to a lesser extent. The rate of pore-scale horizontal interface advancement was constant at each elevation along the height of the micromodels. The average pore-level SAGD sweep rates as well as the net cumulative steam to oil ratio were correlated using an analytical model. [Received: March 15, 2018; Accepted: February 11, 2019]

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: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.578
Threshold uncertainty score0.336

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 head0.069
GPT teacher head0.333
Teacher spread0.264 · 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
Published2020
Admission routes1
Has abstractyes

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