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Record W2137831210 · doi:10.1002/cjce.20117

Steam fingering at the edge of a steam chamber in a heavy oil reservoir

2008· article· en· W2137831210 on OpenAlexafffundvenue
Dharmesh R. Gotawala, Ian D. Gates

Bibliographic record

VenueThe Canadian Journal of Chemical Engineering · 2008
Typearticle
Languageen
FieldEngineering
TopicEnhanced Oil Recovery Techniques
Canadian institutionsUniversity of Calgary
FundersUniversity of Calgary
KeywordsSteam-assisted gravity drainageThermal conductionHeat transferSteam injectionMechanicsConvectionOil sandsPetroleum engineeringGeologyConvective heat transferAsphaltEnvironmental scienceMaterials scienceThermodynamicsPhysics

Abstract

fetched live from OpenAlex

Abstract The steam‐assisted gravity drainage (SAGD) process is one of the key in situ recovery processes being used today to recover heavy oil and bitumen. In this process, steam injected through a horizontal well, flows convectively towards the outer edges of a depletion chamber. At the edges of the depletion chamber, the steam releases its latent heat to the cool oil sand and raises its temperature. The heated oil is mobile and flows under the action of gravity to a horizontal production well located several metres below the injection well. It remains unclear what is the exact mechanism of chamber growth. Some have suggested that in addition to heat conduction, it is by convective steam flow in the form of pointed fingers at the edges of the chamber which penetrate the oil sand. In theory published by Butler [Butler, J. Can. Petroleum Technol. 1987;26(3):70–75], it was determined that the fingers can be as long as 6 m for Athabasca bitumen reservoirs. In this research, a new theory is derived and provides predictions of the rise rate which compare better to estimates derived from field thermocouple data and physical model experimental observations than values obtained from Butler's theory. The results suggest that in the absence of mobile water, heat conduction rather than steam fingers at the chamber edge is the dominant heat transfer mechanism.

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 score0.539
Threshold uncertainty score0.511

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 integrity0.0000.001
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 head0.011
GPT teacher head0.194
Teacher spread0.183 · 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

Citations57
Published2008
Admission routes3
Has abstractyes

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