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Record W1983703022 · doi:10.2118/137729-ms

Flow Regime Transfer Conditions for Two-Phase Flow in a Fracture

2010· article· en· W1983703022 on OpenAlexaff
Saeed Shad, Brij Maini, Ian D. Gates

Bibliographic record

VenueCanadian Unconventional Resources and International Petroleum Conference · 2010
Typearticle
Languageen
FieldEngineering
TopicEnhanced Oil Recovery Techniques
Canadian institutionsUniversity of Calgary
FundersNational Institutes of Health
KeywordsFlow (mathematics)Fracture (geology)MechanicsGeologyHele-Shaw flowTwo-phase flowFluid dynamicsPetroleum engineeringGeotechnical engineeringFlow conditionsMaterials scienceOpen-channel flowPhysics

Abstract

fetched live from OpenAlex

Abstract Overall, oil-laden fractured-rock reservoirs contain between 25 and 30 percent of the total known global petroleum reserves. In these reservoirs, fractures are the dominant flow path, therefore economic oil recoveries from fractured reservoirs rely on better understanding of the flow in fractures and networks of fractures. However, in contrast with gas-liquid flow in fractures, the flow of heavy oil and water has received less attention. Particularly, the flow regime map for two-phase immiscible flow in a thin gap and the transition boundaries are not well studied yet. In this research, a Hele-Shaw apparatus was used to study the flow of water in the presence of heavy oil in a smooth walled fracture. Different flow patterns were observed under different flow conditions and the effects of different fluid and fracture properties on flow regime transition boundaries for a thin gap were evaluated. The results of the experiments demonstrate that, flow regime boundaries strongly depend on fluid and fracture properties. The results also reveal that one correlation can be applied to predict different transition conditions under different fracture orientation.

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: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.002
Threshold uncertainty score0.006

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 communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
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 head0.011
GPT teacher head0.258
Teacher spread0.247 · 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 designObservational
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

Citations2
Published2010
Admission routes1
Has abstractyes

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