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Record W2022260173 · doi:10.2118/157798-ms

Simulation of Miscible Cyclic Injection Flows in Porous Media

2012· article· en· W2022260173 on OpenAlexaff
Qingwang Yuan, Jalel Azaiez

Bibliographic record

VenueSPE Heavy Oil Conference Canada · 2012
Typearticle
Languageen
FieldComputer Science
TopicAdvanced Mathematical Modeling in Engineering
Canadian institutionsUniversity of Calgary
Fundersnot available
KeywordsPorous mediumMechanicsMaterials scienceDispersion (optics)Displacement (psychology)Process (computing)Transverse planeNonlinear systemInverseConstant (computer programming)ConvectionPorosityComposite materialOpticsPhysicsMathematicsGeometryEngineeringStructural engineeringComputer science

Abstract

fetched live from OpenAlex

Abstract The present study examines the development of miscible displacements in homogeneous porous media with a non-constant, time-dependent injection velocity. Numerical simulations for a sinusoidal injection velocity model are conducted to determine the effects of different variables on the efficiency of the displacement. The evolutions of nonlinear fingers with time are analyzed by plotting concentration fields, transversely averaged concentration profiles and sweep efficiency curve. Both the displacing process (injection process) and inverse displacing process (production process) are considered. Convection, transverse dispersion mechanisms, magnitude and frequency of the velocity changes are analyzed and the results are compared with those of a constant injection velocity process.

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 score0.006
Threshold uncertainty score0.013

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 integrity0.0010.000
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 head0.028
GPT teacher head0.252
Teacher spread0.224 · 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

Citations2
Published2012
Admission routes1
Has abstractyes

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