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Record W4256592683 · doi:10.2523/89426-ms

Adomian Solution of Forchheimer Model to Describe Porous Media Flow

2004· article· en· W4256592683 on OpenAlexaff
Belhaj Hadi, Jafar Biazar, Butt Stephen, Islam Rafiq

Bibliographic record

VenueProceedings of SPE/DOE Symposium on Improved Oil Recovery · 2004
Typearticle
Languageen
FieldEngineering
TopicLattice Boltzmann Simulation Studies
Canadian institutionsDalhousie University
Fundersnot available
KeywordsPorous mediumFlow (mathematics)Computer scienceMechanicsPorosityGeotechnical engineeringGeologyPhysics

Abstract

fetched live from OpenAlex

Currently, fluid flow in porous media is mostly calculated by utilizing the well known diffusivity equation based on Darcy’s law. This diffusivity equation is the core fluid flow equation in all modern reservoir simulators used to predict flow behaviors. Inaccurate predictions of reservoir simulators have been reported nevertheless, history matching has been achieved. This dilemma led to questioning the adequacy of the basic governing equation of fluid flow behavior in porous media.This paper suggesting a new governing equation that includes Darcy’s viscous term, Forchheimer’s inertial term and Brinkman’s viscous term all in one model called the Modified Brinkman Model

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: none
Teacher disagreement score0.010
Threshold uncertainty score0.019

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.001
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0010.001
Science and technology studies0.0010.001
Scholarly communication0.0010.002
Open science0.0020.001
Research integrity0.0020.002
Insufficient payload (model declined to judge)0.0040.001

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 head0.017
GPT teacher head0.222
Teacher spread0.205 · 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

Citations0
Published2004
Admission routes1
Has abstractyes

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