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Record W2073743113 · doi:10.2118/164333-ms

Enhanced Oil Recovery by Nanoparticles Injection: Modeling and Simulation

2013· article· en· W2073743113 on OpenAlexaff
Mohamed F. El‐Amin, Shuyu Sun, Amgad Salama

Bibliographic record

VenueSPE Middle East Oil and Gas Show and Conference · 2013
Typearticle
Languageen
FieldEngineering
TopicEnhanced Oil Recovery Techniques
Canadian institutionsKootenay Association for Science & Technology
Fundersnot available
KeywordsBuoyancyCapillary actionSuspension (topology)NanoparticleCapillary pressurePorous mediumEnhanced oil recoveryMaterials sciencePetroleum engineeringMultiphase flowMechanicsWater injection (oil production)Two-phase flowBrownian motionComputer simulationFlow (mathematics)PorosityNanotechnologyGeologyComposite materialMathematicsPhysics

Abstract

fetched live from OpenAlex

Abstract In the present paper, a mathematical model and numerical simulation to describe the nanoparticles-water suspension imbibes into a water-oil two-phase flow in a porous medium is introduced. We extend the model to include the negative capillary pressure and mixed relative permeabilities correlations to fit with the mixed-wet system. Also, buoyancy and capillary forces as well as Brownian diffusion are considered. Throughout this investigation, we monitor the changing of the fluids and solid properties due to addition of the nanoparticles and check for possible enhancement of the oil recovery process using numerical experiments.

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: Simulation or modeling · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.933
Threshold uncertainty score0.662

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 head0.020
GPT teacher head0.207
Teacher spread0.187 · 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 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

Citations34
Published2013
Admission routes1
Has abstractyes

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