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Record W2020388743 · doi:10.2118/64747-ms

Sand Production Simulation in Heavy Oil Reservoirs

2000· article· en· W2020388743 on OpenAlexaff
Liangwen Zhang, Maurice B. Dusseault

Bibliographic record

VenueInternational Oil and Gas Conference and Exhibition in China · 2000
Typearticle
Languageen
FieldEngineering
TopicHydraulic Fracturing and Reservoir Analysis
Canadian institutionsUniversity of Waterloo
Fundersnot available
KeywordsOil productionPressure gradientPorosityMechanicsPorous mediumSteady state (chemistry)Sensitivity (control systems)Production (economics)Yield (engineering)Dynamic equilibriumOil fieldProcess (computing)Field (mathematics)Petroleum engineeringGeotechnical engineeringMaterials scienceGeologyComputer scienceEngineeringPhysicsMathematicsChemistryComposite materialThermodynamics

Abstract

fetched live from OpenAlex

Abstract A new sand production model is developed based on inter-particle contact force variations at the discrete micromechanical level. Two mechanisms for sand production can be expected in the field, dynamic detachment and equilibrium yield. The model discussed in the paper describes the sand production mechanism in the dynamic detachment process. Within the new model formulation, sand is considered to be produced because of either a large porosity gradient or a large pressure gradient. The 1-D steady-state solution of the new model is also presented; it may be used for simple sensitivity analysis for parameters such as the field stress and pressure depletion effect.

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 score0.023
Threshold uncertainty score0.045

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 communication0.0010.001
Open science0.0010.001
Research integrity0.0010.000
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 head0.241
Teacher spread0.230 · 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
Published2000
Admission routes1
Has abstractyes

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