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Record W2305059592 · doi:10.2118/179722-pa

Analytical Solutions and Derivation of Relative Permeabilities for Water-Heavy Oil Displacement and Gas-Heavy Oil Gravity Drainage Under Non-Isothermal Conditions

2016· article· en· W2305059592 on OpenAlexafffund
Francisco J. Argüelles‐Vivas, Tayfun Babadagli

Bibliographic record

VenueSPE Reservoir Evaluation & Engineering · 2016
Typearticle
Languageen
FieldEngineering
TopicEnhanced Oil Recovery Techniques
Canadian institutionsUniversity of Alberta
FundersNatural Sciences and Engineering Research Council of CanadaConsejo Nacional de Ciencia y Tecnología
KeywordsIsothermal processCapillary actionTemperature gradientPermeability (electromagnetism)Displacement (psychology)Relative permeabilityDrainagePetroleum engineeringMechanicsChemistryPorous mediumMaterials scienceThermodynamicsPorosityGeologyGeotechnical engineeringComposite materialPhysics

Abstract

fetched live from OpenAlex

Summary Analytical models were developed for non-isothermal gas/heavy-oil gravity drainage and water-heavy oil displacements in round capillary tubes including the effects of a temperature gradient throughout the system. By use of the model solution for a bundle of capillaries, relative permeability curves were generated at different temperature conditions. The results showed that water/gas-heavy oil interface location, oil-drainage velocity, and production rate depend on the change of oil properties with temperature. The displacement of heavy oil by water or gas was accelerated under a positive temperature gradient, including the spontaneous imbibition of water. Relative permeability curves were greatly affected by temperature gradient and showed significant changes compared with the curves at constant temperature. Clarifications were made as to the effect of variable temperature compared with the constant (but high) temperatures throughout the bundle of capillaries.

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)0.001
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 score0.446
Threshold uncertainty score0.706

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.001
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 head0.027
GPT teacher head0.285
Teacher spread0.258 · 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

Citations3
Published2016
Admission routes2
Has abstractyes

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