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Record W2025341884 · doi:10.2118/64226-pa

Flow Visualization Studies of Solution Gas Drive Process in Heavy Oil Reservoirs With a Glass Micromodel

2000· article· en· W2025341884 on OpenAlexafffund
Rupam Bora, Brij Maini, A. Chakma

Bibliographic record

VenueSPE Reservoir Evaluation & Engineering · 2000
Typearticle
Languageen
FieldEngineering
TopicEnhanced Oil Recovery Techniques
Canadian institutionsUniversity of ReginaUniversity of Calgary
FundersNatural Sciences and Engineering Research Council of Canada
KeywordsMicromodelPetroleum engineeringFlow (mathematics)Process (computing)NucleationProduction (economics)Environmental scienceMechanicsGeologyChemistryEconomicsGeotechnical engineeringPorous mediumPhysicsComputer science

Abstract

fetched live from OpenAlex

Summary A series of flow visualization experiments was carried out to examine the pore scale behavior of the solution gas drive process in heavy oil reservoirs. The main objective was to testify several speculative theories that had been put forward to explain the anomalous production behavior of heavy oil reservoirs producing under the solution gas drive process. Contrary to previous postulations, the asphaltene constituents did not appear to play a significant role in the nucleation and stabilization of the gas bubbles that evolved during the solution gas drive process. Experimental evidence also suggests that the production of heavy oil is not accompanied by a large population of microbubbles. These observations suggest that the production enhancement in the solution gas process in heavy oil reservoirs may be related to other mechanisms such as viscous coupling effects, sand production, wormhole effects, etc.

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)0.000
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: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.002
Threshold uncertainty score0.004

Distilled classifier scores by category (both heads)

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)0.0010.000

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 head0.312
Teacher spread0.284 · 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

Citations105
Published2000
Admission routes2
Has abstractyes

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