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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 OpenAlex

Why this work is in the frame

A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.

affAt least one author lists a Canadian institution in the pinned OpenAlex snapshot.
fundA Canadian funder is recorded on the work.

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.

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 categoriesMeta-epidemiology (narrow)
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 score0.065
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
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 head0.028
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