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Record W2972952348 · doi:10.15446/dyna.v86n210.74983

Laboratory study of cyclic liquid solvent injection process for heavy oil recovery through computed tomography

2019· article· en· W2972952348 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.

fundA Canadian funder is recorded on the work.
no affNo Canadian affiliation: this work is invisible to an affiliation-only frame.
No Canadian affiliation. An affiliation-only frame, the usual design, would never have seen this work. It is one of the works that make the case for inverting the frame.

Bibliographic record

VenueDYNA · 2019
Typearticle
Languageen
FieldEngineering
TopicEnhanced Oil Recovery Techniques
Canadian institutionsnot available
FundersUniversity of Calgary
KeywordsDiluentNaphthaMaterials scienceSaturation (graph theory)SolventPorosityPorous mediumOil productionComposite materialChemistryPetroleum engineeringNuclear chemistryGeology

Abstract

fetched live from OpenAlex

The cyclic solvents injection has been considered for years as an improved non-thermal enhanced oil recovery method for the recovery of heavy oil, which includes three stages: injection, soaking, and production. This paper describes a laboratory study with Computed Tomography and Nuclear Magnetic Resonance of a cyclic solvent injection process in a porous medium, using naphtha as a liquid diluent to recover a Colombian heavy oil in a porous medium at 84 °C. The core was scanned during the soaking time to determine the expansion behavior of the mixing zone by analyzing the density profiles obtained after each scan. It was also scanned after the production stage to observe the distribution of saturation in the porous medium after each cycle. Finally, the fluids recovered from porous medium were taken to a nuclear magnetic resonance equipment to determine the recovery factor.

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)0.000
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: Bench or experimental · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.629
Threshold uncertainty score0.725

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 head0.009
GPT teacher head0.253
Teacher spread0.244 · 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