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Record W4234611948 · doi:10.32920/ryerson.14660529.v1

Determination of concentration-dependent dispersion of propane in vapor extraction of heavy oil

2021· preprint· en· W4234611948 on OpenAlexafffund
Hadil Abu Khalifeh

Bibliographic record

Venuenot available
Typepreprint
Languageen
FieldEngineering
TopicEnhanced Oil Recovery Techniques
Canadian institutionsToronto Metropolitan University
FundersNatural Sciences and Engineering Research Council of Canada
KeywordsDispersion (optics)Petroleum engineeringSolventMixing (physics)PropaneExtraction (chemistry)DiffusionSoil vapor extractionOil fieldMaterials scienceViscosityMass transferChemistryMechanicsChromatographyThermodynamicsComposite materialGeology

Abstract

fetched live from OpenAlex

Vapex (vapor extraction) is a solvent-based non-thermal in-situ heavy oil recovery process. In Vapex process, a vaporized hydrocarbon solvent is injected into an upper horizontal well where the solvent mixes with the heavy oil and reduces its viscosity. The diluted oil drains under gravity to a bottom production well. Two mechanisms control the production rates of heavy oil in Vapex: mass transfer of solvent into heavy oil, and gravity drainage. Both are governed by dispersion, which is composed of molecular diffusion, convection, and other mechanisms that enhance mixing in porous medium. The accurate determination of solvent dispersion in Vapex is essential to predict effectively the amount and time scale of oil recovery as well to optimize the field operations. Motivated by limited dispersion data in the literature, a novel technique is developed to determine experimentally the concentration-dependent dispersion coefficient of propane in Vapex process, The technique employs live oil production rates obtained from Vapex experiments at 21ºC and 0.790 MPa. The salient feature of this technique is that it does not impose any functional form on dispersion as a function of concentration, but allows its natural and realistic determination. The technique could be applied to determine other solvents dispersion coefficient used in the in-situ recovery of heavy oil. Propane dispersion coefficient is determined by the minimization of the difference in experimental and calculated cumulative live oil produced. The necessary conditions for the minimum are fundamentally derived, utilizing the theory of optimal control. A computational algorithm is formulated to calculate the propane dispersion function simultaneously with propane-heavy oil interface mass fraction. Physical models of glass beads of different permeabilities (204-51 Darcy) and drainage heights (25-45 cm) were used to conduct the Vapex experiments. The results show that dispersion of propane is a unimodal function of its concentration in heavy oil, and lies in the range, 0.5x10⁻⁵- 7.933x10⁻⁵ m²/s. Convectional mixing is promoted by higher model drainage heights and lower permeability. Finally, propane dispersion is correlated as a function of propane mass fraction in heavy oil and the packed medium permeability, as well as the drainage height.

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: Bench or experimental · Consensus signal: Bench or experimental
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.001
Threshold uncertainty score0.003

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 studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.001
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 head0.011
GPT teacher head0.258
Teacher spread0.247 · 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 designBench or experimental
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

Citations6
Published2021
Admission routes2
Has abstractyes

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