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Record W2946981379 · doi:10.11575/prism/36604

Phase Behaviour of Mixtures of Heavy Oil and n-Butane

2019· dissertation· en· W2946981379 on OpenAlexfundno aff
Perez Claro

Bibliographic record

VenuePRISM (University of Calgary) · 2019
Typedissertation
Languageen
FieldEngineering
TopicPhase Equilibria and Thermodynamics
Canadian institutionsnot available
FundersNatural Sciences and Engineering Research Council of CanadaSuncor Energy IncorporatedEcopetrolVirtual Materials GroupChina National Offshore Oil CorporationCanadian Natural Resources Limited
KeywordsButaneEnvironmental scienceChemistryPetroleum engineeringOrganic chemistryEngineering

Abstract

fetched live from OpenAlex

Solvent-based methods are a potential alternative to thermal methods for the recovery of heavy oils because they are less water and energy intensive. n-Butane is a solvent of interest for these processes because it has a saturation pressure that is close to the operating (reservoir) pressure of many heavy oil reservoirs. Operating at a pressure near the saturation pressure maximizes the solubility of the solvent in the oil while avoiding the formation of a second liquid phase. To design these processes, it is first necessary to predict the phase behaviour but there are few data available in the literature upon which to base a prediction. The phase behaviour of heavy oil and n-butane mixtures was examined at temperatures up to 230°C and pressures up to 10 MPa. Both vapour-liquid and liquid-liquid regions were observed. The amount of the heavy pitch phase was measured at temperatures from 20 to 180°C and pressures up to 10 MPa. The liquid phase composition (in terms of C5-asphaltene, maltenes, and n-butane) were measured at 130°C and 10 MPa. A ternary diagram was constructed for the liquid-liquid region and equilibrium ratios (K) were determined and used to confirm the consistency of the data. The phase behaviour data for mixtures of n-butane and bitumen were consistent with the trends in the phase boundaries and masses versus carbon number observed with propane (Mancilla-Polanco et al., 2018) and n-pentane diluted bitumen (Johnston et al., 2017b). The Peng-Robinson Equation of State was used to match the data by adjusting the binary interaction parameters with two approaches: 1) temperature-dependent parameters (TDvdW model); 2) composition-dependent parameters (CDvdW model). The TDvdW model matched both the vapour-liquid and liquid-liquid boundaries to within the uncertainty of the measurement but significantly under-predicted the heavy phase masses. The CDvdW model matched not only the phase boundaries but also the phase masses and compositions generally to within the experimental error. The model deviations increased above the critical temperature of n-butane.

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: Other · Consensus signal: none
Teacher disagreement score0.002
Threshold uncertainty score0.005

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0020.000

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 head0.005
GPT teacher head0.201
Teacher spread0.196 · 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
GenreOther

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

Citations0
Published2019
Admission routes1
Has abstractyes

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