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Record W1972714828 · doi:10.2118/170252-pa

Viscosity Measurement and Modeling for Mixtures of Athabasca Bitumen/<i>n</i>-Pentane at Temperatures Up to 200°C

2014· article· en· W1972714828 on OpenAlexafffund
Hossein Nourozieh, Mohammad Kariznovi, Jalal Abedi

Bibliographic record

VenueSPE Journal · 2014
Typearticle
Languageen
FieldChemistry
TopicPetroleum Processing and Analysis
Canadian institutionsUniversity of Calgary
FundersAlberta InnovatesChemical Institute of CanadaUniversity of Calgary
KeywordsAsphaltPentaneViscosityThermodynamicsSolventDissolutionChemistryMixing (physics)Volume (thermodynamics)Oil sandsSurface tensionPetroleum engineeringMaterials scienceOrganic chemistryGeologyComposite material

Abstract

fetched live from OpenAlex

Summary The phase-behavior and thermophysical properties of bitumen/solvent systems are of crucial importance for heavy-oil and bitumen in-situ recovery methods. The viscosity reduction as a result of solvent dissolution and/or steam heating is the main recovery mechanism in the solvent-based bitumen-recovery processes. In this paper, the viscosity of bitumen, pentane, and their mixtures at different pentane weight fractions (0.05, 0.1, 0.2, 0.3, 0.4, and 0.5) are accurately measured. The measurements are conducted under conditions applicable for both in-situ recovery methods and the pipeline transportation of heavy oil. The experiments are taken with Athabasca bitumen at temperatures varying from ambient up to 200°C and at pressures up to 10 MPa. The data for the mixtures are evaluated with predictive schemes as well as with correlation models representing certain mixing rules proposed in the literature. The influences of pressure, temperature, and solvent weight fraction on the viscosity of mixtures are considered in the models and evaluated from the experimental data. The results indicated that the power-law model and the Cragoe model (Cragoe 1933) represent the data better than other models that use a volume-fraction basis.

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 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 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 score0.075
Threshold uncertainty score0.464

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
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 head0.023
GPT teacher head0.259
Teacher spread0.236 · 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 teacher head, 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

Citations31
Published2014
Admission routes2
Has abstractyes

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