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Record W3214197078 · doi:10.1002/cjce.24327

Solubility of carbon dioxide and ethane in <scp>Lloydminster</scp> heavy oil: Experimental study and modelling

2021· article· en· W3214197078 on OpenAlexafffundvenue
Rajkumar Ganapathi, Amr Henni, Ezeddin Shirif

Bibliographic record

VenueThe Canadian Journal of Chemical Engineering · 2021
Typearticle
Languageen
FieldEngineering
TopicPhase Equilibria and Thermodynamics
Canadian institutionsPetroleum Technology Research CentreUniversity of Regina
FundersTotalPetroleum Technology Research CentreShell CanadaBP Exploration Operating Company LimitedDevon Energy Corporation
KeywordsSolubilityAsphaltenePetroleumEnhanced oil recoveryFractionationSolventCarbon dioxideChemistryEnvironmental scienceChemical engineeringPetroleum engineeringOrganic chemistryGeology

Abstract

fetched live from OpenAlex

Abstract Heavy oil reserves in the world represent 5.5 trillion barrels, which are equivalent to five times the conventional crude oil reserves. Heavy oil reserves will be the main petroleum source for the world's future demand for energy. To enhance the recovery of heavy oil/bitumen, solvent‐based recovery seems to be one of the most promising alternatives to costly thermal methods. Phase behaviour studies of light gases in heavy oil are therefore very important when designing surface facilities and for enhanced oil recovery operations. In this study, we present solubility data for carbon dioxide and ethane in Lloydminster heavy oil. Measurements were carried out using a microbalance at 290.2, 298.2, and 313.2 K and at pressures varying from 200–2000 kPa. Experimental data were regressed with the Peng‐Robinson (PR) equation of state. We also report results of the fractionation of the heavy oil and its characterization in terms of SARA (saturates, aromatics, resins, and asphaltenes) fractions. Henry's law constants for gaseous solvents were also regressed and reported. As expected, ethane had a higher solubility than CO 2 in the heavy oil at all temperatures.

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)0.000
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 score0.330
Threshold uncertainty score0.470

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 head0.012
GPT teacher head0.200
Teacher spread0.189 · 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

Citations13
Published2021
Admission routes3
Has abstractyes

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