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Asphaltene Deposition during Bitumen Extraction with Natural Gas Condensate and Naphtha

2017· article· en· W2778083025 on OpenAlexafffund
ZhenBang Qi, Ali Abedini, Atena Sharbatian, Yuanjie Pang, Adriana Guerrero, David Sinton

Bibliographic record

VenueEnergy & Fuels · 2017
Typearticle
Languageen
FieldChemistry
TopicPetroleum Processing and Analysis
Canadian institutionsSuncor Energy (Canada)University of Toronto
FundersNatural Sciences and Engineering Research Council of CanadaCanada Research ChairsSuncor Energy Incorporated
KeywordsNaphthaAsphalteneChemistryTolueneSolventDeposition (geology)HydrocarbonChemical engineeringExtraction (chemistry)AsphaltPhenanthrenePentaneRefining (metallurgy)Organic chemistryMaterials scienceCatalysisGeology

Abstract

fetched live from OpenAlex

Solvent bitumen extraction processes are alternatives to thermal processes with potential for improved economic and environmental performance. However, solvent interaction with bitumen commonly results in in situ asphaltene precipitation and deposition, which can hinder flow and reduce the process efficiency. Successful implementation requires one to select a solvent that improves recovery with minimal flow assurance problems. The majority of candidate industrial solvents are in the form of mixtures containing a wide range of hydrocarbon fractions, further complicating the selection process. In this study, we quantify the pore-scale asphaltene deposition using two commonly available solvent mixtures, natural gas condensate and naphtha, using a microfluidic platform. The results are also compared with those of two typical pure solvents, n -pentane and n -heptane, with all cases evaluated with both 50 and 100 μm pore-throat spacing. The condensate produced more asphaltenes and pore-space damage than the naphtha and exhibited deposition dynamics similar to that of pentane and heptane. This similarity is due to the presence of a large amount of light hydrocarbon fractions in condensate (∼85 wt % of C 5 s–C 7 s) dictating the overall deposition dynamics. Naphtha, which contains heavier fractions (∼70 wt % of C 8 s–C 11 s) and aromatic/naphthenic components, generated less asphaltenes and exhibited a slower deposition rate, resulting in less pore damage and overall better performance.

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: Bench or experimental
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.016
Threshold uncertainty score0.458

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 studies0.0010.000
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 head0.007
GPT teacher head0.233
Teacher spread0.226 · 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

Citations58
Published2017
Admission routes2
Has abstractyes

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