MétaCan
Menu
Back to cohort
Record W2020844788 · doi:10.1021/ef900369r

Asphaltene Nanoaggregates Measured in a Live Crude Oil by Centrifugation

2009· article· en· W2020844788 on OpenAlexaff
Kentaro Indo, John Ratulowski, Birol Dindoruk, Jinglin Gao, Julian Y. Zuo, Oliver C. Mullins

Bibliographic record

VenueEnergy & Fuels · 2009
Typearticle
Languageen
FieldChemistry
TopicPetroleum Processing and Analysis
Canadian institutionsSchlumberger (Canada)
Fundersnot available
KeywordsAsphalteneChemistryBuoyancySolubilityChromatographyAnalytical Chemistry (journal)Light crude oilWaxChemical engineeringOrganic chemistryThermodynamics

Abstract

fetched live from OpenAlex

Asphaltene nanoaggregates have recently been observed in live crude oil by observation of gravitationally induced asphaltene gradients in four different reservoir sands with oil columns up to 1000 m vertical. When the liquid phase is invariant, these gradients can be fit using Archimedes buoyancy in the Boltzmann distribution; the only adjustable parameter in data fitting is the size of the asphaltene nanoaggregate; ∼2 nm is obtained in four reservoir sands and is similar to laboratory results for asphaltene nanoaggregates in toluene. Here, a live crude oil (with dissolved gases) has been spun at modest g forces for long times designed to create a large, equilibrium asphaltene gradient for the presumed 2 nm aggregates. Elevated temperatures (∼91 °C) were employed during centrifugation to mimic reservoir conditions for asphaltene aggregation and prevention of a possible wax phase. Elevated pressures were employed on the hot, live crude oil to maintain dissolved gas concentrations. A total of 13 alliquots of crude oil were removed after centrifugation, and the asphaltene concentrations were determined by optical spectroscopy. Indeed, a large asphaltene gradient was observed, and a 2.6 nm diameter nanoaggregate was obtained using Archimedes buoyancy in the Boltzmann distribution. In addition, a solubility model accounting for the gas/oil ratio (GOR) gradient was used to analyze the asphaltene gradient, giving an asphaltene particle size of 2.0 nm, thus, the same as field observations. In addition, the gradient in bulk resins was shown to be quite small, showing the stark contrast of asphaltene versus bulk resin aggregation. The heaviest resins (or lightest asphaltenes) do show some gradient. These observations allow for the determination of the maximum and minimum asphaltene aggregation number; the range is roughly 3−8. Some modest resin association with asphaltenes, one resin molecule in every asphaltene nanoaggregate, is consistent with our data. These results are discussed within the increasingly successful modified Yen model of asphaltenes.

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: Bench or experimental · Consensus signal: Bench or experimental
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.004
Threshold uncertainty score0.007

Distilled classifier scores by category (both heads)

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)0.0010.000

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 head0.215
Teacher spread0.208 · 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

Citations90
Published2009
Admission routes1
Has abstractyes

Explore more

Same venueEnergy & FuelsSame topicPetroleum Processing and AnalysisFrench-language works237,207