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Record W4245907637 · doi:10.2118/2000-099-ea

Asphaltene Deposition

2000· article· en· W4245907637 on OpenAlexaffabout
H. Yarranton

Bibliographic record

VenueCanadian International Petroleum Conference · 2000
Typearticle
Languageen
FieldChemistry
TopicPetroleum Processing and Analysis
Canadian institutionsUniversity of Calgary
Fundersnot available
KeywordsAsphalteneDeposition (geology)CitationPetroleumLibrary scienceEnvironmental scienceComputer scienceNanotechnologyChemistryChemical engineeringGeologyMaterials scienceEngineeringOrganic chemistrySedimentPaleontology

Abstract

fetched live from OpenAlex

Introduction Production of heavy oils and paraffinic crude reserves often results in the deposition of organic solids, typically waxes or asphaltenes. The organic deposits can reduce the productivity of the reservoir as well as foul piping and surface equipment. Current chemical and mechanical methods for treating deposition are only partially effective partly because the deposition process is poorly understood. A joint research program investigating asphaltene deposition is now underway at the University of Calgary, the University of Alberta and DB Robinson Research Ltd of Edmonton. The various steps of the deposition process (precipitation, aggregation, surface contact and adhesion) are to be investigated under both static and flowing conditions. A key component of the project is an apparatus for non-intrusively measuring asphaltene deposition under flowing conditions using x-ray tomography. This flow-loop apparatus will be designed and constructed with COURSE funding. ASPHALTENE DEPOSITION Asphaltenes are a solubility class and are usually defined as the fraction of a crude oil that precipitates in an aliphatic solvent (typically n-pentane or n-heptane) yet remains soluble in toluene. Asphaltenes are the most aromatic and polar fraction of crude oil and have the largest heteroatom and metal content. They consist of a variety of molecular species with molar masses of at least 1,000 g/mol (1). Asphaltenes appear to self-associate on a molecular level even in aromatic solvents. The degree of association depends on the composition, temperature and likely the pressure of the system. The average size of the associated asphaltenes ranges from 2,000 to 10,000 g/mol or approximately 2 to 6 molecules per aggregate (2). Asphaltenes can also precipitate upon a change in temperature, pressure or composition. The asphaltenes appear to precipitate as small "primary" particles which rapidly aggregate into macro-particles. The size of the primary particles is unknown but is likely in the order of a few microns based on visual observations. The size of the aggregated asphaltenes depends on the solven temperature and pressure but is in the order of several hundred microns (3). Solubilized asphaltenes can adsorb directly onto hydrophilic surfaces probably through interactions with heteroatom functional groups (4). Hence, adsorption can be significant in the reservoir. Direct adsorption is less likely on hydrophobic surfaces such as metals. Deposition on pipes and surface facilities more likely requires the precipitation of asphaltenes, the formation of aggregates and adhesion of aggregates to equipment surfaces. To understand and effectively prevent or treat asphaltene deposition, it is desirable to investigate each step of the deposition process, particularly under the flowing conditions where deposition normally occurs. In this way, potential treatments can be designed for particular steps in the deposition process. Our research is focused on deposition in pipelines. First, the precipitation, aggregation and adhesion of asphaltenes will be investigated at static conditions in order to identify critical deposition factors and to design the flow-loop apparatus. Then, the flow-loop will be employed to assess deposition under flowing conditions and to gather data for deposition models.

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 score0.016
Threshold uncertainty score0.055

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0010.001
Science and technology studies0.0010.000
Scholarly communication0.0010.001
Open science0.0000.001
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0160.009

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 head0.230
Teacher spread0.218 · 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

Citations3
Published2000
Admission routes2
Has abstractyes

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