Evidence map›Paper›PMID 40949226›Full record

ArticleACS omega2025

Influence of SARA Fractions on the Yield Stress of Heavy Oil.

Wenli Ke, Qi Han, Wu Chen

Abstract read
In one paragraph

Article in ACS omega, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 1 paper.

0numbers the graph read from it
0cells of the map it votes in
1citing papers in PubMed
–field-weighted citation impact
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

1 citing paper in PubMed.

  1. Review
4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

3 authors.

Wenli KeYangtze University, Wuhan 430100, China.ORCID https://orcid.org/0000-0002-6323-9372
Qi HanYangtze University, Wuhan 430100, China.
Wu ChenYangtze University, Wuhan 430100, China.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

The depletion of conventional oil resources has rendered heavy oil development a critical challenge in the energy sector. As a core rheological parameter of heavy oil, yield stress directly influences transportation efficiency and the optimization of oil displacement strategies. This study employs a multiscale analytical approach, integrating ultraviolet absorption spectroscopy and rheological testing, to systematically investigate the mechanisms by which saturate, aromatic, resin, and asphaltene (SARA) components in heavy oil and their interactions affect yield stress. The findings reveal distinct impacts of individual components on yield stress: Saturates monotonically reduce yield stress via solvation effects; Aromatics exhibit a peak-valley trend, increasing yield stress to a maximum within a specific content range before decreasing; Resins progressively elevate yield stress with increasing content by forming micellar systems that enhance structural strength; Asphaltenes display a U-shaped influence, initially disrupting resin-asphaltene complexes to reduce yield stress, followed by significant yield stress increases beyond a critical concentration due to the formation of three-dimensional network structures. Molecular interaction analysis demonstrates that saturates exhibit dual effects, aromatics reduce structural integrity by dispersing asphaltene aggregates, resins bind with asphaltenes through multifaceted interactions, and asphaltenesowing to their strongest polarityact as the structural core of networks. Elevated temperatures substantially weaken intercomponent interactions, leading to colloid network destabilization and widespread yield stress reduction. This research establishes a comprehensive theoretical framework bridging molecular mechanisms to macroscopic performance, providing crucial scientific insights for both theoretical advancements in heavy oil rheology and practical engineering applications.

Identifiers

PMID40949226
PMCPMC12423879

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Registered trials

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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.