Asphaltenes View Full Text


Ontology type: schema:Chapter     


Chapter Info

DATE

2017

AUTHORS

Oliver C. Mullins , Andrew E. Pomerantz , A. Ballard Andrews , Rudraksha Dutta Majumdar , Paul Hazendonk , Yosadara Ruiz-Morales , Lamia Goual , Richard N. Zare

ABSTRACT

Asphaltenes are the most enigmatic component of crude oil, and resolution of fundamentals such as asphaltene molecular weight and predominant molecular architecture has been required for the field to advance. Application of many sophisticated analytical methods to asphaltenes has recently given rise to a broad understanding. This chapter reviews many of the most important and recent studies of asphaltene molecular and nanocolloidal structures that have been codified in the Yen–Mullins model. Broad consistency across wide-ranging disciplines has been achieved, yielding robust interpretations. The result that a simple nanoscience model applies to chemically diverse asphaltenes is placed within the context of related similar findings of the gas and liquid components of crude oil. Implications are discussed for thermodynamic modeling of asphaltenes that are specifically treated in the next chapter. In addition to describing measurements of average molecular parameters, this chapter deals with the characterization of the width of certain asphaltene distributions, including molecular weight and the range of asphaltene polycyclic aromatic hydrocarbons (PAHs). The unique role of the asphaltene PAHs in intermolecular interaction is discussed within the context of structure–function relations, for example, for interfacial properties. With these advances, the concept of petroleomics, predictive petroleum science, is realized, even for the most complex fraction of petroleum. More... »

PAGES

221-250

Book

TITLE

Springer Handbook of Petroleum Technology

ISBN

978-3-319-49345-9
978-3-319-49347-3

Identifiers

URI

http://scigraph.springernature.com/pub.10.1007/978-3-319-49347-3_6

DOI

http://dx.doi.org/10.1007/978-3-319-49347-3_6

DIMENSIONS

https://app.dimensions.ai/details/publication/pub.1099749383


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