International scientific journal

ISSN: 2663-0419 (electronic version)

ISSN: 2218-8754 (print version)

International scientific journal

ISSN: 2663-0419 (electronic version)

ISSN: 2218-8754 (print version)

SCImago Journal & Country Rank

Analysis of the influence of the composition and properties of oils on their quality based on fuzzy clustering

Efendiyev G.M.1, Karazhanova M.K.2, Zhetekova L.B.2, Abbasova S.V.3

1 - Institute of Oil and Gas of Azerbaijan National Academy of Sciences F. Amirov 9, Baku, Azerbaijan, AZ1000:

2 - Caspian State University of Technology and Engineering named after Sh. Yessenov 32 microdistrict, Mangistau region, Aktau, The Republic of Kazakhstan, 130003:,

3 - Azerbaijan State Oil and Industry University 20, Azadlyg prosp., Baku, Azerbaijan, AZ 1010: 


The submitted paper studies published data and summarizes contemporary views concerning classification of tight oil reserves and evaluation of their quality. In recent years there has been an increase in the production of tight oil reserves, which are difficult to extract because of their anomalous properties, as well as because of difficult geological conditions, which makes it important and necessary to study the qualitative properties of tight oil. The paper offers results of the analysis and systematization of indicators of the properties of oil samples collected at various Kazakhstani fields and their classification prepared using fuzzy cluster analysis algorithm. Three groups of signs were considered as classification attributes of various types of tight oil reserves:
1) signs characterizing the composition, this is the content of sulfur, chlorides; 2) properties, this group includes oil density and viscosity, 3) mode of occurrence, i.e. in-situ permeability. Preliminary analysis was completed to determine current status of the issue of tight oil reserves classification and quality evaluation. A review of classification results of tight oil reserves demonstrated the need to break down the entire data set (assemblage) into uniform groups using a series of classification attributes, for which fuzzy cluster analysis is the most appropriate solution. A parameter characterizing oil quality was offered, too. Three clusters have been obtained, each of which characterizes the difficulty of extraction, linguistic rules of conformity of a lot of oil characteristics and total quality factor have been formulated.

Keywords: oil, tight oil reserves, membership function, density, viscosity, composition


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DOI: 10.33677/ggianas20220100075