
№ 1,
2022
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Analysis of the influence of the composition and properties of oils on their quality based on fuzzy clustering
1 - Institute of Oil and Gas of Azerbaijan National Academy of Sciences F. Amirov 9, Baku, Azerbaijan, AZ1000: galib_2000@yahoo.com
2 - Caspian State University of Technology and Engineering named after Sh. Yessenov 32 microdistrict, Mangistau region, Aktau, The Republic of Kazakhstan, 130003: mikado_70@inbox.ru, zhetekova81@mail.ru
3 - Azerbaijan State Oil and Industry University 20, Azadlyg prosp., Baku, Azerbaijan, AZ 1010: abbasovasamira@mail.ru
Summary
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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
REFERENCES
Akhmetov D.A., Efendiyev G.M., Karazhanova M.K., Koylibaev B.N. Classification of hard-to-recover hydrocarbon reserves of Kazakhstan with the use of fuzzy cluster-analysis. 13th International Conference on theory and application of fuzzy systems and soft computing – ICAFS-2018, Springer Nature Switzerland. Warsaw, Poland, 27-28 August 2018, pp. 865-872.
Aliev R.A., Guirimov B.G. Type-2 fuzzy neural networks and their applications. Springer International Publishing. 2014, 190 p., http://www.springer.com/us/book/9783319090719.
Antoniadi D.G., Savenok O.V. Analysis of structure of tight oil reserves and growth trends of the increment rate. Geoengineering, Vol. 18, No. 2, 2013, pp. 76-80 (in Russian).
Efendiyev G.M., Mammadov P.Z., Piriverdiyev I.A., Mammadov V.N. Clustering of geological objects using FCM-algorithm and evaluation of the rate of lost circulation. Procedia Computer Science, Vol. 102, 2016, pp. 159-162.
Efendiyev G., Mammadov P., Piriverdiyev I., Mammadov V. Estimation of the lost circulation rate using fuzzy clustering of geological objects by petrophysical properties. Visnyk Taras Shevchenko National University of Kyiv, Vol. 81, No. 2, 2018, pp. 28-33.
Eliseeva O.A., Lukyanov A.S. Systematic assessment of economically acceptable resources of oil and gas provinces of Russia, taking into account innovative technologies. Georesources, geoenergetics, geopolitics (Electronic Scientific Journal), Vol. 9, No. 1, 2014, http://oilgasjournal.ru/vol_9/ eliseeva.pdf (in Russian).
Karazhanova M.K., Zhetekova L.B., Aghayeva K.K. Quality assessment of tight oil based on fuzzy clustering and statistical analysis. 10th International Conference on theory and application of soft computing, computing with words and perceptions – ICSCCW-2019. Advances in Intelligent Systems and Computing book series, Vol. 1095, 2019, pp. 254-258.
Klubkov S. Promoting TRIZ development will help to maintain oil production in Russia. Oil and Gas Journal Russia, No. 7(95), 2015, pp. 6-11 (in Russian).
Kritskaya E.B., Chizh D.V. Study of changes in physicochemical parameters of oils of Ciscaucasia. Proceedings of Voronezh State University. Series: Chemistry, Biology, Pharmacy, No. 1, 2013, pp. 21-23 (in Russian).
Lisovsky N.N., Halimov E.M. Classification of tight oil reserves. Vestnik CKR Rosnedra, No. 6, 2009, pp. 33-35 (in Russian).
Maksutov R., Orlov G., Osipov A. Development of high-viscosity oil reserves in Russia. FEC (fuel and energy complexes) Technologies, No. 6, 2005, pp. 36-40 (in Russian).
Nardone P.J. Well testing project management. Well test description, 2009, pp. 73-105, https://doi.org/10.1016/B978-1-85617-600-2.00003-0.
Oil classification, https://studfiles.net/preview/1772355/page:2/ (in Russian).
Raupov I.R., Kondrasheva N.K., Burkhanov R.N. The mobile device design for oil optical properties measuring in the performance of field-geologic tasks. Electronic scientific journal "Oil and Gas Business", No. 3, 2014, pp. 17-32 (in Russian).
Santos R.G., Loh W., Bannwart A.C., Trevisan O.V. An overview of heavy oil properties and its recovery and transportation methods. Brazilian Journal of Chemical Engineering, Vol. 31, No. 3, 2014, pp. 571-590.
Shpurov I.V., Rastrogin A.E., Bratkova V.G. On the problem of hard-to-recover oil reserves development in Western Siberia. Neftyanoye khozyaystvo, No. 12, 2014, pp. 95-97 (in Russian).
Turksen I.B. Full Type 2 to Type n fuzzy system models. Seventh International Conference on soft computing, computing with words and perceptions in system analysis, decision and control, Turkey, Izmir, 2013, p. 21.
Yashchenko I.G., Polishchuk Yu.M. Tight oils and analysis of their properties based on classification by oil quality. Bulletin of Russian Academy of Natural Sciences (West Siberian Branch), No. 19, 2016, pp. 37-44 (in Russian).
DOI:
10.33677/ggianas20220100075
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
REFERENCES
Akhmetov D.A., Efendiyev G.M., Karazhanova M.K., Koylibaev B.N. Classification of hard-to-recover hydrocarbon reserves of Kazakhstan with the use of fuzzy cluster-analysis. 13th International Conference on theory and application of fuzzy systems and soft computing – ICAFS-2018, Springer Nature Switzerland. Warsaw, Poland, 27-28 August 2018, pp. 865-872.
Aliev R.A., Guirimov B.G. Type-2 fuzzy neural networks and their applications. Springer International Publishing. 2014, 190 p., http://www.springer.com/us/book/9783319090719.
Antoniadi D.G., Savenok O.V. Analysis of structure of tight oil reserves and growth trends of the increment rate. Geoengineering, Vol. 18, No. 2, 2013, pp. 76-80 (in Russian).
Efendiyev G.M., Mammadov P.Z., Piriverdiyev I.A., Mammadov V.N. Clustering of geological objects using FCM-algorithm and evaluation of the rate of lost circulation. Procedia Computer Science, Vol. 102, 2016, pp. 159-162.
Efendiyev G., Mammadov P., Piriverdiyev I., Mammadov V. Estimation of the lost circulation rate using fuzzy clustering of geological objects by petrophysical properties. Visnyk Taras Shevchenko National University of Kyiv, Vol. 81, No. 2, 2018, pp. 28-33.
Eliseeva O.A., Lukyanov A.S. Systematic assessment of economically acceptable resources of oil and gas provinces of Russia, taking into account innovative technologies. Georesources, geoenergetics, geopolitics (Electronic Scientific Journal), Vol. 9, No. 1, 2014, http://oilgasjournal.ru/vol_9/ eliseeva.pdf (in Russian).
Karazhanova M.K., Zhetekova L.B., Aghayeva K.K. Quality assessment of tight oil based on fuzzy clustering and statistical analysis. 10th International Conference on theory and application of soft computing, computing with words and perceptions – ICSCCW-2019. Advances in Intelligent Systems and Computing book series, Vol. 1095, 2019, pp. 254-258.
Klubkov S. Promoting TRIZ development will help to maintain oil production in Russia. Oil and Gas Journal Russia, No. 7(95), 2015, pp. 6-11 (in Russian).
Kritskaya E.B., Chizh D.V. Study of changes in physicochemical parameters of oils of Ciscaucasia. Proceedings of Voronezh State University. Series: Chemistry, Biology, Pharmacy, No. 1, 2013, pp. 21-23 (in Russian).
Lisovsky N.N., Halimov E.M. Classification of tight oil reserves. Vestnik CKR Rosnedra, No. 6, 2009, pp. 33-35 (in Russian).
Maksutov R., Orlov G., Osipov A. Development of high-viscosity oil reserves in Russia. FEC (fuel and energy complexes) Technologies, No. 6, 2005, pp. 36-40 (in Russian).
Nardone P.J. Well testing project management. Well test description, 2009, pp. 73-105, https://doi.org/10.1016/B978-1-85617-600-2.00003-0.
Oil classification, https://studfiles.net/preview/1772355/page:2/ (in Russian).
Raupov I.R., Kondrasheva N.K., Burkhanov R.N. The mobile device design for oil optical properties measuring in the performance of field-geologic tasks. Electronic scientific journal "Oil and Gas Business", No. 3, 2014, pp. 17-32 (in Russian).
Santos R.G., Loh W., Bannwart A.C., Trevisan O.V. An overview of heavy oil properties and its recovery and transportation methods. Brazilian Journal of Chemical Engineering, Vol. 31, No. 3, 2014, pp. 571-590.
Shpurov I.V., Rastrogin A.E., Bratkova V.G. On the problem of hard-to-recover oil reserves development in Western Siberia. Neftyanoye khozyaystvo, No. 12, 2014, pp. 95-97 (in Russian).
Turksen I.B. Full Type 2 to Type n fuzzy system models. Seventh International Conference on soft computing, computing with words and perceptions in system analysis, decision and control, Turkey, Izmir, 2013, p. 21.
Yashchenko I.G., Polishchuk Yu.M. Tight oils and analysis of their properties based on classification by oil quality. Bulletin of Russian Academy of Natural Sciences (West Siberian Branch), No. 19, 2016, pp. 37-44 (in Russian).