Advertisement

Thin Sections Images Processing Technique for the Porosity Estimation in Carbonate Rocks

  • N. D. Nurgalieva
  • N. G. NurgalievaEmail author
Conference paper
Part of the Sustainable Civil Infrastructures book series (SUCI)

Abstract

In present paper we used program Cluster Image which was created in Java to process thin section image of carbonate rock to estimate its porosity on image of any format and with a strong color contrast between the mineral part and pores in thin sections under polarized light. For the experiment the images of thin sections of carbonate rocks of the Carboniferous age were used. Cluster Image does picture clustering with parameters given by user. After opening the program, the picture should be downloaded and parameters should be chosen. A thin section photo in polarized light can be downloaded in any format; also a folder or an URL address containing pictures can be chosen as a material for clustering. It is necessary to specify the number of clusters. To process clustering ISODATA algorithm is preferred because it is iterative and accurate. It is necessary to specify the number of clusters, the percentage of their convergence and the minimum size of one cluster (in pixels). Each pixel’s color can be represented as vector of three components in RGB basis. As a result, the picture is a set of vectors which have to be divided into separate groups according to their coordinates. The total number of groups is given by the number of clusters, while the convergence specifies the accuracy rate within the group and bounds the number of algorithm iterations. The program creates a completely new image in which pixels of a particular group are all colored in average color of the group. Since the pores in the photo are black, the program can recognize them as a separate group. Digital estimation of porosity was made for cores from two wells in comparison with liquid injection method of porosity measuring. The features of digital porosity were explained by porosity genesis.

Notes

Acknowledgments

The work is supported by the Russian Government Program of Competitive Growth of Kazan Federal University.

References

  1. Nurgalieva, N.D., Nurgalieva, N.G.: Porosity estimation of carbonate rocks with Multispec processing technique. ARPN J. Eng. Appl. Sci. 1, 20–24 (2014)Google Scholar
  2. Nurgalieva, N.D., Nurgalieva, N.G.: Cluster Image processing technique for porosity estimation of carbonate rocks. ARPN JEAS 10(4), 1668–1671 (2015)Google Scholar
  3. Nurgalieva, N.D., Nurgalieva, N.G.: New digital methods of estimation of porosity of carbonate rocks. Indian J. Sci. Technol. 9(20) (2016). doi: 10.17485/ijst/2016/v9i20/93750
  4. Nurgalieva, N.G.: Microfacies petrophysics and sequence-stratigraphic frame of carbonate reservoir rocks of Kizelovskian formation. J. Oil Ind. 3, 38–40 (2012)Google Scholar
  5. Mazzullo, S.J.: Overview of porosity evolution in carbonate reservoirs, pp. 1–19 (2004). www.searchanddis-covery.com/documents/2004/mazzullo/images/mazzullo.pdf

Copyright information

© Springer International Publishing AG 2018

Authors and Affiliations

  1. 1.Department of General and Applied PhysicsThe Moscow Institute of Physics and TechnologyDolgoprudnyRussia
  2. 2.Institute of Geology and Petroleum TechnologiesKazan (Volga Region) Federal UniversityKazanRussian Federation

Personalised recommendations