Digital Rock Samples Porosity Analysis by OTSU Thresholding Technique Using MATLAB
DOI:
https://doi.org/10.31699/IJCPE.2020.3.8Keywords:
Digital rock physics, OTSU thresholding, Thin section image, Porosity, Macro pores, Micro poresAbstract
Porosity plays an essential role in petroleum engineering. It controls fluid storage in aquifers, connectivity of the pore structure control fluid flow through reservoir formations. To quantify the relationships between porosity, storage, transport and rock properties, however, the pore structure must be measured and quantitatively described. Porosity estimation of digital image utilizing image processing essential for the reservoir rock analysis since the sample 2D porosity briefly described. The regular procedure utilizes the binarization process, which uses the pixel value threshold to convert the color and grayscale images to binary images. The idea is to accommodate the blue regions entirely with pores and transform it to white in resulting binary image. This paper presents the possibilities of using image processing for determining digital 2D rock samples porosity in carbonate reservoir rocks. MATLAB code created which automatically segment and determine the digital rock porosity, based on the OTSU's thresholding algorithm. In this work, twenty-two samples of 2D thin section petrographic image reservoir rocks of one Iraqi oil field are studied. The examples of thin section images are processed and digitized, utilizing MATLAB programming. In the present study, we have focused on determining of micro and macroporosity of the digital image. Also, some pore void characteristics, such as area and perimeter, were calculated. Digital 2D image analysis results are compared to laboratory core investigation results to determine the strength and restrictions of the digital image interpretation techniques. Thin microscopic image porosity determined using OTSU technique showed a moderate match with core porosity.
Received on 26/06/2020
Accepted on 22/08/2020
Published on 30/09/2020
References
R. C. Gonzalez and R. E. Woods, Digital image processing. New Jersey: Parson, 2008.
L. G. Shapiro and G. C. Stockman, "Computer Vision. 279-325," ed: New Jersey, Prentice-Hall, ISBN 0-13-030796-3, 2001.
L. Barghout and L. Lee, "Perceptual information processing system." U.S. Patent Application 10/618,543, filed March 25, 2004.
P. Shanthakumar and P. Ganesh Kumar, "Computer aided brain tumor detection system using watershed segmentation techniques", International Journal of Imaging Systems and Technology, vol. 25, no. 4, pp. 297-301, 2015.
E. B. George and M. Karnan, "MR brain image segmentation using bacteria foraging optimization algorithm," International journal of engineering and technology (IJET), vol. 4, pp. 295-301, 2012.
J. Delmerico, P. David and J. Corso, "Building facade detection, segmentation, and parameter estimation for mobile robot stereo vision", Image and Vision Computing, vol. 31, no. 11, pp. 1632-1639, 2013.
D. L. Pham, et al., "Current methods in medical image segmentation," Annual review of biomedical engineering, vol. 2, pp. 315-337, 2000.
M. Forouzanfar, N. Forghani and M. Teshnehlab, "Parameter optimization of improved fuzzy c-means clustering algorithm for brain MR image segmentation", Engineering Applications of Artificial Intelligence, vol. 23, no. 2, pp. 160-168, 2010.
S. Kamalakannan, "Double-edge detection of radiographic lumbar vertebrae images using pressurized open DGVF snakes," IEEE Transactions on Biomedical Engineering, vol. 57, pp. 1325-1334, 2010.
N. Otsu, "A Threshold Selection Method from Gray-Level Histograms", IEEE Transactions on Systems, Man, and Cybernetics, vol. 9, no. 1, pp. 62-66, 1979.
O. Wirjadi, "Survey of 3d image segmentation methods," 2007.
H. Mobahi, S. Rao, A. Yang, S. Sastry and Y. Ma, "Segmentation of Natural Images by Texture and Boundary Compression", International Journal of Computer Vision, vol. 95, no. 1, pp. 86-98, 2011.
Md. Abu Bakr Siddique, Mohammad Mahmudur Rahman Khan, Rezoana Bente Arif and Zahidun Ashrafi, "Study and observation of the variations of accuracies for handwritten digits recognition with various hidden layers and epochs using neural network algorithm." In 2018 4th International Conference on Electrical Engineering and Information & Communication Technology (iCEEiCT), pp. 118-123. IEEE, 2018.
Rezoana Bente Arif, Md. Abu Bakr Siddique, Mohammad Mahmudur Rahman Khan and Mahjabin Rahman Oishe, "Study and Observation of the Variations of Accuracies for Handwritten Digits Recognition with Various Hidden Layers and Epochs using Convolutional Neural Network." In 2018 4th International Conference on Electrical Engineering and Information & Communication Technology (iCEEiCT), pp. 112-117. IEEE, 2018.
Mohammad Mahmudur Rahman Khan, Rezoana Bente Arif, Md. Abu Bakr Siddique and Mahjabin Rahman Oishe, "Study and Observation of the Variation of Accuracies of KNN, SVM, LMNN, ENN Algorithms on Eleven Different Datasets from UCI Machine Learning Repository", in 2018 4th International Conference on Electrical Engineering and Information & Communication Technology (IEEE ICT), 2018, pp. 124-129.
M. Sezgin and B. Sankur, "Survey over image thresholding techniques and quantitative performance evaluation", Journal of Electronic Imaging, vol. 13, no. 1, pp. 146-166, 2004.
Y. Zhang and L. Wu, "Optimal multi-level thresholding based on maximum Tsallis entropy via an artificial bee colony approach," Entropy, vol. 13, pp. 841-859, 2011.
A. dos Anjos and H. Shahbazkia, "Bi-Level Image Thresholding-A Fast Method", in BIOSIGNALS (2), 2008, pp. 70-76.
P.-S. Liao, “A fast algorithm for multi-level thresholding," J. Inf. Sci. Eng., vol. 17, 2001, pp. 713-727.
Ghassan H. Ali, Yahya J. Tawfeeq, and Mohammed Y. Najmuldeen, “Comparative estimation of water saturation in a carbonate reservoir: A case study of northern Iraq”, Periodicals of Engineering and Natural Sciences, nol. 7, No. 4, pp.1743-1754, 2019.
Mohammed Y. Najmuldeen, Ali A. Fadhil, and Yahya J. Tawfeeq, “Petrophysical Characterization of The Tertiary Oil Reservoir, Northern Iraq”, Periodicals of Engineering and Natural Sciences, ISSN 2303-4521, Vol. 8, No. 2, 2020.
Karrar Hayder Jassim and Jalal A. Al-Sudani. " Re-evaluation of Petro physical Properties in Yammama Formation at Nasiriya Field”. Iraqi Journal of Chemical and Petroleum Engineering, Vol.20 No.3, 2019, pp. 59 – 66.
Yahya J. Tawfeeq, Mohammed Y. Najmuldeen and Ghassan H. Ali, “Optimal statistical method to predict subsurface formation permeability depending on open hole wireline logging data: A comparative study”, Periodicals of Engineering and Natural Sciences, ISSN 2303-4521, Vol. 8, No. 2, 2020.
Sara S. Zughar, Ahmad A. Ramadhan, Ahmed K. Jaber, "Petrophysical Properties of an Iraqi Carbonate Reservoir Using Well Log Evaluation", Iraqi Journal of Chemical and Petroleum Engineering, vol.21, no.1, pp. 53 – 59,2020.
Fens, T.W., “Petrophysical properties from small rock samples using image analysis techniques”, Ph.D, Delft University of Technology, 2000.
Zerabruk, B.T., Nermoen, A., Nadeau, P.H., “Digital image analysis for petrophysical characterization”, M.Sc., University of Stavanger, 2017.
Sonka, Hlavac and Boyle, “Digital Image Processing and Computer Vision”, India Edition, CENGAGE Learning, 2007.
K. Fukunaga, Introduction to statistical pattern recognition. Elsevier, 2013. pp. 260-267.
J. Gong, L. Li, and W. Chen, “Fast recursive algorithms for two-dimensional Thresholding,” Pattern Recognition, vol. 31, no. 3, pp. 295–300, 1998.
Zhang, Jun and Hu Jinglu, "Image segmentation based on 2D Otsu method with histogram analysis", Computer Science and Software Engineering, 2008 International Conference on. 6: 105–108.
Zhu, Ningbo and Wang, Gang and Yang, Gaobo and Dai, Weiming, "A fast 2d otsu thresholding algorithm based on improved histogram", Pattern Recognition, 2009. CCPR 2009, Chinese Conference on: 1–5.
Varfolomeev, I., Yakimchuk, I., Denisenko, A., Khasanov, I., Osinceva, N., Rahmattulina, A., “Integrated study of thin sections: Optical petrography and electron microscopy”, SPE 182071, 2016.
Lawrence, M., Jiang, Y., "Porosity, pore size distribution, micro-structure." In Bio-aggregates based building materials, pp. 39-71. Springer, Dordrecht, 2017
Downloads
Published
Issue
Section
License
Copyright (c) 2020 Iraqi Journal of Chemical and Petroleum Engineering
This work is licensed under a Creative Commons Attribution-NonCommercial 4.0 International License.
