Two intelligent pattern recognition models for automatic identification of textural and pore space characteristics of the carbonate reservoir rocks using thin section images
| Autor: | Amir Hatampour, Javad Ghiasi-Freez, Omid Borazjani |
|---|---|
| Rok vydání: | 2016 |
| Předmět: |
business.industry
0208 environmental biotechnology Perspective (graphical) ComputingMethodologies_IMAGEPROCESSINGANDCOMPUTERVISION Intelligent decision support system Energy Engineering and Power Technology Pattern recognition 02 engineering and technology 010502 geochemistry & geophysics Geotechnical Engineering and Engineering Geology 01 natural sciences Texture (geology) 020801 environmental engineering Identifier Fuel Technology Dunham classification Pattern recognition (psychology) Carbonate rock Artificial intelligence business Porosity Geology 0105 earth and related environmental sciences |
| Zdroj: | Journal of Natural Gas Science and Engineering. 35:944-955 |
| ISSN: | 1875-5100 |
| Popis: | Over the last two decades pattern recognition approaches have attracted engineers to solve real world problems more accurately through the development of computational technology. In the present research, the capabilities of intelligent systems are employed to develop two algorithms for identification of textural and pore space characteristics of carbonate rocks from thin section images. The texture identifier model classifies the images based on Dunham classification, while the porosity analyzer model determines the percentage of each type of pore spaces in the image. The texture identifier model extracts thirteen features to recognize texture type and the porosity analyzer determines percentage of each type of porosity based on eleven features extracting from the thin section image. Finally, two confusion matrixes are used to evaluate the performance of the developed models. The results show that the models perform reliably from the perspective of petroleum geology for studying carbonate reservoir rocks. |
| Databáze: | OpenAIRE |
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