Porosity classification from thin sections using image analysis and neural networks including shallow and deep learning in Jahrum formation | ||
| Journal of Mining and Environment | ||
| مقاله 19، دوره 9، شماره 2، تیر 2018، صفحه 513-525 اصل مقاله (1.53 M) | ||
| نوع مقاله: Case Study | ||
| شناسه دیجیتال (DOI): 10.22044/jme.2018.6386.1458 | ||
| نویسندگان | ||
| M. Abedini* 1؛ M. Ziaii1؛ Y. Negahdarzadeh1؛ J. Ghiasi-Freez2 | ||
| 1Faculty of Mining, Petroleum & Geophysics Engineering, Shahrood University of Technology, Shahrood, Iran | ||
| 2Iranian Central Oil Fields Company (ICOFC), Subsidiary of National Iranian Oil Company (NIOC), Iran | ||
| چکیده | ||
| The porosity within a reservoir rock is a basic parameter for the reservoir characterization. The present paper introduces two intelligent models for identification of the porosity types using image analysis. For this aim, firstly, thirteen geometrical parameters of pores of each image were extracted using the image analysis techniques. The extracted features and their corresponding pore types of 682 pores were used for training two intelligent models, BPN (back-propagation network) and SAE (stacked autoencoder). The trained models take the geometrical properties of pores to classify the type of six porosity types including intra-particle, inter-particle, vuggy, moldic, biomoldic, and fracture. The MSE values for the BPN and SAE models were found to be 0.0042 and 0.0038, respectively. The precision, recall, and accuracy of the intelligent models for classifying the types of pores were calculated. The BPN model was able to correctly recognize 193 intra-particle pores out of 197 ones, 45 inter-particle pores out of 50 ones, 7 vuggy pores out of 9 ones, 10 moldic pores out of 12 ones, 2 biomoldic pores out of 3 ones, and 6 fractures out of 7 ones. Also the SAE model was able to correctly classify 193 intra-particle pores out of 197 ones, 46 inter-particle pores out of 50 ones, 8 vuggy pores out of 9 ones, 10 moldic pores out of 12 ones, 3 biomoldic pores out of 3 ones, and 7 fractures out of 7 ones. The results obtained showed that the SAE model carried out a bit more accuracy for classification of the inter-particle, vuggy, biomoldic, and fracture pores. | ||
| کلیدواژهها | ||
| porosity classification؛ image analysis؛ Neural network؛ deep learning؛ stacked autoencoder | ||
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