Ooid characteristic extraction mathod of carbonate rocks based on superpixel image segmentation algorithm
JIANG Liwei1, LIU Di2, ZHANG Yang1, ZHANG Yunying1, TAO Zhi1
1. Artificial Intelligence Research Center of China Petroleum Exploration and Development Research Institute, Beijing 10083; 2. Geophysical Technology Research Center, BGP Inc., CNPC, Zhuozhou, Hebei 072751, China
Abstract：Carbonate rocks are a crucial reservoir for oil and gas. The identification of thin section characteristics of carbonate rocks often relies heavily on manual annotation，which is time-consuming and subjective. With the rapid development of machine learning and other technologies，converting existing images into digital images for segmentation，classification，and feature extraction can efficiently improve research and reduce costs. Thus，two me-thods are compared and studied for extracting ooid characteristic of carbonate rocks. One method is based on traditional image segmentation processing algorithms，and the other is based on SLIC superpixel segmentation algorithms. The experimental results show that the superpixel segmentation algorithm has advantages over traditional segmentation algorithms in terms of accuracy，extraction rate，and other aspects.
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