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Fracture recognition in ultrasonic logging images via unsupervised segmentation network

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Abstract

Image well logging is an intuitive approach to identify fractures of reservoir for oil and gas exploration. However, these logging images are rare and nonannotated. A method of unsupervised segmentation network based on convolutional neural network is adopted to automatically extract pixels pertaining to fracture information in this paper. We propose a modified model to accomplish domain adaptation from the source domain with similar fractures information to the target domain, which can improve the accuracy of fracture recognition. The network is trained in the source domain with ground truth and tested in the target domain without any labels. Compared with the experimental results of other classical methods, this method has demonstrated satisfactory performances in terms of accuracy and visual quality even if the logging image dataset is insufficient.

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Acknowledgments

The research is supported by National Science Foundation of China under the Grant numbered 61201131 and Development and industrial application of ultra-high temperature and high-pressure wireline logging system from Science and technology project of CNOOC under the Grant numbered CNOOC-KJ ZDHXJSGG YF 2019-02. We thank all researchers who have supported us.

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Correspondence to Wei Zhang.

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Zhang, W., Wu, T., Li, Z. et al. Fracture recognition in ultrasonic logging images via unsupervised segmentation network. Earth Sci Inform 14, 955–964 (2021). https://doi.org/10.1007/s12145-021-00605-6

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