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Earth Science Informatics

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16.03.2021 | Research Article

Fracture recognition in ultrasonic logging images via unsupervised segmentation network

verfasst von: Wei Zhang, Tong Wu, Zhipeng Li, Shiyuan Liu, Ao Qiu, Yanjun Li, Yibing Shi

Erschienen in: Earth Science Informatics | Ausgabe 2/2021

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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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Titel: Fracture recognition in ultrasonic logging images via unsupervised segmentation network
verfasst von: Wei Zhang
Tong Wu
Zhipeng Li
Shiyuan Liu
Ao Qiu
Yanjun Li
Yibing Shi
Publikationsdatum: 16.03.2021
Verlag: Springer Berlin Heidelberg
Erschienen in: Earth Science Informatics / Ausgabe 2/2021
Print ISSN: 1865-0473
Elektronische ISSN: 1865-0481
DOI: https://doi.org/10.1007/s12145-021-00605-6

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