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.

图像测井是一种用于油气勘探的识别储层裂缝的直观方法。但是，这些日志记录映像很少且未注释。本文采用基于卷积神经网络的无监督分割网络方法，自动提取与裂缝信息有关的像素。我们提出一种改进的模型，以实现从具有相似裂缝信息的源域到目标域的域适应，从而可以提高裂缝识别的准确性。在源域中使用基本事实对网络进行了培训，并在目标域中对网络进行了测试，没有任何标签。与其他经典方法的实验结果相比，