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| Intelligent identification and segmentation method of wellbore fractures in resistivity logging imaging map |
| XIA Wenhe1, ZHU Zhehao1, HAN Yujiao2, YANG Yikai1, LIN Yongxue2, WU Xiongjun2 |
1. School of Electrical Engineering and Information, Southwest Petroleum University, Chengdu, Sichuan 610500, China;
2. SINOPEC Petroleum Engineering Technology Research Institute, Beijing 102200, China |
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Abstract In view of the huge workload, strong subjectivity in artificial identification, and poor stability in the fracture identification and processing of logging images, this paper introduces the computer vision technology and deep learning framework into the analysis and interpretation of logging images, builds an intelligent identification and segmentation network model of fracture morphology, and intelligently identifies wellbore fractures in resistivity logging images. First, the model extracts the shallow and deep features of wellbore images through multi-scale dilated convolution and attention mechanism, and multi-scale fusion of shallow and deep features is conducted to form new features with more representation ability. According to the new features, the two pixel classification is carried out to complete foreground and background type identification of each pixel in the logging images. Several pixels classified as foreground present the contour of the fractured area. The multi-scale feature fusion model can fully retain more contour details of the fracture image from the micro perspective, and the identification and classification accuracy of each fracture pixel reaches almost 80.0%. Finally, by drawing lessons from the evaluation system of human eye visual similarity, a performance evaluation algorithm is designed for intelligently identifying fracture contour from the macro perspective. The evaluation results show that when the visual similarity perception rating is grade II, 81.3% and 80.0% of identification results in the fracture region in the training set and test set images are basically consistent with the artificial identification results. The results indicate that the proposed method can replace artificial interpretation to complete fracture identification and marking, greatly reduce the image analysis workload and carefully outline the fracture contour. Meanwhile, it is conducive to the rapid and timely judgment of wellbore and shaft stability, thus providing technical support for subsequent intelligent quantitative evaluation and calculation of fractured areas.
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Received: 13 September 2022
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2023, Vol. 58 
