Multi-Dimensional Lithology Identification Method Based on Microresistivity Image Logging - LIU Juan; MIN Xuanlin; QI Zhongli; YI Jun; LAI Fuqiang; ZHOU Wei (Cejing jishu)
The traditional microresistivity image logging lithology identification mainly depends on manual identification, and the identification results often affected by manual experience and subjective factors which leads to some issues such as difficulty in lithology characterization. In this paper, a multi-dimensional electrical imaging identification method based on the combination of shape and color is proposed for lithology identification. First, Filtersim algorithm is employed to fill the blank strip of electrical imaging, and K-means++ clustering in pixel-wise is performed on the filled data to mark the weak noise such as cracks and karst caves, so as to avoid introducing noise into color clustering. Then, loss anomaly is used to screen strong noise samples. According to the texture structure and resistivity response characteristics of electro-imaging, the electrical imaging dataset is decoupled into shape set and color set, respectively. Then, a shape and color combined electrical imaging identification model is proposed. To solve the issue of hard labeling by introducing label refining method, Resnet-50 network is established to realize automatic recognition of shape features for different geological structures (massive, layered and laminated). For the electrical imaging color features of different resistivity responses (mudstone, calcareous mudstone and sandy mudstone), K-means++ algorithm is used to screen out the clustering centers of the overall distribution of the data set to achieve fast classification of the electro-imaging colors. Finally, combined with the results of shape classification and color classification, the types of electrical imaging lithology are identified. Lithology recognition experiment is carried out on the electrical imaging image of shale oil reservoir in Jiyang depression. The results show that the recognition accuracy is 83.5%, which has high recognition accuracy. The method can provide fine algorithm support for log interpretation of lithology recognition.