Novel Automatic Classification Method for Geological Structures in Carbonate Formations Based on Electrical Imaging Logging - FU Yafeng; HUANG Ke; ZHU Hanbin; WANG Hui; ZHANG Xu; ZHAO Jie; XIAO Ni (Cejing jishu)
In the processing of electrical imaging logging data for carbonate formations, it is challenging to distinguish mudstone laminae, natural fractures, induced fractures, and vugs due to their similar resistivities and overlapping occurrences. To address this issue, this paper proposes an automatic classification method based on skeleton maps and machine learning. First, an improved K-means clustering algorithm is used to segment regions of interest from the electrical imaging data. Then, image thinning and the Hough transform are employed to obtain a line-segment representation of the skeleton map, and line segments are grouped based on spatial geometric relationships. Next, a directional region-growing algorithm is applied to separate overlapping regions and to extract geometric features (e.g., perimeter, area, average dip, etc.) of each independent connected region. Finally, these features are used to train an extreme gradient boosting (XGBoost) model, enabling the automatic classification of different geological structures. Validation using actual carbonate logging data shows that the classification accuracy of this method exceeds an 80% agreement rate with core observations. Compared with existing research, this study is the first to achieve automatic classification of mudstone laminae, natural fractures, induced fractures, and vugs in carbonate formations based on electrical imaging logs, significantly improving classification efficiency and accuracy.