A deep-learning approach for dynamic region merging applied to feature extraction from borehole microresistivity images

Abstract

The primary purpose of processing borehole resistivity images is to identify and extract high (or low) resistivity anomalous areas, which are associated with resistive fractures and dissolved pores. To improve the accuracy and applicability of these models, a new intelligent method combining dynamic-region merging (DRM) with a deep-learning network (U-net) is proposed. The superpixel method, also referred to as linear spectral clustering (LSC), was applied to segment fractures and dissolved pores that are represented by a resistivity contrast in the original resistivity images. The sequential probability ratio test technique was then used to implement the DRM procedure to group many oversegmented small regions. A convolutional neural network (U-net architecture) model was used to automatically identify geologic features with various scales. The trained neural network was then used to identify the segmented resistivity images that had been processed by the DRM. The results showed that the superpixel algorithm and U-net combination significantly improved the accuracy of the classification and identification of fractures and dissolved pores in different test data sets. Moreover, it solved the problem of few-shot learning, enabling a pretrained model to generalize over new data categories.