Filling borehole image gaps with a partial convolution neural network

Abstract

Borehole images are measured by logging tools in a well, providing a microresistivity map of the rock properties surrounding the borehole. These images contain valuable information related to changes in mineralogy, porosity, and fluid content, making them essential for petrophysical analysis. However, due to the special design of borehole imaging tools, vertical strips of gaps occur in borehole images. We develop an effective approach to fill these gaps using a convolutional neural network with partial convolution layers. To overcome the challenge of missing training labels, we introduce a self-supervised learning strategy. Specifically, we replicate the gaps found in borehole images by randomly creating vertical blank strips that mask out certain known areas in the original images. We then use the original images as label data to train the network to recover the known areas masked out by the defined gaps. To ensure that the missing data do not impact the training process, we incorporate partial convolutions that exclude the null-data areas from convolutional computations during forward and backward propagation of updating the network parameters. Our network, trained in this way, can then be used to reasonably fill the gaps originally appearing in the borehole images and obtain full images without any noticeable artifacts. Through the analysis of multiple real examples, we determine the effectiveness of our method by comparing it with three alternative approaches. Our method outperforms the others significantly, as demonstrated by various quantitative evaluation metrics. The filled full-bore images obtained through our approach enable enhanced texture analysis and automated feature recognition.