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Identifying the property of sandstone slice has important directive to exploration of oil and gas resources. The traditional sandstone slice identification method based on manual observation, which is time-consuming and laborintensive. In recent years, the Convolutional Neural Networks (CNNs) have achieved excellent results in the field of image recognition and a CNN-supported recognition algorithm has practical value in various application. However, there are multiple challenges for using the classic CNN for the sandstone slice image recognition: the industrialized recognition needs efficiency, which means the sandstone slice image recognition should be finished accurately and quickly; the sandstone image recognition requires expert annotations, resulting in lack of data; some mineral particles in sandstone are uncommon, which caused the sandstone image dataset has unbalanced data distribution problem. We invited several geologists to annotate sandstone slice image and start experiments. For sandstone slice image recognition, in order to ensure recognition speed and accuracy of sandstone slice, we built the Res2DwNet model. The model is optimized for the characteristics of the sandstone slice image. In this model, we proposed the Res2Dw module, which combines deep separable convolution, dense connection and residual learning ideas. Combining geology expertise, the sandstone slice image in our model is divided into two types of single-polarized and cross-polarized images for processing, which helps the recognition accuracy improves significantly. To solve the problem of small amount of data, a unique data enhancement method is designed on the basis of the experience of sandstone slice identification. To handle the uneven data distribution problem, the class-balanced softmax loss function is used during training. Compared with other classic Convolutional Neural Network models, the Res2DwNet model has a deeper network structure, which can achieve higher recognition accuracy than models of the same magnitude while maintaining lightweight parameters, and is suitable for sandstone slice image recognition tasks.
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