Video Logging Casing Damage Image Recognition Based on Improved Convolutional Neural Network

Abstract

Abstract Oil casing damage detection is the key point to ensure the smooth production of oil fields. In recent years, the automatic image recognition technology based on deep learning has become a researchful hot topic. But the common deep learning models have some defects in identifying the target features of casing damage images in the complex environment. This paper proposes an oil casing damage image recognition model based on DS-CNN(deep and shallow convolutional neural network). Based on VGG19, this model integrates the shallow convolution neural network. It combines global features extracted by the shallow network and the local features extracted by the deep network to form the input of the fully connected layer. The joint training of the shallow network and the deep network enables the image to be expressed in multiple scales to improve the recognition accuracy of the entire model. The experimental data is obtained from the downhole casing image dataset of an oil field in Sichuan. Experimental result shows that the macro-average F1 scores of the DS-CNN are 4.41 and 5.74 percentage points higher than those of the VGG19 model and the GoogleNet model, indicating that this model improves the recognition accuracy of oil casing damage images.