###### Abstract  
The quality of cement in cased boreholes is related to the production and life of wells. At present, the most commonly used method is to use CBL-VDL to evaluate, but the interpretation process is complicated, and decisions associated with significant risks may be taken based on the interpretation results. Therefore, cementing quality evaluation must be interpreted by experienced experts, which is time-consuming and labor-intensive. To improve the efficiency of cementing interpretation, this paper used VGG, ResNet, and other convolutional neural networks to automatically evaluate the cementing quality, but the accuracy is insufficient. Therefore, this paper proposes a multi-scale perceptual convolutional neural network with kernels of different sizes that can extract and fuse information of different scales in VDL logging. In total, 5500 datasets in Tarim Oilfield were used for training and validation. Compared with other convolutional neural network algorithms, the multi-scale perceptual convolutional neural network algorithm proposed in this paper can evaluate cementing quality more accurately by identifying VDL logging. At the same time, this model's time and space complexity are lower, and the operation efficiency is higher. To verify the anti-interference of the model, this paper added 3%, 6%, and 9% of white noise to the VDL data set for cementing evaluation. The results show that, compared with other convolutional neural networks, the multi-scale perceptual convolutional neural network model is more stable and more suitable for the identification of cementing quality.  
c 2022  
variable density logging (VDL) will not. Therefore, cement bonding logging and variable density logging are the main methods for evaluating cementing quality at present.  
However, the CBL-VDL interpretation is a complex process [5], and cementing quality must be evaluated manually by trained experts. They use their understanding to integrate the logging results and their knowledge about the well to produce an evaluation of the cement status. This also results in a slow cementing interpretation and low efficiency [6]. At the same time, further oil and gas well development may depend on the evaluation results of cementing quality. Therefore, cementing interpretation is performed under time pressure [7]. Thus, the evaluation method urgently needs to be improved.  
Some scholars have carried out relevant research on the automatic interpretation of cement cementing and variable density logging. In 2015, [PERSON] et al. [8] proposed evaluating the cementing quality according to the method of acoustic energy, which has a high interpretation efficiency but insufficient accuracy. In 2020, [PERSON] et al. [9] established different machine learning algorithms, such as CBL-VDL-based random forest and neural network prediction, and ultrasonic imaging data, to output the prediction results of cementing quality and achieve an automatic interpretation of cementing quality. In 2021, [PERSON], [PERSON] et al. [10] used the Gaussian process regression algorithm for training, generated new characteristic curves according to CBL and VDL logging data, and accurately evaluated the cementing quality through the new curves. Although these two methods can realize an automatic interpretation, they require a large amount of pre-processing of data, and the evaluation process is tedious and time-consuming.  
In this study, an automatic interpretation method of cementing quality based on a convolutional neural network is proposed. Based on the trained convolutional neural network, it can identify variable density images and automatically output cementing quality results. This method does not need a large amount of data preprocessing, and greatly improves the accuracy and efficiency of cementing quality evaluation.