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.

Stuck pipe is a common and serious accident in oil drilling processes, which may lead to huge economic losses and safety risks. In recent years, the rapid development of artificial intelligence technology has provided new ideas for stuck pipe prediction. Existing intelligent prediction studies on stuck pipe mostly focus on the optimization and application of a single unsupervised or supervised algorithm, or the research on simple ensemble learning of these two types of algorithms. This paper proposes a stuck pipe prediction method based on mechanism constraints and a deep learning ensemble model. By integrating the advantages of mechanism constraints and various time-series data processing models, this method achieves accurate prediction of stuck pipe. The method first performs preprocessing, feature engineering, and mechanism constraints on multi-parameter time-series data during drilling, then constructs three models, namely, Autoencoder, BiLSTM, and Transformer, for feature extraction and preliminary prediction, respectively. Finally, it integrates the prediction results of multiple models through a meta-model to improve prediction accuracy. The experimental results show that after introducing mechanism constraints, the accuracy of each model increases by an average of 10%. For the stuck pipe prediction task, the accuracy and precision of the proposed ensemble model reach 90.1% and 95.9%, respectively. Compared with single models, the ensemble model achieves an optimal balance between the false alarm rate and missing alarm rate, which are 7.7% and 11.0%, respectively. Its comprehensive performance is significantly better than that of single models, which can provide effective risk early warning for drilling operations.