Drilling and Completion Condition Recognition Algorithm Based on CNN-GNN-LSTM Neural Networks and Applications

Drilling and completion condition identification is of great significance in improving operational efficiency, reducing safety risks and optimizing resource utilization. However, traditional methods rely on experts’ experience and rules and have low recognition accuracy and poor robustness when facing dynamic working condition changes. In recent years, deep learning technology has shown great potential in the field of time series data analysis and multimodal data fusion. In this paper, we propose a hybrid deep learning model (CNN-GNN-LSTM) based on a convolutional neural network (CNN), graph neural network (GNN) and long short-term memory network (LSTM). The model extracts the local spatial features of multi-sensors via a CNN module to reduce the noise interference; models the nonlinear dependency between sensors via a GNN module to capture the complex interaction relationship; and mines the long- and short-term time dependencies via an LSTM module to accurately identify the dynamic change and transition process of the working conditions. This significantly improves the classification accuracy under dynamic changes in multi-conditions. This study compares the performance of four models: a CNN, LSTM, CNN-LSTM, and CNN-GNN-LSTM. The results show that the CNN-GNN-LSTM outperforms the other models in key metrics such as the classification accuracy, recall, F1 score, etc., and is more robust to noise interference and changes in complex working conditions. This study verifies the advantages of the hybrid model in multi-sensor complex scenarios and provides technical support for the intelligent development of drilling and completion condition recognition.