Research on Evaluation Model of Ultra-Deep Wellbore Instability Based on Convolutional Neural Network

Wellbore instability in ultra-deep drilling is a complex problem governed by the coupling of multiple factors. Traditional evaluation methods are often limited in accuracy under such challenging conditions. This study proposes an innovative evaluation model based on Convolutional Neural Networks (CNN) to achieve high-precision prediction. We systematically analyze the key influencing factors of wellbore instability and construct a multi-dimensional feature dataset comprising 12 geomechanical, drilling fluid, and engineering parameters. Innovatively, the feature parameters are reorganized into a 2D matrix with geomechanical significance. A multi-task CNN architecture integrating depthwise separable convolutions and dual channel–spatial attention mechanisms is designed to simultaneously perform stability classification, safety factor regression, and collapse pressure prediction. Validation using field data from major ultra-deep well basins in China shows that the model achieves an overall accuracy of 92.3%, significantly outperforming traditional empirical formulas (76.5%) and BP neural network models (85.1%). This research provides a more reliable technical solution for intelligent evaluation and risk management of wellbore stability in ultra-deep drilling.