A Knowledge-Guided Temporal Graph Neural Network for Predicting Rate of Penetration in Complex Formations

Summary Accurate rate of penetration (ROP) prediction is essential for optimizing drilling performance and reducing operational costs. However, the complexity and uncertainty inherent in drilling operations pose significant challenges to existing data-driven models, limiting their capacity to generalize across diverse conditions and effectively incorporate domain knowledge. In this paper, we propose a knowledge-guided hybrid temporal graph neural network (KHTGNN) for robust ROP prediction. First, explicit domain knowledge is applied to perform physical corrections on drilling parameters during feature engineering, enhancing data quality. Second, an adaptive graph construction method based on node embedding is introduced to dynamically model the evolving correlations among drilling parameters. Third, a hybrid temporal graph neural network (GNN) is designed to capture both intravariable and intervariable dependencies. Moreover, domain-specific rock-breaking mechanism equations are embedded as soft constraints within the model’s loss function to enforce physical consistency. To further enhance generalization, a multiple kernel (MK) maximum mean discrepancy (MMD) approach is integrated to achieve domain-adaptive learning across different wells. The results demonstrate that KHTGNN outperforms current models, achieving a mean absolute percentage error (MAPE) of 9.49%, a mean absolute error (MAE) of 1.64 m/h, and a coefficient of determination (R2) of 0.99. Ablation studies validate the contributions of physical data correction, knowledge constraints, and domain adaptation, emphasizing the critical role of domain knowledge integration in improving predictive performance. This work provides a robust knowledge-guided framework for sequential parameter prediction tasks in complex industrial systems.