Research on constructing a dynamic force prediction model for workover tubing strings based on multisource signal fusion

Artificial intelligence-driven intelligent algorithms demonstrate significant advantages in prediction and control within complex industrial scenarios, particularly for dynamic modeling and anomaly detection of tubing forces during well intervention operations. Addressing the challenges of strong signal heterogeneity, complex time-domain coupling, and prominent noise interference, this study constructs a tubing dynamic force prediction model based on multi-source signal fusion. Methodologically, vibration, torque, pressure, and image signals are fused as inputs. Short-time Fourier transform (STFT) enables time-frequency domain mapping. Convolutional networks and bidirectional gated recurrent units extract temporal features. Multi-head attention mechanisms facilitate cross-modal correlation modeling, while temporal convolutional networks deliver multi-scale prediction outputs. Training incorporates smoothing constraints and attention regularization terms to enhance responsiveness to abrupt load changes. Experimental results demonstrate that the fusion model achieves a 42.3% reduction in mean absolute error and a 38.7% reduction in root mean square error compared to traditional unimodal networks. Dynamic load prediction latency is controlled within 180 ms, exhibiting excellent real-time performance and stability. These findings validate the deep fusion architecture's capability for high-dimensional feature coordination and nonlinear capture during complex force evolution, providing a scalable technical pathway for dynamic monitoring, risk early warning, and adaptive control in intelligent well intervention operations.