Physics-Informed CNN-BiGRU Model for Downhole Weight-on-Bit Prediction Using Surface Measurement-While-Drilling Sensor Data in Horizontal Wells

Accurate estimation of downhole weight on bit (DWOB) is important for drilling parameter optimization and safe drilling in extended-reach horizontal wells. However, DWOB is difficult to obtain continuously because surface weight on bit (SWOB) is attenuated by drill-string friction, wellbore trajectory, and borehole-wall contact. To address this problem, a physics-informed convolutional neural network combined with bidirectional gated recurrent unit (CNN-BiGRU) prediction model optimized by the whale optimization algorithm (WOA) is proposed using surface sensor data and downhole measurement-while-drilling (MWD) measurements. The model first uses a one-dimensional CNN to extract local features from drilling parameters along the measured-depth direction and then employs a BiGRU to capture depth-series dependencies. Meanwhile, a drill-string frictional attenuation relationship is embedded into the loss function as a physical prior, and WOA is used to optimize network hyperparameters and the physics-constrained weight. Field data from a horizontal well were used for validation. The proposed model achieved a coefficient of determination (R2)of 0.9245 on the test set, with root mean square error (RMSE), mean absolute error (MAE), and mean relative error (MRE) values of 0.1856, 0.1135, and 0.0124, respectively. The results demonstrate that the proposed data–physics hybrid framework improves the accuracy and physical consistency of DWOB prediction.