A new prediction model of bottomhole weight on bit in offshore extended-reach drilling based on the integration of intelligent algorithms and the physical models of rate of penetration

Offshore extended-reach drilling is widely employed for the efficient development of deepwater hydrocarbon fields. However, the complex subsea formation environment and downhole frictional losses lead to significant inaccuracies and limitations in bottomhole weight on bit (WOB) calculations, severely impacting drilling operations. Therefore, this study proposes an improved bottomhole WOB prediction model that combines CNN-BiLSTM networks with Transformer encoders. By introducing a loss function (named DRE) based on the rate of penetration equation into the intelligent algorithm, four models were constructed: CNN-BiLSTM, CNN-Encoder-BiLSTM, CNN-BiLSTM (DRE), and CNN-Encoder-BiLSTM (DRE). The eight models were compared and evaluated, including both the established models and conventional models. Experimental results show that the newly developed CNN-Encoder-BiLSTM (DRE) model parameters' influence degree basically conforms to the DRE with high generalization capability. On the test well, it achieved MAE of 0.859, MRE of 0.1, MAPE of 10%, MSE of 1.201, and R² of 0.867, demonstrating good performance. The research results prove that the new model is suitable for bottomhole WOB prediction in offshore extended-reach wells, which is expected to improve drilling efficiency and enable subsequent complex situation analysis, providing new ideas for intelligent drilling research in the marine sector.