Abstract
Data-driven models are widely used to predict rate of penetration. However, there are still challenges on real-time predictions considering influences of formation properties and bit wear. In this paper, a novel data-driven model is proposed to tackle this problem by combining an attention-based Gated Recurrent Unit network and fully connected neural networks. At first, input features of the model are elaborately selected by physical drilling laws and statistical analyzes. Then, four subnetworks are employed to construct the whole model structure, where formation properties are assessed using well-logging data and bit wear is evaluated by introducing an attention-based Gated Recurrent Unit network. Next, the model is dynamically updated with data streams by implementing the sliding window method to realize real-time predictions. Finally, the model performance is thoroughly analyzed based on ten field drilling datasets after optimizing model hyperparameters using the orthogonal experiment method. Results indicate that the model is accurate and robust to give predictions after training with the first several data streams. Compared with the conventional data-driven models, the proposed model shows great superiority due to the sub-network structure, the Gated Recurrent Unit network, and the attention mechanism. The model proposed herein opens opportunities for real-time prediction of rate of penetration in the field with high accuracy and robustness.
NSTL