A new method for the rate of penetration prediction and control based on signal decomposition and causal inference

Abstract

Offshore drilling costs are high, and the downhole environment is even more complex. Improving the rate of penetration (ROP) can effectively shorten offshore drilling cycles and improve economic benefits. It is difficult for the current ROP models to guarantee the prediction accuracy and the robustness of the models at the same time. To address the current issues, a new ROP prediction model was developed in this study, which considers ROP as a time series signal (ROP signal). The model is based on the time convolutional network (TCN) framework and integrates ensemble empirical modal decomposition (EEMD) and Bayesian network causal inference (BN), the model is named EEMD-BN-TCN. Within the proposed model, the EEMD decomposes the original ROP signal into multiple sets of sub-signals. The BN determines the causal relationship between the sub-signals and the key physical parameters (weight on bit and revolutions per minute) and carries out preliminary reconstruction of the sub-signals based on the causal relationship. The TCN predicts signals reconstructed by BN. When applying this model to an actual production well, the average absolute percentage error of the EEMD-BN-TCN prediction decreased from 18.4% with TCN to 9.2%. In addition, compared with other models, the EEMD-BN-TCN can improve the decomposition signal of ROP by regulating weight on bit and revolutions per minute, ultimately enhancing ROP.