# A Hybrid Neural Network Model for Predicting Bottomhole Pressure in Managed Pressure Drilling
## 6 Conclusions  
The contribution of this paper is to predict the BHP accurately and optimize the processing of temporal data in managed pressure drilling.  
The BP model captures time-series data with uniformity, the LSTM model can effectively extract the contextual information of the time-series data, and the CNN model pays more attention to capturing the subtle changes of the data. The hybrid optimization model that combines the advantages of a single intelligent model can significantly reduce the predicted outliers, improve the accuracy and stability, and reduce the average relative error by about 70%.  
The CNN-LSTM model has a strong ability to extract the time-series characteristics of the pressure-while-drilling data and can capture the subtle changes, with an average relative error of 0.031%. However, due to the influence of the size of the convolution kernel, the feature map formed after convolution is large, which reduces the efficiency of model solving, and the running time is about 4.5 times higher than that of the BP-LSTM parallel model. Through trend analysis, the effectiveness and robustness of the CNN-LSTM model is demonstrated.  
Figure 10: Effect of changing well depth on BHP with different drilling fluid density.  
This research can further predict BHP accurately by using a hybrid model method and create a more robust model to deal with complex situations in the field. In the future, we hope to intelligently select the appropriate model according to the time-series characteristics of the data and explore interpretable methods to correctly capture the physical meaning behind the MWD data.  
Conceptualization, Z.Z. and X.S.; methodology, Z.Z.; software, R.Z.; validation, Z.Z., X.S. and R.Z.; formal analysis, Z.Z.; investigation, Z.Z.; resources, X.S.; data curation, R.Z.; writing--original draft preparation, R.Z.; writing--review and editing, Z.Z., L.H., X.H., D.L. and D.Y.; visualization, R.Z. and F.Q.; supervision, Z.Z.; project administration, Z.Z.; funding acquisition, X.S and G.L. All authors have read and agreed to the published version of the manuscript.  
This research was funded by China National Petroleum Corporation Limited-China University of Petroleum (Beijing) strategic cooperation science and Technology special project, grant number ZIZX2020-03, National Science Foundation for Distinguished Young Scholars, grant number 52125401, Science Foundation project of China University of Petroleum (Beijing), grant number 246202 SZBH002 and National Key RESEARCH and Development Program of China, \"Key Scientific Issues of Transformative Technologies\", grant number 2019 YFA0708300.  
Not applicable.  
The data are not publicly available due to involve the information of Chinese oil fields and need to be kept confidential.  
The author would like to thank the academic salon of the High-Pressure Water Jet Drilling and Completion Laboratory of China University of Petroleum (Beijing), and [PERSON] for giving constructive suggestions and ideas, which helped improve the quality of the paper.  
The authors declare no conflict of interest.