# A Model-Based Intelligent Adjustment Method of Toolface for Bent-Housing Motor
## 5 Conclusions  
In this paper, a method based on a drill string dynamics model is proposed to adjust the toolface of a bent-housing motor. Firstly, the dynamics model of the drill string is used to calculate the change value of the bottom hole toolface under the condition that the rotation angle of the drill string at the wellhead is known. Then, the GA-BP neural network is used to fit the calculated change value of the bottom hole toolface, well depth, deviation angle, weight on bit, wellhead drill string rotation angle, and the actual change value of the downhole toolface, and the trained network is used to predict the change value of the downhole toolface in advance. In addition, under the condition of changing the WOB to change the toolface, taking the well depth, deviation angle, and WOB change value as input items and the change value of the toolface as output items, the relationship between the well depth, WOB, and the change value of the toolface is obtained by fitting with the GA-BP neural network. The results show that the prediction error is within  
\begin{table}
\begin{tabular}{c c c} \hline
**Accuracy Results** & **GA-BP** & **BP** \\ \hline MAE-train & 1.4596\({}^{\circ}\) & 63.9412\({}^{\circ}\) \\ \hline MAE-test & 11.5992\({}^{\circ}\) & 57.4219\({}^{\circ}\) \\ \hline maximum absolute error of train set & 4.7383\({}^{\circ}\) & 163\({}^{\circ}\) \\ \hline maximum absolute error of test set & 18.8833\({}^{\circ}\) & 151\({}^{\circ}\) \\ \hline MRE-train & 0.8190\% & 38.8828\% \\ \hline MRE-test & 4.9981\% & 38.0585\% \\ \hline maximum relative error of train set & 3.5112\% & 108.3333\% \\ \hline maximum relative error of test set & 7.7741\% & 121.7742\% \\ \hline R\({}^{2}\)-train & 0.9993 & 0.6669 \\ \hline R\({}^{2}\)-test & 0.9962 & 0.4986 \\ \hline R\({}^{2}\)-all & 0.9951 & 0.6077 \\ \hline \end{tabular}
\end{table}
Table 10: Toolface prediction accuracy parameters of BP and GA-BP in the mode of adjusting the toolface by changing WOB.  
10.9783\({}^{\circ}\) under the condition of rotating the drill string at the wellhead to change the toolface, and the error is within 18.8883\({}^{\circ}\) under the mode of changing the WOB to change the toolface, which can meet the control requirements of the directional well horizontal well trajectory. The proposed method can directly guide the drilling platform workers to adjust and control the toolface quickly and accurately. This method can also provide algorithm support for directional horizontal well trajectory control. With an intelligent drilling rig, downhole measurement, and control and data transmission systems, an intelligent directional horizontal well trajectory control system can finally be formed.  
Conceptualization, T.D.; methodology, T.D.; validation, T.D.; formal analysis, T.D.; investigation, T.D.; data curation, T.D.; writing--original draft preparation, T.D.; writing--review and editing, H.Y.; supervision, Q.L.; project administration, Q.L.; funding acquisition, Q.L.; on-site data collection, H.P. All authors have read and agreed to the published version of the manuscript.  
The present study was supported by the National Key R&D Program of China (grant No. 2019 YFA0708302).  
Not applicable.  
All data generated or analyzed during this study are included in this published article.  
The authors declare no conflict of interest.