# A Model-Based Intelligent Adjustment Method of Toolface for Bent-Housing Motor
## 4 Results and Discussions
### Tooface Results Predicted by GA-BP Method
#### 4.2.2 Toolface-Predicted Results in the Mode of Adjusting Toolface by Changing WOB

Table 6: The input parameters for the artificial intelligence prediction model of the toolface in the mode of adjusting toolface by changing WOB. Figure 13: Toolface prediction results of BP and GA-BP in the mode of adjusting the toolface by changing WOB. \begin{table}
\begin{tabular}{c c c c c c c c c c} \hline \hline
**Influence Factors** & **Method** & **Computing Time (s)** & **MAE\_train** & **MAE\_test** & **MSE\_train** & **MSE\_test** & **R\({}^{2}\_train\)** & **R\({}^{2}\_test\)** & **R\({}^{2}\_all\)** \\ \hline \hline Factor 6 & BP & 3.5680 & 63.9412 & 57.4219 & 6354.4118 & 6591.7346 & 0.6669 & 0.4986 & 0.6077 \\ \cline{2-9} Factor 7 & GA-BP & 25.9130 & 1.4596 & 11.5992 & 4.1686 & 157.1832 & 0.9993 & 0.9962 & 0.9951 \\ \hline Factor 7 & BP & 3.9810 & 63.9412 & 63.2458 & 6354.4118 & 6824.7234 & 0.6669 & 0.4986 & 0.5993 \\ \cline{2-9} Factor 8 & GA-BP & 19.6890 & 2.6422 & 10.1362 & 11.6064 & 130.2901 & 0.9981 & 0.9913 & 0.9938 \\ \hline Factor 6 & BP & 3.8700 & 63.9412 & 63.8266 & 6354.4118 & 6870.5719 & 0.6669 & 0.4987 & 0.5976 \\ \cline{2-9} Factor 8 & GA-BP & 18.5350 & 2.7179 & 16.1463 & 11.4671 & 476.7637 & 0.9979 & 0.9907 & 0.9860 \\ \hline Factor 8 & BP & 3.9210 & 63.9412 & 63.8266 & 6354.4118 & 6870.5725 & 0.6669 & 0.4987 & 0.5976 \\ \cline{2-9}  & GA-BP & 14.9660 & 5.4599 & 9.7241 & 56.4514 & 143.6517 & 0.9898 & 0.9958 & 0.9888 \\ \hline \hline \end{tabular}
\end{table} 
Table 9: Prediction accuracy of GA-BP and BP under different influence factors in the mode of adjusting the toolface by changing WOB. Figure 14: Correlation coefficient of BP and GA-BP in the mode of adjusting the toolface by changing WOB. The training and testing results of the intelligent control model of the toolface under the above two scenarios show that in the 2D wells with the same formation, the same type of bit, the same screw drill, and the same drill assembly, the prediction absolute error under the mode of adjusting the toolface by rotating the drill string at the wellhead is within 10.9783\({}^{\circ}\), and the prediction absolute error under the mode of adjusting the toolface by changing the WOB is within 18.8833\({}^{\circ}\). Reference [8] holds that \"If the measurement value is very close to the target value, for example, within the range of target \(\pm\) 20\({}^{\circ}\), which is generally considered good in slide drilling, the toolface setting job is done.\" So, the established GA-BP neural network can meet the accuracy requirements of the toolface control of the bent-housing motor, and can save the time wasted waiting for the toolface to upload, and the subsequent repeated control. In the actual drilling process, the two scenarios are used together, which can effectively prevent an abnormal drilling time. This kind of intelligent method can replace an experienced drilling engineer to guide the rapid and accurate adjustment of the toolface in the short term. In the later stage, we will continue to optimize our GA-BP intelligent prediction model to be suitable for toolface prediction in different formation wells in the same block using different drill bits, downhole motors, and drill string assemblies. Once the prediction accuracy of the model can be guaranteed, this prediction method will serve as a subsystem to provide algorithm support for the intelligent drilling of the directional well and horizontal well. Combined with an automatic drilling rig, downhole measurement, and control and powerful data transmission systems, an intelligent control system of directional wells and horizontal wells trajectory is finally formed.