# Dynamic Prediction of Shale Gas Drilling Costs Based on Machine Learning
## 4 Experiments and Results
### Analysis of Results
#### 4.8.2 Discussion  
Figure 6 illustrates the results of the evaluation of the three models. The probability density plot modelled to predict shale gas cost regressions is shown, providing detailed information on the model predictions. 1. Characteristic Analysis of the Prediction Distribution  
When the LSTM_SVM_Stacking model is compared with the SVM model and the LSTM model, its probability density points show obvious centralised distribution characteristics, with a large number of prediction points clustered around the standard line; moreover, the degree of dispersion of the predicted values is small, indicating that the prediction results of the model have a high degree of stability, coupled with the symmetry of the distribution pattern of the predicted values, which means that the prediction errors of the model are more balanced in the positive and negative directions. This concentrated distribution indicates that the shale gas cost prediction model has achieved excellent results in this task and is able to accurately capture the correlation between the true value and the predicted value. 2. Analysis of prediction accuracy:  
Further examination of the predicted probability density plots shows that there is a close correlation between the predicted and true values. The LSTM_SVM_Stacking model's close fit of the predicted points to the standard line compared to the SVM model and the LSTM model reflects the model's high prediction accuracy. In addition, the LSTM_SVM_Stacking model has fewer outliers and outliers, indicating that the model maintains stable prediction performance for different input scenarios with good confidence. This close relationship indicates that the model has high prediction accuracy and reliability for the problem under study. To ensure the model's ability to predict shale gas costs for unknown samples, shale gas drilling cost prediction curves were generated on the test set, as shown in Figures 3 and 4. As can be seen in Figure 7, the SVM model, the LSTM model and the LSTM_SVM_Stacking model were used to predict the drilling costs on the test set, and the predicted curves of the LSTM_SVM_Stacking model are basically the same as the actual value curves, which indicates that this study achieves better shale gas drilling cost modelling. In the subsequent shale gas drilling cost prediction, the drilling cost can be simulated with the model of this study on the previously unexamined data, so as to reduce the economic losses caused by the shale gas exploration process. Figure 5: Comparison of RMSE\(\backslash\)MAE\(\backslash\)R\({}^{2}\) Values for Three Models. In addition, this study adopts a stacked integrated learning model of SVM and LSTM, and combines these two models to predictively model the shale gas drilling cost dataset using SVM's sensitivity to spatial data and LSTM's sensitivity to temporal features. By exploiting the respective advantages of SVM and LSTM, this study was able to provide a more comprehensive analysis of the various factors in the dataset, resulting in more accurate predictions. The combined model achieved satisfactory prediction results. The Mean Absolute Error (MAE) is 438.50 and the Root Mean Square Error (RMSE) is 697.37, indicating that the model in this study is able to effectively and accurately predict shale gas drilling costs. This indicates that the model used in this study is able to utilise both spatial and temporal information, thus capturing the essential characteristics of drilling costs and improving the accuracy and reliability of the prediction. In order to present the prediction results in a more intuitive manner, this study conducted a visualisation analysis to clearly demonstrate the relationship between the predicted values and the actual values through graphs and charts, thus revealing a certain degree of fit patterns captured by the model. This further validates the validity and reliability of the shale gas drilling cost prediction model used in this study.