# Dynamic Prediction of Shale Gas Drilling Costs Based on Machine Learning
## 4 Experiments and Results
### Pearson Test  
In this study, a multidimensional correlation analysis of drilling engineering parameters and their influencing factors was carried out using Pearson correlation analysis statistics, a number of correlations with different levels of significance were found, reflecting the existence of a significant correlation between the factors, and the results are shown in Table 2. \begin{table}
\begin{tabular}{c c c c c c c} \hline \hline
**Index** & **Variable Name** & **Index** & **Variable Name** & **Index** & **Variable Name** & **Index** & **Variable Name** \\ \hline
6 & Actual fracturing length & 15 & Degree of policy support & 24 & Area of real roads in cities & 33 & Number of people employed in the transport storage and postal sector \\ \multirow{2}{*}{7} & Number of fracturing sections & 16 & Geological conditions & 25 & Total wages of on-the-job workers in the municipal area & 34 & Number of post offices \\  & Sand addition intensity & 17 & Degree of scientific and technological expenditures & 26 & Number of employed & 35 & Cargo turnover \\ \multirow{2}{*}{9} & Fracturing displacement & & Corporate & Average number of employed & 36 & Investment in drilling and completion of wells \\  & & & Index (CGPI) & & workers & wells \\ \hline \hline \end{tabular}
\end{table} 
Table 1: _Cont._ \begin{table}
\begin{tabular}{c c c} \hline \hline
**Index** & **Variable Name** & **Pearson Correlation** & \(p\)**-Value (Max)** \\ \hline
1 & Actual fracturing length & 0.091781 & 0.042279 \\
2 & Annual GDP & 0.119031 & 0.008352 \\
3 & Area of real roads in cities & \(-\)0.097925 & 0.030209 \\
4 & Average daily temperature & 0.094083 & 0.037349 \\
5 & Average number of employed workers & 0.102400 & 0.023397 \\
6 & Cargo turnover & 0.095721 & 0.034148 \\
7 & Consumer Price Index (CPI) & 0.095045 & 0.035440 \\
8 & Corporate Commodity Price Index (CGPI) & \(-\)0.104520 & 0.020664 \\
9 & Degree of policy support & \(-\)0.125078 & 0.005562 \\
10 & Degree of scientific and technological expenditures & 0.418286 & 0.000001 \\
11 & Fluid intensity & \(-\)0.110961 & 0.013989 \\
12 & Fracturing displacement & \(-\)0.091786 & 0.042268 \\
13 & Fracturing pump pressure & 0.122340 & 0.006700 \\
14 & Geological conditions & 0.159277 & 0.000401 \\
15 & Horizontal section length & \(-\)0.095850 & 0.033905 \\
16 & Number of bridges & 0.238878 & 0.000001 \\
17 & Number of employed persons & \(-\)0.146708 & 0.001126 \\
18 & Number of fracturing sections & 0.106456 & 0.018414 \\
19 & Number of fracturing sections & 0.106456 & 0.018414 \\
20 & Number of people employed in the transport storage and postal sector & \(-\)0.101383 & 0.024816 \\ \hline \hline \end{tabular}
\end{table} 
Table 2: Pearson test results for different shale gas drilling costs features. Through a detailed analysis of the \(p\)-values, these variables were classified into three levels according to their level of significance:  
1. Analysis of factors with very high significance  
The study showed that there was a significant correlation between technical engineering parameters and economic indicators. The level of technological expenditure showed a significant positive correlation (r = 0.418, \(p\approx 0.000001\)) with the total wages of workers, indicating a significant link between drilling technology inputs and labour costs. The level of infrastructure support also showed synergistic effects, with the positive correlation between the number of bridges and the actual road area (r = 0.239, \(p\approx 0.00001\)) reflecting the systematic nature of drilling project support facilities. These associations reveal the complexity of the cost components of drilling projects. 2. Analysis of highly significant factors  
Fracturing technical parameters show significant technical correlations.