## 2 Methodology
### Data Description  
With the core porosity and permeability experimental test data as the target label, the core layer is the section of H8 and S1 in the study area, there are 94 wells and 3389 experimental points, and the logging depth is 3441.43-5119.06 m. We counted the average values of the curves at the same depth of the single-well test points and logging curves and removed some outliers. The distributions of RLLD, RLLS, and RT were more discrete, as shown in Table 1. The distribution of wells is shown in Figure 1b. The laboratory-determined porosity distribution of H8 section was 5.0-10.0% with an average value of 7.0%, the laboratory-determined permeability distribution of H8 section was 0.1-1.0 \(\times\) 10\({}^{-3}\)\(\upmu\)m\({}^{2}\) with an average value of 0.6 \(\times\) 10\({}^{-3}\)\(\upmu\)m\({}^{2}\), the laboratory-determined porosity distribution of S1 section was 4.4-10.0% with an average value of 6.4%, and the laboratory-determined permeability distribution of S1 section was 0.1-1.2 \(\times\) 10\({}^{-3}\)\(\upmu\)m\({}^{2}\) with an average value of 0.51 \(\times\) 10\({}^{-3}\)\(\upmu\)m\({}^{2}\). The maximum-minimum normalization method was applied to the input feature variables and target label values to unify the data range from 0 to 1. This eliminates the \begin{table}
\begin{tabular}{c c c c c c c c c} \hline \hline
**Logging Curves** & **Mean** & \begin{tabular}{c} **First** \\ **Quartile** \\ \end{tabular} & **Median** & \
\begin{tabular}{c} **Third** \\ **Quartile** \\ \end{tabular} & **Min** & **Max** &
\begin{tabular}{c} **Std** \\ **Deviation** \\ \end{tabular} & **Variance** \\ \hline AC (\(\upmu\)s/m) & 216.20 & 203.68 & 212.68 & 225.65 & 181.86 & 379.57 & 19.17 & 367.71 \\ CAL (in) & 25.04 & 23.14 & 24.89 & 26.07 & 21.30 & 39.68 & 2.48 & 6.14 \\ CNL (\%) & 11.12 & 6.17 & 9.27 & 13.51 & 0.13 & 50.59 & 7.05 & 49.73 \\ DEN (g/cm\({}^{3}\)) & 2.53 & 2.49 & 2.56 & 2.62 & 1.29 & 2.85 & 0.16 & 0.03 \\ GR (API) & 66.96 & 38.78 & 59.17 & 85.31 & 15.64 & 437.66 & 35.80 & 1282.35 \\ PE & 2.91 & 2.21 & 2.55 & 3.00 & 0.79 & 13.70 & 1.45 & 2.11 \\ RLLD (\(\Omega\)\(\cdot\)m) & 67.07 & 28.25 & 45.88 & 73.25 & 5.37 & 679.66 & 75.83 & 5751.95 \\ RLLS (\(\Omega\)\(\cdot\)m) & 60.68 & 25.65 & 42.45 & 68.13 & 5.51 & 666.82 & 66.98 & 4487.29 \\ RT (\(\Omega\)\(\cdot\)m) & 66.59 & 28.16 & 45.96 & 72.95 & 0.00 & 676.38 & 75.80 & 5747.06 \\ SP (MV) & 56.52 & 47.81 & 57.45 & 65.93 & \(-\)7.18 & 113.31 & 14.33 & 205.47 \\ K (\%) & 1.32 & 0.69 & 1.18 & 1.79 & 0.11 & 4.32 & 0.77 & 0.60 \\ TH (mg/L) & 8.91 & 4.94 & 7.60 & 11.62 & 1.51 & 42.88 & 5.15 & 26.53 \\ U (mg/L) & 2.36 & 1.36 & 1.88 & 2.88 & 0.22 & 15.78 & 1.52 & 2.31 \\ POR\_L (\%) & 4.55 & 2.16 & 4.69 & 6.74 & 0.00 & 15.20 & 3.16 & 9.97 \\ PERM\_L (10\({}^{-3}\)\(\upmu\)m\({}^{2}\)) & 0.33 & 0.01 & 0.11 & 0.40 & 0.00 & 11.37 & 0.72 & 0.52 \\ \hline \hline \end{tabular}
\end{table} 
Table 1: Statistics of logging curves for experimental data points. Figure 2: Flow chart of XGBoost prediction model for porosity and permeability. influence of the units of measure of different logs and improves the performance of the model operation, as shown in Equation (1). \[x_{i}^{*}=\frac{x_{i}-x_{\text{min}}}{x_{\text{max}}-x_{\text{min}}} \tag{1}\]  
where:  
\(x_{i}\) is the input feature variable data;  
\(x_{i}^{*}\) is the normalized feature variable;  
\(x_{max}\) is the maximum value of feature variable data;  
\(x_{min}\) is the minimum value of feature variable data. The order of magnitude difference is large, and the data transformation using a logarithmic function effectively compresses the data magnitude and improves the stability of the modeling. \[x_{i}^{*}=logx_{i} \tag{2}\]  
Normalization of the data can also be achieved using the inverse tangent function; when using this method, it should be noted that if the interval you want to map is \([0,1]\), the data should all be greater than or equal to 0, and data less than 0 will be mapped to the interval \([-1,0]\).