# Shear Wave Velocity Prediction Based on the Long Short-Term Memory Network with Attention Mechanism
## 3 Data Collection and Preprocessing  
### Data Description  
The well logging data used in this study came from two different wells, Well A and Well B, in the Yengegahai Basin in the South China Sea. Data from these two wells include seven standard logs as follows: shear velocity (Vs) and P-wave velocity (Vp), gamma ray (GR), sandstone volume (V clay), density (RHOB), water saturation (SW), and resistivity (RS). Well A's main gas layer is buried at about 4600-5000 m, mainly composed of low-permeability sandstone, and the reservoir type is delta sandstone. The main gas reservoir of Well B is about 4050-4200 m, with an apparent gas-bearing structure, and the reservoir type is submarine fan sandstone. To verify the effectiveness of the proposed Attention-LSTM network, the network is trained with Well A data and tested with Well B data in the region. The statistical indexes of the logging curves of the two wells are shown in the Table 1. The depth range of Well A is 3302-5042 m, with a sampling interval of 0.125 m, culminating in a total of 13,924 samples. Also, the depth range of Well B is 3335-4076 m, and  
Figure 4: Diagram of the Attention-LSTM structure.  
Figure 5: Flowchart of the whole process.  
the total sampling number is 5924. Figure 6 shows the relative positions of the two Wells in the working area (108\({}^{\circ}\)37\({}^{\prime}\)06\({}^{\prime\prime}\) E-108\({}^{\circ}\)46\({}^{\prime}\)36\({}^{\prime\prime}\) E, 17\({}^{\circ}\)46\({}^{\prime}\)30\({}^{\prime\prime}\) N-18\({}^{\circ}\)05\({}^{\prime}\)42\({}^{\prime\prime}\) N).  
\begin{table}
\begin{tabular}{c c c c c c c c} \hline \multirow{2}{*}{**Well Name**} & **Statistical** & \multirow{2}{*}{**Vp (m/s)**} & \multirow{2}{*}{**GR (API)**} & **RHOB** & \multirow{2}{*}{**SW (\%)**} & \multirow{2}{*}{**V Clay (\%)**} & \multirow{2}{*}{**RS (\(\Omega\cdot\)m)**} & \multirow{2}{*}{**Vs (m/s)**} \\ \cline{3-3} \cline{5-8}  & & & & & & & \\ \hline \multirow{4}{*}{Well A} & Mean & 4221.25 & 74.31 & 2.62 & 0.91 & 0.61 & 11.19 & 2435.39 \\ \cline{2-8}  & Std & 430.95 & 17.01 & 0.07 & 0.15 & 0.31 & 14.96 & 345.11 \\ \cline{2-8}  & Min & 3008.24 & 29.17 & 0.08 & 0.30 & 0.01 & 0.91 & 1634.64 \\ \cline{2-8}  & max & 5599.93 & 138.43 & 2.85 & 1.00 & 0.99 & 25.36 & 3302.90 \\ \hline \multirow{4}{*}{Well B} & Mean & 3882.78 & 107.26 & 2.60 & 0.92 & 0.45 & 4.88 & 2191.43 \\ \cline{2-8}  & Std & 450.95 & 23.05 & 0.09 & 0.19 & 0.29 & 6.68 & 344.60 \\ \cline{2-8}  & Min & 2766.46 & 37.85 & 2.11 & 0.22 & 0.01 & 0.85 & 1498.71 \\ \cline{2-8}  & max & 6221.81 & 168.63 & 3.41 & 1.00 & 1.00 & 15.78 & 3199.33 \\ \hline \end{tabular}
\end{table}
Table 1: Statistical characteristics of the variables of two wells.  
Figure 6: Working area and wells position.  
between the shear wave velocity and other logging data is above 0.15, and the correlation between the two is firm and can meet the general assumption of data regression.  
Different logging attributes have certain indications of the shear wave velocity. The increase in water saturation can cause a slow decrease in the shear wave velocity. VCL and GR can reflect the mud content. The vertical and shear wave velocity ratio of the formation with a high mud content is slightly high. The vertical and shear wave is commonly affected by the rock skeleton and pore fluid of the reservoir. Thus, there is a clear positive correlation between them. The resistivity mainly reflects the pore fluid characteristics of the reservoir. Thereby, it can also show the change in shear wave velocity.