# Physically-Data Driven Approach for Predicting Formation Leakage Pressure: A Dual-Drive Method
## 3 Selection, Processing, and Correlation Analysis of Training Samples
### Construction, Evaluation, Comparison, and Application of the Two Approaches
Figure 12b illustrates that the results obtained from the formula calculation exhibit more pronounced overall fluctuations in comparison to the predictions generated by the Approach 1 and Approach 2 models. Figure 12c displays the linear correlation coefficients of 0.8173 and 0.6451, indicating the degree of alignment between the results obtained from the formula calculation and the predictions of the Approach 1 and Approach 2 models, respectively. These results suggest that the predictions of the Approach 1 model exhibit a stronger alignment with the results obtained from the formula calculation. While direct validation of the predictions remains challenging due to the absence of actual formation pressure loss data, the comparison between the formula calculation and the predictions generated by the models indicates that the Approach 1 model exhibits stronger concurrence with the calculated results. This finding instills a degree of confidence in the predictive capability of the Approach 1 model for estimating formation pressure loss in the M-2 well. Figure 12b reveals that both the three predicted formation pressure loss results for the M-2 well and the formation pressure loss in the M-1 well display elevated values within the middle and lower sections of the D Formation, signifying the occurrence of substantial formation pressure loss in this interval. Nonetheless, a notable discrepancy exists in terms of the precise location of the maximum formation pressure loss. In the M-1 well, the formation pressure loss reaches its peak at a depth of 4427 m, with a recorded value of 1.896 g/cm\({}^{3}\). In contrast, the M-2 well exhibits a maximum predicted formation pressure loss value of 1.905 g/cm\({}^{3}\) at a depth of 4485 m in the Approach 1 model, and 1.909 g/cm\({}^{3}\) at the same depth in the Approach 2 model. Consequently, the maximum predicted formation pressure loss in the M-2 well is situated at a greater depth compared to the location of the maximum formation pressure loss in the M-1 well. Figures 4 and 13 and Table 3 provide evidence that the M-1 and M-2 wells are in close proximity to each other and share comparable lithology. Furthermore, the input layer data for both wells demonstrate significant similarities, despite the presence of a distinct separation between their respective formations. Notably, the starting depths of the B Formation, C Formation, and D Formation in the M-1 well are 2256 m, 2985 m, and 3714 m, respectively. In contrast, the M-2 well exhibits greater depths, particularly in the D Formation, where it is approximately 140 m deeper than the M-1 well, with corresponding \begin{table}
\begin{tabular}{c c c c} \hline \hline
**Top Depth** & **Bottom Depth** & **Drilling Fluid** & **Formation** \\
**(m)** & **(m)** & **Density (g/cm\({}^{3}\))** & **Formation** \\ \hline
2325 & 3765 & 1.18 & B and C1 \\
3765 & 4172 & 1.25 & C1, C2, and D \\
4172 & 4658 & 1.3 & D \\
4658 & 4790 & 1.45 & D \\ \hline \hline \end{tabular}
\end{table} 
Table 4: The actual drilling fluid density used in Well M-2. depths of 2312 m, 3014 m, and 3854 m. As a result, the formation pressure loss in the M-2 well is anticipated to share similarities with that in the M-1 well across different formations. While the predicted formation pressure loss values in the M-1 well can serve as a point of reference, the varying depth ranges of the formations imply an overall deeper distribution of formation pressure loss in the M-2 well in comparison to the M-1 well. This observation aligns with the findings depicted in Figure 12b. The above analysis affirms the viability of utilizing the LSTM-BP model, developed within this study, for predicting the formation pressure loss in the M-2 well. Additionally, the Approach 1 model exhibits superior predictive performance compared to the Approach 2 model.