## 6 Conclusions  
In this study, we explored the efficacy of various machine learning models in predicting the UCS of rocks in oil and gas wells. Through rigorous experimentation and analysis, we evaluated the performance of five distinct models: Linear Regression, Random Forest, Gradient Boosting, XGBoost, SVM-SVR, and MLP-ANN. Our investigation aimed to identify the most accurate and reliable model for UCS prediction, which is crucial for optimizing drilling operations and ensuring wellbore stability in the petroleum industry. While RHOB consistently appears as a significant feature across most models, the importance of other features such as NPHI and DT varies depending on the model used. Ensemble methods like Gradient Boosting, XGBoost, and LightGBM demonstrate a more balanced utilization of features, enhancing their predictive performance. In contrast, Linear Regression relies heavily on RHOB, reflecting its simplicity and limitations in capturing complex relationships. SVM-SVR and MLP-ANN highlight the importance of NPHI and DT, indicating their effectiveness in modelling non-linear interactions. Our findings underscore the superiority of ensemble methods, particularly Gradient Boosting and XGBoost, in accurately predicting UCS values. These models demonstrate robustness, reliability, and superior generalization capabilities, making them ideal choices for practical applications in geotechnical engineering. \begin{table}
\begin{tabular}{c c c c c c c c} \hline \hline
**Indicator** & \begin{tabular}{c} **Linear** \\ **Regression** \\ \end{tabular} & \begin{tabular}{c} **Random** \\ **Forest** \\ \end{tabular} & \begin{tabular}{c} **Gradient** \\ **Boosting** \\ \end{tabular} & \begin{tabular}{c} **XGBoost** \\ \end{tabular} & \begin{tabular}{c} **LightGBM** \\ \end{tabular} & \
\begin{tabular}{c} **SVM-SVR** \\ \end{tabular} &
\begin{tabular}{c} **MLP-ANN** \\ \end{tabular} \\ \hline RMSE & 4.72 & 5.35 & 5.54 & 5.31 & 7.24 & 5.09 & 5.33 \\ MSE & 22.27 & 28.61 & 30.71 & 28.23 & 52.35 & 25.94 & 28.43 \\ MAPE & 3.38 & 4.07 & 4.18 & 3.87 & 5.04 & 3.76 & 3.93 \\ MPE & \(-0.0002\) & \(-0.9450\) & \(-0.6376\) & \(-0.4052\) & \(-2.1977\) & \(-0.0894\) & \(-0.0527\) \\ MAE & 2.993 & 3.404 & 3.563 & 2.578 & 3.277 & 3.337 & 3.531 \\ Geometric MAPE & 3.36 & 3.97 & 4.17 & 3.85 & 4.74 & 3.75 & 3.92 \\ Symmetric MAPE & 3.364 & 3.971 & 4.170 & 3.855 & 4.742 & 3.755 & 3.925 \\ R\({}^{2}\) & 0.8849 & 0.8522 & 0.8414 & 0.8542 & 0.7296 & 0.8660 & 0.8531 \\ Adjusted R\({}^{2}\) & 0.8511 & 0.8087 & 0.7947 & 0.8113 & 0.6500 & 0.8266 & 0.8100 \\ MSLE & 0.0018 & 0.0030 & 0.0026 & 0.0024 & 0.0062 & 0.0020 & 0.0022 \\ MBE & 0.0742 & \(-0.5570\) & \(-0.5725\) & \(-0.2447\) & \(-1.4988\) & 0.2286 & 0.2345 \\ \hline \hline \end{tabular}
\end{table} 
Table 3: Comparison between various statistical performance indicators for developed models. Additionally, our results highlight the effectiveness of MLP-ANN in capturing the complex, non-linear relationships inherent in the UCS dataset. While MLP-ANN exhibits strong predictive performance, it occasionally struggles with extreme values, indicating opportunities for further optimization. Furthermore, Random Forest and LightGBM also exhibit commendable performance, albeit with slightly more variability compared to ensemble methods. These models provide viable alternatives, especially in scenarios where computational efficiency is a concern. On the other hand, Linear Regression and SVM-SVR models, while providing moderate predictive capabilities, fall short of capturing the full complexity of the UCS dataset. These simpler models are outperformed by ensemble methods and MLP-ANN in terms of predictive accuracy and reliability. Our study underscores the importance of employing advanced machine learning techniques for UCS prediction in oil and gas wells. By leveraging these methodologies, the petroleum industry can benefit from enhanced decision-making processes that lead to improved drilling efficiency and safety.