## 4 Conclusions  
This paper proposed a 1D-CNN with adaptive moment estimation for modelling of the SRT dataset focusing on the slurry and sand pack tests that use stand-alone screens as a sand control method to reduce sand production. The proposed method was developed to examine the sand retention efficiency and plugging performance, which can help select the optimal screen and slot size.  
The hyperparameters tuning of 1D-CNN was empirically performed using a trial-and-error approach, leading to the development of six models tested for each output. The 1D-CNN model performance showed that the hyperparameters configuration in model 5 of the slurry test fits the best in predicting the amount of sand produced in grams, retained permeability, and the amount of sand produced per unit area because model 5 gave the lowest average errors and the highest R\({}^{2}\). The best model for both sand productions gave an accuracy of 77%, while the best model for retained permeability gave 99% accuracy. Besides, the set of hyperparameters in model 2, 5, and 6 fit very well with the classification of the plugging sign because all three models gave the same accuracy of 96%. Thus, either model 2, 5, or 6 can be used as the best fit model. In contrast, model 1 of the sand pack test outperformed the other five models in predicting the amount of sand produced per unit area with an accuracy of 84%.  
For comparative model performance, the accuracy of the 1D-CNN model was higher than the other four machine learning models in predicting all the outputs of slurry and sand pack tests. Therefore, the proposed deep learning model outperformed the other four machine learning methods based on validation metrics.  
Since the proposed deep learning model is the first model developed for SRT problem, further optimization can be proposed for future research by focusing more on the feature engineering process and include more observations for the modelling phase.  
Conceptualization, N.N.A.R. and S.J.A.; methodology, N.N.A.R. and S.J.A.; software, N.N.A.R. and M.G.R.; validation, N.N.A.R., S.J.A., and S.N.A.S.; formal analysis, N.N.A.R. and M.G.R.; data curation, N.N.A.R. and S.N.A.S.; writing--original draft preparation, N.N.A.R.;  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Output** & **Test** & \begin{tabular}{c} **Test** \\ **Statistic** \\ \end{tabular} & \(p\)**-Value** & \
\begin{tabular}{c} **Critical** \\ **Value** \\ \end{tabular} \\ \hline Slurry–PLUG\_SIGN &
\begin{tabular}{c} Kruskal- \\ Wallis \\ \end{tabular} & 12.2000 & 0.0159 & 9.4677 \\ \hline Slurry–SAND\_PRODUCED & ANOVA & 11.0518 & \(1.254\times 10^{-8}\) & 2.3881 \\ \hline Slurry–RETAINED\_PRM & ANOVA & 35.4468 & \(4.254\times 10^{-18}\) & 2.4675 \\ \hline Slurry–SAND\_PROD\_PER\_AREA & ANOVA & 23.9964 & \(3.307\times 10^{-18}\) & 2.3894 \\ \hline Sand pack–SAND\_PROD\_PER\_AREA & ANOVA & 35.3620 & \(3.283\times 10^{-25}\) & 2.3948 \\ \hline \hline \end{tabular}
\end{table}
Table 17: Significance analysis using ANOVA and Kruskal–Wallis tests.  
writing--review and editing, N.N.A.R. and S.J.A.; visualization, N.N.N.A.R.; supervision, S.J.A. and M.A.M.; funding acquisition, S.J.A. All authors have read and agreed to the published version of the manuscript.  
This research was supported by PETRONAS Group Research & Technology and Universiti Teknologi PETRONAS funded by GR&T-UTP Collaboration Grant with UTP grant number 015 MD0-24.  
\"Not applicable\" for studies not involving humans.  
The data that support the findings of this study are available from PETRONAS GR&T. Restrictions apply to the availability of these data, which were used under license for this study. Data are available from the authors with the permission of PETRONAS Group Research & Technology (GR&T).  
The authors declare no conflict of interest.