## 12 Conclusions  
In this paper, a comprehensive review of development for Metocean data integration, interoperability based on intelligent operations, and automation using ML techniques is presented. The existing Metocean system generates data using sensors such as wave buoy, anemometer and acoustic doppler current profiler (ADCP) manually. Additionally, these data which appear in ASCII format to the Metocean system are also manual and silos. There is no fundamental method, model, or algorithm that allows the system to reason and \begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**Authors** & **Year** & **Application** & **Technique** & **Pros** & **Cons** \\ \hline [PERSON] et al. [116] & 2019 & Oil and gas risk management & DNN & - & Enhanced robustness & - & Accuracy detection needs enhancement \\ \hline [PERSON] et al. [117] & 2019 & Metocean & LR & - & Works well with classifying unknown records & - & Construct linear boundaries Limited to predict only discrete functions \\ \hline [PERSON] et al. [118] & 2018 & Metocean & CNN & - & High accuracy in image recognition Weight sharing & - & High training data are required \\ \hline \hline \end{tabular}
\end{table} 
Table 3: _Cont._automate intelligently. This slows down provisioning while the monitoring element of the Metocean data path is partial. However, with the current oil and gas crash, digitalization is a good opportunity that will bring value to all its stakeholders, which shows that there is a need to continue in digitalization of the oil and gas sector to achieve the expected, or more of the, stated value by the year 2025. Moreover, the digitalization of the oil and gas production-based industries is driven by enabling technologies which include ML and big data analytics. ML is one of the crucial and key enabling tools that oil and gas industries are now focusing on for digital transformation implementation which allows the industry to automate, enhance production, and have less human capacity. We summarized several research studies exploring the needs of ML in oil and gas industries by investigating in depth the integration of Metocean data interoperability for intelligent operations and automation using an ML-based approach. In addition, a comparative analysis of the current uses of ML algorithms in the oil and gas industries was provided, and we reviewed the transformation and integration of existing Metocean architecture for the use of oil and gas data operators through automation. Lastly, a new model and its integration with the existing Metocean data system using an ML algorithm to monitor and interoperate with maximum performance was proposed. Conceptualization, K.U.D. ; methodology, M.A. and K.U.D. ; software, K.U.D., M.A. and M.Y.A. ; validation, K.U.D., M.S.L. and L.E.S. ; formal analysis, K.U.D. and H.H.H. ; investigation, K.U.D. and M.A. ; resources, H.H.H. and M.S.L. ; data curation, K.U.D., M.A. and L.E.S. ; writing--original draft preparation, K.U.D. and H.H.H. ; writing--review and editing, M.A. and M.Y.A. ; visualization, K.U.D. and M.A. ; supervision, K.U.D. and M.S.L. ; project administration, K.U.D., H.H.H. and L.E.S. ; funding acquisition, H.H.H. and M.S.L. All authors have read and agreed to the published version of the manuscript. This research was funded by Short Term Internal Research Funding (STIRF)grant, grant numbers: 015 LA0-026. Not applicable. We would like to acknowledge [PERSON]. [PERSON], the Head Metocean, Group Technical Solutions (GTS) for providing the necessary support in terms of Metocean. The authors declare no conflict of interest. The following abbreviations are used in this manuscript: