## 7 Metocean Data System
### Metocean Data System in Malaysia  
Oil and gas companies use Metocean data as the main information source for determining the time and other weather factors. The users that benefit from these data comprise the participants of oil and gas companies, the partners, the contributors, as well as the research institutes, and are of extreme value [73; 74; 75]. The Metocean contributors are BP, Chevron, PETRONAS, Statoil, Total and research institutes. In Malaysia, demographically, the main Metocean contributor in Malaysia is PETRONAS, which is the Malaysian-owned oil and gas company that is among the Metocean contributors. It has enormous resources with diverse data for scientific research but they have not implemented AI or machine learning technologies in its Metocean data system. The information supplied by Metocean to users is primarily for oil spill response, satellite locations, environmental platforms and other types of drifting buoys [76]. The Metocean database has high quality of oil and gas datasets which are structured on uniform formats of ASCII, NetCDF and indexed upon the ISO 19115 metadata standard [73]. Metocean has become an industry that handles large amounts of data and metadata which yield immense benefits to oil and gas stakeholders. The results of the Metocean data are in the form of text or graph formats. Undeniably, the Metocean data are the resources for petroleum industries as well as human life activities, which are therefore necessary to be monitored, automated, and interoperable for the benefit of its users.  
As PETRONAS remains the major oil and gas company in Malaysia, it officially intends to diversify the energy sector beyond 2020 with a high competitive advantage [77; 78]. More recently, as reported in the PETRONAS activity outlook 2019-2021, \"PETRONAS is actively seeking ways to deploy technology in terms of digital, data analytics, automation, etc.\" [79]. Thus, PETRONAS must make the Metocean data system fully autonomous and digitalized to  
Figure 6: Gas production profiling using DTS data [66].  
achieve its goal effectively. In addition, some ML initiatives have been seen in the oil and gas sector. While the sector wants to transform into a fully digitally data-driven industry, it is not enough to solve the continuous problem of integrating all oil and gas data over the globe using AI or ML. This indicates the presence of limited integration and few empirical studies of ML, digitalization, and data integration in the oil and gas sector. Hence, the need to fill this gap is apparent. The Metocean data system flow information for assessment process based on risk inspection technique is illustrated in Figure 7.  
A growing body of literature has investigated the Metocean data system in Malaysia. [PERSON] et al. [81] investigated the effectiveness of genetic programming (GA) and support vector machine (SVM) learning models in predicting monthly sea-level variations by comparing model accuracy performance. Adding to this, the model has been validated using datasets from Tioman Island, Kerteh, and Tanjung sedili, Malaysia. Moreover, other methods have been applied in Metocean data systems by providing semantic web technologies for the oil and gas industries, which include an architecture for data integration. The application database utilized includes RDF query (D2 RQ) for setting performance. The study data were obtained from Malaysia's oil and gas industries [82]. More recent studies have investigated Metocean data systems around the globe. [PERSON] et al. [83] implemented a numerical model using Mike 21 to estimate Metocean conditions by evaluating hurricanes and hindcasts on the United States Atlantic coast.