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The existence of soft soil in offshore areas may lead to the amplification of vibration received from offshore facilities, especially from the existing fixed-jacket platforms, which were designed without provision to seismicity, as in Malaysian water. Therefore, this study was designed to develop a seismic microzonation map and a soil amplification factor map according to soil type; we propose horizontal response spectra and site coefficient values (Ca and Cv) for the Malay Basin. A one-dimensional nonlinear analysis of layered soil (NERA) was used in the ground response analysis for six selected seismic events under five return periods of 100, 200, 500, 1000, and 2500 years. Soil amplification factors for soil types D and E showed a decreasing trend from 100 years to 2500 years. Two designed horizontal response spectra are proposed (for soil type D and E) under average and envelope conditions; a comparison with ISO showed that the proposed spectra were higher, especially for soil type E. To summarize, the seismicity effect should be included in the development of offshore industries as findings indicated that soil amplification occurred in soil types D and E at the Malay Basin.

The current oil industry is moving towards digitalization, which is a good opportunity that will bring value to all its stakeholders. The digitalization of oil and gas discovery, which are production-based industries, is driven by enabling technologies which include machine learning (ML) and big data analytics. However, the existing Metocean system generates data manually using sensors such as the wave buoy, anemometer, and acoustic doppler current profiler (ADCP). Additionally, these data which appear in ASCII format to the Metocean system are also manual and silos. This slows down provisioning, while the monitoring element of the Metocean data path is partial. In this paper, we demonstrate the capabilities of ML for the development of Metocean data integration interoperability based on intelligent operations and automation. A comprehensive review of several research studies, which explore the needs of ML in oil and gas industries by investigating the in-depth integration of Metocean data interoperability for intelligent operations and automation using an ML-based approach, is presented. A new model integrated with the existing Metocean data system using ML algorithms to monitor and interoperate with maximum performance is proposed. The study reveals that ML is one of the crucial and key enabling tools that the oil and gas industries are now focused on for implementing digital transformation, which allows the industry to automate, enhance production, and have less human capacity. Lastly, user recommendations for potential future investigations are offered.