Reefing of Oil and Gas structure has become as one of the emerging options for decommissioning of an O&G Structure offshore and requires expert input beyond engineering such as biomarine and environmental sciences. This paper will present the interdisciplinary collaborative effort by industries with academia in Malaysia in developing a reef viability index as the first high level screening to assess the suitability of decommissioning using the reefing option in the region. The results of the reef viability index formulation to identify an offshore area that potential to be used for Rigs-to-Reefs (RTR) program in the South China Sea. The integration of data collection, numerical modelling and Geographic Information System (GIS) aims to review the relationship of coral reefs spawning ground, diversity and planula larvae in the colonization process which to produce a reefing area map. Coral connectivity and spawning behaviour were investigated to reveal the potential source of coral seedling as well as the number of coral larvae based on different taxa released during the spawning seasons. A spatial reef viability index was developed based on seven parameters, i.e. coral larval density, pelagic larval duration, sea currents, temperature, chlorophyll-a, depth, and substrate availability. Hydrodynamic model was developed to emulate the pattern of larval scattering. Based on the simulation and rankings, there were 95 (21%) sites that are most likely for in-situ reefing while the remaining 358 (79%) sites were probably suitable for ex-situ reefing or decommissioning. Validation of the viability index was done using Remotely Operated Vehicle (ROV) media footage assessment.

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Underwater decommissioning is a challenging and expensive job especially when a company is not getting any benefit. The efficient, economical and environmentally friendly technique is highly desirable. The main objective of this paper is to discuss the available techniques for the nondestructive test (NDT) that can be potentially used for the decommissioning project to identify clear cut during cutting. Therefore, this paper discusses the stat of the art techniques that can be used preferably underwater NDT for cut detection application. The approach will focus on existing methods available for NDT. There are various NDTs but the current study only focused on the techniques that can be used underwater monitoring. Therefore, review of past studies and techniques that are commonly used in industry for NDTs will be discussed, particularly for Alternative Current Field Measurement (ACFM), Magnetic Partial (MPI), Eddy-Current (EC), Ultrasonic testing (UT), Radiographic Testing (RT), Visual Testing (VT) and Acoustic Emission (AE) Testing techniques will be discussed with real application in O&G with selected case studies. In the end, the advantages and disadvantages will be discussed to compare the suitability for the decommissioning project. The matrix presented summarized the key elements that need to consider before selecting a system for decommissioning project for considering cut detection.

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.

In the study of structural strength, the reserve strength ratio provides a measure of the ultimate strength capacity of a structure. Under actual site conditions, the reserve strength ratio may vary from its design values with loss of stiffness and changes in structural integrity. Changes in the vibrational response of a structure due to loss of stiffness is observed as a form of structural health monitoring (SHM). The aim of this study is to investigate the relationship and sensitivity of the reserve strength ratio of a structure to changes in natural frequency due to damage occurrences as a measure of global structural integrity. The reduction of stiffness is simulated by the sequential removal of members according loading path within the model. To obtain the values used for comparison, a non-linear pushover analysis and eigenvalue analysis is utilized to obtain the Reserve Strength Ratio (RSR) and eigenvalue for intact as well as simulated progressive damage conditions. The pattern recognized from the analysis performed indicated that as the reserve strength ratio (RSR) is reduced with reduction of stiffness by the removal of primary and secondary members, the eigenvalues for each respective model showing similar reductions.