The objective of the paper is to demonstrate digitalization of Floating Structures Integrity Management Program (FSIMP) and its application for the structural integrity of floating structure assets. The framework of FSIMP is being developed by adopting Risk Based Inspection (RBI) methodology and complemented with technical know-how and industry best-practices. Implementing the methodology provides strategic planning for maintenance by reducing the anticipated risk. Hence, ensuring uninterrupted service of the floating structure assets throughout the service life. This paper presents a systematic approach for digitalization of the integrity management program for a nominated floating structure asset. The methodology offers a procedure to acquire necessary data management gathering, risk assessment, and RBI survey plan to maintain the structural integrity in the centralized web-based platform of FSIMP. RBI process is adopted into the FSIMP to investigate all deterioration and failure mechanisms. These structures will be identified by qualitative and quantitative risk assessment methods. The implementation of FSIMP offers a wide range of capabilities in structural integrity management such as integrating all floating structure fleet assets in a single dashboard of web-based platform, clear line of sight for reliable structural integrity, and an holistic overview across all levels of management. FSIMP with RBI methodology evaluates all data gathering to optimize inspection resources based on the risk assessment through an optimum combination of inspection methods and frequencies. The whole process is aligned to the requirements from Classification to ensure reliability for continuous operations. It also observes the essential need of digitalization for FSIMP during the time of post-COVID19 pandemic and the ever-expanding offshore oil, gas and energy frontiers that demand the adoption of new and advanced technologies, especially in the field of digitalization. It is shown that FSIMP has great potential as a digitalization tool and system to integrate with the RBI risk assessment that aligns to the requirements from Classification. It is strategically to maximize the effectiveness and improved efficiency for inspection and monitoring plan. The paper provides information on the solution of digitalization to the Floating Structures Integrity Management Program (FSIMP) in ensuring that the integrity of floating structure asset during the service life is intact for continuous operation and a holistic overview for all the assigned fleet assets in a centralized dashboard web-based platform. In addition to that, RBI is as added benefit to the FSIMP with its structure methodology of data evaluation and risk assessment in order to objectively optimizing inspection and maintenance resources.
There is an industry need to have a fit for purpose whilst accurate method of predicting geohazard impact to pipelines. Geohazard events are influenced by rain occurrences. The effect of rain intensity and duration has been much researched in the context of understanding slope failures in Malaysia's context, however timely prediction of slope failures triggered by rain events that can cause pipeline damage remains challenging. The major challenge arises from the fact that numerous variables influence the prediction of the target parameters called PR (Risk Index for geohazard impact to pipeline) and pipeline strain predictions. Uncertainties that makes prediction challenging includes but are not limited to soil strength parameters, subterrain geological conditions, occurrences of external disturbances that are outside zone of concerns and also numerous pipeline related parameters that renders understanding influences complex. To further enhance timely and improved accuracy of predicting geohazard impact to pipeline, new Machine Learning capabilities were used to develop a tool called IMGESA (Integrated Meteorological and Geohazard System Advisory) leveraging on probabilities method to study the features of terrain degradation that are impacted by rain intensity and its duration. New probabilistic algorithms can be used to manage uncertainties. Machine Learning methods can provide the basis for continuous improvement to predictions. Two main parts in establishing the impact of geohazards to terrain degradation are discussed in this paper: the first is associated with availability of data, namely which data can be considered as main influencer to terrain degradation; the second is associated with development of methodology in establishing the predictive model of PR (Risk Index) and Strain Prediction that can be acceptable by industry. This paper will explore the issues from these two important parts and will present salient Machine Learning related experiences to provide the much-needed technology enhancement to push the needle in predicting terrain degradation and its impact to onshore assets.
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