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dc.contributor.authorSchleder, Adriana Miralles
dc.contributor.authorMazaira, Leorlen Yunier Rojas
dc.contributor.authorde Abreu, Danilo Taverna Martins Pereira
dc.contributor.authorCruz, João Pedro Bachega
dc.contributor.authorRodríguez, Daniel González
dc.contributor.authorRusso, Ana Carolina
dc.contributor.authorBastos, Bruna Moura
dc.contributor.authorMaturana, Marcos Coelho
dc.contributor.authorOrlowski, Rene Thiago Capelari
dc.contributor.authorM
dc.date.accessioned2026-08-23T08:15:45Z
dc.date.available2026-08-23T08:15:45Z
dc.date.copyright2026/06/01
dc.date.issued2026
dc.identifier.issn0892-7219
dc.identifier.otheromae-25-1155.pdf
dc.identifier.urihttp://yetl.yabesh.ir/yetl1/handle/yetl/4316295
dc.description.abstractAbstract. Remote subsea operations of oil and gas installations could be a complex undertaking related to the inherent risk in the operating processes. This systematic literature review consolidates current knowledge on risks and mitigation strategies in remote subsea operations for offshore oil and gas systems, focusing on water injection, oil–water separation, and oil transfer processes. Addressing the research questions, the study identifies critical risk hotspots, including equipment failures, human–machine interface errors, external events (e.g., environmental conditions), and challenges in real-time monitoring and decision-making. Comprehensive searches were performed in Scopus and Web of Science for studies published between 2000 and 2024, drawing from a final analysis of 44 articles selected. Studies were selected based on predefined inclusion and exclusion criteria relevant to risk assessment in remote subsea operations. The screening process involved independent reviewers and included both title/abstract and full-text review stages. Additionally, a data analysis was performed regarding parameters such as the evolution of publications over the years, the most explored techniques, areas of application, among others. The study concludes that traditional static risk assessment methods are insufficient for real-time remote operations, and the implementation of dynamic models, combined with comprehensive training and robust equipment design, is essential for effective risk mitigation and operational safety enhancement. The effectiveness of Bayesian networks in dynamic risk analysis was highlighted and complemented by other methodologies such as decision trees and event trees. Advancements in monitoring technologies, big data analytics, and machine learning indicate promising pathways for the evolution of risk management practices.
dc.publisherThe American Society of Mechanical Engineers (ASME)
dc.titleRisk Analysis of Remote Subsea Operations: A Systematic Literature Review
dc.typeJournal Paper
dc.journal.volume148
dc.journal.issue3
dc.journal.titleJournal of Offshore Mechanics and Arctic Engineering
dc.identifier.doi10.1115/1.4070803
dc.journal.fristpage1
dc.journal.lastpage10
dc.page10
dc.treeJournal of Offshore Mechanics and Arctic Engineering:;2026:;volume( 148 ):;issue:003
dc.contenttypeFulltext


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