Risk Analysis of Remote Subsea Operations: A Systematic Literature Review
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Date
2026Author
Schleder, Adriana Miralles
Mazaira, Leorlen Yunier Rojas
de Abreu, Danilo Taverna Martins Pereira
Cruz, João Pedro Bachega
Rodríguez, Daniel González
Russo, Ana Carolina
Bastos, Bruna Moura
Maturana, Marcos Coelho
Orlowski, Rene Thiago Capelari
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Abstract. 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.