Data Analytics and Machine Learning for very large Oil&Gas Projects

Description

This paper outlines a comprehensive study aiming to develop tailored risk reduction models for large-scale engineering projects, with a focus on FPSO (Floating, Production, Storage and Offloading) design. The study evaluates critical factors related to piping and fitting in FPSO projects, proposing mitigation strategies. Starting with historical project data analysis, preprocessing involves data cleaning and preparation. Employing Business Intelligence and Data Analytics, the study advances to data transformation and interactive visualization. The complexity of crude oil systems requires advanced analytics, and Machine Learning clusters high-velocity data, enhancing project management with data-driven insights. The research deepens understanding of FPSO risk factors, offering practical strategies. The fusion of traditional methods with advanced analytics presents a novel approach to offshore engineering challenges.

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