Machine Learning and Simulation Modeling Large Offshore and Production Plants to improve Engineering and Construction

Floating Production Storage and Offloading (FPSO) units are essential yet complex assets in offshore oil and gas production. Due to their significant scale and the intricate nature of their construction and conversion processes, FPSO projects frequently face delays and cost overruns. This paper presents a strategic engineering approach integrating risk management frameworks with machine learning to enhance project execution for FPSO units. By focusing on early Material Take Off (MTO) estimations, Critical Path analysis, and Local Content requirements, this approach addresses major challenges in FPSO projects, such as supply chain constraints and fluctuating market conditions for exotic materials. Our hybrid model combines historical data analysis with neural network-based process-specific correlations to improve MTO accuracy and procurement planning. Results demonstrate that this approach not only mitigates project risks but also enhances efficiency, enabling smoother workflows and minimizing delays. The findings offer actionable insights and tools for project managers aiming to optimize FPSO project delivery within the smart industry framework.