Building an Analytical Framework to Measure Offshore Infrastructure Integrity, Identify Risk, and Strategize Future Use for Oil and Gas

Alec Dyer1,2, Lucy Romeo3,4, Madison Wenzlick3,5, Jennifer Bauer3, Jake Nelson1,2, Michael Sabbatino3,6, Patrick Wingo3,4 and Rodrigo Duran7,8, (1)National Energy Technology Laboratory, Albany, OR, United States, (2)Oak Ridge Institute for Science and Education, Oak Ridge, TN, United States, (3)National Energy Technology Laboratory, Albany, United States, (4)Leidos Research Support Team, Albany, OR, United States, (5)Oak Ridge Institute for Science and Education, Oak Ridge, United States, (6)Leidos Research Support Team, Albany, United States, (7)Planetary Science Institute Tucson, Tucson, United States, (8)Theiss Research, Davis, OR, United States
Abstract:
Challenges faced by oil and gas exploration and extraction include potential socio-economic and environmental impacts. Researchers at the Department of Energy’s National Energy Technology Laboratory (NETL) have developed innovative solutions to mitigate risks by analyzing the current state of offshore oil and gas infrastructure to better identify potential future impacts. An analytical approach leveraging structural- and weather-related incidents, infrastructure characteristics, and metocean data has been developed for platforms and rigs in the Gulf of Mexico. Results from this approach include a risk index that identifies spatio-temporal patterns and trends of aged infrastructure.

In parallel with these efforts, NETL has developed the Analytical Offshore Platform (AOP), an evolving cloud-based data visualization and analytical toolset that applies the aforesaid data- and science-driven approach to better predict and quantify potential risks associated with oil and gas infrastructure. Along with infrastructure data, several key tools from NETL’s Offshore Risk Modeling (ORM) suite will be made available through the AOP. These validated tools include NETL’s 4D Blowout and Spill Occurrence Model (BLOSOM), the spatial data density tool, Cumulative Spatial Impact Layers (CSIL), and the scenario-comparison / decision-support tool, Spatially Weighted Impact Model (SWIM). In combination, these models and tools can predict the transport of oil spills while highlighting regional socio-economic and environmental impact risks.

This presentation will cover the analytical approach developed to evaluate the current state of offshore infrastructure using spatio-temporal analytics and statistics. The application of data analytics and big data computing tools will be highlighted, along with how this research is contributing to the understanding of the current state of offshore infrastructure, identifying potential offshore hazards, and mitigating future oil and gas related risks.

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