Demand for energy resources continues to rise globally as the portfolio of resource options continues to diversify. While interest in alternative energy options grows, there remains a need for research that helps ensure both safe and reliable access to energy resources while reducing potential risks and impacts associated with them. Increased access to energy, infrastructure, environmental, and societal data offers new opportunities to inform and address these types of research questions, but working with these large, complex datasets require new approaches and solutions that help bridge the gap between the availability of information and effective data synthesis and analysis. Although numerous big data driven approaches and solutions exist for marketing and business, research applications still struggle for solutions to efficiently find, integrate and utilize relevant, authoritative datasets.
The U.S. DOE’s National Energy Technology Laboratory (NETL) has been developing geo-data science methods and tools for small and big data problems to facilitate science-based evaluations of engineered and natural systems. NETL’s Energy Data eXchange serves as the hub for data discovery, integration, and big data analytics, incorporating tools and models (info on some available at https://edx.netl.doe.gov/tools) to evaluate energy efficiency, resource assessments, subsurface storage potential, geothermal resources, risks with energy infrastructure, water resource impacts, induced seismicity potential, and offshore oil spill prevention and response. In this presentation, we will explore some examples of these efforts and show how they have been used to improve the baseline knowledge, detect new trends, reduce uncertainty, evaluate risks, and support of a range of decision-making needs