Gaining Insights Into Risks to Groundwater Using Big Data and Machine Learning

Monday, December 4, 2017: 4:50 p.m.
101 AB (Music City Center)
Jennifer Bauer , National Energy Technology Laboratory, Department of Energy, Albany, OR

Efforts to improve insights related to the risks to groundwater coinciding with oil and gas development and infrastructure requires an understanding of the dynamics and interactions across the entire engineered-natural system. However, the heterogeneity and ambiguity of these data make it difficult to assess the broad range of potential risks posed to groundwater. But, successful applications of big data and machine learning in other scientific disciplines suggest these approaches are well suited to coping with these types of challenges faced when assessing risks to groundwater. Here, we will present some of our efforts and lessons learned integrating machine learning and big data analytics in various models and assessments to understand risk associated with oil and gas development and infrastructure as it pertains to groundwater.
Jennifer Bauer, National Energy Technology Laboratory, Department of Energy, Albany, OR
Jennifer, a geospatial researcher for the DOE’s National Energy Technology Laboratory, utilizes spatio-temporal analytics to innovate research and develop solutions for energy and natural systems.


NGWA Groundwater Summit is being held in conjunction with Groundwater Week.

Find out more about NGWA and our events.

National Ground Water Association
601 Dempsey Road
Westerville, Ohio 43081
USA
Phone 614 898.7791
(toll-free within the United States 800 551.7379)
Fax 614 898.7786
Email ngwa@ngwa.org

Websites:

http://www.ngwa.org/ —home site of NGWA

http://www.wellowner.org — information for well owners