Putting the FLOW in workflow: Using hydrocarbon plume prediction AI to quantify groundwater risk and liability
About the SESSION
Environmental Contaminated Sites professionals have a challenging role to obtain a clear picture of the contamination impacts to soil and groundwater on their sites. Factors such daily and seasonal groundwater fluctuations, challenging lithology, and the cost of invasive sampling work impede the ability to achieve consistent, representative data required to adequately assess risk and confidently make decisions on next steps. The traditional approach includes:
Routine sampling of water and soil is typically used to estimate pollutant fate and transport.
Owners incorporate this sporadic data into their conceptual site models, then plan and evaluate remedial action plans.
Groundwater and soil fate models become challenging to calibrate with limited data points and often fail to capture changing site conditions even when calibrated.
Environmental Material Science Inc. (EMS) provides a new generation of contaminated site modeling to overcome prior site model’s limitations. By leveraging increased data density from cost-effective IoT sensors, EMS data facilitates models that continuously calibrate, update, and improve predictability and enable adaptive management of contaminated sites.
EMS manufactures and deploys subsurface sensors from our Saskatoon headquarters. These autonomous sensors measure hydrocarbons in soil and groundwater, in addition to measuring temperature, pressure, humidity, as well as methane, carbon dioxide, oxygen, and nitrous oxide concentrations. These sensors transmit data as frequent as every 30 minutes wirelessly via secure LoRaWan channels. From there data is fed into our models and continually updated through highly parameterized inversion using the Parameter Estimation (PEST++) software suite.
EMS presents a family of artificially intelligent models that leverages the latest in modern data assimilation to quantify and reduce the uncertainty in contaminant fate and transport modeling. We will show how these models can be continuously updated with our low footprint, low power, cost-effective sensors to overcome traditional limitations with modeling. Our case studies will highlight real-world examples of EMS’ AI Modeling and Remote Autonomous Sensor Technology providing clear data trends, in months, to enable faster pathways to closure.
About the Speaker
Nico Higgs, Environmental Material Science (EMS)
Nico Higgs is the Director of Software at Environmental Material Science (EMS) where he's tackling soil and groundwater contamination with the power of ones and zeros. Mr. Higgs obtained his Bachelor of Science in Mathematics and Statistics from the University of Saskatchewan (U of S). Awarded the Most Outstanding Graduate in the Department of Statistics, he continued his studies to complete his Master of Science in Computer Science.
Now Mr. Higgs leads the charge in developing cutting-edge analytics and predictive models to help provide Contaminated Site experts with predictive critical decision-making tools. As the lead in developing EMS’ AI Machine Learning Software, patented “PlumeFutures”, he has paved the path for industry to turn complex environmental data into actionable insights and ensure EMS remains at the forefront of environmental technology.
Event Information
Date: October 8, 2024
Venue: The Bow, Mountain View Room, 500 Centre St S, Calgary, AB T2G 0E3 (View Map). This event will also be taking place virtually. Lunch to be catered by Spolumbo’s.
Time: 11:45am - 1:30pm MT
Tickets:
Members (Corporate*, Individual, Retired), $45
Non-Members, $60
Student Members, $25
Virtual, $20
*Corporate members, please log in to your corporate member account to manage employee accounts and provide access to your team members to register at the member rate. To inquire, please contact us.
Registration Deadline: October 3, 2024
Refund policy: No refunds may be processed on or after the registration deadline.
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