Room: Ballroom
Date: 4/18/2023
Session Time: 8:00 AM to 5:45 PM (local time)
De-risking Deep Geothermal Projects: Geophysical Monitoring and Forecast Modeling Advances
Geothermal energy is an emerging renewable energy source and as a green and sustainable energy can make a significant contribution to the current worldwide challenge to reduce the net atmospheric emissions of greenhouse gasses to zero. Geothermal heat extracted from depths in excess of 400 m is defined as deep geothermal energy or Enhanced Geothermal Systems (EGS). EGS usually employ hydraulic fracturing to increase the rock permeability and favor a more efficient exploitation of deep geothermal reservoirs when local geology does not favor natural pathways for fluid circulation. Induced micro-earthquakes in EGS are not therefore undesired by-products but a necessary tool to create effective pathways for fluid migration and heat exchange. Thus, to develop EGS, adaptive, data-driven real-time monitoring and risk analysis of potential seismicity triggered by EGS operations is crucial for assessing the geothermal stimulation effects and demonstrating that safe and sustainable development of deep geothermal energy projects is possible. A current research-oriented EGS laboratory is being developed at the FORGE (Frontier Observatory for Research in Geothermal Energy) geothermal site in Utah, USA. We encourage contributions from FORGE and other different geothermal energy projects and field test sites that focus on geophysical technologies applied to geothermal energy, such as real-time monitoring and characterization of induced seismicity, distributed acoustic sensing, large-N array, active surface seismic, vertical seismic profiling, seismic imaging of faults and fracture zones, laboratory experiments and novel instrumentation. We also welcome submission of abstracts on modeling studies at all scales, seismicity forecasting models, hazard and risk analysis studies as well as presentations dealing with good-practice guidelines and risk assessment procedures that would help in reducing commercial costs and enhancing the safety of future geothermal projects.
Conveners
Federica Lanza, Swiss Seismological Service, ETH Zurich (federica.lanza@sed.ethz.ch)
Kristine Pankow, University of Utah (kris.pankow@utah.edu)
David Eaton, University of Calgary (eatond@ucalgary.ca)
Ryan Schultz, Stanford University (rjs10@stanford.edu)
Nori Nakata, Lawrence Berkeley National Laboratory (nnakata@lbl.gov)
Annemarie Muntendam-Bos, Delft University of Technology (a.g.muntendam-bos@tudelft.nl)
Poster Presentations
| Participant Role | Details | Action |
|---|---|---|
| Submission | Developing a Machine Learning Model to Pick Phase Arrivals on Das Data at the Forge Site | View |
| Submission | Applying Waveform Correlation Analysis to Microseismicity at the Forge Sites to Detect and Characterize Fractures | View |
| Submission | Bidirectional Displacement Waveforms of Hhz Induced Microearthquakes - Evidence for Volumetric Shear-Slip Distributions in Ambient Crust Hydraulic Stimulation | View |
| Submission | Time-Lapse Changes in Velocities at Patua Geothermal Fields Using Seismic Ambient Noise | View |
De-risking Deep Geothermal Projects: Geophysical Monitoring and Forecast Modeling Advances [Poster]
Description