Using machine learning, researchers have tied low-magnitude microearthquakes to the permeability of subsurface rocks beneath the Earth, a discovery that could have implications for improving geothermal energy transfer.
Using machine learning, researchers at Penn State have tied low-magnitude microearthquakes to the permeability of subsurface rocks beneath the Earth, a discovery that could have implications for improving geothermal energy transfer.
Increasing rock permeability is critical to a range of energy extraction methods, Yu said. Rock permeability impacts traditional fossil fuel recovery as well as renewable energies including hydrogen production. Hydrofracturing methods introduce cold fluids into the subsurface through porous rock, which creates high pressures that break the rock in tension or shear. This process creates microearthquakes similar to naturally occurring earthquakes, but at a much smaller scale.
"Machine learning played a key role in uncovering the relationship between seismic activity and rock permeability" said co-author Parisa Shokouhi, professor of engineering science and mechanics in the College of Engineering."It helped identify the important attributes of the seismic data for predicting rock permeability evolution. We constrained the machine learning algorithm to ensure a physically meaningful model.
"Yu's work is part of our effort to advance geothermal exploration and geothermal energy production using machine learning methods, said co-author Chris Marone, professor of geosciences at Penn State."Our lab studies show clear connections between the evolution of elastic properties prior to lab earthquakes, and we are excited to see that similar relationships are observed in nature.
Energy And Resources Physics Thermodynamics Energy And The Environment Renewable Energy Earthquakes Environmental Science
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