Cracking the Code: Microseismicity Findings Point to Improved Geothermal Extraction

New research around microearthquakes is shaking things up for the geothermal community. Using machine learning, Pennsylvania State University (Penn State) researchers have uncovered a connection between the seismic waves produced by common microearthquakes, subsurface rock permeability, and the ability to optimally transfer hot geothermal fluids to the surface. This discovery could boost the efficiency of extracting geothermal energy. 

In order to understand this, let’s break down two foundational aspects of this discovery in relation to geothermal energy: 

1. Permeability is the ease by which a fluid can pass through a material. Generating geothermal power requires subsurface permeability, which allows fluids to flow among hot rocks and be drawn to the surface. More permeability generally means more energy extraction. 
2. Microearthquakes are very low-intensity tremors that frequently occur in deep underground geothermal reservoirs and are rarely felt on the surface.  Like Pop Rocks® candy, they create little bursts of energy—only instead of a teeny popping noise, microearthquakes make tiny seismic waves.  

Using machine learning, Penn State’s scientists were able to identify a relationship between microseismicity and permeability. With these results, geothermal developers could ramp up geothermal energy extraction by using microearthquake measurements to target areas that may have increased permeability. 

This breakthrough has far-reaching potential. Increasing efficiencies around extracting geothermal energy, a renewable resource that offers vast nationwide potential for clean, firm, flexible electricity generation, can help improve the cost-effectiveness of harnessing the heat beneath our feet. Additionally, the work can be beneficial in monitoring gas movement for carbon sequestration and used in the subsurface storage of hydrogen, which can be used across multiple sectors to enable zero or near-zero emissions in other processes and systems. 

Funded by the Geothermal Technologies Office’s (GTO’s) Machine Learning for Geothermal Energy initiative and published in Nature Communications, the research team used datasets from EGS Collab and the Frontier Observatory for Research in Geothermal Energy (FORGE) stimulation to make the discovery. 

GTO’s pioneering research, development, and demonstration continually advance geothermal energy innovations. Read more about the office's work in EGS, how this research supports the Enhanced Geothermal Shot™ and other ways GTO is drilling down into the science of geothermal!