Enhanced geothermal systems (EGS) can enrich the nation’s energy portfolio by increasing the amount of renewable, baseload power available to the electrical grid. Microseismic activity due to fluid resource injection and production provides critical insight into the processes that occur within EGS subsurface fracture networks.
To more precisely characterize these fracture networks, we leveraged recent advances in deep neural network (DNN) image-analysis techniques. Using seismic spectrogram data collected during a mesoscale EGS field experiment as image inputs, we developed a new approach to correlate microseismic events. Our results indicate that our chosen DNN can estimate the correlation value between highly correlated events, i.e., cross-correlation coefficients greater than approximately 0.7, but has difficulty estimating correlation values between poorly correlated events. In physical systems, however, the highly correlated microseismic events are the events of interest. Therefore, by advancing current methods available to image subsurface EGS thermal reservoirs, this data-driven method has shown potential for improving domestic energy and resource security by enhancing the availability of clean and reliable energy to the national electric grid.
Our research created new capabilities supporting Lawrence Livermore National Laboratory’s energy and resource security mission research challenge area within the energy and climate security mission area. We strengthened ties between the Laboratory’s core competencies in the earth and atmospheric sciences and information systems and data sciences to create a new methodology that could promote the availability of clean and reliable energy to the nation’s electric grid. This research also advances the DOE Office Energy Efficiency & Renewable Energy (EERE) Geothermal Technologies Office mission to develop and validate innovative and cost competitive technologies to locate and develop geothermal resources in the United States.
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