FORGEcasting induced seismicity in real-time with statistical and physics-informed models

Enhanced Geothermal Systems (EGS) comprise technologies aiming to harness geothermal energy from the Earth's subsurface by enhancing the productivity of existing or naturally occurring geothermal reservoirs. Unlike conventional geothermal systems (hydrothermal systems) that rely on naturally permeable rock formations, EGS involve creating or enhancing fractures in low permeability or impermeable rock mass through hydraulic stimulation. EGS has the potential to expand the geographical reach of geothermal energy utilization and increase the overall efficiency and sustainability of geothermal power generation.

The injection of pressurised fluids and opening of fractures manifests as micro-seismicity, which is in most cases a normal indication of the reservoir stimulation process. However, some cases have seen unwelcome large magnitude and even damaging events. The US department of Energy has sponsored the Utah Frontier Observatory for Research in Geothermal Energy (FORGE), a flagship demonstration site aiming to demonstrate to the public, stakeholders and the energy industry that EGS technologies have the potential to contribute safely and significantly to future low-carbon power generation. The site is thoroughly instrumented with monitoring boreholes combining geophone chains and fibre-optic cables for DSS and DAS, as well as a dense surface network of seismometers, allowing for the generation of high-resolution seismic catalogues during and after the stimulation phases.

At FORGE, the granitoid reservoir with temperatures exceeding 220°C around 2300 m b.s.l. has been stimulated in hours-long stages in April 2022 and April-May 2024. During both sets of hydraulic stimulations, we monitored the micro-seismicity and ran forecasting models in an ATLS framework (Adaptive Traffic Light System). From the raw real-time catalogue, we use advanced techniques (machine-learning based pickers, template matching, …) to generate enhanced catalogues which help us investigate essential the dynamics of micro-seismicity and reservoir creation. For the ATLS, three forecasting models classes were used: an empirical model that relates injection rates and rate of seismicity based on the seismogenic index model; a machine learning based model able to weight timeseries measurements of past seismicity and hydraulic parameters to output a forecast rate of seismicity; and a hybrid hydromechanical model that generates seismicity based on a linear or non-linear pressure solution. This implementation of an ATLS at full scale and in real-time paves the way for future implementations in Switzerland and abroad in an effort to derisk and generalise EGS.