Bayesian Inversion of Microseismic Events at the FORGE Geothermal Site
Description:
Enhanced geothermal systems (EGS) show great potential as a possible source of low-carbon energy. However, they are only economically feasible where the hot rock is sufficiently permeable for economical amounts of fluid to pass through and accumulate heat. In EGS systems, hydraulic fracturing is used to create this permeability. Accurately characterizing the locations of microseismic events during hydraulic fracturing in EGS allows for a better understanding of the fracturing process and provides guidance to EGS operators. Limited understanding of velocity models and limited spatial coverage of monitoring stations can lead to significant microseismic location uncertainty. Bayesian inference is a widely used approach in inverse problems and model parameter estimation which yields estimates with uncertainty analysis.
We applied the Slice Sampling method to locate events in the Utah FORGE April 2022 microseismic data, which includes both downhole and surface monitoring using both distributed acoustic sensing and traditional seismometers. Our analysis provides a posterior distribution of the source locations. To better understand the limitations of this technique, we present several synthetic test results to evaluate the localization uncertainty under varying receiver geometries and noise levels in the arrival time data.
Results indicate that the Slice Sampling method successfully captured the uncertainty in event locations, and the joint analysis of surface and downhole data significantly reduced the uncertainty of event location. This improved location accuracy may enable better mapping of fractures and more efficient optimization of hydraulic stimulation strategies. Our findings demonstrate the potential of integrated Bayesian approaches for advancing EGS monitoring and development.
Session: Fiber-optic Sensing Applications in Seismology [Poster]
Type: Poster
Date: 4/15/2025
Presentation Time: 08:00 AM (local time)
Presenting Author: Yida
Student Presenter: Yes
Invited Presentation:
Poster Number: 61
Authors
Yida Song Presenting Author Corresponding Author yida_song@mines.edu Colorado School of Mines |
Shihao Yuan syuan@mines.edu Colorado School of Mines |
Eileen Martin eileenrmartin@mines.edu Colorado School of Mines |
|
|
|
|
|
|
Bayesian Inversion of Microseismic Events at the FORGE Geothermal Site
Category
Fiber-optic Sensing Applications in Seismology