EGS Collab Experiment 1: Microseismic Monitoring


The U.S. Department of Energy's Enhanced Geothermal System (EGS) Collab project aims to improve our understanding of hydraulic stimulations in crystalline rock for enhanced geothermal energy production through execution of intensely monitored meso-scale experiments. The first experiment is being performed at the 4850 ft level of the Sanford Underground Research Facility (SURF), approximately 1.5 km below the surface at Lead, South Dakota.

Here we report on microseismic monitoring of repeated stimulation experiments and subsequent flow tests between two boreholes in the Poorman Formation. Stimulations were performed at several locations in the designated injection borehole at flow rates from 0.1 to 5 L/min over temporal durations from minutes to hours. Microseismic monitoring was performed using a dense 3D sensor array including two cemented hydrophone strings with 12 sensors at 1.75 m spacing accompanied by 18 3-C accelerometers, deployed in 6 monitoring boreholes, completely surrounding the stimulation region. Continuous records were obtained over a two-month period using a novel dual recording system consisting of a conventional 96 channel exploration seismograph and a high-performance 64 channel digitizer sampling sensors at 4 and 100 kHz respectively.

Using a standard STA/LTA triggering algorithm, we detected thousands of microseismic events with recorded energy in a frequency range generally above 3 kHz and up to 40 kHz. The locations of these events are consistent with creation of a hydraulic fracture and additional reactivation of pre-existing structures. Using manual pick refinement and double-difference relocation we are able to track the fracture growth to high precision. We estimate the times and locations of the fracture intersecting a monitoring and the production borehole using microseismic events. They are in excellent agreement with independent measurements using distributed temperature sensing, in-situ strain observations and measurements of conductivity changes.

This submission includes a microearthquake catalog, raw event files, a subset of the continuous microseismic monitoring data collected during stimulations and flow test activity on 05/22/2018, 05/23/2018, 05/24/2018, 05/25/2018, 06/25/2018, 07/19/2018, 07/20/2018, 12/7/2018, 12/20/2018, and 12/21/2018 (in binary format), and a binary file interpreter to read the continuous microseismic monitoring data. A Stanford Geothermal Workshop paper is also included to describe microseismic monitoring activities at SURF during these periods.

43 Resources

Related Datasets

Datasets associated with the same DOE project
  Submission Name Resources Submitted Status

Additional Info

DOE Project Name: EGS Collab
DOE Project Number: EE0032708
DOE Project Lead: Lauren Boyd
DOI: 10.15121/1557417
Last Updated: 26 days ago
Data from July, 2019
Submitted Jul 29, 2019


Lawrence Berkeley National Laboratory


Publicly accessible License 


Martin Schoenball
Lawrence Berkeley National Laboratory
Jonathan Ajo-Franklin
Lawrence Berkeley National Laboratory
Michelle Robertson
Lawrence Berkeley National Laboratory
Todd Wood
Lawrence Berkeley National Laboratory
Doug Blankenship
Sandia National Laboratories
Paul Cook
Lawrence Berkeley National Laboratory
Patrick Dobson
Lawrence Berkeley National Laboratory
Yves Guglielmi
Lawrence Berkeley National Laboratory
Pengcheng Fu
Lawrence Livermore National Laboratory
Timothy Kneafsey
Lawrence Berkeley National Laboratory
Hunter Knox
Sandia National Laboratories
Petr Petrov
Lawrence Berkeley National Laboratory
Paul Schwering
Sandia National Laboratories
Dennise Rempleton
Lawrence Livermore National Laboratory
Craig Ulrich
Lawrence Berkeley National Laboratory


geothermal, energy, EGS Collab, SURF, hydraulic, fracturing, stimulation, Sanford Underground Research Facility, experiment, EGS, microseismic monitoring, meso-scale stimulations, Sandford Underground Research, mesoscale experiments, crystalline rock, 3D sensor, Lead, South Dakota, STA/LTA triggering algorithm, microseismicity, catalog, raw data, processed data, binary file interpreter, Python, geospatial data


Export Citation to RIS
Submission Downloads