Eric Matzel (14-ERD-051)
Induced seismicity is an inherent issue associated with several underground energy technologies of strategic interest to Livermore, including geologic carbon sequestration, enhanced geothermal systems, and shale gas development. If fracturing fluids from shale gas development, for example, are injected in proximity to a pre-existing fault or fracture system, the resulting elevated pressures may lead to dynamic earthquake slip. We plan to develop an approach for predicting and avoiding induced earthquakes, with the central goal of deployment as an on-site, real-time tool for use by field operators in the energy sector. We will combine innovative techniques for analyzing microseismic data with a physics-based inversion model to forecast microseismic cloud evolution (a group of spatially correlated individual microseismic events). Inversion models are used to convert observed measurements into information about objects or systems. Fast-running tools of the type we propose will allow operators to respond quickly to changing subsurface conditions and thus develop underground resources in a responsible manner.
Current state-of-the-art methods in this field of energy exploration and development contain almost no insight into the equilibrium and motion of fluids and of solid-body processes leading to earthquakes. Our research will improve the foundational science and practical capabilities necessary to develop predictive tools. The ultimate deliverable is a real-time methodology for continuously evaluating the likelihood of triggering a large earthquake at any given field site. This evaluation will be dynamically updated every few hours as new microseismic or injection-rate data becomes available, and it will allow operators to quickly react to changing subsurface conditions. A central goal is that these processing approaches be fast enough that they can be applied in real time and synchronized with data acquisition.
A rational strategy for assessing and mitigating seismic risk must be developed if large-scale fluid injection operations are to continue responsibly. Our proposed work will produce both foundational science and a practical capability in both advanced monitoring techniques and in addressing public and regulatory concerns over seismic risk, supportive of a central Laboratory mission in energy to advance the nation's security through the production, development, and deployment of energy resources and technology.
FY15 Accomplishments and Results
We have achieved great progress in developing the techniques and applying them to microseismic data. Specifically, we (1) created sharp three-dimensional models, resolved moment tensors, and refined locations of microseismicity using interferometric (electromagnetic wave information) methods; (2) determined that these models are precise enough that we can study the full seismic waveform, including the scattered energy seen in microquake records; (3) mapped these microquakes in space using the Bayesian statistical multiple seismic-event locator and observed that the relocated seismicity closely follows the structures identified by the three-dimensional image; and (4) used the ambient noise correction and the virtual seismometer method (see figure) to resolve moment tensors of individual seismic events.
Publications and Presentations
- Matzel, E. M., et al., Looking inside the microseismic cloud using seismic interferometry. AGU Fall Mtg., San Francisco, CA, Dec. 14–18, 2015. LLNL-ABS-679274.
- Matzel, E. M., et al., “Microseismic techniques for avoiding induced seismicity during fluid injection.” Energ. Procedia. 63, 4297 (2014). LLNL-ABS-648453. http://dx.doi.org/10.1016/j.egypro.2014.11.465
- Morency, C., and E. Matzel, Virtual seismometers for induced seismicity monitoring. AGU Fall Mtg., San Francisco, CA, Dec. 14–18, 2015. LLNL-POST-670444.
- Matzel, E. M., et al., “Virtual seismometers in geothermal systems: Using microquakes to illuminate the subsurface.” 40th Workhop on Geothermal Reservoir Engineering 2015, SGP-TR-204, Stanford Geothermal Program, Menlo Park, CA (2015). LLNL-CONF-666405.