SHale gas Exploration and Exploitation induced Risks This project has received funding from the European Union s Horizon 2020 research and innovation programme under grant agreement No 640896. IGF PAS activity in WP4 M. Staszek, B. Orlecka-Sikora, S. Lasocki, K. Leptokaropoulos, J. Mirek, H. Marcak, IGF PAS, Napoli First Annual Meeting Napoli - June 7-9, 2016 Hotel Palazzo Esedra, Piazzale Tecchio, 50 80125
Presentation plan Topics currently realized by IGF PAS within WP4: 1. Seismic interferometry for monitoring of S-wave velocity changes in the reservoir due to fluid injection. 2. Multi-dimensional clustering in equivalent dimensions for identification of physical processes controlling seismogenesis. 3. Static stress drop distribution and its application in seismic hazard assessment. 2
1. Seismic interferometry Goal: use of ambient seismic noise for monitoring of S-wave velocity changes due to hydraulic fracturing at Wysin site Small velocity perturbations from correlations of seismic noise can be a useful tool for studying the contiuous time evolution of stress regime in the vicinity of seismogenic faults. Brenguier et al., 2008. Science measure changes between cross-correlation functions of ambient seismic noise computed for different dates for the same receiver pair determine changes in seismic velocity 3
1. Seismic interferometry It has been observed that changes in seismic activity correlate well with seismic velocity changes. Brenguier et al., 2008 4 predict change in seismic activity / track dynamic changes in the medium due to hydraulic fracturing
1. Seismic interferometry S-wave velocity will be determined on the basis of Rayleigh wave dispersion curve determined from cross-correlation functions Rayleigh wave dispersion curve determined from crosscorrelation functions for KUHL-BETS station pair at Soultzsous-forets site (Lehujeur et al., 2015). Frequency Theoretical dispersion curve for Wysin site determined on the basis of geological structure information. Dispersion curve obtained from cross-correlations will be compared with theoretical one in order to get the most reliable velocity information. 5
1. Seismic interferometry Four additional broadband seismic stations (NPH1-NPH4) has been installed in the Wysin area in order to record ambient seismic noise. Current stage of research: installation of additional broadband stations finished 6 preparation of tools to deal with big sets of data for cross-correlation in progress.
2. Multi-dimensional clustering Goal: use of multi-dimensional clustering in equivalent dimensions for identification of physical processes controlling seismogenesis at The Geysers geothermal site The analysis is perfomed using methodology proposed by Lasocki (2014): transformation of multi-dimensional dataset into equivalent dimensions identification of clusters using Ward s hierarchical method (Ward, 1963) 7
2. Multi-dimensional clustering The Geysers geothermal field (NW part): injection wells Prati-9 and Prati-29 (in SHEER database: THE GEYSERS: extra data) 8 Kwiatek et al., 2015 353 events with calculated spectral parameters Relocated catalogue (1254 events) (Kwiatek et al., 2015) Time range: Dec 2007 Apr 2014 Completeness magnitude: M C = 1.4
2. Multi-dimensional clustering Does events closely clustered in XYZ space reveal the same connection in 7-D space? Dataset analysed comprises 353 events and the parameters that are taken into consideration are: Hypocentral coordinates (X, Y, Z) Origin Time (T) Seismic Moment (M 0 ) Stress drop (Sd) Source Radius (R) 1. TED in 3-parameter space (X,Y,Z) Selection of clusters (C3 j ) with the smallest LDs. 2. TED in 7-parameter space (X,Y,Z,T,M 0,Ds,r 0 ). 3. Determine the linkage distance (LD) of events from C3 j clusters in 7-parameter space. 9
2. Multi-dimensional clustering 18 clusters with LD<0.07 in transformed XYZ were found 2 examples: 10
2. Multi-dimensional clustering LD of 18 clusters in 7-parameter space 18 LD~38 11
2. Multi-dimensional clustering Consideration of source parameters seem to introduce a considerable influence on clustering. 12 Publication: Leptokaropoulos K., Staszek M., Lasocki S., Kwiatek G., ESC 2016 conference abstract submitted: Preliminary results for Space-Time Clustering of Seismicity and its Connection to Stimulation Processes, in North-Western Geysers Geothermal Field
3. Static stress drop distribution Goal: verification of the possibility to use static stress drop distribution for seismic hazard assessment at The Geysers geothermal site Idea: static stress drop distribution determines the occurence of induced seismic events in the regions of fluid injection Observation: increase of static stress drop with radial distance from injection well (with radial distance 10-300m stress drop changes by a factor of ca 5) stress drop correlates with pore pressure perturbations due to injection 13 Goertz-Allmann et al., 2011
3. Static stress drop distribution Analysis of event sequences: 08 Feb 2011 interpolation time window: 60 days interpolation method: spline 14
3. Static stress drop distribution Analysis of event sequences: 27-29 Mar 2011 15
3. Static stress drop distribution Analysis of event sequences: 14 Apr 2011 16
3. Static stress drop distribution Analysis of group of 24 events: 28 Apr 2011 07 Sep 2011 Publication: Staszek M., Orlecka-Sikora B., Kwiatek G., ESC 2016 conference abstract submitted: Static stress drop of induced earthquakes in seismic hazard assessment: Preliminary results from The Geysers geothermal site 17
References 1. Brenguier F., Campillo M., Hadziioannou C., Shapiro N. M., Nadeau R. M., Larose E., 2008. Science 321: 1478-1481. 2. Goertz-Allmann B., Goertz A., Wiemer S. (2011), Stress drop variations of induced earthquakes at the Basel geothermal site. Geophysical Research Letters, 38, L09308, doi:10.1029/2011gl047498. 3. Kwiatek G., Martínez-Garzón P., Dresen G., Bohnhoff M., Sone H., and Hartline C. (2015), Effects of longterm fluid injection on induced seismicity parameters and maximum magnitude in northwestern part of The Geysers geothermal field. J. Geophys. Res. Solid Earth, 120, doi:10.1002/2015jb012362. 4. Lasocki, S. (2014), Transformation to equivalent dimensions a new methodology to study earthquake clustering, Geophys. J. Int., 197, 1224-1235. 5. Lehujeur M., Vergne J,, Schmittbuhl J., Maggi A., 2015. Characterization of ambient seismic noise near a deep geothermal reservoir and implications for interferometric methods: a case study in northern Alsace, France. Geothermal Energy 3:3. 6. Martínez-Garzón P., Kwiatek G., Sone H., Bohnhoff M., Dresen G., and Hartline C. (2014), Spatiotemporal changes, faulting regimes, and source parameters of induced seismicity: A case study from The Geysers geothermal field. J. Geophys. Res. Solid Earth, 119, 8378 8396, doi:10.1002/2014jb011385. 7. Ward, J.H., 1963. Hierarchical grouping to optimize an objective function. J. Am. Stat. Assoc., 58, 236 244. 18