Noise-based monitoring of the reservoir-stimulating injection experiment in Basel, Switzerland

Transcription

1 Noise-based monitoring of the reservoir-stimulating injection experiment in Basel, Switzerland Stephan Husen Swiss Seismological Service, ETH Zürich, Switzerland, Gregor Hillers Institute des Sciences de la Terre, Université Joseph Fourier, CNRS, Grenoble, France Acknowledgements Geopower Basel and Geothermal Explorers for providing continuous seismic data used in this study. Day of the year HALTI JOHAN.5 2 Hz Time [s] ERC WHISPER Project for software tools.

2 INTRODUCTION Method Data Results Discussion Injection of large volumes of fluid in geothermal projects will change physical parameters of the subsurface: Pore fluid pressure, Temperature, Permeability Stress A better understanding of these changes is valuable for reservoir engineering and induced seismicity. How can these changes be imaged/ monitored? Source: Geothermal Explorers

3 INTRODUCTION Method Data Results Discussion Estimates of pore fluid pressure (Allmann et al., GRL, 211) Depending on the model pore fluid pressures estimated for the Basel reservoir vary by a few orders of magnitude. Important consequence on the stress state in the reservoir! Figure 3. (a) Distance from the injection point versus Julian day during th drop. Circle size of events is scaled by magnitude. Inset shows source spec dashed line marks shut in time. White (Terakawa starset mark al., JGR, the four 212) largest events durin with M L = 3.4). (b) Wellhead pressure versus time. Red line represents sim pore pressure perturbation at each event location. Dashed lines denote isobars turbation. Squares are median stress drops per pressure bin with standard erro the median stress drops. (e) Stress drop versus radial distance. Squares are m dard error. Vertical dashed line marks 3 m distance from the injection po after 5 days.

4 INTRODUCTION Method Data Results Discussion Imaging/monitoring of sub-surface properties Traditional tomographic methods are expected to have poor lateral and temporal resolution due to limited extent of seismicity and network. true model recovered models (Julian & Foulger, GJI, 21)

5 Time shifts averaged over 91 receiver pairs and coherence measured between the reference stacked and 3-day stacked cross-correlation functions (frequency Data band,.1 to.9 Hz). Results INTRODUCTION Method Discussion Imaging/monitoring of sub-surface properties Noise correlation interferometry is very sensitive to changes in the subsurface but requires stable noise sources (wave field). EGS a natural laboratory for noise correlation interferometry? L2132 Fig. 3. Seismic velocity changes, surface displacements from GPS, and tremor activity near Parkfield. The red curve represents the postseismic fault-parallel displacements along the San Andreas fault as measured by GPS at station pomm (Fig. 1) (29). The tremor rates are averaged over a centered 3-daylength moving time window. SENS-SCHÖNFEDLER AND WEGLER: PASSIVE IMAGE INTERFEROMETRY Seismic velocity changes at Merapi volcano (Sens-Schönfelder & Wegler, GRL, 26) Sep Seismic velocity changes in the Parkfield region Figure 4. Measured and modeled velocity variations at Merapi volcano. (a) Red dots mark the measureme time window. Individual measurements in the 2 4 s, 4 6 s, and 6 8 s seconds lapse time windows an deviation are indicated by light gray dots and gray shading respectively. Results of the active CWI experi al., 26] are indicated by green and blue lines. Inset shows a close up around these active measurement corresponds to the measurements at the GRW station (used here). Green line shows measurements from stat southern flank of Mt. Merapi. (Brenguier (b) (bottom) et Daily al., Science, precipitation28) rate (blue) and modeled ground water lev Measured (light red dots) and modeled (dark red line) velocity variations in the 2 4 s time window. Sam time window in blue. Jul Downloaded from on May 13, SEPTEMBER 28 VOL 321 SCIENCE

6 Introduction METHOD Data Results Discussion Coda wave interferometry Coda waves are more sensitive to changes of the medium due to their longer ray paths. Ultrasonic laboratory experiments show changes in the coda due to changes of the medium (temperature)(e.g. Weaver & Lobkis, 2). In real Earth applications coda waves of repeating earthquakes can be used (e.g. Poupinet et al., 1984). Repeating earthquakes are rare and allow continuous monitoring only in very limited cases/circumstances.! (Snieder et al., Science, 22)

7 Introduction METHOD Data Results Discussion STEHLY ET AL.: ORIGIN OF THE SEISMIC NOISE B136 Cross-correlation of ambient noise The Greenʼs function (GF) between two receivers can be recovered by crosscorrelation of ambient noise. B136 STEHLY ET AL.: ORIGIN OF THE SEISMIC NOISE B An equipartioned wave field (even distribution of sources around two receivers) is needed to fully recover the GF. In many applications distribution of sources is not even -> correlation of longer time series to compensate for that. Cross-correlation of long times series yields poor temporal resolution! Figure 1. Schematic illustration of the effect of inhomogeneous noise sources distribution on the degree of symmetry of cross correlation. (a) Symmetric cross correlation between 1 and 2 obtained when the sources of noise are evenly distributed. (b) Asymmetric cross correlation (but symmetric travel times) associated with a nonisotropic distribution of sources. (Stehly et al., JGR, 26) measurements at different arrays will allow us to determine the location of main sources of the seismic noise. fundamental Rayleigh wave. The Green function is reconstructed in the causal part. This indicates that m the noise is propagating from PHL to MLAC, i.e., fro coastline to the continent. This confirms that at p between 5 and 1s most of the noise is dominated by generated by nonlinear interaction between the swe the coast line. [9] The behavior is very different when consi Figure 1. Schematic of the effect of inhomogeneous noise sources distrib 3. Origin ofillustration Seismic Noise Observed in California [8] cross We first correlation. consider one pair of in California cross correlation between 1 and 2 of symmetry of (a)stations Symmetric (MLAC and PHL, Figure 2a). We analyze 1 year (23) of sources of noise are vertical evenly distributed. (b)crossasymmetric cross correlation (but sym continuous records. Before computing corre-

8 Introduction METHOD Data Results Discussion Ambient noise monitoring (or noise correlation interferometry) Laboratory experiments confirm that for monitoring velocity changes it is NOT necessary to recover the full GF (Hadziioannou et al., 29). Active configuration 8 mm 64 mm S=R Air-Gel mix Array of transducers Passive configuration FIG. 2. Experimental setup. Left: the active experiment in the pulse-echo configuration. Right: the passive experiment in the transmission configuration before autocorrelation. S R Ambient noise correlation can be used for monitoring velocity changes under these assumptions: Stable noise sources over time, High signal-to-noise ratio of the coda normalized pressure 1 1 Pulse echo Auto corr Coherence=2% Time (µ s) FIG. 8. Comparison of the pulse-echo data h R,R,t obtained in the active experiment and the average autocorrelation t h S,R,t h S,R,t. (Hadziioannou et al., J. Acoust. Soc. Am., 29)

9 Introduction Method DATA Results Discussion Seismic network in Basel Data used in this study were recorded by borehole network. 4.5 Hz geophones with Galperin orientation -> rotated to XYZ system. FRANCE JOHAN HALTI Stimulated volume OTER1&2 GERMANY RIEH2 Continuous recordings from (doy 22) to (doy 2). MATTE Data provided by Geothermal Explorers. SWITZERLAND Figure 2. Seismic (Deichmann stations in Basel & Giardini, and surroundings SRL, 29) during the stimulation in December 26 and for about six months thereafter. The darker shaded areas correspond to the city of Basel and surrounding towns, while woodland and farmland are the light gray and white patches. The epicenters of the induced seismicity and the Basel injection well are located immediately

10 Introduction Method DATA Results Discussion Data pre-processing Data (EHZ channels) were decimated to 5 Hz and deconvolved with instrument response. Data example for DOY 323/26: 24 hr raw data Amplitude statistics on 4 h segments to remove poor traces (glitches, earthquakes). Whitening (.1-1 Hz) and amlitude clipping ( 3 x stdv of 4 h windows). Amplitude hr preprocessed data Hours PSD Frequency [Hz]

11 Introduction Method DATA Results Discussion Noise cross correlations Cross-correlation was done for each station pair using 4 h windows -> stack of 4 h segments to 1-day correlation function. Data example for DOY 323/26: Bandpass filter to retrieve cross-correlation function in the desired frequency band, e.g..5-2 Hz. Stack of 7 days to determine velocity changes. Goal is to retrieve stable cross-correlation functions with high SNR. stack of 7 days

12 Introduction Method DATA Results Discussion Measuring velocity changes Daily correlation functions are characterized by slightly asymmetric main arrival (phase) and very stable coda. Stretching technique (Lobkis & Weaver, 23; Sens-Schönfelder & Wegler, 26) to determine cross-correlation coefficient (CC) and velocity change (dv/v) in the coda (1 s - 2 s). Day of the year Daily correlation functions: HALTI JOHAN.5 2 Hz Time [s] coda main arrival coda

13 Introduction Method Data RESULTS Discussion Velocity changes during the entire data period Reference is the entire stack. Daily velocity changes are computed (using stacks of +/- 3 days). Strong fluctuations at the beginning and at the end. De-correlation and velocity decrease shortly after stimulation phase! dv/v x 1e 3 CC Day of the year individual station pairs 5 average +/- error individual station pairs 5 9/6 1/6 11/6 12/6 1/7 2/7 3/7 4/7 5/7 6/7 7/7 Date Frequency range:.75-3 Hz

14 Introduction Method Data RESULTS Discussion Velocity changes during stimulation phase Reference is stack Daily velocity changes are computed (using stacks of +/- 3 days). Strong decrease in CC and small velocity decrease starting 2 days after stimulation. Recovered to prestimulation level beginning of 27. CC dv/v x 1e Day of the year reference stack individual station pairs average +/- error individual station pairs / / /7 Date Frequency range:.75-3 Hz Stimulation

15 Introduction Method Data Results DISCUSSION Influence of induced seismicity? No, because changes occur after main phase of seismicity, (Häring et al., Geothermics, 28) data during highest event rate of induced seismicity is not used, induced seismicity shows higher frequency content than frequencies analyzed (.75-3 Hz). 1 Day of the year CC.5

16 Introduction Method Data Results DISCUSSION Location of changes? Ambient noise interferometry gives only magnitude of change but not location. Inversion for location of changes needs sensitivity kernel -> Pacheco & Snieders (25). Inversion of changes in CC for location in a probabilistic sense (Larose et al., 21). Poor spatial resolution due to low number of stations. Sensitivity kernel for La Reunion: K(s 1,s 2,x,t) y (km) 2 1 x (km) (Obermann et al., in preparation) Figure 4. Spatial representation of the sensitivity kernel. The two peaks correspond to the position of the station pair. Probability of location of medium change study the response of the deeper crust to the Wenchuan 28 earthquake by Froment (211) Sensitivity kernel Latitude As we have seen previously, the apparent relative velocity changes of the seismograms can be quantified by a stretching factor ε. This OTER1 coefficient can be related to a local velocity perturbation using the sensitivity kernel introduced by Pacheco and Snieder 25: K(s 1, s 2, x,t)= 47.5 t 3 p(s 1, x,u)p(x, s 2,t u)du p(s 1, s 2 probability (4),t) where s 1 and s 2 are the positions of the respective station, x is the position of the 7.5 local velocity variation and 7.6t is the center of the 7.7 time interval in the coda where the stretching Longitude is evaluated. Here, p(a, b,t) is the probability that the wave has travelled from a to b during time t. This can be approximated by the intensity of the wave field from a to b at time t. An example of the sensitivity kernel is shown in Figure 4. As several interstation distances are smaller than the 3 km that we determined approximatively for the scattering mean free 3.2 Inversion of the app To obtain a 2D model of a of interest, we pose this di equations in matrix form a d = Gm, Probability of location of changes in CC: MATTE JOHAN HALTI high RIEH2 where d = ε i (i = 1...n changes that we measured technique. G ij = s K t ij for all cells evaluated at th surface of the cells ( s). m cal) velocity changes. As t local velocity changes, the sign of the elements nor tain. Therefore we can use method for linear problem 1982). The solution for th m = m + C m G t (GC m where m is the initial m we do not possess any a changes. C d represents th C m the covariance matric For our study we use Easting and from 764 to imately 35 km 25 km. F 25 = 875 cells of 1 km into account the topograph only consider the previous correlation coefficient sup tories. This results in an in 255 data sets and 875 mo independent parameters w from correlations of neigh ing is introduced in the co exponential function. The matrix are given by: C m (i, j) =(σ m λ λ )2 exp where d(i, j) is the distan trix is weighted by (σ m λ λ

17 Introduction Method Data Results DISCUSSION Conclusions Cross-correlation of ambient noise data at borehole stations in the Basel area yields stable coda -> suitable for ambient noise interferometry. Ambient noise interferometry shows strong decrease in CC and small negative velocity change following the stimulation -> changes in physical properties of the crust?. Observed changes start about 5 days after stimulation and last for about 15 days -> diffusion processes?

18 Introduction Method Data Results DISCUSSION Outlook Use of all 3 components to determine full 3x3 correlation tensor, Compare time domain (stretching) versus frequency domain (doublet) changes, Use of surface stations (accelerometers) to stabilize inversion for location of changes, Correlate observed changes with enviromental data (rain fall, temperature) to exclude causality with dv/v changes, Relate observed changes to physical processes in the reservoir.

19 Introduction Method Data Results DISCUSSION Thank you for your attention! Day of the year dv/v x 1e 3 CC reference stack Stimulation individual station pairs average +/- significant level 2 3 individual station pairs / / /7 Date Frequency range:.75-3 Hz