Geophysical Journal International

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1 Geophysical Journal International Geophys. J. Int. (2014) 198, Advance Access publication 2014 May 23 GJI Seismology doi: /gji/ggu010 Combining microseismic and geomechanical observations to interpret storage integrity at the In Salah CCS site Bettina P. Goertz-Allmann, 1 Daniela Kühn, 1 Volker Oye, 1 Bahman Bohloli 2 and Eyvind Aker 3 1 NORSAR, Gunnar Randers vei 15, 2007 Kjeller, Norway. bettina@norsar.no 2 Norwegian Geotechnical Institute, Sognsveien 72, 0855 Oslo, Norway 3 AGR Petroleum Services AS, Karenslyst allé 4, P.O.Box444 Skøyen, 0213 Oslo, Norway Accepted 2014 January 13. Received 2014 January 8; in original form 2013 September 24 SUMMARY We present results from microseismic monitoring and geomechanical analysis obtained at the industrial-scale CO 2 sequestration site at the In Salah gas development project in Algeria. More than 5000 microseismic events have been detected at a pilot monitoring well using a master event cross-correlation method. The microseismic activity occurs in four distinct clusters and thereof three clearly correlate with injection rates and wellhead pressures. These event clusters are consistent with a location within the reservoir interval. However, due to insufficient network geometry there are large uncertainties on event location. We estimate a fracture pressure of 155 bar (at the wellhead) from the comparison of injection pressure and injection rate and conclude that reservoir fracture pressure of the injection horizon has most likely been exceeded occasionally, accompanied by increased microseismic activity. Our analysis of 3-D ray tracing for direct and converted phases suggests that one of the event clusters is located at a shallower depth than the reservoir injection interval. However, this event cluster is most likely unrelated to changes in the injection activity at a single well, as the event times do not correlate with the wellhead pressures. Furthermore, this event cluster shows b-values close to one, indicating re-activated natural or tectonic seismicity on pre-existing weakness zones rather than injection induced seismicity. Analysis of event azimuths and significant shear wave splitting of up to 5 per cent provide further valuable insight into fluid migration and fracture orientation at the reservoir level. Although only one geophone was available during the critical injection period, the microseismic monitoring of CO 2 injection at In Salah is capable of addressing some of the most relevant questions about fluid migration and reservoir integrity. An improved monitoring array with larger aperture and higher sensitivity is highly recommended, as it could greatly enhance the value of this technique. As such, real-time microseismic monitoring can be used to guide the injection pressure below fracture pressure, thus providing a tool to mitigate the risk of inducing felt seismicity and compromising seal integrity. Key words: Geomechanics; Fracture and flow; Earthquake source observations; Body waves; Seismic anisotropy. 1 INTRODUCTION The In Salah carbon capture and storage (CCS) project in central Algeria is a world pioneering onshore CO 2 capture and storage project (Mathieson et al. 2010). Carbon dioxide from several gas fields is separated from the production stream and then compressed, transported and sequestered at 1.9 km depth in a 20-m-thick Carboniferous sandstone unit in the downdip aquifer of a producing gas field at Krechba. Injection commenced in 2004, via three horizontal injection wells (KB-501, KB-502 and KB-503, see Fig. 1) and since then nearly 4 Mt of CO 2 have been stored in the subsurface. The In Salah CO 2 Joint Industry Project (JIP) was set up to develop monitoring technology and to cost-effectively verify secure longterm geological storage at the site (Ringrose et al. 2013). A range of different monitoring technologies was tested including satellite interferometry, microseismic monitoring and time-lapse (4-D) seismic reflection imaging (Mathieson et al. 2010). Owing to the desert environment with little vegetation cover, one particularly successful monitoring technique at this site has been interferometric synthetic aperture radar (InSAR), which showed that the CO 2 injection is accompanied by surface uplift (Ringrose et al. 2009; Vasco et al. 2010; Gemmer et al. 2012; see Fig. 1). The surface deformation C The Authors Published by Oxford University Press on behalf of The Royal Astronomical Society. 447

2 448 B.P. Goertz-Allmann et al. Figure 1. Krechba field, In Salah, Algeria showing the location of two horizontal CO 2 injection wells (KB-502 and KB-503), one abandoned test well in the area (KB-5), and the vertical microseismic monitoring well (KB-601). Injection well KB-501 is located outside of this region to the South (not shown). Locations of the microseismic event clusters (A D) are shown by red stars. Known faults mapped by 3-D seismic are shown down to a depth of 2000 m (white lines). The direction of the maximum horizontal stress σ H is indicated by the grey arrow. The background colour indicates the observed surface deformation due to CO 2 injection (data obtained from Tele-Rilevamento Europa TRE). The red box shows the region of interest. response to the injection at depth can be simulated numerically using coupled CO 2 flow and geomechnical models (e.g. Bissell et al. 2011; Rutqvist et al. 2011). Microseismic monitoring, which we focus on in this paper, is routinely applied in the mining industry (Spottiswoode & McGarr 1975; Urbancic & Young 1993) as well as in hydraulic fracturing operations in the oil and gas industry, for example, shale-gas extraction (Duncan & Eisner 2010; Maxwell et al. 2010). It has also been successfully employed in enhanced geothermal reservoirs (e.g. Majer et al. 2007; Deichmann & Giardini 2009). However, its application to CO 2 injection is limited, and it is currently unclear to what extent the rock responds with brittle failure to CO 2 injection. Brittle rock failure at CO 2 injection sites is generally undesirable and it could potentially compromise the seal integrity (Zoback & Gorelick 2012). For the Krechba CO 2 injection site, Gemmer et al. (2012) show in a geomechanical study that a purely elastic model can only account for about half the amount of observed surface uplift and that fractures are required to explain the double-lobe uplift feature at well KB-502, as proposed, for example, by Vasco et al. (2010). On the other hand, Rutqvist et al. (2011) infer that there is a relatively low potential for injection-induced microseismicity at Krechba. However, they point out that already small variations in the assumed maximum horizontal effective stress may change their conclusions, indicating that the predictive power in using stateof-the-art modelling software to couple flow and mechanics still strongly relies on input parameters and calibration. In this paper, we focus on the analysis and interpretation of microseismic data recorded at a pilot microseismic monitoring well at the In Salah CO 2 storage site between 2009 August and 2011 June. We first describe the monitoring array and event detection methodology and show how we detected more than 5000 events with clear correlation to the CO 2 injection. We proceed with an analysis of event clusters, which allows us to address questions of fluid migration, principal stress orientation and seal integrity. Clearly observable anisotropy and lateral variation of magnitude statistics provide deeper insights into the properties of the reservoir and its response to injection. We relate the seismicity to the geomechanical properties within and around the storage formation. By correlating the injection pressure and rate with microseismic data, we are able to separate the injection history into two characteristic modes: fracture and matrix injection. 2 ACQUISITION AND EVENT DETECTION A 1-D array consisting of 48 three-component downhole geophones with 15 Hz natural frequency was deployed in a single vertical well (KB-601), ranging from about 30 to 500 m depth with 10 m spacing (e.g. Oye et al. 2013). From 2009 August, geophones from six of the 48 levels were connected to three REF TEK digitizers, recording continuous waveform data at 500 Hz sampling frequency. Unfortunately, the GPS time tracking in between the three digitizers were not consistent. Furthermore, the recorded waveform data were disturbed with strong electronic noise and most of the geophones provided redundant or noisy data. We therefore conducted a full

3 health check of the system during a field visit in 2011 June. During the field visit we measured every channel for resistivity and cable length and measured the P-wave traveltime from the surface to the geophone by hammer strikes close to the well head. We were then able to conclude that only the uppermost of the six connected geophones (at approx. 80 m depth) provided reliable three-component data and could thus be used for further monitoring. The 500 m deep monitoring well is located above and about 500 m to the NE from the injection well (KB-502, Fig. 1). The perforation zone in the injection well is located at a depth of 1.9 km within the Carboniferous reservoir sandstone along a horizontal section oriented in NE direction. An effective method for detecting a known signal in a noisy timeseries is to cross-correlate a waveform template with successive time segments of continuous data (e.g. Gibbons & Ringdal 2006). Any segments of the continuous data stream that display a high degree of similarity to the template or the master waveform result in a high cross-correlation value. The method is able to detect weak signals that are otherwise hidden in the background noise, compared to standard short-term/long-term average (STA/LTA) detectors. The computed lag time between signals can be used to get precise estimates of relative arrival times. We perform a master event waveform cross-correlation with only one master event to detect microseismic events within the continuous data recordings. The master event is chosen from previously manually picked events (Fig. 2a). The main selection criteria are a clear P-/S-wave arrival and a good signalto-noise ratio (SNR). Since we are primarily interested in detecting any detectable seismic events (rather than repeated events with almost identical waveforms), we choose a low correlation coefficient threshold of 0.7 for P wave and 0.6 for S wave in this analysis. Using different master events has little influence on event detection. A Butterworth bandpass filter between 1 and 80 Hz was applied before cross-correlation. The time window used for cross-correlation is 0.5 s for both P and S waves. We first compute the cross-correlation of the master event P wave using the vertical component continuous data. If an event is detected, a cross-correlation of the master event S wave is performed on both horizontal components by shifting a 0.5-s-long time window over a 2 s subsequent time window following the detected P wave (Fig. 2b). All events detected automatically are visually checked and false detections or misaligned phase arrival picks are corrected manually. Using this approach, we detect more than 5000 microseismic events in the investigated time period (Fig. 3). Using waveform cross-correlation results in a significant increase in the number of detected events (about a factor of three) compared to a more conventional event detection method applied previously to the same data (Oye et al. 2013). Microseismics and geomechanics at In Salah ANALYSIS OF MICROSEISMIC EVENTS 3.1 Event locations For all data analysed in this study, only records from one threecomponent receiver are reliable, and therefore no accurate event locations can be determined. However, we can estimate distances between events and the receiver, deducted from the differential S P wave traveltimes, provided that the velocity model is accurate. The smaller the S P arrival time difference, the closer the events are located to the receiver. About 3800 events include both P and S picks. Several events with similar differential traveltime can be identified (Fig. 4). At the beginning of the year 2010, just before the main injection phase at KB-502, most events have an S P traveltime of about 670 ms (arrows in Fig. 4). Events with similar waveforms occur also during the main injection phase, but in addition, two event clusters with differential traveltimes of about 700 ms (including most events) and 750 ms become apparent. It is also clear (Fig. 4) that only events with differential traveltimes of about 700 ms persist after the main injection phase. These events still occur during the year Events with differential traveltimes of about 670 and 750 ms both cease after the main injection phase. In addition, we observe events at further distances (S P traveltimes of about 930 ms), which have no apparent change in activity during the entire investigation period. From the polarization analysis of the P-wave onset, we determine azimuth and inclination of each event. We filter the data between 10 and 100 Hz and apply the polarization analysis over 0.03-slong time windows starting at the P-wave onset. For P waves that arrive almost vertically at the geophone, and especially for small magnitude events, the P-wave energy on the horizontal components may be hidden in the noise and the azimuth can hence not be determined with certainty. We therefore restrict this analysis to events with P-wave peak amplitudes that are clearly above the noise level on the horizontal components, which reduces the data set to about 560 events (coloured circles in Figs 4 and 5). Note that although azimuth and inclination can be determined using only one station, these data only represent emergence angles at the receiver, which may differ from the true farfield azimuth direction due to lateral velocity heterogeneity. They should therefore be interpreted with caution. The polar coordinate plots (Fig. 5) show that: (i) Event clusters (marked as cluster A D) can be separated by combining S P traveltime, azimuth, and inclination. Example waveforms for these clusters are shown in Fig. 6. Figure 2. (a) Master event used for waveform cross-correlation analysis. (b) Example sketch of waveform cross-correlation for 5 min of continuous data. Two events are detected within this time interval. The enlargements of the seismogram show the detected events (black) compared to the master event (red). Event 1 is the master event itself. Event 2 is a smaller magnitude event detected during this time period. The respective correlation functions are shown below.

4 450 B.P. Goertz-Allmann et al. Figure 3. Histograms of detected microseismic events (red) compared to wellhead pressure (blue line) and injection rate (green line) at well KB-502 for (a) the entire investigated time period with events per day, (b) an enlargement with events per day and (c) an enlargement with events per hour. The orange line shows the cumulative number of events. The grey shaded area in a) indicates periods of missing seismic data. The black dashed boxes mark the time period of the enlarged plots below. (ii) The main alignment of event locations with respect to the injection point corresponds to the orientation of the maximum horizontal stress (Gemmer et al. 2012). 3.2 Shear wave splitting analysis A significant time delay between the shear wave arrivals of the two horizontal components (blue and green traces) is observed (Fig.6). This splitting of shear waves is a manifestation of anisotropy within the medium (e.g. Crampin 1984). Regardless of the type of anisotropy, we can describe the ray path-dependent percentage of anisotropy by a dimensionless factor a, which is proportional to the observed time delay t between shear wave arrivals and can be approximated by a = (v S t)/r, wherev S is the average shear wave velocity and r the source receiver distance. In this analysis, we use a simplified averaged homogenous velocity model, where the source receiver distance r is computed from the S P traveltime (t S t P ) and the phase velocities (v P = 3400 m s 1 and v S = 2000 m s 1 ) according to v S v P r = (t S t P ). v P v S The time delay t is particularly evident for clusters B and C with t up to 0.1 s. In addition, the locations of these event clusters with respect to the injection well are well aligned to the principal horizontal stress direction. Data processing has been performed using the Split-Lab software (Wuestefeld et al. 2008), which allows the user to focus on quality control by manually analysing shear wave splitting measurements of individual events. We analyse shear wave splitting using the eigenvalue λ 2 method (see e.g. Crampin 1984; Wuestefeld et al. 2010). In general, a grid-search for the splitting parameters F (fast axis) and t is performed, which best linearize the particle motion in the E N plane. The processing of an example event with strong splitting is shown in Fig. 7. In total we include

5 Microseismics and geomechanics at In Salah 451 Figure 4. S P traveltime versus origin time for about 3800 events (grey and coloured circles) reveals several event clusters. Black boxes mark the area plotted in the enlargement below. The grey shaded areas mark time periods of missing data. Colour-coded circles show events with computed azimuth (i.e. P-wave peak amplitude on the horizontal components is above the noise level). The largest magnitude events with M w > 0.5 are indicated by black stars. The arrows in the middle panel mark event clusters around 670, 700 and 750 ms. 83 events, where a good splitting result could be obtained. Overall, we obtain up to 5 per cent of anisotropy for clusters B, C and D (Fig. 8). Less than 2 per cent of anisotropy is observed for cluster A. Analysing each cluster separately no systematic temporal variation of anisotropy, indicating, for example, fracture development (Wuestefeld et al. 2011), could be resolved. However, due to the low number of events this result may not be representative for the total injection period. Because of the lack of azimuthal coverage and depth resolution, we cannot determine the type of anisotropy. A full inversion for anisotropy parameters (e.g. Verdon et al. 2009) is thus not possible. 3.3 Moment magnitude estimation To estimate moment magnitudes M w, we calculate spectra using a time window with length 0.4 s starting 0.04 s before the P- and S-wave onsets, respectively, employing a multitaper approach (Park et al. 1987; Prietoet al. 2009). Two example time-series and spectra, which are corrected for the instrument response, are shown in Fig. 9. The low-frequency level of the source displacement spectrum is proportional to the seismic moment M 0 (Brune 1970). Before the spectral level can be determined, we apply a distance and attenuation correction to the spectra. A quantitative estimation of the quality factor Q, by fitting the seismic moment, corner frequency, and Q simultaneously was not successful, mainly due to the fact that this data set comprises only recordings from one geophone. To avoid any overcompensation of the high-frequency part of the spectrum, we chose rather conservative values for attenuation correction Q P = 300 and Q S = 200 for P and S wave, respectively. The seismic moment is defined as (Brune 1970): M 0 = 4πρc3 0, F where ρ is the average density (2500 kg m 3 ), c is the P-orS-wave velocity (3400 and 2000 m s 1, respectively), 0 is the spectral level and F is the radiation coefficient. To determine 0, we compute the mean value of each spectrum starting from a value between 10 and 15 Hz up to 20 Hz. The lower frequency bound is variable and depends on the SNR. We require SNR 3 for each frequency sample within this range. The radiation coefficient F is usually assumed to average the amplitudes due to variations in the radiation patterns by summing over recordings from all stations, implying a good azimuthal coverage of the focal sphere. Since we have only one station available for this analysis, we decided to set F equal 1. This assumes that the geophone is situated in direction of maximum energy radiation from the source. Note that we provide thus a min-

6 452 B.P. Goertz-Allmann et al. Figure 5. (a) Polar coordinate plot displaying the difference in azimuth between event clusters for all events where the P-wave peak amplitude on the horizontal components is above the noise level (black and coloured circles). The radius indicates the S P traveltime. Large magnitude events with M w > 0.5 are marked by black stars. The approximate separation of event clusters A, B, C and D is indicated by the coloured circles defined by varying azimuth and S P traveltime. (b) Polar coordinate plot showing event azimuth versus computed incidence angle for all events in (a). imum magnitude estimate. Using the Hanks & Kanamori (1979) relation, the moment magnitude M w is defined as: M w = 2 3 (log 10 M 0 9.1), where M 0 is defined in Nm. Fig. 10 shows the histogram distribution of all estimated moment magnitudes for both P and S wave. P-wave moment magnitudes could be computed from about 3140 spectra, S-wave moment magnitudes from about 2660 spectra. Most values range between M w = 1 and 0. The largest observed magnitudes are about M w 1. The expected power law decay of the frequencymagnitude distribution is observed. For most events, M w of the P- wave estimates are larger compared to the S-wave estimates, which is probably mainly an effect of the differences in radiation patterns. 4 GEOMECHANICAL INTERPRETATION Analysis of injection pressure versus injection rate (both measured at the wellhead) can be used to infer the reservoir fracture pressure as well as the maximum matrix injection rate (Fig. 11). The injection rate is displayed in million standard cubic feet per day (1 mmscfd = m 3 d 1 ). To evaluate the formation fracture pressure a bi-modal fit is applied to the data as is standard practice for step rate tests (SRTs) and leads to an estimate of 155 bar. This value is roughly in agreement with other estimates (Bissell et al. 2011). Monitoring of the injection pressure can therefore be used to differentiate between periods of matrix (green arrows in Fig. 12) and fracture injection (orange arrows). Matrix injection refers to injection of fluid into the rock matrix without altering its permeability, whereas fracture injection implies a permeability change due the opening or creation of fractures by the injected fluid. As long as the injection pressure is below the fracture pressure the injection rate is stable. Once the fracture pressure is exceeded, already a slight increase in injection pressure leads to a strong increase in injection rate, implying fluid flow into a fracture instead of fluid diffusion within the matrix. This effect is illustrated by the bi-linear behaviour in Fig. 11 and also manifested in the changes in injectivity (Fig. 12), previously identified by Bissellet al. (2011) and Shi et al. (2012). Our analysis confirms that maximum applied pressure in all three wells has most probably exceeded the fracture pressure of the injection horizon for certain periods of time (Oye et al. 2013). The injectivity index is defined as the injection rate divided by the wellhead pressure (green and blue curves in Fig. 3). The injectivity index is larger during fracture injection as compared with matrix injection. In addition, the injectivity index decreases gradually during the injection period (Fig. 12). There could be several mechanisms causing the reduction, for example, mobilization of finely grained material from within the fractures blocking the pores, or a global increase of reservoir pressure due to long-term injection. Both phenomena are known from water-flooding enhanced oil recovery (EOR, Economides & Nolte 2000). For KB-501 the matrix injectivity is almost constant implying that the global reservoir pressure is also constant (not shown). With the trend shown in Fig. 12, fracture injectivity would decrease towards the matrix injectivity level if injection was continued. 5 DISCUSSION 5.1 Confining event depths To determine whether the reservoir cap rock formation has been fractured, the event depths are crucial. Due to the malfunctioning of all but one station we are not able to make a definite conclusion, but we can try to constrain the event depths. On selected wave-

7 Microseismics and geomechanics at In Salah 453 Figure 6. (a) Example waveforms of event clusters A, B, C and D. Waveforms are normalized to the trace maximum. Cluster A has an azimuth of about 300 and S P traveltime of about 930 ms. Cluster B has an azimuth of about 300 and S P traveltime of about 700 ms. Cluster C has an azimuth of about 130 and S P traveltime of about 700 ms. Cluster D has an azimuth of about 330 and S P traveltime of about 700 ms. Red, blue and green show vertical, north and east components, respectively. The bottom three traces in each panel display stacked waveforms. The aligned P-arrival time is marked by a dashed vertical line. Note that waveforms within one cluster show a high degree of similarity. forms of cluster A (Fig. 6), an additional phase arrival is observed on the vertical component between the direct P and S wave (arriving 500 ms after the P-wave first onset), occasionally having even larger amplitudes than the direct P and S wave. We interpret this phase (thereafter referred to as SP) to be converted from an S to a P wave at the strongest velocity contrast within the reservoir at about 850 m depth, corresponding to the Hercynian unconformity (between Cretaceous sandstones above and Carboniferous mudstones below; Figs 13 and 14). At this horizon the P-wave velocity increases from 2.49 to 3.65 km s 1,theS-wave velocity increases from 1.59 to 1.83 km s 1, and the density increases from 2182 to 2400 kg m 3 (Fig. 13a). This horizon is also clearly identified as a strong reflector in 3-D seismic data (Ringrose et al. 2011). The fact that all events with observed SP converted phase have higher S/P amplitude ratios compared with all other events strengthens the hypothesis of an SP converted phase. A difference in the radiation

8 454 B.P. Goertz-Allmann et al. Figure 7. An example of a shear wave splitting measurement. (a) Waveforms of east, north, and vertical components, respectively. Picked P- and S-wave arrival times are marked by vertical lines. The yellow shaded area shows the window used for splitting analysis. (b) Particle motion in SH-SV coordinates before (dashed blue) and after (red) correction. Note the linear polarization after correction. (c) Map of error surfaces. The location of the minimum results in a best-fitting time delay ( t) of 88 ms (red cross and grey dashed lines). pattern (smaller S/P amplitude ratios) can be the reason that the SP phase is not visible or concealed in the P-wave coda for these other events. We now use 3-D ray tracing to identify this converted SP phase and to better constrain the depth location of cluster A. The 3-D velocity model used for ray tracing is derived from two 3-D seismic surveys, core and log data (Mathieson et al. 2010; Gemmer et al. 2012). The baseline seismic survey was recorded in 1997 and a second higher-resolution survey was acquired in 2009 after five years of CO 2 injection. Since the properties of the uppermost layers are crucial in estimating the incidence angles at the geophones, a partially saturated vadose zone, not resolved by the seismic surveys targeting the reservoir, has been assumed above the main groundwater zone (at 270 m depth), and included in the velocity model by reducing the P-wave velocity by 5 per cent and the density by 10 per cent. Fig. 13 shows an example 1-D velocity model extracted from the 3-D velocity model at the location of borehole KB-502. The velocity model layers within the region of interest (see Fig. 1) are nearly horizontal with a maximum dip of 2 o over a distance of 6 km (Fig. 13b). To perform event locations, we first propagated seismic wave fronts from the receiver through the model towards a grid of possible source locations close to clusters A and B, employing the wave front construction method (Vinje et al. 1993, 1996a, b). Then we back track seismic rays of direct P and S phases, and also SP converted rays for the selected clusters (A /A and B; Fig. 14). Cluster A has a similar S P traveltime ( 930 s) as cluster A but only cluster A has an SP converted phase with similar theoretical traveltimes as observed in the real data (Fig. 13). The resulting range of possible source locations results from the intersection of acceptable differential traveltimes (S P) with acceptable SP traveltimes (red shaded area in Fig. 15). The range of acceptable theoretical differential traveltimes ( s for S P and s for SP-P) agrees with observed traveltimes for cluster A (see Fig. 6). Based on the premise that the observed phase is indeed an SP conversion at 850 m formation depth, our result suggests event depths for cluster A of about 1.7 km, which is above the top of the reservoir within the lower caprock, but well below the top of the main caprock (Fig. 13). Another indication that cluster A may be situated at more shallow depth than the other three clusters stems from the observation that seismogram traces for this cluster show nearly no time delay in shear wave splitting, which suggests that the anisotropy occurs mainly in the layers below cluster A.

9 Figure 8. Polar coordinate plot (azimuth versus S--P traveltime) displaying the percentage anisotropy for about 80 shear wave splitting measurements. The approximate separation of event clusters A, B, C and D is indicated by the blue-shaded ellipses. 5.2 b-value analysis The Gutenberg Richter law (Gutenberg & Richter 1942) describes the relationship between magnitudes and total number of earthquakes for a given earthquake catalogue. It can be expressed as logn = a bm,wheren is the number of events with magnitudes larger or equal to M, a is a constant describing the productivity of the sequence, and b the ratio of small to large events. For tectonic earthquakes worldwide it has often been observed that on average the b-value approaches the constant one. However significant systematic variations of b-value have been observed for different faulting regimes (Schorlemmer et al. 2005). Using a maximum likelihood fit (see, e.g. Aki 1965), we compute the b-value from the slope of the frequency-magnitude distribution using both M w estimated from P- and S-wave spectra for each event cluster separately (Fig. 16). Again, b-values should be interpreted very carefully, since the estimation of the original magnitudes was performed using records from only one station and thus are afflicted with large uncertainties, because spectra could not be corrected for differences in P-andS-wave radiation across the focal sphere. Using the maximum curvature method (Woessner & Wiemer 2005), the magnitude of completeness M C was determined for each cluster in order to obtain an unbiased b-value estimate and ranges between 0.5 and 0.3. The b-value is similar within each cluster for both P- ands-wave derived moment magnitudes but variations are observed between clusters. The lowest b-value (b 1) is observed for the furthest event cluster A (S P traveltime: 930 ms, azimuth: 300 ). As expected, events from this cluster include the highest number of larger magnitude events (M w > 0.5, see Figs 4, 6 and 16). A larger b-value ranging between 1.5 and 2 is observed for the remaining Microseismics and geomechanics at In Salah 455 clusters. This is consistent with other studies finding high b-values for events induced by fluid injection (Bachmann et al. 2012) or by injection into a hydraulic fracture in a hydrocarbon reservoir settings (Maxwell et al. 2009). Recently, Goertz-Allmann & Wiemer (2013) showed that the b-values are related to the in situ stress regime: high b-values occur, when new fractures (with tensile components) open due to high pore pressure close to the injection point and low b-values prevail when pre-existing fractures are reactivated. The deviation of the cumulative number of events at larger magnitudes from the determined best-fitting b-value may be explained by the maximum fracture length limited by the stimulated rock volume, reducing the probability of occurrence of larger magnitude events (Shapiro et al. 2011). It is interesting to note that cluster A occurs at the end of a pre-existing fault zone (Fig. 1). Whereas clusters B-D are probably located within the naturally fractured Carboniferous storage unit, though fractures are not seen on 3-D repeat seismic. Mapped faults in the vicinity of clusters B-D are all located at much shallower depth. A key question is the nature of stimulation of new or existing fractures by CO 2 injection. This question cannot be answered with certainty but the relatively high b-values (although affiliated with large uncertainties) observed for cluster B-D point towards new fracture creation. 5.3 Comparison of microseismic activity and injection data A comparison of microseismic activity and injection data at borehole KB-502, such as wellhead pressure and CO 2 injection rate, is shown in Fig. 3 and 17. A comparison of event number and injection data at KB-502 (with a high time resolution of events per hour) during a period in mid-2010 exhibiting the highest injection rate is shown in Fig. 3(c). In particular, a high correlation between the occurrence of microseismic events of cluster B, C and D and the injection rate is observed (Fig. 17). On the other hand no correlation is observed between events of cluster A and the injection data at KB-502 within the observation period. Also, no clear correlation is found between detected events and injection data at the other two wells, KB-501 and KB-503, both situated farther away (comparison not shown). The injection history at KB-502 shows a period of fracture injection about mid-year, and at the same time, the number of microseismic events clearly increases. For both injection phases, we observe a time delay between the increase of injection rate and the onset of seismicity, whereas a decrease of injection rate is followed immediately by a decay of the seismicity rate according to the Omori law (Utsu et al. 1995). Also important to mention is that only very few or no microseismic events occurred during the transition between years 2009 and 2010, when re-injection started at KB-502 (Fig. 3a), although the pressure occasionally exceeded the fracture pressure. This behaviour could be explained by the Kaiser effect (Kaiser 1950), since the formation had been pressured during injection and probably hydro-fractured in the preceding years , followed by a relaxation due to an injection stop from mid-2007 to the end of In this context it is also interesting to note that a CO 2 breakthrough was detected at some time between 2006 August and 2007 June at a suspended appraisal well (KB-5) about 1.3 km to the NW of injection well KB-502 (Ringrose et al. 2009; Fig. 1). During re-injection, the pressure is slowly built up again and will only generate microseismic events once it exceeds previous levels. The same process may explain: (i) The delay in the onset of seismicity compared to an increase in well-head pressure and injection rate;

10 456 B.P. Goertz-Allmann et al. Figure 9. Time-series (top, middle) and displacement spectra (bottom) of two example events. The spectra have been corrected for instrument response, distance, and attenuation. Left-hand side: master event used for waveform cross-correlation; right-had side: largest magnitude event within the data set. Colours mark different phase time windows used for computing the spectra: red = P wave on vertical component, green = pre-p-wave noise window on vertical component, blue = S wave on north component, orange = pre-s-wave noise window on North component. The grey box marks the maximum frequency range used for magnitude estimation. Figure 11. Injection pressure versus injection rate; at about 155 bar, the two fitted lines intersect (red circle). At this pressure the injection rate increases suddenly with increasing injection pressure and this value is thus assumed to represent the formation fracture pressure. (ii) The absence of events close to the horizontal injection well at KB-502 (shorter distances than 670 ms differential traveltime). Figure 10. Histogram distribution of computed moment magnitudes for P wave (upper panel) and S wave (lower panel). The number of computed magnitude estimates for P and S wave is indicated. 6 CONCLUSIONS Despite the limitations resulting from the malfunctioning of all but one station, we detected more than 5000 microseismic events

11 Microseismics and geomechanics at In Salah 457 Figure 12. (a) Wellhead pressure (WHP, blue) and injectivity index (orange) over time at well KB-502. The reservoir fracture pressure (red dashed line) separates data into periods of matrix injection (green arrows) and fracture injection (yellow arrows). The black dashed line shows the decreasing trend of injectivity over time. Figure 13. (a) 1-D velocity model extracted from 3-D velocity model at KB-502 (red line: S-wave velocity, green line: density, blue line: P-wave velocity); the reservoir layer, the lower caprock, and main caprock are marked by coloured boxes; the Hercynian unconformity is indicated; (b) exemplary layer surface topographies within region of interest, from top to bottom: ground surface and reservoir. Note that the 1-D velocity model refers to depth below surface, whereas the elevation is based on mean sea level.

12 458 B.P. Goertz-Allmann et al. Figure D view of theoretical traveltimes: S P traveltime is shown within a band of 0.92 and 0.94 s (green), SP converted phase P traveltime is shown within a band of 0.49 and 0.51 s (blue). The overlapping area is indicated in red. using waveform cross-correlation. A clear correlation between microseismic activity and injection data shows that microseismic monitoring could provide a cost effective way for both ensuring site integrity and for optimizing the CO2 injection rate. In particular, we successfully validated the geomechanical interpretation of differentiation into periods of matrix injection and fracture flow regimes by comparison with microseismic activity, thus allowing for the development of pressure-managed injection concepts. We conclude that a sudden increase of microseismic activity implies formation fracturing, and thus provides a good monitoring technique to detect the exceedance of reservoir fracture pressure. Figure 14. Cross-section showing the receiver location at 80 m depth (triangle) and potential cluster locations A, A and B (stars) together with direct P and S wave ray path (black and red lines, respectively). In addition, the ray path of an S-to-P converted phase at about 850 m depth is shown for cluster A. The background colours represent the P-wave velocity model.

13 Microseismics and geomechanics at In Salah 459 Figure 16. Magnitude analysis of clusters A, B, C and D. (a) Comparison of moment magnitudes estimated of S-wave spectra versus P-wave spectra for different event clusters. (b) Polar coordinate plot of individual event clusters (azimuthal distribution of S P traveltime). (c) Frequency-magnitude distribution and b-value estimates for P wave (blue) and S wave (red) derived moment magnitudes. Dashed lines indicate the magnitude of completeness. However, the biggest limitation related to the pilot microseismic monitoring well KB-601 at In Salah is the inability to properly locate the microseismic events. Particularly, the event depth is critical in order to assess and control fractures, which might propagate upwards into the cap-rock. We have applied a technique of using polarization angles, S P traveltime differences and a converted phase to better constrain event locations. Thus, we could differentiate between four clusters of events, apparently forming two different classes: (i) Class I events (cluster A) show the largest S P differential traveltimes and a b-value close to 1, indicating triggered seismicity on pre-existing faults. 3-D seismic images have identified the end of a fault zone close to cluster A. Event occurrences are not correlated to the injection history at KB-501, KB-502 nor KB-503. The events are most likely occurring about 150 m above the reservoir formation top, and in the lower caprock. (ii) Class II events (clusters B D) feature relatively small differential traveltimes and a high b-value of Their occurrence is highly correlated to the injection history in KB-502 and there are no major faults identified on 3-D seismic in proximity to clusters B, CandD. We therefore conclude that class I events are most likely occurring on pre-existing tectonic faults. Their occurrence is not directly related to the CO 2 injection history of a single well. Events may either occur naturally or are triggered by the deformation and uplift of the region, as the events occur at the edge of the uplifted region, where most differential uplift is present (Fig. 1). Class II events, in contrast, most probably indicate the extent of either the CO 2 plume or its pressure fronts and may indicate the opening of tensile fractures due to high pore pressure.

14 460 B.P. Goertz-Allmann et al. Figure 17. Histogram distribution of microseismic events (red) compared to wellhead pressure (blue) and injection rate (green) at well KB-502. At the top all events with available azimuthal information are shown, followed by the event distribution of individual event clusters A, B, C and D. Grey shaded area indicated periods of missing seismic data. Note the correlation between microseismic activities of cluster B D with injection data. ACKNOWLEDGEMENTS We thank Gassnova and the In Salah JIP partners for financial contribution to the MIMOSA project (no ), and the CLIMIT program of the Norwegian Research Council (SafeCO2 project no ). The authors like to acknowledge In Salah Gas Joint Venture and their partners BP, Statoil, and Sonatrach for providing field data and valuable discussions. We thank Andreas Wüstefeld for providing his Split-Lab software (Wuestefeld et al. 2008) and his help with the application. We also thank K. Iranpour, P. Zhao, T. Kaschwich and K. Tronstad for their contribution to this work. REFERENCES Aki, K., Maximum likelihood estimate of b in the formula log N = a - bm and its confidence limits, Bull. Earthq. Res. Inst. Tokyo Univ., 43, Bachmann, C., Wiemer, S., Goertz-Allmann, B.P. & Woessner, J., Influence of pore pressure on the size distribution of induced earthquakes, Geophys. Res. Lett., 39, L09302, doi: /2012gl Bissell, R.C., Vasco, D.W., Atbi, M., Hamdani, M., Okwelegbe, M. & Goldwater, M.H., A full field simulation of the in Salah gas production and CO 2 storage project using a coupled geo-mechanical and thermal fluid flow simulator, Ener. Proced., 4(C), Brune, J.N., Tectonic Stress and Spectra of Seismic Shear Waves from Earthquakes, J. geophys. Res., 75, Crampin, S., Effective anisotropic elastic constants for wave propagation through cracked solids, Geophys. J. R. astr. Soc, 76, Deichmann, N. & Giardini, D., Earthquakes induced by the stimulation of an enhanced geothermal system below Basel (Switzerland), Seismol. Res. Lett., 80(5), Duncan, P.M. & Eisner, L., Reservoir characterization using surface microseismic monitoring, Geophysics, 75(5), Economides, M.J. & Nolte, K.G., Reservoir Stimulation, John Wiley.

15 Gemmer, L., Hansen, O., Iding, M., Leary, S. & Ringrose, P., Geomechanical response to CO 2 injection at Krechba, In Salah, Algeria, First Break, 30(2), Gibbons, S.J. & Ringdal, F., The detection of low magnitude seismic events using array-based waveform correlation, Geophys. J. Int., 165(1), Goertz-Allmann, B.P. & Wiemer, S., Geomechanical modeling of induced seismicity source parameters and implications for seismic hazard assessment, Geophysics, 78(1), KS25 KS39. Gutenberg, B. & Richter, C.F., Earthquake magnitude intensity, energy and acceleration, Bull seism. Soc. Am., 32, Hanks, T.C. & Kanamori, H., A moment magnitude scale, J. geophys. Res., 84, Kaiser, J., Untersuchungen über das Auftreten von Geräuschen beim Zugversuch, Technische Hochschule München. Majer, E.L., Baria, R., Stark, M., Oates, S., Bommer, J., Smith, B. & Asanuma, H., Induced seismicity associated with enhanced geothermal systems, Geothermics, 36(3), Mathieson, A., Midgley, J., Dodds, K., Wright, I., Ringrose, P. & Saoula, N., CO 2 sequestration monitoring and verification technologies applied at Krechba, Algeria, Leading Edge, 29(2), Maxwell, S.C. et al., Fault activation during hydraulic fracturing, in SEG Technical Program Expanded Abstracts, pp Maxwell, S.C., Rutledge, J.T., Jones, R. & Fehler, M., Petroleum reservoir charaterization using downhole microseismic monitoring, Geophysics, 75(5), Oye, V., Aker, E., Daley, T.M., Kühn, D., Bohloli, B. & Korneev, V., Microseismic monitoring and interpretation with associated injection data from the In Salah CO 2 storage site (Krechba), Algeria, Ener. Proced., 37, Park, J., Lindberg, C.R. & Vernon, F.L., Multitaper Spectral Analysis of High-Frequency Seismograms, J. geophys. Res., 92, Prieto, G.A., Parker, R.L. & Vernon, F.L., A Fortran 90 library for multitaper spectrum analysis, Comput. Geosci., 35, Ringrose, P., Atbi, M., Mason, D., Espinassous, M., Myhrer, Ø., Iding, M., Mathieson, A. & Wright, I., Plume development around well KB- 502 at the In Salah CO 2 storage site, First Break, 27, Ringrose et al., Characterisation of the Krechba CO 2 sotrage site: critical elements controlling injection performance, Ener. Proced., 4, Ringrose, Mathieson, A., Wright, I.W., Selama, F., Hansen, O., Bissell, R., Saoula, N. & Midgley, J., The In Salah CO 2 storage project: lessons learned and knowledge transfer, Ener. Proced., 37, Rutqvist, J., Liu, H.-H., Vasco, D.W., Pan, L., Kappler, K. & Majer, E., Coupled non-isothermal, multiphase fluid flow and geomechanical modeling of ground surface deformations and potential for induced microseismicity at the In Salah CO 2 storage operation, Ener. Proced., 4(C), Microseismics and geomechanics at In Salah 461 Schorlemmer, D., Wiemer, S. & Wyss, M., Variations in earthquakesize distribution across different stress regimes, Nature, 437, Shapiro, S.A., Krüger, O.S., Dinske, C. & Langenbruch, C., Magnitudes of induced earthquakes and geometric scales of fluid-stimulated rock volumes, Geophysics, 76(6), 1 9. Shi, J.Q., Sinayuc, C., Durucan, S. & Korre, A., Assessment of carbon dioxide plume behaviour within the storage reservoir and the lower caprock around the KB-502 injection well at In Salah, Int. J. Green. Gas. Control, 7, Spottiswoode, S.M. & McGarr, A., Source parameters of tremors in a deep-level gold mine, Bull seism. Soc. Am., 65(1), Urbancic, T. & Young, R., Space-time variations in source parameters of mining-induced seismic events with M<0, Bull seism. Soc. Am., 83(2), Utsu, T., Ogata, Y. & Matsu ura, R.S., The centenary of the Omori formula for a decay law of aftershock activity., J. Phys. Earth, 43, Vasco, D.W., Rucci, A., Ferretti, A., Novali, F., Bissell, R.C., Ringrose, P.S., Mathieson, A.S. & Wright, I.W., Satellite-based measurements of surface deformation reveal fluid flow associated with the geological storage of carbon dioxide, Geophys. Res. Lett., 37, doi: /2009gl Verdon, J.P., Kendall, J.-M. & Wuestefeld, A., Imaging fractures and sedimentary fabrics using shear wave splitting measurements made on passive seismic data, 179, Vinje, V., Iversen, E. & Gjøystdal, H., Traveltime and amplitude estimation using wavefront construction, Geophysics, 58, Vinje, V., Iversen, E., Åstebøl, K. & Gjøystdal, H., 1996a. Estimation of multivalued arrivals using wavefront construction. Part I, Geophys. Prospect., 44, Vinje, V., Iversen, E., Åstebøl, K. & Gjøystdal, H., 1996b. Estimation of multivalued arrivals using wavefront construction. Part II: tracing and interpolation., Geophys. Prospect., 44, Woessner, J. & Wiemer, S., Assessing the quality of earthquake catalogues: estimating the magnitude of completeness and its uncertainty, Bull. seism. Soc. Am., 95, Wuestefeld, A., Bokelmann, G.H., Zaroli, C. & Barruol, G., Split- Lab: A shear-wave splitting environment in Matlab., Comput. Geosci., 34, Wuestefeld, A., Al-Harrasi, O., Verdon, J.P., Wookey, J. & Kendall, J.M., A strategy for automated analysis of passive microseismic data to image seismic anisotropy and fracture characteristics, Geophys. Prospect., 58, Wuestefeld, A., Verdon, J.P., Kendall, J.-M., Rutledge, J., Clarke, H. & Wookey, J., Inferring rock fracture evolution during reservoir stimulation from seismic anisotropy, Geophysics, 76(6), doi: /geo Zoback, M.D. & Gorelick, S.M., Earthquake triggering and large-scale geologic storage of carbon dioxide, PNAS, 109(26),