Microseismic Network Performance for Induced Seismicity Monitoring

Microseismic Network Performance for Induced Seismicity Monitoring Junwei Huang*, Devin Garrett, Philip Usher, Shawn Maxwell IMaGE (Itasca Microseismic and Geomechanical Evaluation), Canada *[email protected] Summary In this case study, a surface array was used to monitor a multi-lateral hydraulic fracturing project in Northeastern British Columbia, Canada. Prior to the project, the network was designed for accurate and unbiased determination of locations and magnitudes of microseismic events. After the project, the array performance was benchmarked against the monitored results. We show that the predicted magnitude sensitivity and location uncertainty are consistent with the observation. Our array analysis workflow can be used to design local or regional surface monitoring network and for quality control of existing surface monitoring results. Introduction Injection induced seismicity is an increasing concern in various areas, but particularly western Canada. For example, historical seismic events associated with waste water injection and hydraulic fracturing from this region (BCOGC, 2014) follows Gutenberg-Richter distribution, and we can estimate the magnitude of completeness (Mc) around 1.6 and the b-value as 1.1 (Figure 1). To mitigate potential seismic hazard, deployment of local broadband stations is required for accurate determination of locations and magnitudes of seismic events. The density of the stations is limited by the budget and the logistical deployment constraints. In this study, we focused on array analysis for a dedicated local array to monitor a hydraulic fracturing project from Northeastern British Columbia, Canada. The goal of deploying local stations is to increase the sensitivity to detect small magnitude events, improve location accuracy and to comply with the local traffic light regulations. In this case study, four broadband seismometers have been budgeted and thus 4 locations should be selected (Figure 2). The detection threshold is controlled by many factors such as density and distribution of the stations, sensor sensitivity, site condition, and source characteristics. Many factors remain unknown prior to the baseline survey. Therefore, we designed the geometry focusing on the coverage and location accuracy assuming detectable signals on all 4 stations. The minimum detectable magnitude can be estimated for multiple noise levels and later calibrated by a baseline survey of the actual noise levels. With background noise characterization, we can predict the location uncertainty and the minimum detectable moment magnitude as a function of noise level. We show that our predictions are consistent with the processed results.

Figure 1: (a) Historical earthquake catalogue for Northeastern BC from 2013 to 2016 recorded by NRCan network (BCGGC, 2014). The color represents the time and the size is correlated with the local magnitude of the events. (b) the detected earthquakes follow a Gutenberg-Richter distribution with a bvalue equal to 1.1. The magnitude of completeness is around 1.6.

Figure 2: Treatment well trajectory and the four stations (S1, S2, S4 and S5) form a rectangular shape surrounding the well pad with 3~4 km distance between them. The density color is the magnitude threshold pattern discussed in the text. Given sufficient signal-to-noise ratio, any events occurred within the dash line marked region can be detected by all stations and no significant spatial bias is introduced by the array geometry. The colorbar is for the magnitude threshold using 200 nm/s as the noise level.

Array Analysis Workflow The injection formation is approximately 2.5km below the surface and thus the offset of the surface stations is empirically decided to be 1.5~2 times of the depth. Assuming all stations have detectable signal, we arrange the stations in a rectangular shape that can provide the most comprehensive coverage of the site with no significant

Induced Seismicity Network Design and Analysis

spatial bias (Figure 2). The performance of this array is determined from the minimum magnitude of the event that is detectable on all stations. For a given source position (e.g. on the plane shown in Figure 2), the velocity spectrum can be calculated from |𝑉(𝑓)| =

1 4𝜋𝜌𝑉𝑠 3

∙

𝑅𝑠 𝑀0 𝑟

∙

−𝜋∙𝑟∙𝑓

1 𝑓 (1+( 𝑐⁄𝑓)

1 𝛾𝑛 𝛾

∙ e 𝑄𝑉𝑐

(1)

)

where Rs is the averaged radiation coefficient, M0 the seismic moment, ρ density, VS S-wave velocity, r the distance between the source and the station, fc the corner frequency, Q the attenuation factor, and VC is the speed for P-wave (VP) or S-wave (VS). We used generalized Brune source spectrum model with n=2 and γ=2 (Abercrombie, 1995). The seismic moment M0 is related to the assessed moment magnitude Mw by 2 𝑀𝑤 = log10 𝑀0 − 6.1, (2) 3 and the corner frequency can be calculated from M0 by 𝑓𝑐 =

𝐾𝑐 𝑉𝑠 2𝜋𝑅0

3

, with 𝑅0 = √

7𝑀0

16∆𝜎

(3)

where R0 is the source radius and Δσ the static stress drop. Further descriptions can be found in Udías et al (2014). A small frequency window around the dominant frequency fdom is used to effectively calculate the noise velocity amplitude in time domain (Aki and Richards, 2002). A source with velocity amplitude above the sensor noise floor has thus the minimum detectable magnitude. Empirically we chose signal-to-noise-ratio SNR = 2 for the smallest detectable event.

Figure 3: Location uncertainty with picking error only using Monte Carlo simulation. (a) the plan view shows the epicentral uncertain is 100 m along North-South and 80 m along East-West directions. (b) the depth view shows the depth accuracy is about 200 m.

We then estimate the location uncertainty (Figure 3). Three representative positions have been chosen at the treatment depth: relatively close to the heel on the north side (A), close to the toe on the south side (B), and close to the middle of the well path within the treatment zone (C). With 10 ms sample interval, we can reasonably assume all stations have identifiable P- and S-wave first arrivals with 10 ms and 20 ms uncertainty respectively. The location error is generally the combination of picking errors, polarization errors, and velocity uncertainty. In this case study, no polarization constraint is included as the source vector tends to be vertical and is contaminated by multiple arrivals and coda wave in surface monitoring. The layered velocity model is an uncalibrated model based on dipole sonic well logs from a nearby well. Using Monte Carlo simulation, we evaluated the location uncertainty due to picking and velocity errors by randomly perturbing P-, S-wave arrivals and velocity independently. The perturbation is Gaussian with zero mean and standard deviation equal to the arrival uncertainty (10ms and 20ms for P- and S-wave respectively) and/or 5% of the velocity value (about ±250m/s and ±140 m/s for P- and Swave respectively). The spatial distribution of perturbed events (green dots in Figure 3 and blue dots Figure 4) provide the epicentral and depth uncertainty.

Figure 4: Location uncertainty with picking error and additional velocity errors. (a) the plan view shows the epicentral uncertainty increases. (b) the depth view shows the vertical uncertainty almost doubled as the depth accuracy is more sensitivity to the velocity than to the picking errors (see Figure 3).

Without velocity error, the location uncertainly is ~100 m along North-South, and ~80 m along East-West direction

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with depth accuracy at ~200 m. If the velocity model has 5% error, the epicentral uncertainty has different level of increases. The depth error is almost doubled and tilting outward suggesting the depth accuracy is more sensitivity to velocity than to the first arrivals. In general, the array provides comprehensive coverage with no significant biases due to acquisition geometry.

threshold was selected to harvest as many discrete events as possible. In total 3400 high quality events were located with magnitude varying between 0.54 to 2.78 (80% with Mw≥1.0). The predicted minimum magnitude is consistent with the observation as shown by the location of the minimum detected event in our catalogue (Figure 6a) and thus confirms that the site condition is well characterized by the array analysis parameters. Advanced analysis for potential improvement on the array performance thus becomes available. In the following section, we analyze that with the same parameters (such as noise floor, stress drop, SNR) how the minimum magnitude varies with the number of stations and acquisition geometry.

After acquisition geometry is decided, survey crews can be mobilized and a baseline noise survey is recommended before fluid injection. This allows update of the survey performance prior to the operation and avoidance of noisy sites. Typically, we need to measure the ambient noise level at the site in the frequency band of interest to estimate the magnitude threshold. In this case study, there are over one week of pre-operation recordings and we averaged 6 days with relatively light local activities. The estimated noise floor is at 2x10-7 m/s based on visual inspection of the waveform in both time and frequency domain (Figure 5). In addition, noise level could be increased when fluid injection started. Therefore, it is useful to estimate the magnitude threshold for different levels of noise.

Figure 6: (a) The minimum detectable magnitude with identifiable arrivals from at least 3 stations, SNR≥ 2 and one time noise floor (2e-7 m/s). For most region, the threshold is 0.4~0.5 and the value increases with noise level. The event with the minimum magnitude in our catalogue (Mw=0.54) is marked by the green dot. (b) The sample waveform for the minimum magnitude event. Each channel is normalized separately.

Comparison with Alternate Array Geometries

Figure 5: An example for first order estimation of ambient noise level in both time (a) and frequency (b) domain. A bandpass filer (1~40 Hz) has been applied. The RMS trace is calculated from North-East-Down channels. The averaged ambient noise floor at 2e-7 m/s or 200 nm/s is used.

We compared the network sensitivity between surface or shallow stations in uniform grid distribution with a conventional vertical borehole array (Figure 7). The 10x10 grid distribution has 500-meter grid interval and centered around the well pad covering a 5 km by 5 km region with one dimension along the well trajectory. The downhole string consists of 12 geophones with 50 m distance between each geophone and is located above the treatment formation, beside the well path to the north side. With increased the number of surface stations, the magnitude threshold decreases from 0.4 in this project to 0.1, indicating almost 3fold increase on the expected number of events using the bvalue in Figure 1b.

In this case study, the estimated minimum detectable moment magnitude is ~0.4 in the majority area of the monitored region (Figure 6). If the ambient noise level is accessed in different periods, the time dependent magnitude threshold can be estimated based on the table in Figure 6a. Monitored Results The monitoring project lasted 4 weeks and the continuous data stream was triggered and processed automatically. All the event locations and picks were manually verified or relocated to ensure accurate results. The STA/LTA window method was used for event triggering and a relatively low

As expected, the downhole monitoring provides the lowest magnitude threshold (Figure 7), as the ambient noise level is lower (e.g., 10nm/s) and closer to the treatment zone. With

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detectable signals on at least 3 stations, the minimum magnitude is -2.0 near the borehole and increases to -1.2 near the edge of the treatment region. The circular shape of magnitude threshold contours, however, suggested that the borehole monitoring has the most significant detection biases along the radial direction. Quality control such as measurement of magnitude variation with distance must be performed to correct the geometry foot print in the microseismic catalogue. The surface array on the other hand is more flexible in geometry and can provide less biased coverage but with relatively low sensitivity. Stacking nearby stations could potentially increase the sensitivity.

calibrated. We show that in this case, the prediction is consistent with the observation confirming the site conditions are well characterized by the chosen array analysis parameters. Based on the same parameters, we further evaluate the potential improvement on the minimum magnitude using 100 grid-shaped surface stations and compare the surface network to conventional downhole monitoring array with 12 geophones. This array analysis workflow can be used to evaluate local or regional surface array performance and control the data processing quality. Reference Abercrombie, R., (1995), Earthquake source scaling relationships from -1 to 5 ML, using seismograms recorded at 2.5 km depth, J. Geophys. Res., Vol. 100, pp. 2401524036. Aki, K. and P. Richards (2002), Quantitative Seismology, 2nd Ed., University Science Books. BC Oil and Gas Commission (BCGGC) (2014), Investigation of Observed Seismicity in the Montney Trend, https://www.bcogc.ca/node/12291/download (accessed on April 3 2017). Udías A, R. Madariaga, and E. Buforn (2014), Source Mechanisms of Earthquakes: Theory and Practice, Cambridge University Press, 311 pages.

Figure 7: Comparison of the minimum magnitude between conventional downhole (a) and uniform grid surface array (b). As expected, the downhole array is more sensitive (-2.0) but with recording biases along the radial direction. Extra quality control measure should be performed to correct for the geometry footprint. The grid surface array on the other hand provides a homogeneous coverage of the monitored region, although the minimum detectable magnitude is 0.15.

Conclusion In this paper, we presented a case study of assessing array performance for induced seismicity monitoring. The minimum detectable magnitude can be determined by parameters including site noise level, formation density, static stress drop, SNR, and the minimum number of stations. In the pre-acquisition phase, the pattern of the minimum magnitude detectable on all stations and the distribution of location uncertainty can be used to ensure unbiased coverage of the monitored region. We used the baseline survey before fluid injection to estimate the ambient noise level, so that the magnitude threshold can be

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