style="font-size:15px;"> 5 Repeat Step 4 for all possible seismic networks. Networks with different seismic stations can be created by varying the spacing of seismic stations.6 Analyze the relationship between the event location accuracy and the number of seismic stations. The optimal seismic network corresponds to the distribution with the best trade‐off between the event location and the number of seismic stations (or cost).
We demonstrate how to employ these procedures to design an optimal microseismic monitoring network using a synthetic model for the Kimberlina site in California.
4.3. OPTIMAL DESIGN OF A SURFACE SEISMIC ARRAY
4.3.1. Models for the Kimberlina Site
The Kimberlina site is located around the Kimberlina power plant in the southern San Joaquin Valley of California (Fig. 4.2). The San Joaquin Valley is the southern extension of the elongate Great Valley. Site characterization was performed for this region (Wagoner, 2009; Coblentz et al., 2014). The area has operational oil fields, depleted oil and gas reservoirs, and saline aquifers. The target reservoir is a saline sandstone formation, the Vedder formation, located at a depth of approximately 2,100 m. The Vedder formation is a dipping formation with a thickness of approximately 400 m at the proposed injection location. The overlying low‐permeable Temblor‐Freeman shale provides the caprock. The three major faults at the Kimberlina site are the Greeley Fault, Pond‐Poso Creek Fault, and New Hope Fault. The proposed injection well is located between the Greeley Fault and Pond‐Poso Creek Fault.
We build a 1D velocity model for this site based on geological layers and previous seismic refraction survey in this area (Walter & Mooney, 1987; Birkholzer et al., 2011). Layers with thickness less than 200 m are merged with their neighboring layers to create a simple model. Figure 4.3 shows the layered P‐wave velocity model. The S‐wave velocity model is similar to the P‐wave velocity model with a V P/V S = 1.73.
Figure 4.2 The location of the Kimberlina CCUS (carbon capture, utilization, and storage) pilot site. Major faults are shown as blue lines. The red star is the location of the injection well.
4.3.2. Synthetic Event Locations With Different Surface Seismic Networks
For a synthetic demonstration, we take a portion of the Pond‐Poso Creek Fault, and study the optimal design of surface seismic network for microseismic monitoring. The Pond‐Poso Creek Fault is a kilometer‐wide zone of northwesterly striking normal faults with large dip angles. For simplicity, we assume the fault is vertical. We create hundreds of microseismic events on the fault from depths between 1 km to 3 km to cover a wide enough depth range around the reservoir (Fig. 4.4). These events are regularly spaced with a horizontal and vertical spacing of 400 m.
Based on the guiding principles for geometrically strong network distribution, we design the network such that the seismic stations surround the fault. For simplicity, we use regularly spaced seismic stations within a rectangular area. Setting the monitoring network on a circular area surrounding the monitoring target would also be a good choice. We fix the farthest four seismic stations, and then increase the number of stations from 4 to 5, 9 (3X3), 16 (4X4), 25 (5X5), 36 (6X6), and 49 (7X7). Examples of the distribution of seismic stations are shown as green triangles in Fig. 4.5.
Figure 4.3 P‐wave velocity model for the Kimberlina site.
Figure 4.4 The true locations of microseismic events (black dots) used in the synthetic study: (a) map view and (b) depth view. The cyan color shows the simplified surface location of a portion of the Pond‐Poso Creek fault.
For each network distribution, we compute synthetic P‐wave and S‐wave arrival times for the true locations of microseismic events (Fig. 4.4) through ray tracing using the velocity model in Figure 4.3. We add a random Gaussian error with standard deviations of 0.02 s or 0.05 s to the computed travel times (e.g., Kijko, 1977b). We then use these travel times to solve for the locations of the microseismic events assuming that the velocity models are known. We obtain the linearized least‐squares solutions of event locations and origin times using an iterative inversion scheme (Paige & Saunders, 1982). The initial locations (blue dots in Fig. 4.5) are generated by adding a random Gaussian error with a mean of approximately 500 m to the true locations, and can be considered as an initial guess or some preliminary locations. Because microseismic events are clustered, we also use differential travel times between pairs of events in addition to absolute travel times to better locate the events (Waldhauser & Ellsworth, 2000; Zhang & Thurber, 2003). Improved relative locations using differential travel times are useful for studying the geometry of fractures and faults.
The location results obtained using both P‐wave and S‐wave arrival times with a Gaussian time error of 0.02 s are shown as red dots in Figure 4.5. By increasing the total number of seismic stations, the location results improve with varying degrees. For the horizontal direction, the located events all seem to follow well with true locations (the left column (a), (c), (e), (g) of Fig. 4.5). For the vertical direction, the events are poorly constrained by the 4‐station network because of the lack of a station with small epicentral distances (Fig. 4.5b). The addition of one station at the center greatly improves the depth location accuracy for the events beneath the station (Fig. 4.5d). Clear improvement in the event depth is also observed when increasing from 5‐station to 9‐station, and then to 16‐station networks. The event location results with the 25‐station, 36‐station, and 49‐station networks do not seem to show significant difference (Fig. 4.5 f and h).
To show the dependence of location results on different input data, we also conduct the same location inversion process using only P‐wave arrival times and a different Gaussian time error (0.05 s). The location results for these different scenarios are similar to the results shown in Figure 4.5 in terms of the improvement of event locations with the increase of the total number of seismic stations. Examples of the location results for these different scenarios are shown in Fig.
