**3. Acquisition**

Based on the current state-of-the-art for microseismic monitoring, a number of important technological questions are presently under debate, such as:

**•** What conditions favour surface versus borehole microseismic acquisition? Surface acquis‐ ition involves the deployment of large numbers of receivers and has the inherent advantage of more extensive azimuthal coverage (solid angle); in principle, this should improve the condition number for hypocentre inversion and moment-tensor analysis (Eaton and Forouhideh, 2011). On the other hand, placement of geophones in deep boreholes (currently the norm for microseismic monitoring in western Canada) has the advantage of better signal-to-noise characteristics due to the closer proximity to the microseismic sources, generally quieter background noise levels (less anthropogenic noise), often better instru‐ ment coupling and predominantly horizontal (layer-parallel) instead of vertical (layerperpendicular) wave propagation leading to less wave scattering. Conversely, surface acquisition is significantly more cost effective as there is no need to drill observation wells or deploy instrumentation inside wells, and permits deployment of one or two orders of magnitudes more instruments.

ous-recording 3-component geophones within observation well(s) near the zone of interest, and/or a large number of surface sensors. Although relatively new to the oil and gas industry, similar monitoring technologies for earthquakes have been honed and developed by the seismological and mining research communities for decades (e.g. Gibowicz and Kijko, 1994; Bolt, 1984; Stein and Wysession, 2003). The goal of microseismic monitoring is to detect, locate and characterize microseismic events, which often occur in large numbers within cloud-like distributions that reflect underlying fracture networks. This approach enables monitoring of frac treatments in real-time in order to detect the extent of the stimulated rock volume and thus the success of the treatment, as well as predict likely improvements in subsequent

Applications of microseismic monitoring within industry, particularly in oil and gas, have seen remarkable growth during the past 10 years (Warpinski, 2009; Maxwell, 2010). This has not been limited to hydraulic fracture treatment for shale-gas and other tight-gas plays, but has included stimulation technologies such as fracturing or steam injection applied to tight-oil or heavy-oil fields and also techniques for maximizing recovery from producing reservoirs. It is estimated that over one million hydraulic fracture treatments have been performed in the US in the past 60 years (King, 2012), and that currently 3-5% of fracs in North America involve microseismic monitoring. Oil and gas companies have made significant expenditures (con‐ servatively \$100's MM) for microseismic monitoring, but face extraordinary technological challenges to fully utilize the results. Their efforts are hampered by a number of factors, including an incomplete understanding of seismological and geomechanical processes

In the next sections we will review current pertinent research questions on microseismic acquisition, processing and interpretation. Since many items are intimately intertwined it is

Based on the current state-of-the-art for microseismic monitoring, a number of important

**•** What conditions favour surface versus borehole microseismic acquisition? Surface acquis‐ ition involves the deployment of large numbers of receivers and has the inherent advantage of more extensive azimuthal coverage (solid angle); in principle, this should improve the condition number for hypocentre inversion and moment-tensor analysis (Eaton and Forouhideh, 2011). On the other hand, placement of geophones in deep boreholes (currently the norm for microseismic monitoring in western Canada) has the advantage of better signal-to-noise characteristics due to the closer proximity to the microseismic sources, generally quieter background noise levels (less anthropogenic noise), often better instru‐ ment coupling and predominantly horizontal (layer-parallel) instead of vertical (layerperpendicular) wave propagation leading to less wave scattering. Conversely, surface acquisition is significantly more cost effective as there is no need to drill observation wells

inescapable that some points may be revisited throughout the chapter.

technological questions are presently under debate, such as:

reservoir drainage.

442 Effective and Sustainable Hydraulic Fracturing

**3. Acquisition**

associated with induced microseismicity.


A university-led project to acquire microseismic data was undertaken in northern British Columbia, Canada. This experiment involved the recording of several multistage hydraulic fracture treatments performed in two horizontal wells (Figure 2). The microseismic data were collected using both surface and borehole sensors. The borehole tool string consisted of a 6 level broadband system with downhole digitization. Surface sensors included a 12-channel array with a mix of vertical-component and 3-C geophones, and 22 broadband sensors deployed in 7 localized arrays over an area of ~ 0.5 km2 .

The unusual setup was designed to investigate multiple objectives. First, microseismic monitoring was performed using both surface and borehole equipment to compare acquisition strategies and determine their respective advantages and inconveniences such as ease of deployment, costs, detectability of events, other signals and associated noise levels. In addition, the experiment is unique in that both broadband and short-period equipment are deployed. The approximate lowest recording frequencies for the various equipment are; broadband

slow deformation processes. Obviously it remains possible to dissect the recordings to

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Initial analysis of these data reveals the existence of numerous high-frequency (> 100 Hz) microseismic events with moment magnitudes ranging from -2.3 to -1.4. These events are detected to distances of up to 1.2 km using the borehole system. In addition, perforation shots are well recorded to distances of about 2 km. More interestingly spectral analysis shows the existence of complex rupture patterns such as rapid opening and closing of tensile cracks (Eaton, 2012). Moment tensor analysis on other experiments has shown complex deformation as well in hydraulic-fracturing experiments (Baig and Urbancic, 2010); yet such moment-tensor analysis normally requires two or more observation wells (Eaton and Forouhideh, 2011). The current observations are obtained using a single observation well.

Time-frequency analysis of the continuous recordings demonstrates the existence of resonance frequencies during hydraulic fracturing (Tary and Van der Baan, 2013). The resonances are mainly in the frequency band between 5 and 20 Hz. Other resonances are visible on the broadband recordings. They likely correspond to environmental or anthropogenic noises. Noticeably, the resonances are recorded by the downhole geophones, which are close to the horizontal part of the injection well at depth, and by the broadband arrays A and B, which are near the well head. The broadband arrays C or D, closest to the fluid injection during the first stages, do not exhibit any resonance frequencies. This indicates that the injection well is likely the cause of these resonance frequencies (Figure 2). In other cases, however, resonance frequencies may be indicative of the extent of the induced, interconnected fracture network

It is clear from the above discussion that many key acquisition questions are intimately linked to the need to enhance our arsenal of tools for processing and interpretation of microseismic

Rapid turnaround has been a high priority within the microseismic industry to reduce acquisition durations and deliver analysis results such as event locations in near realtime to completion engineers, who are required to make decisions such as starting a new fracturing stage based on assessment of a microseismic event "cloud" distribution. This requirement has led to the development of near real-time event-picking, classification and hypocentre-location algorithms; such rapid turnarounds demand robust techniques based on straightforward assumptions, often accompanied by large reductions in information content. For instance, in the case of hydraulic fracture stimulations, the fracture size and orientation are often inferred using a few events comprising the edges of the "cloud" of

extract individual events as well.

(Tary and Van der Baan, 2012).

**4. Microseismic data processing**

microseismic hypocentres.

data.

**Figure 2.** Experimental setup of the microseismic experiment, as well as the time-frequency transforms of stage H1-4 for one downhole geophone and one broadband station (hot colors correspond to high amplitudes). The stars indi‐ cate the position of the perforation shots and hence of the horizontal part of the wells. H1 and H2 are two different horizontal wells. After: Tary and Van der Baan (2013).

surface-based seismometers: 0.0083 Hz (= 120 s); borehole equipment: 0.1 Hz; short-period surface array: 5 Hz. Data analysis of the variously recorded signal thus helps reveal if signif‐ icant energy is present below the 5 Hz limit imposed by most standard monitoring equipment. This may help resolve the observed energy imbalance between injected and seismically released energy.

Conventional analysis of microseismic recordings involves first identifying and extracting individual events, e.g., via a semi-automatic triggering system. This poses problems if many overlapping events are simultaneously recorded and if individual event strengths hover around the noise level. It also may obscure proper identification of so-called slow earthquakes (Ide et al., 2007) occurring on much longer time scales than conventional earthquakes resulting from abrupt brittle failure.

Direct analysis of continuous data streams on the other hand offers much greater flexibil‐ ity and is not subject to the shortcomings described above. For instance, analysis of continuous recordings of acoustic emissions generated during laboratory rock-fracturing experiments have greatly aided in improving our understanding of active microcracking and deformation processes in volcanoes and the earth in general (Benson et al., 2008; Thompson et al., 2009). These continuous data streams are analyzed using various timefrequency transforms such as short-time Fourier transforms, S-transforms and wavelet transforms (Reine et al., 2009) to examine variations in local frequency content and highlight slow deformation processes. Obviously it remains possible to dissect the recordings to extract individual events as well.

Initial analysis of these data reveals the existence of numerous high-frequency (> 100 Hz) microseismic events with moment magnitudes ranging from -2.3 to -1.4. These events are detected to distances of up to 1.2 km using the borehole system. In addition, perforation shots are well recorded to distances of about 2 km. More interestingly spectral analysis shows the existence of complex rupture patterns such as rapid opening and closing of tensile cracks (Eaton, 2012). Moment tensor analysis on other experiments has shown complex deformation as well in hydraulic-fracturing experiments (Baig and Urbancic, 2010); yet such moment-tensor analysis normally requires two or more observation wells (Eaton and Forouhideh, 2011). The current observations are obtained using a single observation well.

Time-frequency analysis of the continuous recordings demonstrates the existence of resonance frequencies during hydraulic fracturing (Tary and Van der Baan, 2013). The resonances are mainly in the frequency band between 5 and 20 Hz. Other resonances are visible on the broadband recordings. They likely correspond to environmental or anthropogenic noises. Noticeably, the resonances are recorded by the downhole geophones, which are close to the horizontal part of the injection well at depth, and by the broadband arrays A and B, which are near the well head. The broadband arrays C or D, closest to the fluid injection during the first stages, do not exhibit any resonance frequencies. This indicates that the injection well is likely the cause of these resonance frequencies (Figure 2). In other cases, however, resonance frequencies may be indicative of the extent of the induced, interconnected fracture network (Tary and Van der Baan, 2012).

It is clear from the above discussion that many key acquisition questions are intimately linked to the need to enhance our arsenal of tools for processing and interpretation of microseismic data.

### **4. Microseismic data processing**

surface-based seismometers: 0.0083 Hz (= 120 s); borehole equipment: 0.1 Hz; short-period surface array: 5 Hz. Data analysis of the variously recorded signal thus helps reveal if signif‐ icant energy is present below the 5 Hz limit imposed by most standard monitoring equipment. This may help resolve the observed energy imbalance between injected and seismically

**Figure 2.** Experimental setup of the microseismic experiment, as well as the time-frequency transforms of stage H1-4 for one downhole geophone and one broadband station (hot colors correspond to high amplitudes). The stars indi‐ cate the position of the perforation shots and hence of the horizontal part of the wells. H1 and H2 are two different

Conventional analysis of microseismic recordings involves first identifying and extracting individual events, e.g., via a semi-automatic triggering system. This poses problems if many overlapping events are simultaneously recorded and if individual event strengths hover around the noise level. It also may obscure proper identification of so-called slow earthquakes (Ide et al., 2007) occurring on much longer time scales than conventional earthquakes resulting

Direct analysis of continuous data streams on the other hand offers much greater flexibil‐ ity and is not subject to the shortcomings described above. For instance, analysis of continuous recordings of acoustic emissions generated during laboratory rock-fracturing experiments have greatly aided in improving our understanding of active microcracking and deformation processes in volcanoes and the earth in general (Benson et al., 2008; Thompson et al., 2009). These continuous data streams are analyzed using various timefrequency transforms such as short-time Fourier transforms, S-transforms and wavelet transforms (Reine et al., 2009) to examine variations in local frequency content and highlight

released energy.

from abrupt brittle failure.

horizontal wells. After: Tary and Van der Baan (2013).

444 Effective and Sustainable Hydraulic Fracturing

Rapid turnaround has been a high priority within the microseismic industry to reduce acquisition durations and deliver analysis results such as event locations in near realtime to completion engineers, who are required to make decisions such as starting a new fracturing stage based on assessment of a microseismic event "cloud" distribution. This requirement has led to the development of near real-time event-picking, classification and hypocentre-location algorithms; such rapid turnarounds demand robust techniques based on straightforward assumptions, often accompanied by large reductions in information content. For instance, in the case of hydraulic fracture stimulations, the fracture size and orientation are often inferred using a few events comprising the edges of the "cloud" of microseismic hypocentres.

#### **4.1. Analysis and attenuation of coherent noise**

Before discussing picking and event location it is important to realize that a principal aspect of microseismic data processing is the recognition and attenuation of coherent noise. Coherent noise is defined here as any repeatedly recorded energy on one or more traces that is not a body wave (P or S) arrival. The noise is often persistent, repeatable, and may be caused by various types of waves travelling in the borehole. A cemented wellbore with steel casing has the potential to propagate many types of waves. P and S waves can be transmitted in a wellbore in the steel casing, or the cement (Raggio et. al., 2007). The P wave can also be transmitted in the fluid in the wellbore. There are also a number of modes of tube waves (Rayleigh waves travelling at the wellbore fluid and adjacent solid interface) that can be transmitted.

(2012) who demonstrate that mis-identification of arrivals is a prominent source of event

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At the source side, resonance frequencies can be generated by repetitive events if perfectly periodic, or by the resonance of fluid-filled cracks as in the case of volcanic tremors (Aki et al., 1977). Resonances in fluid-filled cracks are generated by interface waves and depend mainly on the crack geometry, the crack stiffness and the source parameters that trigger the resonance (Ferrazzini and Aki, 1987). The latter are significantly less likely to mask strong direct arrivals; yet they offer promise for enhancing our understanding of the geomechanical reservoir deformations during hydraulic fracturing (Tary and Van der Baan, 2012, 2013) as indicated in

Event-detection and time-picking are critical steps for microseismic data processing. Due to the large volume of data acquired during a microseismic survey, these steps are typically performed using an automated method. These steps have been implemented using various algorithms, such as the short- and long-time average ratio (STA/LTA) technique (e.g. Sharma et al., 2010), modified energy-ratio (MER) (Han et al., 2009) and Akaike information criterion (AIC) (Oye and Roth, 2003). Akram et al. (Automatic event-detection and time-picking algorithms for downhole microseismic data processing, manuscript in preparation for Geophysical Prospecting) have developed a dynamic-threshold approach for event detection that reduces false detections and offers improved capability to identify weak signals. They have also developed several hybrid approaches for automatic arrival-time picking that

Calculation and interpretation of the locations of seismic events (hypocentres) are critical first-order components of microseismic monitoring. Compared to conventional earth‐ quake methods, borehole microseismic surveys are relatively poorly constrained because of the fewer number of geophones and less desirable azimuthal coverage (Han, 2010; Jones et al., 2010). Most hypocentre localization methods require knowledge of P- and S-wave arrival times (Xuan and Sava, 2009). For borehole microseismic surveys, the distance between source and receiver can be computed using the arrival time difference of P- and S- waves and azimuth and dip information obtained from polarization analysis (Albright and Pearson, 1982; Eisner et al., 2009; Han, 2010; Jones et al., 2010). A probability density function can also be computed from the observed and modeled arrival time delays of Pand S-waves (Michaud et al., 2004). Surface microseismic methods are better suited to migration-based methods, which do not require P- and S-wave arrivals time picking information and can locate weak events by focusing energy at the source using time reversal (Gajewski, 2005; Chambers et al., 2009; Fu and Luo, 2009; Xuan and Sava, 2009). The drawbacks of the migration-based methods include high computational cost and their requirement of data redundancy (Xuan and Sava, 2009; Han, 2010). A semblance-weight‐ ed stacking method can also be used for microseismic source location, where the maxi‐

combine existing methods to improve performance with real microseismic data.

mislocations.

the previous section.

**4.3. Locations**

**4.2. Traveltime picking**

St-Onge and Eaton (2011) have observed another type of coherent noise source that may be related to the tuned response of a clamped geophone array. This response is manifest‐ ed as discrete, high-amplitude spectral peaks that can have a negative effect on weak signals recorded within the primary bandwidth of borehole microseismic recordings (i.e., several hundred Hz). These observations show that noise can be high in amplitude, persistent in time, and may adversely affect the recording of P and S wave signal energy in microseis‐ mic data (St-Onge and Eaton, 2011). Due to the nature of the data acquisition, the types of noise observed in microseismic surveys differ from typical noise sources in convention‐ al seismic profiling. In many cases, datasets are contaminated by Lamb waves, which are a type of elastic guided wave that travels along a plate surface such as the cylindrical surface of borehole casing. These coupled longitudinal and transverse waves were first described by Lamb (1917) and in a cylindrical casing exhibit longitudinal, torsional and flexural modes. Lamb waves are dispersive, and their frequency characteristics have been described by Karpfinger (2009). St-Onge and Eaton (Lamb waves recorded in wellbores and their potential to predict cement bond failure, in preparation for Geophysics) are exploring various ways in which these harmonic signals can be suppressed or even exploited to characterize the borehole environment as their propagation velocity is influenced by the bonding characteristics of the cement.

Tary and Van der Baan (2012) divide resonance frequencies into three broad categories, namely those generated by source, receiver or path effects. This categorization can also be applied to microseismic noise if we are interested solely in the microseismic direct arrivals for location purposes and estimation of the associated source mechanism. At the receiver side, resonance frequencies and other noise result from wave reverberations in the borehole (Sun and McMe‐ chan, 1988), either the whole borehole or between secondary sources such as the geophones (St-Onge and Eaton, 2011). Resonances and noise can also be due to internal resonance of the geophone if its clamping or damping is flawed.

Along the ray path, resonances arise from constructive and destructive interferences of seismic waves, waves focusing in low-velocity waveguides or multiple wave scattering. Which frequency band is favored depends on the layer spacing, thickness and mechanical properties (van der Baan et al., 2007, van der Baan, 2009). Likewise (multiple) reflections and refractions can also confound the picking of direct arrivals. A prime example on how such secondary arrivals can complicate event picking and location is shown in Kocon and Van der Baan (2012) who demonstrate that mis-identification of arrivals is a prominent source of event mislocations.

At the source side, resonance frequencies can be generated by repetitive events if perfectly periodic, or by the resonance of fluid-filled cracks as in the case of volcanic tremors (Aki et al., 1977). Resonances in fluid-filled cracks are generated by interface waves and depend mainly on the crack geometry, the crack stiffness and the source parameters that trigger the resonance (Ferrazzini and Aki, 1987). The latter are significantly less likely to mask strong direct arrivals; yet they offer promise for enhancing our understanding of the geomechanical reservoir deformations during hydraulic fracturing (Tary and Van der Baan, 2012, 2013) as indicated in the previous section.

#### **4.2. Traveltime picking**

**4.1. Analysis and attenuation of coherent noise**

446 Effective and Sustainable Hydraulic Fracturing

influenced by the bonding characteristics of the cement.

geophone if its clamping or damping is flawed.

Before discussing picking and event location it is important to realize that a principal aspect of microseismic data processing is the recognition and attenuation of coherent noise. Coherent noise is defined here as any repeatedly recorded energy on one or more traces that is not a body wave (P or S) arrival. The noise is often persistent, repeatable, and may be caused by various types of waves travelling in the borehole. A cemented wellbore with steel casing has the potential to propagate many types of waves. P and S waves can be transmitted in a wellbore in the steel casing, or the cement (Raggio et. al., 2007). The P wave can also be transmitted in the fluid in the wellbore. There are also a number of modes of tube waves (Rayleigh waves

travelling at the wellbore fluid and adjacent solid interface) that can be transmitted.

St-Onge and Eaton (2011) have observed another type of coherent noise source that may be related to the tuned response of a clamped geophone array. This response is manifest‐ ed as discrete, high-amplitude spectral peaks that can have a negative effect on weak signals recorded within the primary bandwidth of borehole microseismic recordings (i.e., several hundred Hz). These observations show that noise can be high in amplitude, persistent in time, and may adversely affect the recording of P and S wave signal energy in microseis‐ mic data (St-Onge and Eaton, 2011). Due to the nature of the data acquisition, the types of noise observed in microseismic surveys differ from typical noise sources in convention‐ al seismic profiling. In many cases, datasets are contaminated by Lamb waves, which are a type of elastic guided wave that travels along a plate surface such as the cylindrical surface of borehole casing. These coupled longitudinal and transverse waves were first described by Lamb (1917) and in a cylindrical casing exhibit longitudinal, torsional and flexural modes. Lamb waves are dispersive, and their frequency characteristics have been described by Karpfinger (2009). St-Onge and Eaton (Lamb waves recorded in wellbores and their potential to predict cement bond failure, in preparation for Geophysics) are exploring various ways in which these harmonic signals can be suppressed or even exploited to characterize the borehole environment as their propagation velocity is

Tary and Van der Baan (2012) divide resonance frequencies into three broad categories, namely those generated by source, receiver or path effects. This categorization can also be applied to microseismic noise if we are interested solely in the microseismic direct arrivals for location purposes and estimation of the associated source mechanism. At the receiver side, resonance frequencies and other noise result from wave reverberations in the borehole (Sun and McMe‐ chan, 1988), either the whole borehole or between secondary sources such as the geophones (St-Onge and Eaton, 2011). Resonances and noise can also be due to internal resonance of the

Along the ray path, resonances arise from constructive and destructive interferences of seismic waves, waves focusing in low-velocity waveguides or multiple wave scattering. Which frequency band is favored depends on the layer spacing, thickness and mechanical properties (van der Baan et al., 2007, van der Baan, 2009). Likewise (multiple) reflections and refractions can also confound the picking of direct arrivals. A prime example on how such secondary arrivals can complicate event picking and location is shown in Kocon and Van der Baan Event-detection and time-picking are critical steps for microseismic data processing. Due to the large volume of data acquired during a microseismic survey, these steps are typically performed using an automated method. These steps have been implemented using various algorithms, such as the short- and long-time average ratio (STA/LTA) technique (e.g. Sharma et al., 2010), modified energy-ratio (MER) (Han et al., 2009) and Akaike information criterion (AIC) (Oye and Roth, 2003). Akram et al. (Automatic event-detection and time-picking algorithms for downhole microseismic data processing, manuscript in preparation for Geophysical Prospecting) have developed a dynamic-threshold approach for event detection that reduces false detections and offers improved capability to identify weak signals. They have also developed several hybrid approaches for automatic arrival-time picking that combine existing methods to improve performance with real microseismic data.

#### **4.3. Locations**

Calculation and interpretation of the locations of seismic events (hypocentres) are critical first-order components of microseismic monitoring. Compared to conventional earth‐ quake methods, borehole microseismic surveys are relatively poorly constrained because of the fewer number of geophones and less desirable azimuthal coverage (Han, 2010; Jones et al., 2010). Most hypocentre localization methods require knowledge of P- and S-wave arrival times (Xuan and Sava, 2009). For borehole microseismic surveys, the distance between source and receiver can be computed using the arrival time difference of P- and S- waves and azimuth and dip information obtained from polarization analysis (Albright and Pearson, 1982; Eisner et al., 2009; Han, 2010; Jones et al., 2010). A probability density function can also be computed from the observed and modeled arrival time delays of Pand S-waves (Michaud et al., 2004). Surface microseismic methods are better suited to migration-based methods, which do not require P- and S-wave arrivals time picking information and can locate weak events by focusing energy at the source using time reversal (Gajewski, 2005; Chambers et al., 2009; Fu and Luo, 2009; Xuan and Sava, 2009). The drawbacks of the migration-based methods include high computational cost and their requirement of data redundancy (Xuan and Sava, 2009; Han, 2010). A semblance-weight‐ ed stacking method can also be used for microseismic source location, where the maxi‐ mum value of the product of P- and S-wave semblances on a time window define the location of microseismic source (Eaton et al., 2011).

This approach has two important advantages. First the method is rather insensitive to the chosen velocity model since any inaccuracies will not obscure revealed geologic features but only change their size (Got, 1994, De Meersman et al., 2009). Secondly, mispicks and missing picks are automatically corrected for via the cross-correlation procedure. In addition, a crossplot of waveform correlation coefficients versus hypocentre separation distances of every event pair automatically reveals hypocentre location errors by examining location distances of identified multiplets. This technique enabled Kocon and Van der Baan (2012) to ascertain that events could be mislocated by 350m in a heavy-oil dataset due to erroneous traveltime

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Once the multiplet groups are detected, the double-difference method can be applied. This is a relative relocation method that seeks to reduce the effects of errors due to unanticipated velocity heterogeneities in the structure (Waldhauser and Ellsworth, 2000). An advantage of this method is that no master event is needed, which induces spatial limitations, since all events must be correlated with the reference event. The main assumption in this method is that ray paths between two events will be very similar if their hypocentral separation is small compared to the source-receiver distances; therefore, the relative travel-time difference at a common station will be mainly due to the spatial offset between both events. In other words, the effects of most velocity heterogeneities will cancel out, such that only knowledge of the velocities in the source region is required. Castellanos and Van der Baan (2012) apply this method to data from a mining environment. Results clearly reveal a linear feature after relocation, possibly

Likewise, De Meersman et al. (2009) use relative locations to delineate a graben-like extensional structure in the caprock of a producing reservoir in the North Sea, UK (Figure 3). This grabenlike structure was not visible in the original absolute locations which revealed solely two large microseismic clusters. Next they re-examine temporal changes in anisotropy as found by Teanby et al. (2004a) using the automated shear-wave splitting methodology of Teanby et al. (2004b) for this same dataset. They then argue that their integrated analysis of relocated sources, seismic multiplets, and S-wave splitting supports a model whereby stresses in this reservoir recharge cyclically. Effective stress builds up in response to reservoir compaction as a result of oil production, and stress is released by means of microseismic activity once criticality is reached on slip planes. These changes cause variations in seismic anisotropy and

**5. Better understanding of physical processes associated with**

The microseismic case studies by De Meerman et al. (2009) andCastellanos and Van der Baan (2012) do not include fluid injection; yet they already demonstrate that analysis of the micro‐ seismic cloud of event locations can reveal important insights into the local geology and subsurface deformations. Pore pressure and stress changes during hydraulic fracturing lead to a propagating cloud of microseismic events, which can be recorded and analyzed to

picks.

related to horizontal drilling activities.

the microseismic source mechanisms over time.

**microseismicity**

There are also several techniques (for example, hypocentroidal decomposition and doubledifference tomography), which determine the relative location of the seismic source (Shearer, 1999). It has been recognized that the near real-time hypocentre locations may have large associated uncertainties, preventing high-resolution post-treatment interpretation (Figure 3). A first concern is that different service companies may obtain different event locations, even for the same dataset. This is caused by fundamental uncertainties in how to determine the most appropriate velocity model, the use of different event location algorithms but also elemental problems on how to pick consistently P- and S-wave arrivals in large datasets (sometimes consisting of 1000s of events recorded by 10s or 100s of 3-component receivers).

Much current research focuses on improved workflows for direct estimation of absolute hypocentres and on accurate relative event locations. Multiplet analysis can for instance be used to address the issues of unknown velocity models as well as inconsistent picking on final event locations (De Meersman et al., 2009; Kocon and Van der Baan, 2012). A doublet is a pair of events produced by nearly identical source mechanisms from closely spaced locations; a multiplet is a group of three or more of such events. The waveforms of multiplets are nearly identical, with the principal exception of additive random noise. Multiplets can be readily identified using cross correlation (Poupinet et al. 1984; Arrowsmith and Eisner, 2006). All events in each multiplet group are then relocated to improve their relative location accuracy (Figure 3), thereby revealing lineations and active faults planes.

**Figure 3.** Microseismic events contain a wealth of information that can be used to determine planes of weakness along which fluid migration could occur. (a) Original source locations; (b) new source locations after application of a high-resolution relocation technique; (c) multiplets extracted and best fault plane solutions depicted in two major clusters; (d) obtained fault planes overlain onto the top-reservoir fault map interpreted from 3D surface seismic data (after De Meersman et al., 2009).

This approach has two important advantages. First the method is rather insensitive to the chosen velocity model since any inaccuracies will not obscure revealed geologic features but only change their size (Got, 1994, De Meersman et al., 2009). Secondly, mispicks and missing picks are automatically corrected for via the cross-correlation procedure. In addition, a crossplot of waveform correlation coefficients versus hypocentre separation distances of every event pair automatically reveals hypocentre location errors by examining location distances of identified multiplets. This technique enabled Kocon and Van der Baan (2012) to ascertain that events could be mislocated by 350m in a heavy-oil dataset due to erroneous traveltime picks.

mum value of the product of P- and S-wave semblances on a time window define the

There are also several techniques (for example, hypocentroidal decomposition and doubledifference tomography), which determine the relative location of the seismic source (Shearer, 1999). It has been recognized that the near real-time hypocentre locations may have large associated uncertainties, preventing high-resolution post-treatment interpretation (Figure 3). A first concern is that different service companies may obtain different event locations, even for the same dataset. This is caused by fundamental uncertainties in how to determine the most appropriate velocity model, the use of different event location algorithms but also elemental problems on how to pick consistently P- and S-wave arrivals in large datasets (sometimes

Much current research focuses on improved workflows for direct estimation of absolute hypocentres and on accurate relative event locations. Multiplet analysis can for instance be used to address the issues of unknown velocity models as well as inconsistent picking on final event locations (De Meersman et al., 2009; Kocon and Van der Baan, 2012). A doublet is a pair of events produced by nearly identical source mechanisms from closely spaced locations; a multiplet is a group of three or more of such events. The waveforms of multiplets are nearly identical, with the principal exception of additive random noise. Multiplets can be readily identified using cross correlation (Poupinet et al. 1984; Arrowsmith and Eisner, 2006). All events in each multiplet group are then relocated to improve their relative location accuracy

**Figure 3.** Microseismic events contain a wealth of information that can be used to determine planes of weakness along which fluid migration could occur. (a) Original source locations; (b) new source locations after application of a high-resolution relocation technique; (c) multiplets extracted and best fault plane solutions depicted in two major clusters; (d) obtained fault planes overlain onto the top-reservoir fault map interpreted from 3D surface seismic data

consisting of 1000s of events recorded by 10s or 100s of 3-component receivers).

(Figure 3), thereby revealing lineations and active faults planes.

(after De Meersman et al., 2009).

location of microseismic source (Eaton et al., 2011).

448 Effective and Sustainable Hydraulic Fracturing

Once the multiplet groups are detected, the double-difference method can be applied. This is a relative relocation method that seeks to reduce the effects of errors due to unanticipated velocity heterogeneities in the structure (Waldhauser and Ellsworth, 2000). An advantage of this method is that no master event is needed, which induces spatial limitations, since all events must be correlated with the reference event. The main assumption in this method is that ray paths between two events will be very similar if their hypocentral separation is small compared to the source-receiver distances; therefore, the relative travel-time difference at a common station will be mainly due to the spatial offset between both events. In other words, the effects of most velocity heterogeneities will cancel out, such that only knowledge of the velocities in the source region is required. Castellanos and Van der Baan (2012) apply this method to data from a mining environment. Results clearly reveal a linear feature after relocation, possibly related to horizontal drilling activities.

Likewise, De Meersman et al. (2009) use relative locations to delineate a graben-like extensional structure in the caprock of a producing reservoir in the North Sea, UK (Figure 3). This grabenlike structure was not visible in the original absolute locations which revealed solely two large microseismic clusters. Next they re-examine temporal changes in anisotropy as found by Teanby et al. (2004a) using the automated shear-wave splitting methodology of Teanby et al. (2004b) for this same dataset. They then argue that their integrated analysis of relocated sources, seismic multiplets, and S-wave splitting supports a model whereby stresses in this reservoir recharge cyclically. Effective stress builds up in response to reservoir compaction as a result of oil production, and stress is released by means of microseismic activity once criticality is reached on slip planes. These changes cause variations in seismic anisotropy and the microseismic source mechanisms over time.
