Journal of Applied Geophysics 150 (2018) 278–283

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Journal of Applied Geophysics

j ourna l homepage: www.e lsev ie r .com/ locate / j appgeo

Target-oriented imaging of hydraulic fractures by applying the stainingalgorithm for downhole microseismic migration

Ye Lin, Haijiang Zhang ⁎, Xiaofeng JiaWantai Microseismic Lab of School of Earth and Space Sciences, University of Science and Technology of China, 96 Jinzhai Road, Hefei, Anhui 230026, China

⁎ Corresponding author.E-mail address: zhang11@ustc.edu.cn (H. Zhang).

https://doi.org/10.1016/j.jappgeo.2018.01.0240926-9851/© 2018 Elsevier B.V. All rights reserved.

a b s t r a c t

a r t i c l e i n f o

Article history:Received 13 April 2017Received in revised form 20 December 2017Available online 2 February 2018

For microseismic monitoring of hydraulic fracturing, microseismic migration can be used to image the fracturenetwork with scattered microseismic waves. Compared with conventional microseismic location-based fracturecharacterization methods, microseismic migration can better constrain the stimulated reservoir volume regard-less of the completeness of detected and locatedmicroseismic sources. However, the imaging results frommicro-seismic migration may suffer from the contamination of other structures and thus the target fracture zones maynot be illuminated properly. To solve this issue, in this study we propose a target-oriented staining algorithm formicroseismic reverse-timemigration. In the staining algorithm, the target area is first stained by constructing animaginary velocity field and then a synchronized source wavefield only concerning the target structure is pro-duced. As a result, a synchronized image from imaging with the synchronized source wavefield mainly containsthe target structures. Synthetic tests based on a downholemicroseismicmonitoring system show that the target-oriented microseismic reverse-time migration method improves the illumination of target areas.

© 2018 Elsevier B.V. All rights reserved.

Keywords:Microseismic migrationScattered microseismic wavesTarget-oriented staining algorithmDownhole microseismic monitoring system

1. Introduction

Hydraulic fracturing is an engineering tool to create fractures forbetter recovering hydrocarbons from low permeability reservoirs suchas tight sand and shale. During the fracturing process, proppants areinjected into fracked reservoirs to support created fractures to makepaths for hydrocarbon migration, which is generally associated withmicroseismic events (e.g. van der Baan et al., 2013). Based on the factthat microseismic events are mostly swarmed at the faces and the tipsof opening fractures, microseismic monitoring has been widely usedto estimate the geometry of fracture area and to evaluate the frackingprocess (e.g. Fisher and Warpinski, 2012). The current efforts of micro-seismicmonitoringmainly focus on the locations and focal mechanismsof detected microseismic events (e.g. Maxwell et al., 2010; Rodriguezet al., 2012; Li et al., 2013; Anikiev et al., 2014; Douma and Snieder,2015; Liang et al., 2016).

However, conventional location-based fracture characterizationmethods strongly depend on the completeness of detected microseis-mic events and their location accuracy, which could be greatly biased.Because of weak microseismic signals, it is challenging to detect allinduced events, especially small-magnitude oneswith the currentmon-itoring instruments and event detection methods (e.g. Maxwell et al.,2009; Williams-Stroud et al., 2013; Vaezi and van der Baan, 2014;

Caffagni et al., 2016). The signal of detected microseismic event mayhave low SNR or undistinguished first arrivals for accurate source loca-tion, such as long-period long-duration (LPLD) signals detected duringthe fracking process (Zoback et al., 2012; Das and Zoback, 2013). Al-though the finding of LPLD signals by Zoback et al. (2012)may be ques-tionable, it has been shown recently indeed hydraulic fracturing of theMarcellus Shale can induce LPLD events (Kumar et al., 2017). Further-more, it lacks a physical relationship between the spatial distributionof microseismic events and the stimulated reservoir volume (SRV)under the regional geology (Mayerhofer et al., 2010; Johri, 2013;Cipolla and Wallace, 2014).

For the multi-stage fracturing treatment, fractures are created fromtoe (first-stage) to heel (last-stage). Recorded microseismic waveformsfrom a recent stage may contain scattered waves excited by fracturezones from previous stages because fractures with proppants/fluidswould act as strong scatterers for microseismic signals. Based on theabove facts, Lin and Zhang (2016) proposed to apply the reverse timemigration (RTM) method with microseismic scattering waves todirectly image the fracking zone. Actually, in recent years, seismic mi-gration methods that were originally developed for active sourcessuch as Kirchhoff migration, Fresnel volume migration, generalizedRadon transform and local reverse-time migration have been intro-duced for passive source imaging (Chavarria et al., 2003; Zhang et al.,2009; Reshetnikov et al., 2010; Shabelansky, 2015). These studiesshowed that fault structures or some reflectors can be well imagedwith passive seismic waveforms (Chavarria et al., 2003; Zhang et al.,2009; Reshetnikov et al., 2010; Shabelansky, 2015).

279Y. Lin et al. / Journal of Applied Geophysics 150 (2018) 278–283

Compared to location-based fracture characterization, this migration-basedmethod does not require to have the completeness ofmicroseismicevents to fully characterize fractures. In fact, several large-magnitudeevents that canbewell locatedmaybe sufficient. Formulti-stage fracking,if some stages are aseismic or are accompanied by few microseismicevents, the microseismic migration method would be still able to imagefracturing zones by several detected stronger events. Lin and Zhang(2016) showed that downhole microseismic RTM is effective to imagethe fracture zone based on a 2D synthetic dataset. Recently Grechkaet al. (2017) applied conventional 3D prestack Kirchhoff depthmigrationto downhole microseismic datasets in Woodford and Bakken fields toimage fracture zones for different stages. These modeling studies andreal data applications showed thatmicroseismic imaging based onmigra-tion is a potentially powerful tool to characterize fracture zones inducedby hydraulic fracturing.

However, the fracture imaging result frommicroseismic reverse-timemigration not only is subject to the spatial distribution of downhole sen-sors and seismic events, but may also be contaminated by surroundingstructures (Lin and Zhang, 2016). Hence, illumination improvementand target-oriented imaging of the fracturing area of interest is neces-sary. For these reasons, in this study we propose to apply the stainingalgorithm developed by Chen and Jia (2014) to downhole microseismicreverse-timemigration to improve the illumination of the target frackedzone. In addition to the original wavefield, reverse-time migration withthe staining algorithm creates a synchronized wavefield that is onlysensitive to the stained structure and produces a synchronized imagearound the stained structure besides the original image. In this paper,we will first introduce the microseismic reverse-time migration methodand the staining algorithm as well as the imaging condition. Then toillustrate this target-oriented microseismic imaging method, we designa downhole microseismic monitoring system with multiple frackingstages and apply microseismic reverse-time migration with the stainingalgorithm to the synthetic dataset.

2. Microseismic reverse-time migration with staining algorithm

In the observation geometry of downhole microseismic monitoring,microseismic events are induced for each fracking stage and their wave-forms are recorded by sensors installed in the neighboring monitoringwells. The microseismic events are first located and then can be treatedas active sources in microseismic imaging. Here, we use the reverse-time migration method based on 2D acoustic wave equation (Baysalet al., 1983; Zhang and Sun, 2008; Xiao and Leaney, 2010):

1c2

∂2

∂t2−Δ

!pF x; z; tð Þ ¼ δ x−xs; z−zsð Þ

Z t

0f t0ð Þdt0 ð1Þ

1c2

∂2

∂t2−Δ

!pB x; z; tð Þ ¼ 0

pB x ¼ xr ; z; tð Þ ¼ D xr ; z; xs; zs; tð Þ

8><>: ð2Þ

where c= c(x,z) is the velocity, t is the time, (x,z) is the 2D spatial point,Δ is the Laplace operator, f(t) is the source function located at (xs,zs), pFis the source wavefield that needs to be extrapolated from zero time tomaximum time, D(xr,z;xs,zs; t) is the microseismic waveform recordedin the borehole at x= xr, and pB is the receiver wavefield that needs tobe extrapolated from maximum time to zero time.

The staining algorithm can create an extra source wavefield synchro-nized with the conventional source wavefield and this new wavefieldonly contains information relevant to the target (or stained) region ofthe model (Chen and Jia, 2014). For the staining algorithm based onthe 2D acoustic wave equation

∂2p∂t2

¼ c2Δp ð3Þ

it involves extending both velocity c and wavefield p into the complexdomain as c ¼ cþ i ~c; and p ¼ pþ i ~p. The imaginary velocity ~c is non-zero only within the staining zones and is generally chosen to be muchsmaller than the real velocity (for details refer to Chen and Jia, 2014).When the imaginary velocity ~c is close to zero, the imaginary partof the source wavefield ~p is synchronized with the real part of wavefieldp. Physically, the imaginary velocity only exits in the staining zones, andthe imaginarywavefield is an extra sourcewavefield induced by conven-tional source wavefield arriving at the staining zones. Then, the waveequation can be separated into two parts

∂2p∂t2

¼ c2Δp ð4Þ

∂2~p∂t2

¼ c2Δ~p ð5Þ

By solving the above wave equations by 2nd order temporal andspatial finite-difference (FD) schemes, the propagating sourcewavefield in the real and imaginary domains can be formulated as

plþ1m;n ¼ c2m;nP

l þ 2plm;n−pl−1m;n−2cm;n~cm;n

~Pl−~c2m;nP

l ð6Þ

~plm;n ¼ c2m;n~Pl þ 2~plm;n−~pl−1

m;n þ 2cm;n~cm;nPl−~c2m;n

~Pl ð7Þ

where Pl =(τ/h)2(pm+1, nl + pm−1, n

l + pm, n+1l + pm, n−1

l − 4pm, nl ), sub-

scripts m, n and superscript l denote spatial and temporal samplingpoints, and τ and h represent time step and grid interval, respectively.

The staining algorithm works out by setting the imaginary velocityzero for the whole model region but with values much smaller thanreal velocities for the stained region. By adding the term of sourcewave-let sre and ignoring all terms containing ~cm;n in Eq. (6), we can get

plþ1m;n ¼ c2m;nP

l þ 2plm;n−pl−1m;n þ slþ1

re ð8Þ

By ignoring the term holding ~c2m;n in Eq. (7), we get

~plm;n ¼ c2m;n~Pl þ 2~plm;n−~pl−1

m;n þ 2cm;n~cm;nPl ð9Þ

When comparing Eqs. (8) and (9), the last term on the right side ofEq. (9) acts as a synchronized source term and it will hold zero unless

both ~cm;n≠0 and Pl≠ 0. It means that the imaginary wavefield will stay

zero and make no propagation until the real wavefield reaches thestained zone for the first time. Once the real wavefield touches thestained zone, the synchronized wavefield is excited and begins to prop-

agate. Note that the synchronized source term 2cm;n~cm;nPl ¼ 0 after the

real wavefield has passed Pl ¼ 0. Thus, the real wavefield interacts with

the entire model while the imaginary wavefield only contains informa-tion relevant to the stained structure. For more details on the stainingalgorithm, please refer to Chen and Jia (2014).

The source wavefield Sre(x,z, t) and the imaginary source wavefieldSim(x,z, t) are both forward propagated with the staining algorithmand the receiver wavefield R(x,z,t) is extrapolated backward. To obtainthe conventional image and the imaginary image, we can apply thecross-correlation imaging condition to two source wavefields and onereceiverwavefield by separately cross-correlating two sourcewavefieldswith the receiver wavefield

Ire ¼ ∑tmaxt¼0Sre x; z; tð ÞR x; z; T−tð Þ ð10Þ

Iim ¼ ∑tmaxt¼0Sim x; z; tð ÞR x; z; T−tð Þ ð11Þ

Fig. 1. (a) Configuration of two-dimensional downholemicroseismic monitoring system. Themonitoring well is located at X= 445m and is installed with 36-level sensors starting fromthe depth of 975 mwith a spacing of 30m. The synthetic test simulates four stages of fracking with three existing fracture zones (white shapes) in the early 3 stages and 8 microseismicevents (white asterisks) associated with stage 4. There are two staining zones (dash-dotted lines) located at X= 925m and X= 1075m, respectively. The background is the 1D velocitymodel. (b) Stacked migration image from 8 microseismic events by the conventional reverse-time migration.

280 Y. Lin et al. / Journal of Applied Geophysics 150 (2018) 278–283

where Ire is the conventional image, which is the same as the conven-tional reverse-time migration image, and Iim is the imaginary imageonly relevant to the stained area. Because themigrationwith the stainingalgorithm needs to extrapolate the imaginary source wavefield, it costsabout 50% more time than the conventional method. To suppress theimaging noise, wavefields at different directions are used in Eqs. (10)and (11) by the wavefield-separation method (Liu et al., 2011; Xiaoand Leaney, 2010)

Ire ¼ ∑tmaxt¼0S

�re x; z; tð ÞR∓ x; z; T−tð Þ ð12Þ

Iim ¼ ∑tmaxt¼0S

�im x; z; tð ÞR∓ x; z; T−tð Þ ð13Þ

The superscripts “+” and “−” in Eqs. (12) and (13) denote the dif-ferent directions of wave propagation.

3. Synthetic tests of target-oriented microseismic imaging method

To test the feasibility, we design a 2D borehole microseismic moni-toring system for multi-stage hydraulic fracturing with located micro-seismic events and accurate P-wave velocity model (Fig. 1a). For the

Fig. 2. Stacked images produced by reverse-time migration with the staining algorithm whecorrelating the real part of complex source wavefield and the receiver wavefield; (b) the stackand the receiver wavefield.

microseismic monitoring system, there are 36 receivers installed in amonitoring well at X = 445 m. There are 8 detected microseismicevents induced in the fourth stage and three oval-shaped fracturezones are created in the first three stages. Fracture zones are simulatedby setting their velocity values 15% lower than the background velocity.Here we assume that the oval-shaped fracture zone is an equivalentfracture system representing many small fractures created duringeach hydraulic fracturing stage. A 40 Hz Ricker wavelet is used forrepresenting the microseismic sources.

Fig. 1b displays the stacked image from the conventional reverse-time migration method (Lin and Zhang, 2016). The stacked imageclearly shows the existence of three fractures. However, because ofthe limited geometry of sensors, fractures and events, there is notenough information from the lower part of fractures and this restrictionleads to poor illumination of the lower part. Furthermore, for fracturesfarther away from the monitoring well, the worse they are illuminated.For example, the migration image of fracture III is strongest while theimages of fractures II and I are weak.

To better illuminate the fracture zones farther away from the moni-toring well, we set up two staining zones: one is located between frac-ture zones I and II and the other is located between fracture zones IIand III (Fig. 1a). Because the imaginary wavefield is an extra source

n the staining zone is located at X = 925 m. (a) The stacked image produced by cross-ed image produced by cross-correlating the imaginary part of complex source wavefield

Fig. 3. Stacked images produced by reverse-time migration with the staining algorithm when the staining zone is located at X = 1075 m. (a) The stacked image produced by cross-correlating the real part of complex source wavefield and the receiver wavefield; (b) the stacked image produced by cross-correlating the imaginary part of complex source wavefieldand the receiver wavefield.

281Y. Lin et al. / Journal of Applied Geophysics 150 (2018) 278–283

wavefield induced by conventional source wavefield when propagatingat the staining zones, to obtain better imaging result the staining zonesshould be set at or around the target area. With the staining algorithm,Fig. 2 displays stacked images produced by the real and the imaginarypart of the sourcewavefield respectivelywith the staining zones locatedat X= 925m between fracture zones III and II. Fig. 2a shows the imageproduced by cross-correlating the real part of the source wavefield andthe receiver wavefield. Obviously, Fig. 2a is the same as the result of theconventional reverse-timemigrationmethod (Fig. 1b), because they arealmost equal in equation. In addition, Fig. 2b shows the synchronizedimage produced by cross-correlating the imaginary part of the sourcewavefield and the receiver wavefield. Compared with the conventionalmigration image (Fig. 1b or Fig. 2a), fractures I and II are much betterilluminated while fracture III, which is located to the left of the stainedstructure, was not imaged (Fig. 2b).

Similar to the case of Fig. 2, Fig. 3 shows the imageswhen the stainingzone is located between fractures I and II. Through comparing with theconventional migration image (Fig. 1b or Fig. 3a), it could be observedthat only fracture I is well illuminated while the other two fracturesare not illuminated (Fig. 3b). Thus, the staining algorithm achieves thetarget-oriented imaging capability through properly setting the stainingzones.

Furthermore, we explain how the target-oriented imaging capabilityis achieved according to the propagation behavoir of the complexsource wavefield. In the case that the staining zone is located at X =1075 m between fracture zones I and II, the real part of the complex

Fig. 4. The snapshots of the complex source wavefield from one microseismic event at 0.0952wavefield; (b) the imaginary part of the source wavefield.

sourcewavefield is first excited at its location, then passes through frac-ture zones III and II before touching the staining zone. At this point, theimaginary part of the source wavefield is excited and then begins topropagate from the staining zone towards fracture zone I. Fig. 4 showsthe snapshots of real and imaginary parts of the complex sourcewavefield from one microseismic event after 0.0952 s of propagation.It can be seen that the real part of the sourcewavefield arrives at the re-gion around X= 1075m. At this time the imaginary wavefield (Fig. 4b)is excited and begins to propagate towards fracture zone I once the realpart of the sourcewavefield (Fig. 4a) touches the stained structure. Thisis to say, the imaginary part of the source wavefield is only related tofracture zone I. As a result, the imaginary image only shows the struc-ture pertaining to fracture I, not fractures II and III. Similarly, when thestaining zone is located between fractures II and III, the imaginary partof the source wavefield is only related to fractures I and II, not fractureIII (Fig. 1a). Therefore, in this case the staining algorithm can better re-solve fractures I and II, and is not affected by fracture III.

Fig. 5 gives the comparison of zoomed images around the three frac-tures for 8 individual events and the stacked images from conventionalRTMmethod and staining algorithm, respectively. For the images fromindividual events, it can be seen that fracture images from conventionalRTM are contaminated and blurred by the lower layer, while fractureimages from the staining algorithm are less affected by the layer inter-faces because of narrow range of illumination.

Fig. 6 describes what effect the staining algorithm brings to theenergy distribution of image. Fig. 6 shows the normalized amplitude

s in the case the staining zone is located at X= 1075 m. (a) The real part of the source

Fig. 5. Local migration images around fracture zones from 8 individual events (the first 8 columns) and the stackedmigration image from all events (the last column) by the conventionalRTM and staining algorithm. The 1st row shows the migration images from the conventional RTM while 2nd and 3rd rows show the imaginary migration images from the stainingalgorithm with staining zones at X= 925m (2nd row) and X= 1075m (3rd row), respectively.

282 Y. Lin et al. / Journal of Applied Geophysics 150 (2018) 278–283

of stacked images at depth of 2.05 km (indicated by the white horizon-tal lines in Figs. 1b, 2b and 3b). By comparing the imaginary migrationimages from the staining algorithm (Fig. 6b and c) with the migrationimage from the conventional RTM (Fig. 6a), it can be seen that theenergy mainly focuses on the fracture(s) behind the stained zone.Thus it shows that the staining algorithm not only improves the illumi-nation of fractures but also achieves the target-oriented imaging capa-bility of fractures.

4. Discussions and conclusions

For microseismic reverse-time migration, the objective is to imagemultiple fracture zones that were created in earlier stages of frackingby using microseismic events in later stages. To improve the illumina-tion of target fracture zones and alleviate the adverse effects fromother structures, following Chen and Jia (2014) we have developedthe staining algorithm for microseismic reverse-time migration. Thestaining algorithm makes a synchronized source wavefield onlyconcerning the target structures by properly staining the zones rightin the front where the conventional wavefield will arrive. To achievethe illumination of target structures, the stain areas do not have to over-lap with the target structures.

We have tested this algorithm for the case of three existing fracturezones using 8 microseismic events based on a 2D downhole microseis-mic monitoring system. The synthetic tests show that through properly

Fig. 6.Normalized profiles through the stackedmigration images at depth of 2.05 km (white linestaining zone at X= 925 m, and (c) the staining algorithm with the staining zone at X= 1075

staining we can partly alleviate the restriction of microseismic observa-tion geometry and greatly improve the illumination of target fracturezones. For the 2D synthetic test in this study, we choose an ideal geom-etry for receivers and microseismic events with fracturing stage 1 isfarthest away from the monitoring well. In this way, fractures createdby previous stages can be always acted as scatterers for waveformsemitted from later stage microseismic events. In practice, monitoringwell may be located in the middle of fracturing stages (Chen et al.,2017). As a result, some fracture zones created in previous stages maynot be illuminated by microseismic events in later stages. In this case,microseismic event locations should be used as a proxy for fractures.Therefore, it is best to combine microseismic migration images withmicroseismic event locations to characterize fracture zones.

Similar to what was discussed in Lin and Zhang (2016), downholemicroseismic migration with the staining algorithm is also affected byvarious factors including the noise level in the microseismic data anduncertainties in event location, focal mechanism and velocity model.Because the proposed method is based on standard downhole micro-seismic migration, the tests conducted on these factors by Lin andZhang (2016) are also valid for the migration with the staining algo-rithm. In brief, as shown in Lin and Zhang (2016), downhole microseis-mic migration is less affected by the noise and the fracture zone couldstill be well imaged when the signal to noise ratio is lower than 1. Sim-ilar to othermigrationmethods, downholemicroseismicmigrationwiththe staining algorithm is affected by the accuracy in the background

s in Figs. 1b, 2b and 3b) from(a) the conventional RTM, (b) the staining algorithmwith them, respectively.

283Y. Lin et al. / Journal of Applied Geophysics 150 (2018) 278–283

velocitymodel. For shale gas reservoirs, a 1D velocitymodel can be usedto well approximate the backgroundmodel, which can be derived fromsonic logging data and further calibrated by perforation shots (Zhanget al., 2016). With more advanced event locating algorithms, locationuncertainties could become very small and are on the order of 10 s ofmeters, and thus only slightly affect the imaging results (Lin andZhang, 2016). For different source mechanisms that result in differentpolarities of the first arrival waveforms, it is possible to alleviate thisissue by squaring the instantaneous phase of recorded seismic tracestreated as analytic signals (Barnes, 2016; Grechka et al., 2017).

Similar to Lin and Zhang (2016), the proposed downhole microseis-micmigrationwith the staining algorithm is only tested on 2D syntheticmodel in this study. As demonstrated by Grechka et al. (2017) on realmicroseismic datasets, the proposed method should also be applicableto real data applications. Here the downhole microseismic migrationwith the staining algorithm is developed with the acoustic wave equa-tion. This can be applied for real microseismic data if P and S wave seg-ments are separately used. This is because if only P or S microseismicwaveforms are used for migration, we can treat themodel as the acous-tic model. For example, Grechka et al. (2017) applied downhole micro-seismic migration separately to P and S waveforms based on acousticwave equation. For microseismic waveforms, it is relatively easy toseparate P and S wave segments. Generally, S waveforms have higheramplitudes andmay be used for better illuminating fractures. However,if the medium is strongly anisotropic such as shale, migration using Swaveforms needs to take into account anisotropic properties of themedium.

Microseismicmonitoring has gradually started to be used as a usefultool for guiding the hydraulic fracturing process by providingmicroseis-mic event locations in real time (e.g. Daniels et al., 2007). For downholemicroseismicmigration proposed here, it is also possible to conduct it innear real time oncemicroseismic event locations and velocitymodel areavailable. In addition, the migration images can be updated by sequen-tially stacking migration results for individual microseismic events.Therefore, downhole microseismic migration could providemore infor-mation on hydraulic fracturing by combining with microseismic eventlocations.

Acknowledgements

This research is supported byNational Natural Science Foundation ofChina under Grant Numbers 41274055 and 41374006.

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