Journal of Earth Science, Vol. 26, No. 4, p. 495–501, August 2015 Printed in China DOI: 10.1007/s12583-015-0565-4
ISSN 1674-487X
Velocity Modeling and Inversion Techniques for Locating Microseismic Events in Unconventional Reservoirs Jianzhong Zhang*, Han Liu, Zhihui Zou, Zhonglai Huang College of Marine Geosciences, Ocean University of China, Qingdao 266100, China; Key Laboratory of Submarine Geosciences and Prospecting Techniques, Ministry of Education, Qingdao 266100, China ABSTRACT: A velocity model is an important factor influencing microseismic event locations. We review the velocity modeling and inversion techniques for locating microseismic events in exploration for unconventional oil and gas reservoirs. We first describe the geological and geophysical characteristics of reservoir formations related to hydraulic fracturing in heterogeneity, anisotropy, and variability, then discuss the influences of velocity estimation, anisotropy model, and their time-lapse changes on the accuracy in determining microseismic event locations, and then survey some typical methods for building velocity models in locating event locations. We conclude that the three tangled physical attributes of reservoirs make microseismic monitoring very challenging. The uncertainties in velocity model and ignoring its anisotropies and its variations in hydraulic fracturing can cause systematic mislocations of microseismic events which are unacceptable in microseismic monitoring. So, we propose some potential ways for building accurate velocity models. KEY WORDS: microseismic location, velocity model building, velocity error, anisotropy, unconventional reservoir. 0
INTRODUCTION Hydraulic fracturing is an essential way in enhancing natural permeability of tight formations for the exploitation of unconventional oil and gas reservoirs (e.g., shale gas, tight sand reservoir, carbonate reservoir, etc.). Hydraulic fracturing perturbs the underground stress state and thus induces microseismicity. Microseismic monitoring has become an imperative technology to detect the induced events, locate their locations, and provide possibly detailed information about hydraulic fracture propagation. Ultimately, well developed microseismic monitoring is able to boost effective production and reduce environment impact. To describe hydraulic fractures, we first need to determine where they are by locating microseismic events, similar to the problem of locating earthquake hypocenters. In general, there are mainly three methods for the locating (Kushnir et al., 2014; Maxwell et al., 2010a): (1) Arrival time inversion: the microseismic locations are estimated using P-wave and/or S-wave arrival times or their combinations at multiple stations with either a pre-estimated velocity model, or a velocity model simultaneously estimated in the inversion. (2) A hodogram technique: the direction between the recording sensor and the microseismic location is determined from the particle motion of the direct P- and/or S-wave arrivals; and the distance to the *Corresponding author:
[email protected] © China University of Geosciences and Springer-Verlag Berlin Heidelberg 2015
microseismic event is determined from the difference in arrival times between the direct P- and S-waves and a prior information of the P- and S-wave velocities. (3) Wavefield inverse imaging: with a pre-estimated velocity model, the recorded waveforms are back propagated into subsurface by time-reverse processing or waveform migration so that the P- and/or S-wavefields that relate to all possible microseismic locations are reconstructed. Although these methods differ from each other in their principles, they all share the same prerequisite for accurate velocity models. In fact, velocity model building plays the fundamental role in microseismic monitoring techniques, because it eventually controls how the microseismic event locations are determined with regard to their accuracy. In this article, we review some of the velocity modeling and inversion methods and their impact on microseismic locations in several aspects, including velocity characteristics of the formations, the relationship between inaccuracies in velocity models and errors in microseismic locations, and methods for building velocity models. Also, we propose the potential investigation areas where velocity models might be better reconstructed in future research. 1 THE VELOCITY CHARACTERISTICS OF UNCONVENTIONAL RESERVOIRS The main characteristics of unconventional reservoirs in velocity model building for microseismic locations are heterogeneity, anisotropy, and variability. 1.1
Manuscript received September 2, 2014. Manuscript accepted December 20, 2014.
Heterogeneity The Earth is a heterogeneous medium with variations in geological and/or geophysical properties. Unconventional res-
Zhang, J. Z., Liu, H., Zou, Z. H., et al., 2015. Velocity Modeling and Inversion Techniques for Locating Microseismic Events in Unconventional Reservoirs. Journal of Earth Science, 26(4): 495–501. doi:10.1007/s12583-015-0565-4. http://en.earth-science.net
Jianzhong Zhang, Han Liu, Zhihui Zou and Zhonglai Huang
496 ervoirs exist in sedimentary formations. The heterogeneity can exhibit at different scales in both the reservoirs and their surrounding sedimentary layers. Maxwell et al. (2002) showed that hydraulic fracturing generated complex fracture geometry in the Barnett Shale in the Fort Worth Basin. The fracture geometry in turn caused a significant heterogeneity around the Barnett Shale. Heterogeneity is often accompanied with anisotropy of the formations which can result in major velocity changes. For example, even a small scale of vertical transverse isotropic (VTI) heterogeneity produces measurable velocity changes in the Haynesville Shale reservoir (Sena et al., 2011). Furthermore, the heterogeneity of reservoirs varies in space and in time as well during hydraulic fracturing due to new fractures and the induced fluid leakage in the reservoirs. The heterogeneity of reservoirs and their surrounding formations is reflected in microseismic velocities and affects the microseismic wave propagation. Microseismic waves recorded on the surface and in a borehole all inherently carry the information of heterogeneity. Therefore without the consideration for the medium’s heterogeneity, it would be very difficult to locate microseismic events accurately. Grechka et al. (2011) demonstrated the feasibility to build heterogeneous anisotropic models for microseismic locations using microseismic data recorded in a borehole. But one of the questions that still remain is how to effectively consider the either vertical or lateral or arbitrary heterogeneity for microseismic locations. 1.2
Anisotropy Anisotropy describes the directional dependence of seismic velocities in a medium. It is often classified into horizontal transverse isotropy (HTI), vertical transverse isotropy (VTI) and tilted transverse isotropy (TTI). The superposition of the HTI and VTI symmetries results in an orthorhombic system. By and large, sedimentary formations all have seismic velocity anisotropy due to the alignment of cracks and fractures, the alignment of minerals, fine layering and non-hydrostatic stresses, etc.. Shales, characterized by breaks along thin laminae or parallel layering or bedding, have a strong intrinsic anisotropy. Grechka et al. (2011) found a convincing evidence for anisotropy in the examined shale formation at each stage of hydraulic fracturing. Zhang et al. (2013) demonstrated that the best-fitting isotropic velocity model for the microseimic traveltimes has a 122% larger vertical velocity than the measured calibration shot model. The heterogeneity did not fit well the traveltime residuals but the VTI type of anisotropy could well explain for the data. Erwemi et al. (2010) reported that there was 10% anisotropy in the Eagle Ford Shale. Teanby et al. (2004) studied the spatial and temporal variations in anisotropy of the Valhall oilfield in the North Sea. Shear-wave splitting is one of the most robust indicators of seismic anisotropy and is considered to be one of the most successful ways for detecting and characterizing fractures. Al-Harrasi et al. (2011) presented an evidence of seismic anisotropy of a carbonate reservoir based on observations of shear-wave splitting in an oilfield in Oman. The anisotropy is interpreted in terms of aligned fractures or cracks superimposed on an intrinsic VTI formations. They showed that shear-wave splitting from microseismic data could be used to characterize
fractures and was able to provide important information for the exploitation of many reservoirs. Most reservoir formations have anisotropy, which may vary both laterally and vertically. Fracturing formations generally produce man-made crack-induced anisotropy superimposing on the preexisting in-situ anisotropy (Grechka and Yaskevich, 2014), which certainly aggravates the anisotropic effect of the formations. Even weak velocity anisotropy can make great influence on microseismic event locations. Erwemi et al. (2010) showed a 10% anisotropy in the Eagle Ford Shale produced a large error of locations on the order of a couple hundred feet. In addition, the amount of anisotropy can vary with geologic layers. Thus some variability of anisotropy in the model is also necessary (Warpinski et al., 2009). Anisotropy is an important property of reservoir formations. Accounting for anisotropy can help estimate the fracture network geometry and improve microseismic locations as well. It is thereby necessary to estimate the anisotropy of hydraulically fractured formations and incorporate it into velocity models built for locating microseismic events. 1.3
Variability Hydraulic stimulation alters the formations by increasing the pore pressure and producing fractures in the rock mass. The new fractures, fluid leakage from the fractures and the corresponding pore pressure changes then alter the rock velocities. Zhang et al. (2014) used the Coates-Schoenberg method (Coates and Schoenberg, 1995) to calculate the equivalent elastic matrix in the fractured area where the rock is influenced by the fracturing process. Given the real-time distribution of fractures and pore pressure, the corresponding equivalent elastic matrix and velocity are calculated. Figure 1 is their numerical simulated microseismic events and P-wave velocity distribution at time of 100 s for a three-layered model. The black, green and blue dots represent pore-pressure-diffusion-induced microseismic events during 10 to 20 s, 40 to 50 s and 90 to 100 s in time, red dots are hydraulic-fracture-induced microseismic events during 0 to 100 s. The microseismicity area has the lowest velocity, the amount of the velocity variation can reach to 250 m/s. It is shown that velocity and anisotropy parameters near the hydraulic fracture have time-lapse change during hydraulic fracturing. These time-lapse changes influence the microseismic travel time and propagation direction. Therefore, locating microseismic events needs to account for the time-lapse changes in velocity. 2 IMPACT OF VELOCITY MODELS’ INACCURACIES ON MICROSEISMIC LOCATIONS As microseismic locations depend upon a velocity model, it is helpful to quantify the sensitivity of an event location to errors in the velocity models. In this section, we present several typical cases of mislocations associated with velocity model uncertainty. 2.1
Velocity Errors and Mislocations A number of studies have showed that velocity errors result in the migrations of microseismic events from their true locations. In the Ekofisk microseismic experiment, Jones et al. (2014) showed that small errors in traveltimes and velocity
Velocity Modeling and Inversion Techniques for Locating Microseismic Events in Unconventional Reservoirs
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Figure 1. 3D microseismic distribution (a) and P-wave velocity distribution (b) at time of 100 s (from Zhang et al., 2014).
Anisotropy and Mislocations Anisotropy can have a large impact on the accuracy of microseismic event locations associated with hydraulic fracturing. For anisotropic formations, using an isotropic velocity model could result in unrealistic velocity values. Van Dok et al. (2011) demonstrated how a microseismic event can be incorrectly located if an isotropic velocity model is assumed in the presence of
actual anisotropic velocities. Figure 3 shows the elliptical wavefront in a HTI medium. A three-component geophone is used at Point P in an observation. The arrival angle of the P-wavefront in the anisotropic medium from a microseismic event is shown as the short gray arrow perpendicular to the wavefront, while the arrival angle of a wavefront assuming a homogeneous isotropic medium is shown as a long black arrow. If an isotropic velocity model is assumed, then the microseismic location will be erroneously projected back along the dotted line. If a correct anisotropic velocity model is used, then correct location of the microseismic event can be obtained. Assuming there is only a 100 Location error (m)
models can lead to large errors in locations, especially near the velocity model discontinuities where events tend to cluster. And the location inaccuracies due to errors in velocity models are twice as large as that due to arrival time picking errors. Maxwell et al. (2010b) used a synthetic example based on the Barnett Shale and showed that a 5% velocity variation over the true velocity model could lead to a systematic mislocations varying spatially from 10’s to 100’s of feet in Cartesian directions. Usher et al. (2013) examined the influence of a velocity model and the microseismic source frequency on microseismic waveforms and event locations. The microseismic data were synthetized based on the Cotton Valley hydraulic fracture experiment. In their study, three velocity models with a vertical heterogeneity were generated from surface active seismic data, the VSP data and sonic log data, respectively. The differences between these plausible velocity models manifest in perturbations in arrival times of the P- and S-wave phases of approximately 3.5 and 8.5 ms, respectively, and lead to approximately a 20 m difference in location. As the source frequency increases, the influence of the velocity model increases as well. Using a 1D synthetic velocity model and a vertical recording array, Yin et al. (2013a) estimated the location errors due to errors in velocity models. Figure 2 shows their result where the lines with diamonds, squares, triangles and asterisks represent the location errors in the XYZ directions and distance, respectively. It can be seen that the location error due to a negative velocity error (i.e., the used velocity is less than the true velocity) is larger than that due to a positive velocity error (i.e., the used velocity is more than the true one) with the same magnitude. For example, the velocity errors of -5% and 5% lead to location errors of approximately 55 and 35 m, respectively. These errors cannot be neglected in the world of accurate fracture’s delineation. Errors in velocity models are the most significant source of errors in event locations and they must be taken into account in the analysis of microseismic locations.
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x
z
y
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60 40 20 0 -10%
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Figure 2. Microseismic location errors with velocity errors (from Yin et al., 2013a).
Mislocated microseismic event
Microseismic event location P
2.2
Figure 3. Close-up view of particle motion for isotropic (black) and anisotropic (gray) velocity models arriving at Point P (from van Dok et al., 2011).
Jianzhong Zhang, Han Liu, Zhihui Zou and Zhonglai Huang
498 4 difference between the isotropic and anisotropic raypath angles in Fig. 3, if the microseismic event occurs 1 500 feet from observation well, then the lateral location error due to ignoring anisotropy can be over 100 feet in the transverse direction. This type of errors usually exceeds other positioning errors caused by incorrect velocity values. Woerpel (2010) did a test on a 1D horizontally layered model with a vertical recording array. He showed that a constant horizontal velocity, being independent of incidence angle, is inadequate to match the observed times. Mean depth location errors in the isotropic model were 14 times larger than for the anisotropic model. Erwemi et al. (2010) demonstrated the locations of the microseismic events in the Eagle Ford Shale shifted by several hundred feet if a 10% polar anisotropy is ignored. Apparently ignoring anisotropy can produce significant error in event locations. As demonstrated above with both synthetic and real examples, small errors and the anisotropic effect in the velocity models can lead to large errors of event locations. These examples were all based on simplified 1D velocity models. In reality, a 3D complex velocity model with dipping layers and variable anisotropy should be introduced in the next level of research. 3
METHODS TO BUILD VELOCITY MODELS There are many methods for building velocity models for microseismic locations. Usually, an initial velocity model is constructed using non-microseismic data (i.e., sonic logs or active seismic data), and the calibration shot data are applied to improve the initial velocity model. However, the improved model only approximates the pre-fracturing formation velocity. Since hydraulic fracturing changes the formation velocity, a direct way to build actual velocity models is to use microseismic data (e.g., arrival times and waveforms). 3.1
Using Non-Microseismic Data Active-source surface reflection data provides 2D or 3D velocity models. But the models do not have sufficient vertical and horizontal resolutions as in high frequency microseismic data. On the scale of hydraulic stimulation, the volume of stimulated formation may appear to be relatively homogeneous within the models. Vertical seismic profile data could produce velocity models with predominantly a higher vertical resolution than active surface data, but the velocity models become more localized around the boreholes. Crosswell seismic tomography can produce 1D, 2D or 3D high resolution velocity models. Its low signal strength and high cost have limited its use in practice. Sonic logs can provide 1D velocity model with the highest vertical resolution. Such a model is often used in practice, even though it may be far away from the reality in 2D or 3D. Furthermore, modern dipole sonic logging techniques are capable of measuring in-situ variations in velocity in various directions, depending on the orientation of the logged borehole (Walsh et al., 2007). These advanced sonic measurements can provide a measure for both the velocity and the anisotropy and its variability along the borehole (Erwemi et al., 2010). In addition, sonic logs can provide a better starting velocity and anisotropy model for microseismic data inversion.
3.2
Velocity Calibration The velocity models constructed using non-microseismic data, as mentioned above, often have a large uncertainty. Uncertainties in the velocity models can be minimized by calibrating the location accuracy with calibration shots. Calibration of the velocity model involves some form of velocity adjustment through an inversion that better matches the observed arrival times and also by correctly locating the calibration shots. Perforation shots are an ideal calibration shot, being located in the zone of influence and thus an effective calibration of the apparent velocity between receivers and the shot position. However, the number of shots is often limited and the inversion is non-unique. This could lead to an increasing uncertainty as the microseismic event moves away from the shot location. Additional shots could help improve the velocity model confidence. Microseismic events recorded early in the injection could also be used, although the true location is unknown but could be assumed to be close to the injection location (Maxwell et al., 2010b). Anisotropy parameters obtained using non-microseismic data can also be improved using calibration shot data. Maxwell et al. (2010b) described a method for inverting velocity and VTI parameters to improve the velocity model. To determine a calibrated model, Woerpel (2010) suggested interactively adjusting Thomsen anisotropy parameters and P- and S-wave scale factors until the modelled arrival times matched the observed times. Zhang et al. (2013) developed a method to obtain anisotropic velocity model from calibration shots or microseismicity recorded by a surface array. Using a layered 1D isotropic model derived from check shots as an initial velocity model, they inverted P-wave arrival times to obtain effective Thomsen anisotropic parameters in a VTI medium. The nonlinear inversion is performed by iteration between linearized inversion for anisotropic parameters and microseismic origin times or depths. Pei et al. (2014) proposed a method for velocity model calibration using both direct and reflected arrivals. Because reflected waves provide a wider inclination-angle coverage and longer travel distance in the velocity model, adding the reflected arrivals in a velocity calibration can improve the accuracy of the final inverted model. Their synthetic examples showed that the velocity model calibrated using both direction and reflected arrivals yielded more accurate microseismic event locations than those using the direct arrivals only. Microseismic events close to the calibration shot can often be accurately located as compared to the far shots. Events farther from the shot will have increasing error. Therefore a recalibration of velocity model during each stage of a hydraulic fracture treatment may be necessary for the accurate location. From numerical simulation and actual perforation relocation processing, Yin et al. (2013b) indicated that one perforationcalibrated velocity model led to great location errors in multi-stage fracturing. The farther distance it is away from the perforation, the larger location error is. Thereby, they proposed a multi-stage perforation velocity calibration technique to reduce the influence of velocity variations in multi-stage fracturing for building effective velocity models. Bardainne and Gaucher (2010) showed that it is essential to perform a multi-stage inversion to derive a better updated velocity model.
Velocity Modeling and Inversion Techniques for Locating Microseismic Events in Unconventional Reservoirs 3.3
Arrival Time Inversion In the fracturing process, the injected fluid creates fractures and enhances the pore pressure nearby, which might entail observable time-lapse changes of the velocity models. Velocity models obtained using the above approaches are likely to be suboptimal because they usually ignore the velocity variations that are inherited by the microseismic events. So the way to account for those velocity variations within the framework of arrival time inversion is to derive the velocities from microseismic data itself. In microseismic monitoring, the event locations and the velocity model can be inferred simultaneously. The double difference tomography (Zhang and Thurber, 2003) was used to locate microseismic events simultaneously with building isotropic velocity models (Zhang et al., 2009). Fehler et al. (1998) presented a time-varying tomography method to build velocity models using the microseismic data from throughout a long hydraulic fracturing operation where the time-varying velocity is considered. Figure 4 shows their results of inverting for S-velocity model using synthetic data by the arrival time tomography with and without temporal velocity change, respectively. This result demonstrates that considering time-varying velocity in tomography improves the tomographic inversion model. They also showed that more structure existed in the pattern of microseismic locations found by the time-varying tomography than that by a tomography scheme with no temporal change, using the arrival times of microseismic waves induced by the water injection in a region of Precambrian crystalline rock at the Fenton Hill, USA. Building azimuthally anisotropic models from microseismic data have been attempted recently. Grechka et al. (2011) extended passive seismic tomography to anisotropic media and applied it to borehole microseismic geometries. They estimated portions of the stiffness tensors of effective triclinic media simultaneously with locations of microseismic events. Verdon and Kendall (2011) inverted the effective anisotropy parameters of VTI media containing vertical fractures using measurements of the shear-wave splitting. Unfortunately, their inversion turned out to suffer from the trade-off between Thomsen parameters epsilon and delta. Grechka and Yaskevich (2013) theoretically demonstrated the feasibility of joint inversion of microseismic arrival times and polarizations for event locations
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and parameters of layered triclinic media. Grechka and Yaskevich (2014) inverted full stiffness tensors of triclinic layers to build azimuthally anisotropic velocity models with data that possess a wide directional aperture. One of the most straightforward surface microseismic source location algorithms is called Seismic Emission Tomography (SET). SET uses maxima of the semblance cost function over space, time, and channels to locate microseismic events (Kiselevitch et al., 1991). The first application of SET for monitoring a hydraulic-fracture well stimulation was carried out in 2004 (Duncan, 2005). Kochnev et al. (2007) applied long-time-interval stacking similar to semblance to detect elevated semblance over intervals of several seconds for a fixed target depth and then developed some variations from the SET approach. Kushnir et al. (2013) modified the SET location algorithm to compute a semblance cost function in the frequency domain. Theoretically, SET is the maximum likelihood algorithm for the statistical model of noise assumed to be the Gaussian white random process in time and space (Kushnir et al., 2014). Microseismic arrival time inversion to build anisotropy models remains challenging because of the multi-parameter nature and the non-uniqueness of the anisotropy estimation problem. In addition, event detection and arrival time picking are mandatory for microseismic arrival time inversion (Liu and Zhang, 2014) but always difficult with low signal to noise ratio data. To increase the application of arrival time inversion in practice, development of automatically picking arrival time from microseismic data with a low signal to noise ratio is desirable. 3.4
Waveform Tomography Conventional waveform tomography updates model by matching the full waveforms of the modeled data and the observed data without picking arrival times. This is called data-domain waveform tomography, or waveform inversion. Data-domain waveform tomography is often applied to active source seismic data. But it is hard to be applied to microseismic data because of the need to estimate the source function and the source onset time. Fish (2012) proposed an image-domain waveform tomography method for updating velocity models by imaging microseismic wavefield back to optimal focuses spatially and temporally. Theoretically the image-domain
Figure 4. Horizontal S-velocity slices of a true model (a), the inverted model by the time-varying tomography (b), and the inverted model by a tomography without temporal velocity change (c) (from Fehler et al., 1998).
500 tomography can overcome the difficulties in the data-domain tomography for microseismic data. However, the imagedomain tomography produces velocity models with lower resolutions than the data-domain tomography. Fish (2012) thus suggested the velocity models estimated with the imagedomain inversion may then be used as the starting models for the data-domain tomography. This combination of tomography methods would produce more accurate and higher resolution velocity models. 4
CONCLUSIONS Uncertainties in the velocity model can cause systematic mislocations of microseismic events and then misinterpretation of hydraulic fracture geometries. Small errors, as five percent, in the velocity models can result in large errors, as several tens of meters, in event locations. This is unacceptable in microseismic monitoring. An isotropic velocity model used for actual anisotropic formations can lead to unrealistic velocity values. Ignoring anisotropy can produce significant error in event locations. In the Eagle Ford Shale, for instance, the error in event locations can reach to several hundred feet if a ten percent polar anisotropy is ignored. Hence anisotropy and its spatial and temporal variation of formations need to be introduced to build velocity models for event locations. Hydraulic fracturing changes the velocity of the formations and influences the microseismic arrival time and propagation direction. Accurately building velocity models remains challenging for event locations, because of heterogeneity, anisotropy and variability of the reservoir formations. A preferred way to consider the variations of the formations in fracturing process is to estimate velocity models from the microseismic data. To avoid the difficulties in arrival-time picking from microseismic data, further developing the image-domain waveform tomography might be a potential approach for building fine velocity models. ACKNOWLEDGMENTS This study was supported by the Specialized Research Fund for the Doctoral Program of Higher Education (No. 20130132110023), and by the National Natural Science Foundation of China (Nos. 41230318, 41074077). We thank Xiaolin Zhang, Chen Yin, Richard van Dok, Michael Fehler, et al. for the figures adopted from their articles. We also thank the two reviewers for their helpful comments. REFERENCES CITED Al-Harrasi, O. H., Al-Anboori, A., Wüstefeld, A., et al., 2011. Seismic Anisotropy in a Hydrocarbon Field Estimated from Microseismic Data. Geophysical Prospecting, 59(2): 227–243. doi:10.1111/j.1365-2478.2010.00915.x Bardainne, T., Gaucher, E., 2010. Constrained Tomography of Realistic Velocity Models in Microseismic Monitoring Using Calibration Shots. Geophysical Prospecting, 58(5): 739–753. doi:10.1111/j.1365-2478.2010.00912.x Coates, R. T., Schoenberg, M., 1995. Finite-Difference Modeling of Faults and Fractures. Geophysics, 60(5): 1514–1526.
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