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Downloaded 09/14/16 to 50.244.108.113. Redistribution subject to SEG license or copyright; see Terms of Use at http://library.seg.org/ Improve sensor orientation using both drop-ball and microseismic events Yuanhang Huo*, Wei Zhang, Jie Zhang, University of Science and Technology of China (USTC) Summary The event locations are essential quantities in microseismic monitoring for mapping hydraulic fractures. For downhole monitoring, the accuracy of the event locations depends on the accuracy of the P-wave polarization angle, which consequently depends on the accuracy of sensor orientation. Sensor orientation angle is generally determined by known location events such as perforation or drop-ball event, which is characterized by low signal-to-noise ratio (SNR). The accuracy of sensor orientation can be verified by the waveform coherence for various events after applying sensor orientation. In this study, we develop a new method utilizing both perforation/drop-ball event and high SNR microseismic events at unknown locations to derive orientation angles. In this approach, the waveform coherence of unknown events is directly used in the determination of sensor orientation angle. We use both synthetic data and real data to demonstrate the feasibility and reliability of the new method, which is more accurate than the traditional approach using drop-ball event alone. Introduction Microseismic monitoring is an efficient technique for mapping hydraulic fractures. Event locations can provide information about the geometry and development of the hydraulic fractures for evaluating the stimulation. For downhole monitoring with a single monitoring well, the location procedure usually consists of two steps: the first step locates events in a 2D vertical plane, the second step projects the 2D locations to 3D by P-wave polarization angle, which represents the back-azimuth of the event. But the direction of the horizontal components of the threecomponents seismic sensors in downhole are usually unknown and random orientated, which needs a sensor orientation approach to correct to the right direction. The error of the sensor orientation angle will directly propagate to the derived P-wave polarization angle, which may cause less coherent waveform of the events and large error in calculation of event azimuth. Sensor orientation can be done by performing a polarization analysis to derive P-wave propagation direction, then the difference between P-wave propagation direction and geometry direction from event to sensor indicates the rotation angle, which can be applied to rotate three-component traces to the correct orientation. Becquey and Dubesset (1990) developed a method with polarization analysis to derive three-component sensor orientation in a deviated observation well. Unlike processing geophones deployed in land, Nakamura et al. (1987) adopted air-gun © 2016 SEG SEG International Exposition and 87th Annual Meeting shots to determine the location and orientation of OBS stations. Besides, Di Siena et al. (1984) proposed a power maximization approach to obtain the azimuthal orientation of three-component geophones in a vertical observation well, which consists on maximizing the signal energy of the first P-wave arrival by orientation on a particular axis. Utilizing SH wave, Michaels (2001) applied a technique based on principal component analysis to acquire the sensor orientation relative to the source polarization direction. Differently from the common approaches calculating orientation of every sensor directly, Zeng and McMechan (2006) used traces cross-correlation to infer relative rotation angles among all sensors in borehole arrays. However, we do not know the true rotation angles of the sensors for real data. The derived rotation angle is affected by noise and contains error. To investigate the accuracy of the rotation angle, we can apply rotation on the data and check the coherence of the waveform along the sensor array. If the rotation angle is correct, we expect the maximum coherency of the waveform to be observed. However, if the data does not show strong coherence, the rotation angle may contain error, but there is no solution to improve it. The error of the rotation angle generally comes from noise. Surface orientation shots may show good SNR but may not be available in all the projects. For those cases without surface orientation shots, we have to use perforation shots or ball-drop events to obtain the orientation angles. The low SNR of those events, especially drop-ball event data, may cause large error in the calculated rotation angles. In this study, we introduce the constraint of waveform consistency of unknown events into the calculation of orientation angles and develop a new method for orientation utilizing both perforation/drop-ball event and high SNR microseismic events of unknown locations. Since we can generally always find some microseismic events with high SNR, the accuracy of the new method should be higher than the traditional approach using perforation or drop-ball event due to the higher SNR of those events. Theory In our method, we first scan all the data to find several high SNR events even though we are not aware of their locations. We utilize these events to maximize the waveform consistency between adjacent sensors to compute relative rotation angles between sensors. After rotations with those relative angles, all the sensors are orientated to the same direction with an absolute rotation angle. This absolute rotation angle can be obtained by comparing the Page 2642 Downloaded 09/14/16 to 50.244.108.113. Redistribution subject to SEG license or copyright; see Terms of Use at http://library.seg.org/ Improve sensor orientation using drop-ball and microseismic events polarization angle of each sensor to the geometry angle of the drop-ball event in the least-squares sense. (a) Determination of relative orientation A rotation of the vector, composed of the two horizontal components (X and Y), can be described by a rotation matrix in two dimensions under the assumption that all vertical components are parallel to the casing of the vertical borehole. The rotation matrix is X rot cos sin X ori (1) Y sin cos Y , ori rot where X ori and Yori are original components, denotes the rotation angle (counter-clockwise is positive), X rot and Yrot are rotated components. Our method relies on the fact that the waveform misfit reaches a global minimum for two aligned sensors, while larger misfit implies little similarity. Because we just invert one independent angle every time, ensuring no rankdeficient and ill-conditioned problems, the objective function can be imposed without regularization terms in a quite simple form (2) (m) || Pref Prot (m) ||2 , (b) Determination of absolute orientation After relative rotations of all other sensors with respect to the reference sensor, the absolute orientation of reference one can be determined with the algorithm described in this section. The polarization within a time window is estimated as following. The terms of covariance matrix are evaluated as PP T 1 N S jk pij pik , (6) N N i 1 where P is the data matrix in given time window, pij is the ith sample of component j and N is the number of samples (Jurkevics, 1988). In addition, the estimation of principal axes involves computing the eigenvalues ( 1 , 2 , 3 ) and eigenvectors ( u1 , u2 , u3 ), which are nontrivial solutions to (7) ( S 2 I )u 0 , where I is the 3×3 identity matrix and 0 is a column vector of zeros. The azimuth of P-wave propagation can be calculated from u (8) Azimuth tan 1 ( x1 ) , u y1 where m is the relative rotation angle, Pref is observed where ux1 and u y1 are the X and Y direction cosines of waveform data for the sensor we choose as the reference sensor and Prot is rotated waveform data for adjacent sensor with respect to the reference one. Since the vertical component is not affected by rotation transform, we only take into account the horizontal components from several unknown microseismic events and define Pref as eigenvector u1 corresponding to the largest eigenvalue. Pref ( Px11 Px12 ... Pxl1 , Py11 Py12 ... Pyl1 , Px21 Px22 ... Pxl2 , Py21 Py22 ... Pyl2 , ... ... , Pxk1 Pxk2 ... Pxlk , Pyk1 Pyk2 ... Pylk ) lated in the least-squares sense to minimize Az , (3) and Y components associated with the kth event, respectively. We can get the rotation angle by utilizing Gauss-Newton method to minimize (m) and solve the norm equation (4) J (m) m F (m) , where 1 Prot (m) Pref1 2 P (m) Pref2 . (5) F (m)= rot 2 k l 2 k l Prot (m) Pref 2 M i 1 where Pxlk and Pylk denote the lth sample point data of X © 2016 SEG SEG International Exposition and 87th Annual Meeting The absolute orientation of it, Azleast squares , can be calcu- i Azleast squares , (9) where M denotes the number of sensors and Azi represents the calculation result of ith sensor. Synthetic example To prove the feasibility and reliability of the above approach, we apply it to synthetic data generated with General Reflection and Transmission Method (GRTM) for a homogeneous layered model (Zhang et al., 2003). The acquisition geometry consists of twelve threecomponent sensors from the depth of 1350 m to 1625 m, with an interval of 25 m. Eight microseismic events are located in different positions and the drop-ball event is located at (800 m, 100 m, 1400 m), the depth within between sensors (Figure 1). For the homogeneous isotropic model, P wave velocity is 3000 m/s and ratio of Vp/Vs is equal to 1.7. Page 2643 Downloaded 09/14/16 to 50.244.108.113. Redistribution subject to SEG license or copyright; see Terms of Use at http://library.seg.org/ Improve sensor orientation using drop-ball and microseismic events (a) (b) Figure 1: Acquisition geometry with 12 sensors (green triangle), 8 microseismic events (red asterisk) and 1 drop-ball event (blue asterisk). We use random rotation angles (Table 1) to rotate the synthetic waveforms to model the situation that the orientation of every sensor is unknown and distributed randomly. (a) Figure 3: (a) Waveforms of X and Y components after orientation correction with traditional approach in Event 8. (b) Waveform of X and Y components after orientation correction with traditional approach in drop-ball event. (a) (b) Figure 2: (a) Waveforms of X and Y components with 5% Gaussian noise added after forward rotations in Event 8. (b) Waveforms of X and Y components with the same level noise added after forward rotations in drop-ball event. Random noise of the same level is added to both events and drop-ball event. Figure 2a shows the horizontal components (X and Y) of P-wave waveform for Event 8 (800m, 500m, 1500m) and Figure 2b is for the drop-ball event. It should be noted that the drop-ball event has lower amplitude. The same noise level roughly corresponds to 5% Gaussian noise for Event 8 while 20% Gaussian noise for the drop-ball event. Figure 2a and Figure 2b are scaled separately for better visualization. We use all the 8 events to calculate 11 relative rotation © 2016 SEG SEG International Exposition and 87th Annual Meeting (b) Figure 4: (a) Waveforms of X and Y components after orientation correction with our method in Event 8. (b) Waveforms of X and Y components after orientation correction with our method in dropball event. angles with our developed method. Using the sixth sensor as the reference one, other 11 sensors are rotated towards the reference sensor using corresponding relative angles. Then the absolute orientation is estimated using all the sensors in the least-squares sense. Figure 3a and Figure 3b show the orientated X and Y components of Event 8 and drop-ball event using orientation angles from the conventional approach; while Figure 4a and Figure 4b are corresponding results by using our new method. The higher level of coherence of the waveform by the new method at Page 2644 Downloaded 09/14/16 to 50.244.108.113. Redistribution subject to SEG license or copyright; see Terms of Use at http://library.seg.org/ Improve sensor orientation using drop-ball and microseismic events Sensor 7 and 8 and 9 is obvious, which indicates the higher accuracy of the derived rotation angle by the new method. Table 1 shows the comparison between the results by our method and the ones obtained using the traditional approach with the drop-ball event alone, which shows more accurate and reliable results of the new method quantitatively. Sensors Number True Orientation Angle (°) 1 2 3 4 5 6 7 8 9 10 11 12 -5 100 -50 20 30 -35 -20 55 -90 10 -79 -100 Conventional Technique (°) Angle Error Our Method (°) Angle Error -6 102 -47 21 35 -37 -24 56 -91 16 -77 -97 -5.0 100.1 -49.9 20.3 30.1 -35.0 -20.0 55.3 -89.8 10.1 -79.1 -99.9 -1 2 3 1 5 -2 -4 1 -1 6 2 3 0 0.1 0.1 0.3 0.1 0 0 0.3 0.2 0.1 -0.1 0.1 Table 1: Comparison between orientations obtained with our method and the estimated ones with conventional technique. ensure better waveform similarity after applying sensor orientation. Synthetic examples and real data suggest that our approach can provide more accurate and reliable results than the conventional one. (a) (b) Figure 5: (a) Raw data. (b) Rotated data with our new approach. (a) (b) Real data example We test our method in a real data example, where two microseismic events with unknown locations and high SNR are selected to calculate the relative orientation angles and one drop-ball event is applied to determine absolute orientation of the first sensor using the algorithm in this paper. The calculated orientation angles are shown in Table 2 for both methods, which have a difference from 0.6° to 10.3°. Figure 5a shows raw data of a microseismic event and its rotated traces using the orientation angles obtained with our new method are in Figure 5b. We can see a high coherence between adjacent sensor pairs. Figure 6 shows the distribution of event azimuth from the 12 sensors for this event after orientation correction by the two methods. Obviously, the event azimuths in Figure 6b are more concentrated and have a smaller variance, implying our method superior performance than the traditional method. Conclusions We develop a new method for determining the orientations of sensors deployed in the vertical observation well, taking into account both unknown microseismic events and dropball event together. Due to constraining the waveform coherence between adjacent sensor pairs, accuracy of relative angles calculated can be improved. Compared with the orientation angles obtained with drop-ball event alone, our new approach can derive the orientation angle that can © 2016 SEG SEG International Exposition and 87th Annual Meeting Figure 6: Event azimuth (cross symbol) (a) after orientation correction with conventional technique; (b) after orientation correction with our method. Sensor Number Conventional (°) Our Method (°) Sensor Number Conventional (°) Our Method (°) 1 2 3 4 5 6 87 90.0 -35 -44.9 124 -127.8 45 43.5 49 42 23 26.2 7 8 9 10 11 12 21 14.5 -16 -12.0 29 32.3 146 156.3 -103 -103.6 -36 -27.0 Table 2: Comparison of orientation angles obtained with conventional technique and our method. Acknowledgements We appreciate the support of GeoTomo, allowing us to use the MiVu software package to perform this study. Page 2645 Downloaded 09/14/16 to 50.244.108.113. Redistribution subject to SEG license or copyright; see Terms of Use at http://library.seg.org/ EDITED REFERENCES Note: This reference list is a copyedited version of the reference list submitted by the author. Reference lists for the 2016 SEG Technical Program Expanded Abstracts have been copyedited so that references provided with the online metadata for each paper will achieve a high degree of linking to cited sources that appear on the Web. REFERENCES Becquey, M., and M. Dubesset, 1990, Three-component sonde orientation in a deviated well: Geophysics, 55, 1386–1388, http://dx.doi.org/10.1190/1.1442786. Di Siena, J. P., J. E. Gaiser, and D. Corrigan, 1984, Horizontal components and shear wave analysis of three component VSP data, in M. N. Tosoz, and R. R. Stewart, eds., Vertical seismic profiling, part B: Advanced concepts, 175–235. Grigoli, F., S. Cesca, T. Dahm, and L. Krieger, 2012, A complex linear least-squares method to derive relative and absolute orientations of seismic sensors: Geophysical Journal International, 188, 1243–1254, http://dx.doi.org/10.1111/j.1365-246X.2011.05316.x. Jurkevics, A., 1988, Polarization analysis of three-component array data: Bulletin of the Seismological Society of America, 78, 1725–1743. Michaels, P., 2001, Use of principal component analysis to determine downhole tool orientation and enhance SH-waves: Journal of Environmental & Engineering Geophysics, 6, 175–183, http://dx.doi.org/10.4133/JEEG6.4.175. Nakamura, Y., P. L. Donoho, P. H. Roper, and P. M. McPherson, 1987, Large-offset seismic surveying using ocean-bottom seismographs and air gun: instrumentation and field technique: Geophysics, 52, 1601–1611, http://dx.doi.org/10.1190/1.1442277. Zeng, X., and G. A. McMechan, 2006, Two methods for determining geophone orientations from VSP data: Geophysics, 71, V87–V97, http://dx.doi.org/10.1190/1.2208935. Zhang, H., X. Chen, and S. Chang, 2003, An efficient numerical method for computing synthetic seismograms for a layered half-space with sources and receivers at close or same depths: Pure and Applied Geophysics, 160, 467–486, http://dx.doi.org/10.1007/PL00012546. © 2016 SEG SEG International Exposition and 87th Annual Meeting Page 2646