VSP polarization angles determination: Wysin1 processing case study
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Abstract
In this paper, we present an analysis of borehole seismic data processing procedures required to obtain highquality vertical stacks and polarization angles in the case of walkaway VSP (vertical seismic profile) data gathered in challenging conditions. As polarization angles are necessary for more advanced procedures like anisotropy parameters determination, their quality is critical for proper media description. Examined Wysin1 VSP experiment data indicated that the best results can be obtained when rotation is performed for each shot on data after denoising and vertical stacking of unrotated data. Additionally, we proposed a procedure of signal matching that can substantially increase data quality.
Keywords
Walkaway VSP Polarization Signal processing S/N ratio Azimuth and inclinationIntroduction
Obtaining more accurate geological information based on the analysis of seismic waves is now even more important than it was in the past. 3C VSP walkaway seismic survey can refine surface seismic observations, provide additional information about the geology (Hinds et al. 1996; Trela 1999), and ultimately allow one to obtain complete information on the anisotropic elasticity tensor (Dewangan and Grechka 2003). Additionally, it gives the possibility of obtaining a highquality seismic section in the domain of a common depth point. This is due to the fact that the seismic signal propagates through the geological media in such a way that there is only one passage through the lowvelocity zone (LVZ). This zone, due to the often high variability of its components, a small compaction, and usually a large amount of pore space, has a significant impact on the energy of seismic waves. This is caused mostly because of energy dissipating on inhomogeneities and by using it for surface waves generation. Additionally, seismic borehole surveys greatly reduce the impact of spherical divergence and energy attenuation as a result of nonelastic interactions (Trela 1996a, b; Gulati et al. 2004; Kuzmiski et al. 2009). Walkaway VSP surveys also improve the accuracy of microseismic observations by helping in the proper determination of perforation parameters for hydraulic fracturing in shale formations (Pei et al. 2017). Developing an optimal technique for walkaway VSP data processing will consequently allow for the minimization of errors and the maximization of the signaltonoise ratio. It is a key factor for correct interpretation and application of these data for hydrocarbon exploration.
In the case of a classic, nearoffset 3C VSP survey, it is assumed that each of the receiver’s components registers only one type of medium vibrations. Obviously, this assumption is only true if the medium is isotropic and homogeneous. Vertical component Z measures longitudinal wave, while horizontal components H1 and H2 record transversely or longitudinally polarized transverse waves. In the case of a multilevel borehole receiver tool (like 96channel BSR Slimhole Array System used in Wysin1 experiment), the walkaway VSP allows obtaining detailed seismic information at a given depth in a function of the offset. This type of measurement allows one to obtain a better quality seismic signal (Payne et al. 1994; Bartoń 2014) and more detailed information about seismic attenuation (Xu et al. 2001). Eventually, highquality VSP allows one to obtain accurate information about local anisotropy (Dewangan and Grechka 2003; Grechka and Mateeva 2007) and deposit parameters (Xiange et al. 2009).In this case study considering Wysin1 VSP experiment, we examine processing sequences that allows the most efficient vertical stacking and, in consequence, stabile azimuth and inclination angles determination with limited errors. Accurate determination of both polarization angles or, with some assumptions, only inclination in every receiver is a critical factor for the estimation of local anisotropy by using P wave only inversion (Grechka and Mateeva 2007).
We took under consideration signal matching for every SP separately before performing the vertical stacking of records. Finally, we wanted to point out the most practical position for polarization analysis in the processing sequence.
In this study, we use signaltonoise ratio in a function of offset and depth as a quantitative evaluation criterion, as well as the error values for determining the inclination angles for individual depth levels. As an additional qualitative criterion, we used the quality of the seismic record in the common shotpoint domain. The analyzed data were gathered in the Liniewo area in the Pomeranian Voivodship. The seismic survey was carried out by the company Geofizyka Toruń SA on behalf of the Department of Geology, Geophysics and Environmental Protection of the AGH University of Science and Technology in Kraków, as part of the “Polish Technologies for Shale Gas” project.
The characterization of acquisition and region
Walkaway VSP acquisition and wavefield separation
Typically, receivers in the borehole are 3C geophones. When the well is vertical, axis Z is perpendicular to the component V (vertical) axis, and components H1 and H2 axes can be in a random position in the horizontal plane. The angle between H1 and H2 is always 90°. One of the important steps in walkway VSP processing is to isolate the downgoing P wave energy (Hinds et al. 1996). Hardage (1985) proved that SH and SV propagate along with a downgoing P wave path.
The idea of the wavefield separation for the compression P wave and longitudinal SV and SH waves is based on the analytical method of the determination of the medium particles’ direction of motion (Galperin 1984).
After that, the reorientation of components is performed according to estimated angles. As the final result, energy is maximized on the V component, and the energy which was not possible to obtain on V component is projected on the H1 and the rest of the energy is stored in H2 component. In this paper, the method of wavefield separation proposed by DiSiena et al. (1984) was applied and expanded upon. The final rotation effect of all three components was compared depending on three methods of the component’s rotation angles calculation: peak vector amplitude, maximum power search methods, and the principal component method (compare—Kirlin and Done 1999).
Peak vector amplitude (PVA) is a method based on the analysis of the magnitude vector for a given time sample. Please note that, in this approach, the word “magnitude” cannot be directly related to the seismological definition. For the needs of seismic surveys, the magnitude vector for a given time series t_{n} in the interval \( \varTheta = (n_{a} ,n_{z} ),\;n_{a} < n_{z} \,\varLambda \,n \) belongs to Θ and will be defined as the supremum of the sum of squares of amplitudes in the range Θ for all registered components on given time samples belonging to Θ. In this method, the angle of rotation is determined based on the direction of the maximum amplitude vector, on the basis of the time point t_{x} found in the window Θ in the way described above.
Maximum power search (MPS) is a method of finding a new coordinate system in which components get maximized energy. The proposal coordinates system differing from the initial position by the angle δ_{n} are defined using a Δδ step. Then, for each new position, the energy maximum values for each axis are calculated based on the samples in the given time window. The final step in the procedure is to choose a system that gives maximum energy for the determined inclination and azimuth.
Noise attenuation and vertical stacking
The amplitude and hence the signal strength is influenced by many different factors—from the acquisition itself through the features of the geological media, the nonstationarity of the seismic signal, to the phenomena like interference and noise (Varela et al. 1996; Kowalski 2016).

OPTION 1 (O1): rotation is performed on raw data, before vertical stacking for each shot separately. Then vertical stacking and denoising are applied.

OPTION 2 (O2): rotation is performed on raw data after vertical stacking, and then denoising is applied.

OPTION 3 (O3): rotation is performed on data after denoising, which is performed for each shot separately, and then vertical stacking is performed.

OPTION 4 (O4): rotation is performed for each shot on data after denoising and vertical stacking of unrotated data.
 1.
Perform bandpass filtering using a single, zerophase, and a single timeinvariant Ormsby filter (16 to 80 Hz passband filter with a 8 Hz wide lowcut ramp and a 40 Hz wide high cut ramp).
 2.
Remove monofrequency noise (which were frequent for the analyzed registrations) on the basis of determining the mean of the arrhythmic value of such disturbance along a single seismic trace (this kind of noise is connected with tool generated harmonic noise).
 3.
Provide frequency filtration based on time–space analysis of the record using a shorttime Fourier transform (STFT) with replacing disturbance, connected to surface noise transmitted by a cable or the tool resonance noise, using values calculated on the basis of neighboring traces that were considered as undisturbed. (The time window was 200 ms long and has the five traces aperture. Those traces are used to calculate the median spectral amplitude when the threshold amplitude on particular sample has been reached. Threshold multiplier values were set on 3. The frequency of interest for STFT was 20–80 Hz.)
 4.
Provide the suppression of highenergy noise using horizontal and vertical median filtration. [The length of the vertical median computation window was 100 ms, and the width of horizontal median computation window was 30 traces. The 50 ms length cosine taper zone has been used with no scaling factor. This kind of noise can be inducted by tool slippage due to weak anchorage and tube waves (which are a problem only for nearoffset shot points).]
 5.
Top mute the energy over first breaks.
(Some types of noise (like tool resonance) are normally removed by moving the tool to another location; however, in this specific case, the tool had not been moved up during the whole acquisition. Moreover, in some parts of the well, there was no casing between the inner and outer tube, which leads to the generation of additional noise. When the casing is unbounded, the ring noise can be present too. It has to be notice that the last receivers are hanging on the cable of a length over 3 km. If the anchorage is not perfect, the various forces have an effect on receivers and could lead to unexpected noise generation that is hard to classify. The results of the described procedures are shown in Fig. 9.)
It is easily noticeable that error and inclination values for O1 and O2 are significantly higher than for O3 and O4. At this stage, we chose O4 as the best option for most accurate polarization angle determinations; however, the expectation was that the course of changes will continue the increasing trend or will be stable instead of changes that can be described by a seconddegree polynomial curve. In the next step, the novel signal processing has been added to exclude the described abnormal trend of changes in the behavior visible in O4 result.
Signal matching
It is possible that the observed phenomena are due to fact that the well was not properly cased in the depth interval above the 41th receiver. The observed effect of changes in the correlation values may be related to the changes in the quality of the receivers’ anchorage or some near well effects and geology itself. However, at this stage of the study, it is hard to fully explain that effect. Another reason for existing differences between vertical and horizontal components can be explained by the fact that only P wave was transmitted from source and most energy observed on horizontal components is transmitted through converted waves. A drastic decrease in correlation coefficients observed for selected receivers (e.g., 6, 22, 33) is most probably due to waddle anchorage.
It is clearly visible that, on some receivers, it was possible to obtain up to 25% higher S/N ratios (Fig. 16) and consequently obtain a better estimation of polarization angles (Fig. 14, Fig. 15).
Conclusion
 1.
The order of four main procedures is critical for proper polarization angle evaluation based on a three component walkaway VSP survey. In this paper, we have shown that the best results can be obtained when rotation is performed for each shot on data after denoising and vertical stacking of unrotated data (Option 4).
 2.
In the case of repeated shots at a given shot point, in conditions of high humidity and ground instability, the calculation and application of the signal matching filter based on pilot registration, being a static representation of all shots at the particular shot point, are justified.
 3.
The matching filter should be calculated and applied after the noise is removed.
 4.
Obtained values are strongly related to lithological complexes (determined on welllogs results).
 5.
There are changes in inclination and azimuth values within particular lithological complex that are not related to the lithology. It is likely that this phenomenon is caused by the elastic inconsistency of a particular complex.
Notes
Acknowledgements
We express our gratitude to Jerzy Trela (Director of Geophysics in GT Services) for assistance with VSP processing and interpretation problems. We also thank Michal Podalak (former R&D Department Chief in GT Services) and Henryk Kowalski (R&D Chief in GT Services) for their help, critical remarks, and constructive comments. Additionally, we thank PGNiG S.A. for allowing us to use seismic and welllog data acquired as a part of the project “Polish technologies for shale gas.”
References
 Bartoń R (2014) Determination of directional velocity changes of transverse waves in nearwellbore zone based on VSP 3C. Nafta Gaz 70(8):483–492Google Scholar
 Dewangan P, Grechka V (2003) Inversion of multicomponent, multiazimuth, walkaway VSP data for the stiffness tensor. Geophysics 68(3):1022–1031. https://doi.org/10.1190/1.1581073 CrossRefGoogle Scholar
 DiSiena JP, Gaiser JE, Corrigan D (1984) Horizontal components and shear wave analysis of threecomponent VSP data. In: Toksoz N, Stewart RR (eds) Vertical seismic profiling: advanced concepts, vol 15 B. Geophysical Press, LondonGoogle Scholar
 Galperin EI (1984) The polarization method of seismic exploration. Springer, DordrechtGoogle Scholar
 Grechka V, Mateeva A (2007) Inversion of P wave VSP data for local anisotropy: theory and case study. Geophysics 72(4):69–79. https://doi.org/10.1190/1.2742970 CrossRefGoogle Scholar
 Gulati JS, Stewart RR, Parkin JM (2004) Analyzing threecomponent 3D vertical seismic profiling data. Geophysics 69:386–392. https://doi.org/10.1190/1.1707057 CrossRefGoogle Scholar
 Hardage BA (1985) Vertical seismic profiling, 2nd edn. Geophysical Press, LondonGoogle Scholar
 Hinds CR, Anderson LN, Kuzmiski DR (1996) VSP interpretative processing. Society of Exploration Geophysicist, Tulsa. https://doi.org/10.1190/1.9781560801894 CrossRefGoogle Scholar
 Hoteling H (1933) Analysis of a complex of statistical variables into principal components. J Educ Psychol 24:417–441CrossRefGoogle Scholar
 Jolliffe (1986) Principal component analysis. Springer, New YorkCrossRefGoogle Scholar
 Kirlin LR, Done JW (1999) Covariance analysis of seismic signal processing. Society of Exploration Geophysics, Tulsa. https://doi.org/10.1190/1.9781560802037 CrossRefGoogle Scholar
 Klemperer S (1987) Seismic noisereduction techniques for use with vertical stacking: an empirical comparison. Geophysics 52(3):322–334. https://doi.org/10.1190/1.1442306 CrossRefGoogle Scholar
 Kowalski H (2016) Inverse Q filtering build into the prestack depth migration. Prace Naukowe INiGPIB, Kraków, p 209Google Scholar
 Kumar L, Sinha DP (2008) From CMP to CRS: an overview of stacking techniques of seismic data. In: 7th Biennial international conference and exposition on petroleum geophysics, p 414Google Scholar
 Kuzmiski R, Chartes B, Galbraith M (2009) Processing considerations for 3D VSP. Recorder 34(04):31–40Google Scholar
 Michaels P (2001) Use of principal component analysis to determinate downhole tool orientation and enhance SHwaves. JEEG 6(4):175–183. https://doi.org/10.4133/JEEG6.4.175 CrossRefGoogle Scholar
 Payne MA, Eriksen EA, Rape TD (1994) Considerations for highresolution VSP imaging. Lead Edge 14:173–180CrossRefGoogle Scholar
 Pei D, Carmichael J, Zhou R, Knapo G (2017) Enhanced microseismic anisotropic velocity optimization by checkshot and walkaway VSP. SEG technical program expanded abstracts, pp 2867–2871. https://doi.org/10.1190/segam201717538927.1
 Scheevel JR, Payrazyan K (1999) Principal component analysis applied to 3D seismic data for reservoir property estimation. In: SPE technical conference and exhibition. Society of Petroleum Engineers Inc., SPE 56734Google Scholar
 Trela J (1996a) VSP data processing. Nafta Gaz 8Google Scholar
 Trela J (1996b) VSP data acquisition. Nafta Gaz 5Google Scholar
 Trela J (1999) 3C VSP data processing. Wavefields separation using polarization method. Nafta Gaz 3:1999Google Scholar
 Varela LC, Rosa A, Ulrych T (1996) VSP data processing. Nafta Gaz 8Google Scholar
 Xiange S, Yun L, Jun G, Desheng S, Jixiang L (2009) Anisotropic parameter estimation based on 3D VSP and full azimuth seismic data. SEG technical program expanded abstracts, pp 4105–4109. https://doi.org/10.1190/1.3255728
 Xu C, Stewart RR, Osborne AC (2001) Walkaway VSP processing and Q estimation: Pikes Peak, Sask. In: CSEG annual conferenceGoogle Scholar
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