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2006 Special Edition CSEG RECORDER 65 Continued on Page 66 Advances in Land Multicomponent Seismic: Acquisition, Processing and Interpretation Coordinated by Robert Kendall VeritasDGC Inc, Calgary, Canada Introduction Land multicomponent seismic has garnered significant atten- tion and demonstrated substantial growth in the last five years. The bulk of this growth in multicomponent activity has been in western Canada. While multicomponent seismic has been around for many years, historical cost and quality concerns have limited its use as a routine exploration tool. The current resurgence of multicomponent seismic on land is a result of acquisition advances, specifically the development of digital multicomponent sensors (Micro Electro Mechanical System, or MEMS) and processing developments including statics, noise attenuation advances such as polarization filters and prestack migration. One remaining difficulty to be over- come is in the interpretation – or perhaps the interpreter’s misconceptions - of multicomponent data. On the acquisition side, MEMS technology is driving a multi- component-specific re c o rding revival. These tiny silicon dioxide accelerometers have been available for over a decade, yet only recently has technology allowed these miniature accelerometers to be manufactured with a noise performance compatible with seismic requirements. Both Input/Output and Sercel have adopted this technology in the design of unique micro-machined digital accelerometers specifically targeted at the seismic acquisition industry. Recent processing advances include better statics algorithms, application-specific noise attenuation and prestack time migration. Although the routine use of multicomponent prestack depth migration is still a few years off, the depth domain is the most natural place for comparing and inter- pretting images from the different modes. From an interpreter’s point of view, multicomponent data present numerous headaches and uncertainties. Today’s stan- dard interpretation software does not lend itself well to multicomponent interpretation. Furthermore, the PS and SS volumes are much noisier and of lower frequency, and typi- cally bear little resemblance in character to the familiar PP volumes. The registration process (event matching between the PP, PS and SS) is largely driven by the availability of dipole sonic logs. These dipole sonics are not as common as standard sonics and tend to be more affected by washouts and other problems with the borehole. Therefore, the quality control and petrophysics required for PS and SS interpreta- tion can be much more complex. Furthermore, many interpreters have not been able to iden- tify a clear-cut situation where the additional components have been absolutely necessary. It is precisely this mind-set ("killer-application" or nothing) that hampers the further acceptance of multicomponent seismic data as an exploration and exploitation tool. Just as we would never drill a well based on AVO alone or on a prestack depth migration image alone, we should not expect multicomponent seismic to be a "silver bullet". Like any other type of additional information, it contributes supplementary independent data to help reduce risk when choosing well locations. There is, however, some room for optimism: the potential benefits of multicomponent data include improved conven- tional P-wave data, more reliable rock property information, lithology discrimination, fluid identification, fracture and stress identification and characterization as well as improved imaging (for example below gas clouds, beneath salt and basalt and in low impedance PP reservoirs). In this article, we review some of the latest developments in multicomponent technology including a brief overview of the MEMS sensors, recent processing advances including statics, ground roll attenuation and converted-wave PSTM, and four interpretation case histories that highlight the added value of multicomponent seismic data. Acquisition The New Generation of Recording Equipment John Gibson, Howard Watt, Jim Roy, and Robert Kendall VeritasDGC Inc., Houston, Texas For standard P-wave exploration, the analog coil geophone has served the industry well for over seventy years. It is rela- tively inexpensive, rugged and reliable, and allows for flex- ible array designs, but it also presents limitations. The natural resonance of the analog coil geophone (for example, 10 Hz) limits the re c o rded signal fidelity at lower frequencies. F u r t h e r m o re, the advent of 24-bit re c o rding, impro v e d processing options and cost constraints have lessened the concern, but not the need, for careful receiver-array design. For multicomponent applications the conventional geophone’s limitations become more noticeable. Vector fidelity and the response of one vertical and two horizontally deployed elements are severely limited unless the geophone is planted within a few degrees of perfectly level. MEMS sensors offered to the seismic industry by Sercel (DSU3) and Input/Output (VectorSeis“) provide some decided advantages over prior multicomponent sensors. They include single sensor (point receiver) recording, direct digital output, improved vector fidelity, broadband linear phase and amplitude response, low harmonic distortion, measurement of sensor tilt, and reduced power consumption. These are important performance characteristics for multi- component seismic data acquisition, and directly relate to the quality of recorded data and their value in understanding the subsurface. It is essential that noise be minimized during converted-wave acquisition when using a single sensor at each station. Direct digital output produced through the inte- gration of the sensor and field electronics virtually eliminates

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2006 Special Edition CSEG RECORDER 65

Continued on Page 66

Advances in Land Multicomponent Seismic:Acquisition, Processing and InterpretationCoordinated by Robert KendallVeritasDGC Inc, Calgary, Canada

Introduction

Land multicomponent seismic has garnered significant atten-tion and demonstrated substantial growth in the last fiveyears. The bulk of this growth in multicomponent activity hasbeen in western Canada. While multicomponent seismic hasbeen around for many years, historical cost and qualityconcerns have limited its use as a routine exploration tool.The current resurgence of multicomponent seismic on land isa result of acquisition advances, specifically the developmentof digital multicomponent sensors (Micro Electro MechanicalSystem, or MEMS) and processing developments includingstatics, noise attenuation advances such as polarization filtersand prestack migration. One remaining difficulty to be over-come is in the interpretation – or perhaps the interpreter’smisconceptions - of multicomponent data.

On the acquisition side, MEMS technology is driving a multi-component-specific re c o rding revival. These tiny silicondioxide accelerometers have been available for over a decade,yet only recently has technology allowed these miniatureaccelerometers to be manufactured with a noise performancecompatible with seismic requirements. Both Input/Outputand Sercel have adopted this technology in the design ofunique micro-machined digital accelerometers specificallytargeted at the seismic acquisition industry.

Recent processing advances include better statics algorithms,application-specific noise attenuation and prestack timemigration. Although the routine use of multicomponentprestack depth migration is still a few years off, the depthdomain is the most natural place for comparing and inter-pretting images from the different modes.

From an interpreter’s point of view, multicomponent datapresent numerous headaches and uncertainties. Today’s stan-dard interpretation software does not lend itself well tomulticomponent interpretation. Furthermore, the PS and SSvolumes are much noisier and of lower frequency, and typi-cally bear little resemblance in character to the familiar PPvolumes. The registration process (event matching betweenthe PP, PS and SS) is largely driven by the availability ofdipole sonic logs. These dipole sonics are not as common asstandard sonics and tend to be more affected by washoutsand other problems with the borehole. Therefore, the qualitycontrol and petrophysics required for PS and SS interpreta-tion can be much more complex.

Furthermore, many interpreters have not been able to iden-tify a clear-cut situation where the additional componentshave been absolutely necessary. It is precisely this mind-set("killer-application" or nothing) that hampers the furtheracceptance of multicomponent seismic data as an explorationand exploitation tool. Just as we would never drill a wellbased on AVO alone or on a prestack depth migration imagealone, we should not expect multicomponent seismic to be a

"silver bullet". Like any other type of additional information,it contributes supplementary independent data to helpreduce risk when choosing well locations.

There is, however, some room for optimism: the potentialbenefits of multicomponent data include improved conven-tional P-wave data, more reliable rock property information,lithology discrimination, fluid identification, fracture andstress identification and characterization as well as improvedimaging (for example below gas clouds, beneath salt andbasalt and in low impedance PP reservoirs).

In this article, we review some of the latest developments inmulticomponent technology including a brief overview of theMEMS sensors, recent processing advances including statics,ground roll attenuation and converted-wave PSTM, and fourinterpretation case histories that highlight the added value ofmulticomponent seismic data.

Acquisition

The New Generation of Recording EquipmentJohn Gibson, Howard Watt, Jim Roy, and Robert KendallVeritasDGC Inc., Houston, Texas

For standard P-wave exploration, the analog coil geophonehas served the industry well for over seventy years. It is rela-tively inexpensive, rugged and reliable, and allows for flex-ible array designs, but it also presents limitations. The naturalresonance of the analog coil geophone (for example, 10 Hz)limits the re c o rded signal fidelity at lower fre q u e n c i e s .F u r t h e r m o re, the advent of 24-bit re c o rding, impro v e dprocessing options and cost constraints have lessened theconcern, but not the need, for careful receiver-array design.For multicomponent applications the conventionalgeophone’s limitations become more noticeable. Ve c t o rfidelity and the response of one vertical and two horizontallydeployed elements are severely limited unless the geophoneis planted within a few degrees of perfectly level.

MEMS sensors offered to the seismic industry by Sercel(DSU3) and Input/Output (VectorSeis“) provide somedecided advantages over prior multicomponent sensors.They include single sensor (point receiver) recording, directdigital output, improved vector fidelity, broadband linearphase and amplitude response, low harmonic distortion,measurement of sensor tilt, and reduced power consumption.

These are important performance characteristics for multi-component seismic data acquisition, and directly relate to thequality of recorded data and their value in understanding thesubsurface. It is essential that noise be minimized duringconverted-wave acquisition when using a single sensor ateach station. Direct digital output produced through the inte-gration of the sensor and field electronics virtually eliminates

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Multicomponent analysis Cont’dAdvances in Land Multicomponent...Continued from Page 65

pick-up noise, cross-talk and sensitivity to leakage. Additionally,harmonic distortion is reduced considerably relative to conven-tional multicomponent geophones.

Vector fidelity is of considerable importance in accurately meas-uring true earth motion. It is determined primarily by thecoupling of the sensor package to the ground, but also dependson the consistency of element sensitivity, cross axis rejection,sensor orthogonality and tilt of the deployed unit. MEMS-based3C digital sensors provide vastly improved specifications for theabove attributes together with capability for measuring andcorrecting sensor tilt. The net result is vector fidelity superior tothat of conventional multicomponent systems.

The overall complexity and physical attributes of modern multi-component acquisition systems are vastly reduced from previous

generations. Figure 1 compares the system configurations forconventional geophone array, conventional multicomponent(3C), and MEMS 3C recording. Note that with MEMS-basedrecording systems, an array of receiver sensors is replaced by asingle 3C MEMS sensor, or "point receiver." Traditional triphone( 3C geophone ) recording often employs arrays.

Point receivers are preferable for multicomponent recording.They are essential for implementing tilt corrections and estab-lishing sensor orientation. Further, they provide isotro p i crecording for improved azimuthal analyses and higher resolu-tion through elimination of intra-array effects, notably S-wavestatics. On the other hand, single-sensor recording does notdirectly provide ambient and coherent noise rejection and thusproper sampling for converted-wave acquisition requires partic-ular attention.

The operational advantages of re c o rding multicompo-nent data with these new purpose-built multicompo-nent sensors are substantial. They re q u i re fewerconnections and less cable, so the overall weight of theequipment is reduced. Field productivity is enhancedand sensor coupling improved since the sensor neednot be adjusted for levelling purposes. The re c o rd i n gsystems allow for the segregation of individual compo-nents into individual files thus reducing pro c e s s i n gtime and uncertainty. The final result is more accurateand aff o rdable multicomponent acquisition.

Multicomponent Processing

Overview

In the last five years there there have been significantadvances in multicompomponent processing and, inparticular, converted-wave processing. Some of thebiggest obstacles to obtaining high-quality (high S/N)and high frequency results were deficiencies indatuming, statics, noise attenuation, azimuthalanisotropy considerations and anisotropic prestacktime migration. All of these processing steps are criti-

cally intertwined and overall improvements in the finalsection will be a result of small improvements at eachstage. Datuming and statics go hand in hand; for inter-pretation puposes, it is important that the two volumes(PP and PS) be processed at the same datum and allstatics re f e renced back to this common datum.Furthermore, shear statics are typically large and unre-lated to the P-wave statics and it is not possible toderive them with conventional refraction methods.Polarization techniques using single sensor multicom-ponent receivers have shown great success in ground-roll attenuation. Azimuthal anisotropy corre c t i o n sallow for more accurate velocity and static solutions.F i n a l l y, anisotropic converted-wave prestack timemigration offers an affordable and relatively accurateimaging solution.

F i g u re 1. Comparison of various seismic acquisition system configurations. (a) Conventionalgeophone arrays ( 6 elements ). (b) Conventional multicomponent triphones; system complexitycan produce wiring errors and system weights and power re q u i rements can be onerous. (c)MEMS - single cable, light weight, reduced power re q u i rements, easy deployment (shadedMEMS sensors re p resent an alternate design using half station interval).

F i g u re 2. Schematic illustration of travel times due to topographical variations. ‘ts’ is the traveltime from source to CCP, ‘tr’ is the travel time from CCP to re c e i v e r, ‘to’ the two-way zero offsettime. Note the assymetric raypath: the conversion point is shifted towards the re c e i v e r.Assuming that the travel path for the PP (source) leg is close to that of the PS (receiver) leg is agood appro x i m a t i o n .

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Multicomponent Processing – Datums, Binning and NMOGraziella Kirtland GrechVeritasDGC Inc., Calgary, Canada

The conventional P-wave NMO equation assumes that all shot-receiver pairs for traces within a midpoint gather are at the sameelevation. Data are typically processed to meet this assumptionby the application of statics. While this method is applicable ina reas of minimal topography, it pro g ressively breaks down ina reas where topographical variations exceed a few tens of meters,making accurate velocity analysis difficult. In order to account forelevation diff e rences between the shot, receiver and commonconversion point (CCP), the paths and, there f o re travel times,f rom shot to the CCP and from the CCP to receiver have to bec o n s i d e red separately (Figure 2).

Elevation diff e rences can be accounted for in NMO by describingthe total travel time from source to receiver by using the doubles q u a re root (DSR) equation - similar to that commonly used inp restack imaging – rather than the conventional single squareroot NMO equation.

In practice, because of the presence of the near-surface low-velocity or weathering layer, drift static corrections have to becomputed first. These can be done in a surface-consistent way toa floating datum, and NMO with the DSR equation can follow.Velocity analysis is performed by scanning a cube of constantvelocity stacks, or by other techniques. A temporally and laterallyvarying velocity function is interactively picked and applied tothe gathers. Such a flow is also compatible with prestack timemigration surface-consistency re q u i rements.

ReferenceG rech, M. G., Miao, X., and Zhu, T., 2004, A Velocity analysis pro c e d u re for multicompo -nent data with topographic variations, CSEG National Convention, May 2004, Calgary.

Multicomponent Processing – An SVD-polarization filter forground roll attenuation on multicomponent data Kristof DeMeersmen, University of Leeds, Leeds England and Robert Kendall, VeritasDGC Inc., Calgary, Canada

In many cases, the success of a land survey partially depends ong round roll suppression and the recovery of low frequency re f l e c-tions. These issues are important insurvey design and data pro c e s s i n g .The traditional approaches to atten-uate ground roll include the use ofs o u rce and receiver arrays, the stackarray method (Anstey, 1986), orfiltering in the fre q u e n c y, f-k, _-p, orwavelet domains. Here we focus onthe ground roll problem for multi-component data. The manner inwhich these data are acquired oftenp revents the use of many traditionalattenuation techniques. The use ofs o u rce and receiver arrays becomesespecially impractical for 3D surveys,and more generally, intra-array S-wave statics can not be neglected. In3D, the tight restrictions the stack

array method puts on fold and CDP bin population are often notmet (Anstey, 1986). Despite this, multicomponent data provide thegeophysicist with the opportunity to apply powerful ground ro l lattenuation methods. One such method that uses full wavefieldinformation is adaptive polarization filtering.

The polarization filter we present here combines previous eff o r t sby Franco and Musacchio (2001) and Liu (1999) with the concept ofcomplex polarization analysis (Vidale 1986). SVD decomposes thedata into its elliptically and linearly polarized components. Incontrast with f-k and tau-p filtering methods, the polarizationfilters are essentially a station by station process which is inde-pendent of spatial sampling. This feature makes polarization filtersapplicable in 3D, where spatial sampling in the cross-line dire c t i o nis a problem for other methods. Also, the design of the 3D surveyscan focus more on sampling the signal than on sampling the noise.

Filter Theory

For the 3C case, our adaptive filter in matrix notation is:

with

1)

Here, D f is the t by 3 matrix in which the columns hold thefiltered vertical, inline and crossline seismograms of a given 3Cstation. D is a matrix with same dimensions as D f, but itscolumns now hold the unfiltered analytic signals of the vertical,inline and crossline components. Dlp is the t by 3 matrix withlow-pass filtered analytic signals from the n selected neighboring3C stations. A constant moveout correction, relative to the 3Cstation of interest can be applied to the different samples in Dlp.F(t) is a 3 n by 3 n time-dependent filter operator and S is thesparse matrix whose nonzero elements relate to the 3C stationthat is being filtered. The factor (D lp F(t) S) represents theanalytic signals of the estimated ground roll. The filter operator,F(t) is derived using the first eigenvector v(t)1 of the time-

Multicomponent analysis Cont’dAdvances in Land Multicomponent...Continued from Page 66

F i g u re 3. Results of synthetic tests. Left: the synthetic showing the ground roll. Right: the same record after the polariza -tion filter was applied. The synthetic was used to understand which filter parameters were optimal for removing gro u n droll, while preserving signal.

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dependent data covariance matrix C(t). C(t) is calculated usingthe rows d(t)lp of Dlp for the time-window [t-T t+T]:

and 2)

Data Examples

We first use a synthetic shot to test the filter. A wide range offilter parameters were tested on the synthetic shot gather todetermine the filter that best removes ground roll whilepreserving the signal. Tests showed that the main parametersc o n t rolling filter performance are the frequency applicationbandwidth and the design window. The length mainly controls

the amount of energy that is removed and wider filters are betterat preserving reflected energy. Figure 3 shows that the polariza-tion filter effectively removes the ground roll noise whilepreserving the reflection signal.

We next test the filter on two real data examples (Figures 4 and5), one from Eastern Europe (2D-3C) and the other from EastTexas (3D-3C). Both examples use MEMS recording technology.The polarization filter is applied on raw 3C shot gathers after tiltcorrection. Ground roll frequencies are usually restricted to the 0-15 Hz band and peak at approximately 8 Hz. Moreover, withinthis band the signal-to-noise ratio is very low and a >30 dBpower difference between ground roll and signal is common.The ground roll is dispersive and spatial sampling is sufficient to

avoid spatial aliasing. However, wewill also show that our polarizationfilter works on aliased ground ro l lwhen the filter width is one trace.

F i g u re 4 shows an example fro mEastern Europe. In this case we showthe shot gathers for both the verticaland inline components, before andafter filtering. The ground roll in thisarea is very strong and dispersive andthe polarization filter does a good jobof attenuating the surface noise.

Figure 5 shows the stacked sectionsfrom the previous example for the non-filtered, f-k filtered and polarizationfiltered. In this extreme case of groundroll, the noise has remained throughthe deconvolution and stack processes.While the f-k filter does a good job ofattenuating the noise, the polarizationfilter does a better job.

In Figure 6, a shot re c o rd from East Te x a sis shown. Figure 6a is the raw shot;F i g u re 6b is the shot after the applicationof the polarization filter; Figure 6c showsthe diff e rence. Note that only the zonethat was contaminated by ground ro l lhas been affected by the filtering. This isdue to the fact that the polarization filtercan also detect the ground roll in addi-tion to being able to remove it (Jin andRonen, 2005). The detection is particu-larly beneficial to the preservation of thesignal since we can apply the filter onlywhen the ground roll is detected. It alsomakes the polarization filter more prac-tical by eliminating the re q u i rement ofg round roll picking.

In both the shot re c o rd examples(Figures 4 and 6), the signal-to-noiseimprovement is obvious as reflectionsthat were previously invisible are nowclearly resolvable on the gathers.

Multicomponent analysis Cont’dAdvances in Land Multicomponent...Continued from Page 67

F i g u re 4. Vertical and inline shot gathers of a re p resentative shot. A: Raw shot vertical, B: with polarizationf i l t e r, C: raw shot inline, D: with polarization filter. The filter design window is 84 ms long, 7 stations wideand has a linear moveout of 2 ms/m. The filter operates in the 0-15 Hz band, and was applied only to the databelow the red line.

F i g u re 5. Results on stack sections. A: the non-filtered stack; B: f-k filtered stack; C: polarization-filtered stack.The most noticeable differences are seen in the area outlined. For this extreme case of ground roll, the polariza -tion filter is doing a better job than the f-k filter and these differences are apparent on the stack sections. This2D survey was designed so the ground roll would not be aliased for the f-k filter. However, spatial aliasing ofthe ground roll in other surveys, especially in 3D surveys, often makes f-k filtering untenable, while the polar -ization filter will perform well even when the ground roll noise is spatially aliased.

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Adaptive Polarization Filtering Summary

Our work demonstrates the use of SVD polarization filters toattenuate ground roll on 3C data. The filter can be appliedstation-by-station and is there f o re not subject to spatialsampling. In particular, it is applicable in 3D without having todesign the survey with the dense sampling required for avoidingspatial aliasing of the noise in the inline and in the crosslinedirections. Using the test-data we were able to improve signal-to-noise in the ground roll band on the vertical component byapproximately 30 dB.

ReferencesAnstey, N., 1986, Whatever happened to ground-roll?, The Leading Edge, 5, 40-46.

Franco, R., and G. Musacchio, 2001, Polarization filter with singular value decomposi -tion, Geophysics, 66, 932-938.

Jin, S. and Ronen, S., 2005, Ground roll detection and attenuation by 3C polarizationanalysis, 67th Mtg.: Eur. Assn. Geosci. Eng., B020

Lui, X., 1999, Ground roll suppression using the Karhunen-Loeve transform, Geophysics,64, 564-566.

Vidale, T., 1986, Complex polarization analysis of particle motion, Bulletin of theSeismological Society of America, 76, 1393-1405.

Multicomponent Processing – Anisotropic Converted-wavePrestack Time Migration Xiaogui Miao, Sam Gray, Robert Kendall VeritasDGC Inc., Calgary, Canada

Preserving amplitudes in converted-wave prestack migration isimportant for subsequent AVO prestack processing and interpre-tation. However, it has not received as much attention as ampli-tude preservation for P-wave migration. As multicomponentland and OBC data acquisitionbecome more widely available,converted-wave prestack migrationis becoming a more routine process inseismic exploration and develop-ment. Converted-wave data alsocontain additional information aboutvelocities (Vp and Vs) and other rockproperties. Extracting this informa-tion from converted-wave data bymeans of PS AVO, joint PP/PS inver-sion, and other interpre t i v eprocessing has attracted some atten-tion; in addition, it has placed a crit-ical requirement for the preservationof amplitude information inprocesses such as prestack migration.

For the converted wave, few papersaddress the true amplitude issue forprestack Kirchhoff migration becauseof complications due to asymmetricscattering angles. We start fro mBleistein’s Kirc h h o ffinversion/migration formula andderived true amplitude weight func-tions in a v(z) medium for commonoffset Kirchhoff migration for both3D and 2.5D. Please see Miao (2005)for a full derivation.

Since 2.5D ray theory and migration characterize in-plane prop-agation of waveforms from a point source in 3D, they moreclosely represent reality than 2D in-plane propagation, whichdoes not recognize the out-of-plane geometrical spreading ofwaves. We have derived a 2.5D amplitude weighting scheme andimplemented it for our 2D migration. We also tested it withsynthetic data, which shows more accurate amplitude behaviorsthan the 2D weight.

In our implementation, the true amplitude weights for both 3Dand 2.5D have been simplified to functions of travel time andvelocities, which can be easily obtained from pre-computedtravel time tables. The simplified versions demonstrate negli-gible computational cost over that of conventional migration.The numerical modeling tests have shown well-contro l l e damplitude behavior that successfully recovers amplitudes alongdepth and offset for both 2.5D and 3D cases.

We have prestack depth migrated a converted-wave syntheticdata set that was modeled on North Sea structures with our trueamplitude weights. The non-scaled migrated converted-wavedepth image is shown in Figure 7. The amplitudes on themigrated image are very well balanced for the whole section,and the migrated structures agree extremely well with the deriv-atives of PS impedance (rho*Vs) model.

ReferenceMiao,Xiaogui, et al, Converted wave true amplitude prestack Kirchhoff migration,2005 CSEG National Convention, May 2005, Calgary

Multicomponent analysis Cont’dAdvances in Land Multicomponent...Continued from Page 68

F i g u re 6. A and B are the vertical components before and after polarization filtering on data from East Texas. C is the difference. Note the complete removal of the spatially aliased ground roll while preserving the re f l e c t i o n s .While these effects would not necessarily show up on the stack volume, they could pose problems for any AVO work.

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Multicomponent Processing – StaticsRobert Kendall,VeritasDGC Inc., Calgary, Canada

Static corrections are a key step in converted-wave processingand also one of most difficult. Converted waves have been usedfor reservoir characterization and seismic imaging for manyyears, however, only in the last few years have robust converted-wave statics correction methods been developed. Converted-wave (P-SV) statics have many features diff e rent fro mconventional PP data and require special consideration. Oneconsideration is the large magnitude of converted wave staticswhich can be up to ten times greater than the P-wave statics( F i g u re 8, right). This often produces cycle skips whenattempting to use conventional residual statics algorithms toresolve them. Another feature is that the converted waves aregenerally much noisier than P-P data. This makes it less reliable

to pick time delays in CCP (Common-Conversion-Point) gathers.P-wave statics are typically not related to S-wave statics in asimple and predictable manner (Figure 8, right). One reason forthis is that the shear waves respond differently to the water tablethan do the P-waves. Consequently, most P-wave staticsmethods do not work well for converted-wave data.Furthermore, some misalignment in reflectors may be due toanisotropy and the resultant shear-wave splitting (PS1 and PS2).So running residual and trim statics on the PSV or radial datamay indeed be removing some of the information that is neces-sary for azimuthal anisotropy analysis.

Recent advances have been made in solving the receiver-sideshear statics of the converted-mode in the last few years. Since aconverted-wave first break does not exist, we have to use othermethods to determine the very slow, shear weathering statics.We use combinations of P-wave horizon-based pilots for

correcting the receiver stacks (Grech, 2004),and cro s s - c o r relation techniques in thereceiver domain (Jin, et al., 2004).

Assuming the shot statics are obtainedfrom conventional P-wave processing andapplied to the converted waves, only theshear receiver statics are then required. Jinet al. (2004) have shown that a modificationto Cary and Eaton’s (1993) method ofobtaining an initial estimate of large, short-wavelength receiver statics by optimizingthe trace-to-trace coherence of the CommonReceiver Stack (CRS) can be very effective.The solution of the inverse problem is theone that minimizes the trace-to-trace timed i ff e rence within a trace window. Thisinversion can better handle the uncertaintyof time delay picks in the presence of noise,because the cro s s - c o r relation coeff i c i e n t sare used as weights in the optimizationprocess to limit the influence of bad pickson the solution. Figure 8 (left) is a syntheticdata example that demonstrates how this

technique can resolve large statics.F i g u re 8a shows the CommonReceiver Stack with significantstatics. Random noise has beenadded to make the data more real-istic. Note that the reflectors arealiased due to large statics. For thiskind of data, pilot trace methods areimpractical. Figure 8b shows the CRSsections after statics corre c t i o nobtained by the weighted inversion.Although the data are so noisy thatfew maximum crosscorrelation coef-ficients are larger than 0.5 and manypicks are erroneous, we can see thatthe method is still correctly esti-mating most of the statics.

Multicomponent analysis Cont’d

F i g u re 7. Migrated image. Left: Converted wave depth migration of the Valhall data. Right: The derivative ofPS impedance for Valhall model.

F i g u re 8. Left (a): Synthetic CRS section. (b): CRS section after statics correction by the inversion. Right: Crossplot ofshear statics on the vertical axis versus compressional statics on the right. Note how the shear statics are an order ofmagnitude greater than the compressional statics and the lack of any trend through the points that would suggest arelationship between shear and compressional statics.

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ReferencesCary, P.W. and Eaton, D.W.S., 1993. A simple method for resolving large converted-wave(P-SV) statics. Geophysics, 58: 429-433.

Grech, M., Cheadle, S., Miao, X., and Zhu, T., A Velocity analysis procedure for multi -component data with topographic variations, 2004 CSEG National Convention, 11 – 13May 2004, Calgary

Jin, S., Li, J., and Ronen, S., 2004, Converted wave statics correction by inversion of tracetime shifts of common-receiver stack, EAGE 66th Conference & Exhibition, Paris,France, 7 - 10 June 2004.

Multicomponent Processing – Azimuthal Anisotropy AnalysisRichard Bale,VeritasDGC Inc., Calgary, Canada

The most direct seismic method of estimating vertical fractureorientation and intensity is by analysis of shear-wave splitting.S h e a r-wave splitting arises when a shear wave naturally polar-izes into a fast wave (S1) parallel to the fractures and a slow wave(S2) perpendicular to them. This can happen when the seismicwave travels through a fractured media. A common way to detectthe direction of fractures from shear-wave splitting is to rotate thehorizontal components to radial and transverse components, andthen search for azimuthal directions in which the transversecomponent changes polarity.

Azimuthal sampling can have an impact on standardmethods of estimating fracture orientation from shear-waves. Although regular azimuthal coverage can beensured by using large analysis bins and offset ranges, itmay not be necessary to do this. A new least-squaresalgorithm (Bale et al., 2005) using transverse componentamplitudes is designed to be less sensitive to the azimuthdistribution of the input. Comparison with a purepolarity based approach shows the new method to bemore accurate on a synthetic test. A field data example isshown in Figures 9 and 10, comparing the estimates offracture direction using the least-squares method with astandard technique based upon scanning for polaritychanges. In Figure 9, all azimuths were included in theanalysis, leading to very comparable results for the twomethods. In Figure 10, half of the azimuths have beenomitted, corresponding to the first and third quadrants.The polarity based results are adversely affected in someareas [compare Figures 9(a) and 10(a)], whereas the least-s q u a res method remains largely unaffected by theazimuth decimation [Figures 9(b) and 10(b)].

ReferenceBale R.A., Li J., Mattocks B., Ronen S., 2005, Robust estimation of fracturedirections from 3-D converted-waves, 75th Ann. Internat. Mtg: Soc. of Expl.Geophys.

Multicomponent Interpretation

P e rhaps the largest obstacle in the continued developmentand ultimate acceptance of multicomponent seismic is theseismic interpre t e r. From an interpre t e r’s point of view,multicomponent data introduces additional complexityand thus generates new headaches and uncertainties.Conventional P-wave interpretation software does notlend itself well to multicomponent interpre t a t i o n .

F u r t h e r m o re, the PS and especially SS volumes are much noisier,lower frequency and typically bear little resemblance in characterto the PP volumes. The registration process (event matchingbetween events on the PP, PS and SS volumes) is largely driven bythe availability of dipole sonic logs. These dipole sonics are not ascommon as standard sonics and can often contain erroneous infor-mation, making the quality control and petrophysics re q u i red forPS and SS interpretation potentially much more involved. Theanalysis of this extra information will, of course, take more time.As a result these additional data might be viewed by the inter-p reter as more of a hindrance, not helping to narrow down uncer-tainties about reservoir properties.

New, multicomponent-specific interpretatation software such asVeritas Hampson-Russell’s (VHR) ProMC, make the interpreta-tion of multicomponent seismic much more convenient. The newsoftware provides the means of incorporating dipole sonic andconventional sonic logs into the interpretation workflow.Horizon picking and event registration are easy to do onmultiple sections simultaneously. Furthermore attribute genera-tion, such as Vp/Vs maps, instantaneous frequency, amplituderatios, etcetera is possible. Once the registration is done, furtheranalysis, such as PS-AVO can be pursued.

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F i g u re 9. Estimate of fracture direction, field data example. Results from analysis on allazimuths using: (a) polarity based method; (b) least-squares method. Fast shear (S1) dire c -tions are indicated by line segments, color display indicates time delays.

(a) (b)

(a) (b)

F i g u re 10. Analysis results on a limited azimuth range, using: (a) polarity based method; (b)l e a s t - s q u a res method. The estimated directions are similar to the full azimuth estimates ofF i g u re 9, with slightly more variability on the polarity based method.

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The following case histories are examples of how multicompo-nent seismic is adding value, reducing risk, and proving theadditional interpretation effort worthwhile.

Case Study 1: Improved P-wave Imaging of the Darby ThrustFault using Multi-Component Seismic ReceiversShuki Ronen, VeritasDGC Inc., Houston, Texas

Seismic imaging in rough terrain is often a challenge. To evaluatethe applicability of 3C point receiver MEMS in rough terrain we

conducted a test over the Darby thrust fault in southwestWyoming. The source was 5kg Pentolite at 18m hole depth, with50m shot interval. The wavefield was recorded by both 12-element linear and 36-element areal geophone group arrays at50m interval as well as by 3C sensors at 25m interval. Theprocessing sequence included tomographic refraction statics,c o h e rent and random noise attenuation, surface consistentdeconvolution, two iterations of velocity analysis usin the DSRequation and residual statics, and pre-stack migration. Weapplied polarization filters to the 3C data to separate signal andnoise. No such polarization filtering is possible of course withany 1C data.

The test results (Figures 11 and 12) indicate that single sensorthree component receivers provide better data than conventionalgroups of geophones in rough terrain. The images from the 3Cdata have better resolution and better imaging of dipping reflec-tors. Arguably, the benefits of the 3C data are much more signif-icant than the small increase in random noise that we observe onthe single sensor data. The benefits are due to a combination offactors: the smaller (25m) DSU receiver interval compared to the(50m) group interval, the polarization filters, the use of the DSRequation, the avoidance of intra-array statics with pointreceivers, the MEMS sensors, and the tilt correction. Our resultsare in general agreement with Behr (2005).

Acknowledgements

Thanks to Marvin Johnson and Vinny Buffenmeyer ofExxonMobil and Mark Wagaman for initiating, funding,designing, and supervising the data acquisition. Thanks to ChrisAnsorger for processing the data.

ReferencesBehr, J., 2005. Multicomponent Receivers for P-wave Seismic over Rough Topography:Motivation and Results. CSEG meeting.

Case Study 2: Heavy Oil Paul Anderson, formerly VeritasDGC Inc., and Robert Kendall, VeritasDGC Inc., Calgary, Canada

A multicomponent reservoir characterization workflow wasdeveloped on a heavy oil project from northeastern A l b e r t a ,Canada, with two primary goals. First, estimate key re s e r v o i rparameters, Density & Volume Shale (Vsh) to aid in placing SAGDwell pairs optimally within the McMurray re s e r v o i r. Second,determine if the multicomponent data add value in describing there s e r v o i r. The workflow used, described in Figure 14, describes amethodology for incorporating the additional data available withmulticomponent seismic into a reservoir model to help in moreeconomic well placement.

Initially, the seismic data were processed together, where veloci-ties and statics are carried from the PP processing to the PSprocessing. At this point, we perform basic interpretation of thePP seismic data, correlating wells and picking key horizons. Thisthen leads into the PS processing where dipole shear sonic logs(when available) are used to correlate the wells to the PS seismicand guide picking horizons on the PS data that are geologicallyconsistent with the PP horizons. In this example, the horizonsused included a Clearwater marker, the Top McMurray and the

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F i g u re 11. Image produced from the conventional geophone (groups of 12) data. Notethe loss of image under the ridge in the middle of the line.

F i g u re 12 Image produced from the multi-component single sensors (DSU) data.Note the improved image under the ridge compared to Figure 11 .

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Devonian and resulted in a Vp/Vs ratio of approximately 3within the McMurray interval. Three-term PP-AVO and two-term PS-AVO analyses were then performed using angles out to50 degrees, using the background Vp/Vs from registration andvelocities from well logs. These attributes were then inverted toimpedance and then used for neural network analysis. Next, theattributes were combined in a multi-attribute analysis program(for example, VHR-EMERGE) to determine how attributes relateto Density and Vsh, and use those relationships to build thecorresponding reservoir models. Remember, a key objective wasto derive an estimate of Vsh. Five of the nine attributes used tocalculate Vsh were derived from PS data!

The cross section shown in Figure 15 shows theresulting Vsh-Model along with the shale content fromlog data. The wells show a good correlation to the shalecontent within the McMurray (log values not calculatedoutside the McMurray interval), including the right-most well. This well, while excluded from the analysis,shows a good correlation to the shale-plug in themiddle-to-upper McMurray.

ReferencesAnderson, P., Gray, F.D., and Chabot, L.., 2005, A Proposed Workflow forReservoir Characterization Using Multicomponent Seismic Data, 75th Ann.Internat. Mtg.: Soc. of Expl. Geophys.

Barson, D., Bachu, S., Essslinger, P., 2001, Flow systems in the MannvilleGroup in the east-central Athabasca area and implications for steam-assistedgravity drainage (SAGD) operations for in situ bitumen production, Bulletinof Canadian Petroleum Geology, 49, 3, 376-392

Hampson, D.P., Schuelke, J.S. and Quirein, J.A., 2001, Use of multiat -tribute transforms to predict log properties from seismic data: GEOPHYSICS,Soc. of Expl. Geophys., 66, 220-236.

Case Study 3: North Emerald 3C3DSteve Roche, VeritasDGC Inc., Houston, TExas

The following example (Figures 16 and 17) is from the AnadarkoBasin in Oklahoma, a major gas production province withmature fields and on-going exploration efforts. In the surveyarea, thin sands (less than 8m each) produce natural gas from theMississippian Springer Formation (approximately 70m thick), atdepths near 3.2km. Production is primarily related to porositydevelopment. Wells with 8-12% porosity are generally commer-cial. An embedded, static 3C patch was recorded concurrentlywithin a P-wave survey. The source was 5 pound dynamite at20m depth.

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F i g u re 14. Schematic flow chart for multicomponent seismic reservoir characterization.

F i g u re 15. Estimated Shale volume (Vsh) section from multiattribute analysis. The well on theright, excluded from the analysis, correlates well with the log data within the McMurray.

F i g u re 13. Regional stratigraphy of the heavy oil target for this pro j e c t .The target is the McMurray formation (Barson et al. 2001). TheMcMurray interval is a thick sand, largely bitumen-saturated thro u g hmost of its interval.

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Figure 16 shows the P-wave (PP) data and converted-wave data(PS). The PS data is presented in PP time. Differing reflectivityindicates additional information is available when using bothwave modes for interpretation. When we examine the Springerreservoir level, we observe changes in both P-wave (PP) andconverted-wave (PS) reflectivity associated with gas production.We used a linear regression waveform classifier to quantitativelydetermine if using the PP and PS data together could provide abetter empirical estimate of gas production than using PP dataonly. For seventeen wells in the 3C3D image area with Springerpenetrations and tests, we created a simple log trace representingcumulative gas produced. Using cumulative gas as the target, aneural network was trained and an estimate of gas productionusing the seismic data was derived.

F i g u re 17 shows the empirical results for predicting cumulativegas production. Results using both PP and PS data correctly iden-tified the three wells with higher cumulative gas pro d u c e d .C rossplots of estimated gas versus actual gas production showthat using PP and PS data together produced a more accuratelinear fit than when using the conventional P-wave data alone.

Reference"Anadarko Basin survey shows value of multicomponent acquisition", Steve Roche,Mark Wagaman, Howard Watt, First Break, volume 23, February 2005

Case Study 4: Carbonate Fracture Identification andCharacterization: Onshore USBruce Mattocks, VeritasDGC Inc., Houston, Texas

In this onshore US example, the initial objectiveswere to examine the essential feasibility of PS-waveimaging in middle Ordovician carbonates at a depthof 2800 m, and to verify the capacity of the PS-wavedata to determine the orientation of maximum hori-zontal stress. To minimize the cost of this evaluation,the PS-wave acquisition was implemented as anembedded test (Figure 18), where three-componentS e rcel DSU-3 accelerometers are co-located withproduction single-component geophone groups overa subset of the survey. The resulting seven squarekilometer PS-wave image is approximately centeredon a recent well in which a dipole sonic wasacquired.

To permit evaluation of the azimuthal anisotropy, thePS-wave data are initially processed in a conven-tional isotropic manner, assuming neither azimuthalnor polar variation in any of the parameters. Aftercorrection for tilt and orientation, the receivers arerotated from the global acquisition coordinate systemto a radial-tangential coordinate system, local to eachshot. Source and receiver statics are surface-consis-tent, while deconvolution operators are designedindependently on each trace. Asymptotic ConversionPoint (ACP) binning is used to preserve surface-consistent relationships for iterative velocity analysiswith residual statics. Following normal moveoutcorrection, a large ACP superbin (600 m in radius) isselected and the binned data are sorted andsubstacked over ten-degree azimuth sectors.

The azimuth stacks of this embedded test display allthe expected characteristics of azimuthallyanisotropic PS-waves (Figure 19) as described by Li(1998). The radial component shows azimuthal vari-ation in arrival times of the various reflections, withthe fastest arrivals occurring approximately east-west, and the slowest arrivals north-south. The trans-verse component – which would be free of reflectionsin isotropic media – shows polarity reversals sepa-rated by azimuths without reflections, at both N80°Eand N170°E. The "phase flip" semblance displayF i g u re 17. Predicted gas production based on seismic data attributes. Two estimates of gas pro d u c -

tion were calculated, one using the PP data only (left map panel), the second using both PP and PSdata (right map panel). Crossplots of estimated vs. actual gas production show a better fit is obtainedwhen using both the PP and PS data. Gas production (bcf) is noted for key wells.

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F i g u re 16. PP and PS data comparison (3C sensors). Migrated profiles through the 3C3D datavolume. P-wave data are on the left, PS data on the right. PS data are presented in PP time.Annotated horizons were picked on the P-wave volume, posted, and then transferred to the PSsection. Note the differing reflectivity between the PP and PS sections. Native frequency contentis approximately 8-80Hz (PP), 8-35 Hz (PS).

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permits the orientation of the symmetry planes to be determinedgraphically; however, the 90-degree ambiguity remains. This isresolved through the azimuthally varying travel-time of theradial component, in which travel-time minima coincide withthe fast-shear axis (or isotropy plane) and travel-time maximacoincide with the slow-shear axis (or symmetry-axis plane). Inthis case, the fast shear axis is N80°E, which represents the orien-tation of maximum horizontal stress.

Using a smaller 800 ft bin diameter, the analysis is extendedspatially and the anisotropy evaluated after rotating the data intothe natural coordinate system; the results are summarized inFigure 20. These results represent the cumulative anisotropyabove the reservoir. Nonetheless, the average orientation isconsistent with the regional orientation of maximum horizontalstress from borehole breakouts.

ReferencesLi, X. -Y., 1998, Fracture detection using P-P and P-S waves in multicomponent sea-floordata: 68th Ann. Internat. Mtg: Soc. of Expl. Geophys., 2056-2059.

Reinecker, J., Heidbach, 0., Tingay, M., Connolly, P., and M¨uller, B., 2004, The 2004release of the World Stress Map (available online at www.world-stress-map.org).

Mattocks, B., Li, J., and Roche,S., 2005, Converted-wave azimuthal anisotropy in acarbonate foreland basin: 75th Ann. Internat. Mtg: Soc. of Expl. Geophys.

Conclusions

It is clear that recent advances in acquisition, processing andi n t e r p retation software are already making multicomponentdata an increasingly valuable tool for today’s interpre t e r.Modern digital multicomponent sensors are providing accurateand affordable solutions for acquisition. Processing advancessuch as improved statics algorithms, more accurate binning andvelocity analysis, polarization filtering, azimuthal anisotropycompensation and anisotropic prestack time migration arehelping to improve the S/N, frequency content and overallquality of the data. Finally, recent advances in multicomponentinterpretation and analysis are setting the stage for significantbreakthroughs in multicomponent exploration technology andquickly repositioning multicomponent seismic from unmanage-able to indispensable. R

Corresponding author: Robert Kendall ([email protected])

Multicomponent analysis Cont’d

F i g u re 18. Acquisition geometry of the embedded multicomponent test, with thesurface topography shown as relative elevation. The digital (MEMS) 3-C re c e i v e r soccupy a roughly circular patch near the center of the survey, and parallel to thereceiver lines of the larger P-wave 3-D survey (not shown). Source interval is 100m and receiver interval is 33.5 m. The segmented circle shows the location of theanalysis superbin of Figure 19.

F i g u re 19. Radial and transverse azimuth stacks from a single ACP superbin. Bothcomponents show evidence of PS-wave azimuthal anisotro p y.

F i g u re 20. The spatial distribution of polarizations determined from PS-wave bire -fringence (Figure 19) is displayed in the rose diagram at left. At right is a re g i o n a lcompilation of stress orientations obtained from the World Stress Map pro j e c t(Reinecker et al, 2004). This summarizes borehole breakout results from 12 locationswithin approximately 100 miles of the PS-wave survey.

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