Walkaway VSP

Geophysical Prospecting, 2011, 59, 464476 doi: 10.1111/j.1365-2478.2010.00933.x

Seismic interferometry experiment in a shallow cased boreholeusing a seismic vibrator source

Flavio Poletto, Lorenzo Petronio, Biancamaria Farina and Andrea SchleiferIstituto Nazionale di Oceanografia e di Geofisica Sperimentale, OGS, Borgo Grotta Gigante n. 42/c, 34010 Sgonico (TS), Italy

Received February 2009, revision accepted October 2010

ABSTRACTWe present the results of a seismic interferometry experiment in a shallow casedborehole. The experiment is an initial study for subsequent borehole seismic surveysin an instrumented well site, where we plan to test other surface/borehole seismictechniques. The purpose of this application is to improve the knowledge of the reflec-tivity sequence and to verify the potential of the seismic interferometry approach toretrieve high-frequency signals in the single well geometry, overcoming the loss andattenuation effects introduced by the overburden. We used a walkaway vertical seis-mic profile (VSP) geometry with a seismic vibrator to generate polarized vertical andhorizontal components along a surface seismic line and an array of 3C geophonescemented outside the casing. The recorded traces are processed to obtain virtualsources in the borehole and to simulate single-well gathers with a variable source-receiver offset in the vertical array. We compare the results obtained by processingthe field data with synthetic signals calculated by numerical simulation and analysethe signal bandwidth and amplitude versus offset to evaluate near-field effects in thevirtual signals. The application provides direct and reflected signals with improvedbandwidth after vibrator signal deconvolution. Clear reflections are detected in thevirtual seismic sections in agreement with the geology and other surface and boreholeseismic data recorded with conventional seismic exploration techniques.

Key words: Overburden, Seismic interferometry, Vibroseis.

INTRODUCTION

Seismic interferometry uses the cross-correlation of recordedtraces to obtain virtual sources at the position of the re-ceivers (Claerbout 1968; Bakulin and Calvert 2004; Calvert2004). The method allows exploration geophysicists to sim-ulate source points where the possibility to use real sourcesis limited, as in the case of borehole geophysics. Several ex-amples of interferometry for seismic exploration are shownin the literature with vertical seismic profiling (VSP) data

This paper is based on extended abstract P278 presented at the 70thEAGE Conference & Exhibition Incorporating SPE EUROPEC 2008,912 June 2008 in Rome, Italy.E-mail: [email protected]

sets, where the seismic virtual source method provides suc-cessful results for the detection and separation of wavefields(e.g., Bakulin et al. 2007; Mateeva et al. 2007). In these ap-plications, important aspects are the available coverage as afunction of the distribution of the real exploration sourcesilluminating the receivers and the source spacing required tominimize and prevent spatial aliasing (Mehta et al. 2008).We apply seismic interferometry to process the data recordedby an array of 3C receivers fixed in a borehole of an instru-mented test site facility. This survey is an introductory study toevaluate borehole signals in the near-surface and to providereference signals in the well, which can be used to performacquisitions with other conventional techniques and, poten-tially, to experiment with borehole instrumentations. We usea seismic vibrator at the surface as the exploration source and

464 C 2010 European Association of Geoscientists & Engineers

Seismic interferometry experiment in a shallow cased borehole 465

simulate the virtual sources at any recording depth, with theaim to obtain high-resolution data. Albeit this study is sub-stantially an application of the seismic interferometric methodto a multi-component walkaway VSP experiment, we focusour attention on the typical geometry of single well imaging(Hornby 1989; Chabot et al. 2001) in this work. The tar-get of the single well imaging sonic techniques is to imagethe near-borehole structure using full-waveform sonic data(Hornby 1989) with a signal frequency of several thousandHz. In this experiment the signal bandwidth is confined to alower frequency range, so that the result is a low-frequencyapproximation of sonic single-well-imaging wavefields. Theeffort in the processing phase is to improve the seismic fre-quency content, exploiting the potential of the interferometrymethod and to recover the high-frequency signal of the sur-face vibrator source between closely-spaced receivers. Virtualsingle-well sections are obtained by gathering the traces withvirtual sources and receivers at very close positions. These sig-nals are interpreted and can be considered as reference resultsrepresentative of single-well sections in the site area, in thelow-frequency approximation with respect to sonic logs andwith higher frequency content with respect to conventionalseismic.

ACQUIS IT ION

In this work we use the seismic data specifically recorded forthe purposes of interferometry at the Osservatore di GeofisicaSperimentale (OGS) test site (Poletto et al. 2008). This siteis located in the Toppo inter-mountain plain (north-easternItaly) on the external thrust-belt of the eastern Southern Alps(Zanferrari, Poli and Rogledi 2002) where Jurassic carbon-

ate overlays Miocene sedimentary sequences. In the studiedarea, quaternary alluvial sediments, mainly gravels, overly theMiocene conglomerate (Montello conglomerate) formation.At this geophysical site, three closely spaced wells were drilledto depths of 280 m, 380 m and 420 m below ground level.Near-surface overburden conditions, which affect seismic sur-veys in this area, are due to the presence of loose gravel in theshallower part in conjunction with a deep water table (locatedat a depth of approximately 120 m below ground level).The field layout of this experiment (Fig. 1) consists of an in-

strumented well with 30 3C geophones cemented outside thecasing and a surface seismic source line crossing the well. Bore-hole sensors (borehole geophones 15 Hz natural frequency)were installed between 35240 m below ground level with adepth interval of 10 m in the shallower section (35145 mdepth) and 5 m in the deeper one (>145 m depth). A 2500-pound seismic vibrator (minivib IVI) energized three series of80 shot points polarized in the P- (vertical), SV- (horizontalin-line) and SH-wave (horizontal cross-line) configurations.The maximum horizontal offset from the wellhead was 150m. The source intervals were 2.5 m below and 5 m above50 m offset. These small source-sampling intervals were cho-sen to avoid aliasing effects and to improve the S/N ratio(vanManen, Curtis and Robertsson 2006; Mehta et al. 2008).We used a linear upsweep of 12 s duration, the theoreticalsweep ranging from 10400Hz, with 5 sweeps for each sourceposition. Raw data consisting of borehole geophone data andvibroseis pilot signals were recorded with a 1 ms samplingrate. The pilot signal traces were the baseplate acceleration,the reaction-mass acceleration, the ground force signal ob-tained by the weighted sum of the baseplate and reaction-mass signals (Sallas 1984) and the theoretical sweep. After

Figure 1 Schematic layout of the single well interferometry test (not to scale).

C 2010 European Association of Geoscientists & Engineers, Geophysical Prospecting, 59, 464476

466 F. Poletto et al.

Figure 2 Real vibroseis signals. P-wave energizations: pilot signal(ground force) power spectra. Surface terrain and ground couplingconditions vary along the line and affect the sweep-signal bandwidth.

in-field quality control on the pilot signals, the field data werecross-correlated with the ground force signal obtained by theweighted sum and stacked for each source position. However,all the recorded raw field signals, consisting of uncorrelatedgeophone and pilot traces, were available for subsequent re-processing.Figure 2 shows the power spectra of the groundforce pilot

signals recorded in the P-wave energizations. These spectra areobtained by Fourier frequency transforming the stacked auto-correlations of the groundforce pilot signals for each sourceposition. The reference signal variability along the energiza-tion line depends on the different vibroseis ground-couplingconditions, related to the presence of different near-surfaceterrains.Figure 3 shows example vibroseis cross-correlated signals

of P-, SV- and SH-wave energizations recorded by 3C down-hole geophones. The data have a good S/N ratio. Tube wavesdo not significantly affect this VSP data set, so no particu-lar attention was given in the processing phase to avoid thecontribution of the tube-wave noise induced by near-offsetsources (Gaiser, Vasconcelos and Ramkhelawan 2009). Theweak amplitude of the borehole tube waves is interpreted asdue to the location of the borehole geophones outside thecasing.

THE PROCESS ING METHOD

The processing method adopted in this work for the synthesisof the virtual signals uses the standard interferometry by cross-correlation algorithm

CAB() =

i

SAi ()SBi (), (1)

where SAi and SBi are the Fourier frequency transforms of thepreprocessed vibrator signals from the i-th field source po-sition recorded by two receivers in A and B, respectively, is the angular frequency and denotes complex conjugate.The summation in the interferometry-by-correlation equation(1) is extended over the offset domain (or P, SV and SH sub-domains) of the surface vibrator sources (here we omit forsimplicity the multi-component tensorial notation). The sig-nal CAB is a band-limited estimate of the Greens function(Wapenaar and Fokkema 2006) between two receiver pointsB and A. Even if interferometry by correlation can also be ap-plied using the raw vibrator data (Halliday, Curtis and Kragh2008), in this work we prefer using preprocessed data as inputto equation (1). This approach makes it possible to interpretand analyse the signal wavefields in the seismograms prepro-cessed prior to interferometry (a similar analysis was done fordrill-bit signals by Poletto, Corubolo and Comelli (2010)). Weused two approaches to preprocess the vibrator data.

Interferometry using vibroseis correlation data

In the first approach, we used as input to equation (1) thevibrator signals SAi obtained using the conventional pilot-cross-correlation approach

SAi () = XAi ()Pi (), (2)

where Pi is the groundforce pilot-sweep signal of the i-thsource point and XAi is the signal recorded in A (similarreasoning holds for SBi ). Here we omit for simplicity theshot stacking index. Using equation (2), the interferometry-by-cross correlation equation (1) can be rewritten as

CAB() =

i

XAi ()XBi ()Pi ()Pi (). (3)

Later in this paper we show that this approach provides goodquality virtual seismic signals in the lower frequency band-width, say with maximum frequency up to 100140 Hz, forthe data set of this experiment. Assuming that the signal radi-ated from the i-th source point and recorded in A is the com-position of the Greens functionGAi from the source point i tothe receiver point A and of the true groundforce signal Vi ofthe vibrator (Sallas 1984; Baeten and Ziolkowski 1990), wehave (neglecting the response of the recording instrumentationand omitting tensorial notation for multi-components)

XAi () GAi ()Vi (), (4)



Figure 3 Vibroseis cross-correlated signals. P-, SV- and SH-wave energizations recorded by 3C downhole geophones. A notch filter (50 Hz)is applied to the data to remove AC power noise. Z, R and T represent the vertical, horizontal in-line and horizontal cross-line components,respectively.

where the symbol means equal apart from a global scalingfactor (a similar equation holds for B). Equation (3) becomes

CAB()

i

GAi ()GBi ()Vi ()Vi ()Pi ()P

i (). (5)

In the representation by equation (5) we neglect the anti-causalterm CAB (Wapenaar and Fokkema 2006), not relevant forthe purposes of our analysis. Assuming that P() is a re-liable approximation of V(), we obtain that equation (5)ultimately contains a fourth power of the vibroseis source sig-nal. Even if the theoretical source sweep was designed using a

theoretical linear sweep in the frequency bandwidth rangingbetween 10400 Hz, the effective pilot signals were recordedwith a significant amplitude attenuation for frequencies higherthan 100140 Hz. Moreover the weighted groundforce pilotsignal contains distortions due to baseplate bending vibra-tions (Baeten and Ziolkowski 1990) and the source signalcontains frequency peaks due to amplification by the localsoil-vibrator system response. During acquisition a significanteffort was made to minimize these effects and to optimize thespectrum of the emitted signal. However, this task was onlyin part achieved by expending additional time to optimize the



adaptation of the baseplate to the soil surface at each vibrationlocation. Figure 2 shows the power spectra of the groundforcepilot signals acquired at different positions. These pilot-signalpower spectra contain undesired coloured effects, which areamplified by the power (in this case second, according to equa-tion (5)) and ultimately limit the bandwidth in the recoveredGreens function synthesized using the vibroseis correlationdata.

Interferometry using vibroseis deconvolution data

As interferometry provides a local response and preserveshigh frequencies, we tested the method by using the vibroseisdeconvolution input traces, aimed at improving bandwidthand at compensating for local effects at each source location(Poletto et al. 2008; for a discussion on the role of energyequipartitioning for interferometry see Snieder,Wapenaar andWegler 2007). In this approach, we use as input to equation(1) the vibroseis signals deconvolved by the pilot signals (Brit-tle, Lines and Dey 2001)

SDAi () =XAi ()Pi ()

, (6)

where using some additional white noise to bias the pilot sig-nal P() before the spectral division is beneficial. Using equa-tion (6), equation (1) can be rewritten as

CDAB() =

i

SDAi ()(SDBi ())

=

i

XAi ()XBi ()Pi ()Pi ()

. (7)

Equation (7) shows that interferometry by the cross-correlation of the vibroseis deconvolution signals is similar tothe conventional interferometry-by-deconvolution approach(Vasconcelos and Snieder 2008a,b); with the difference thatequation (7) removes only the reference source signature anddoes not remove propagation effects. Moreover, using equa-tion (4) in equation (7) gives

CDAB()

i

GAi ()GBi ()Vi ()Vi ()Pi ()Pi ()

. (8)

The key problem in vibroseis deconvolution is that theweighted groundforce estimate deviates from the true signalemitted in the formation at the baseplate (Sallas 1984; Baetenand Ziolkowsky 1990; Mewhort, Bezdan and Jones 2002).This leads to frequency distortions, which make vibroseis de-convolution difficult to be applied to the individual shots ofour experiment. However, we may observe in Fig. 2 that thepilot power spectra are variable along the acquisition line.For this reason the deconvolution response is different fordifferent source positions. Averaging the deconvolved terms

in equation (8) improves the S/N in the virtual-signal resultsobtained by using the vibroseis deconvolved signals. The ap-proach is robust with respect to the phase fluctuations anddistortions after deconvolution as the averaged signature is

W () =

i

Qi , (9)

where

Qi () = Vi ()Vi ()

Pi ()Pi (). (10)

The deconvolved signature W() is a zero-phase signal forconstruction. Other possible approaches to removing thesource-signature effects are to deconvolve the interferometry-by-correlation result CAB given by equation (3) by theaveraged-energy deconvolution operator

DAV () = 1[i Pi ()P

i ()

]2 , (11)

or, also, by using the fourth power of averaged synchronized-sweep signals | Pi ()|. If we assume that the source sig-nature and the propagation effects are independent, i.e., as-suming statistical independence for Qi and (GAiGBi ), usingequations (9) and (10) we can rewrite equation (8) as

CDAB() W ()

i

GAi ()GBi (). (12)

The source-deconvolution approach in equations (8) and (12)is similar to the semblance technique for performing optimal,least-squares deconvolution of VSP data, in which the oper-ator is estimated using moveout aligned traces (Haldorsen,Miller and Walsh 1994). However, the interferometry vibro-seis deconvolution approach using equations (8) and (12) isrephased for construction and does not require estimating thesource signature from the seismic data.Conventional interferometry-by-deconvolution methods

are also tested with the correlated vibrator data of equa-tion (3), to remove the source signature and to improve the vir-tual signal signature. In the following we present only the re-sults obtained with the standard approach of interferometry-by-correlation (equation (1)) with the vibrator correlated(equation (2)) and vibrator deconvolved (equation (6)) datasets, which provided good-quality results for subsequent sig-nal analysis. This analysis includes the processing of tracesof different components and data gathering to simulate thegeometry of a survey with a source/receiver downhole tool inthe well.



BOREHOLE DATA ANALYSIS

The real interferometry data are compared to the syntheticdata generated by a 2D elastic finite difference code, with aregular spatial grid interval x = z = 1 m, representing thegeological model of the test site. These synthetic data werecomputed to guide the interpretation of the redatumed wave-fields. The numerical simulation of the signals recorded alongthe casing does not include the borehole.Figure 4 shows the synthetic data, P- and SV-waves, ob-

tained with the vertical and the horizontal sources, respec-tively, located in the borehole at elevation 132 m. The am-plitude versus-offset decay in the frequency bandwidth 60140 Hz is shown in Fig. 5. The real P and SV in-terferometry data computed at the same source posi-tion (virtual source at 132 m elevation above sea level)are shown in Fig. 6. The analysis of the amplitudesin the field interferometry traces is performed by select-ing the compressional and shear direct arrivals at differ-ent offsets of the deeper receivers from the virtual source(Fig. 7). These results are obtained with all the real sources(2.5 m and 5 m interval) and with an equi-spaced (regular)subset of them (5 m). The real-signal amplitude results (Fig. 7)are compared to the amplitudes of the synthetic data (Fig. 5).The amplitudes of the data obtained with equi-spaced sourcesare more in agreement with the trends showed by the syn-thetic data, where near-offset effects (in the 2D model ap-proximation) are included. This result is in agreement withthe theory, for which the retrieval of the Greens functionby cross-correlation depends on the appropriate spatial distri-

Figure 5 Synthetic signals. P and SV direct signal amplitudes at av-erage frequency 100 Hz versus source-receiver offset in the well. Theamplitude is relative to the amplitude of the signal at the source loca-tion.

bution of the real sources in terms of energy equipartioning(Snieder et al. 2007).In the single well imaging technique, a critical point is the

large amplitude (including near-offset effects) of the boreholewaves with respect to the magnitude of the investigated reflec-tions. In this case, the data are not strongly contaminated bytube waves probably because the sensors are installed outsidethe cased borehole.Due to source illumination conditions from the surface with

the stationary-phase region at the well head, the coverage is

Figure 4 Synthetic results obtained with the source at 132 m elevation in the well: a) P-waves and b) SV-waves.



Figure 6 Real vibrator interferometry results obtained with the virtual source at 132 m elevation in the well: a) P-waves and b) SV-waves.

Figure 7 Real-vibrator virtual signals. Comparison of direct signalamplitudes at average frequency 100 Hz versus source-receiver offsetin the well. The solid line is obtained by using a regular vibratorinterval and the dashed line is obtained by using all the source points.The amplitude is relative to the amplitude of the signal at the sourcelocation.

appropriate for traces located below the reference receiver inwhich the virtual source is simulated. The signal arrivals inthe borehole above the source are represented by the recip-rocal virtual signals, observable in Figs 6 and 8 at negativetimes at shallower receiver positions with respect to the refer-ence trace used as the virtual source. Here reciprocal meansthat if we interchange the order of two receiver traces in the

cross-correlation, i.e., interchange the reference trace in equa-tion (1), we obtain the time reversed result (see, for compari-son, the synthetic signals of Fig. 4).The comparison between interferometric virtual-shot data

obtained by processing cross-correlated vibroseis data and de-convolved vibroseis data is shown in Fig. 8 for P-wave gathers.The sweep-based deconvolution improves the bandwidth ofthe signal produced by vibrators and the interferometry cross-correlation and stacking improves the signal-to-noise ratio inthe virtual signals by attenuating the deconvolution noise anddistortions. The sweep-correlation and sweep-deconvolutionvirtual results are compared in Fig. 8(a,b), respectively. Theaverage improvement in the spectrum of the interferometry bycorrelation using the vibroseis deconvolution signal beyondthe observed sweep bandwidth (however, within the nominalsweep bandwidth) can be appreciated in Fig. 9 (Poletto et al.2008). Similar results of improving the signal bandwidth wereshown by Haldorsen and Borland (2008) for walkaway VSPusing the downgoing energy to estimate the deconvolutionoperator.

S INGLE-WELL RESULTS

The interferometry processing is repeated for all the receiversto obtain a virtual source for each sensor of the borehole array.Subsequently, the data are selected and gathered in single-wellsections.The single-well interferometry signals are gathered by P-

and SV-components by extracting the traces vertical and



Figure 8 Interferometry by a) vibroseis cor-relation and by b) vibroseis deconvolution.

Figure 9 Amplitude spectra comparison: interferometry by vibroseiscorrelation and by vibroseis deconvolution.

horizontal components of the receivers used as virtualsources (Fig. 10). This gather simulates (approximates) asingle-well imaging sectionwith zero offset between the virtualsource and receiver. In this case the seismic traces are stackedautocorrelations, i.e., diagonal elements Ckk of equation (1), kbeing the receiver index. A dipping event, corresponding to areflection from a layer encountered during subsequent drilling(and confirmed by the well lithology results and log data), canbe recognized in both these seismic data sets.Figure 11a shows the traces (P-components) extracted with

10 m offset from the virtual source to the (lower) receiver. The

data are filtered in the frequency bandwidth between 80300Hz. The 10-m-offset single well section of Fig. 11a is com-pared to the zero-offset section (Fig. 11b) obtained by select-ing the virtual-source traces used to calculate Fig. 11(a). Wecan observe the difference in the amplitude of the interpretedreflection at minimum elevation (maximum depth). This effectis due to the fact that the amplitude of the reflection relative tothe amplitude of the normalized direct arrival, represented bythe event sub-aligned and aligned at zero time in Fig. 11(a,b),respectively, has strong variations at short source-receiver off-sets along the borehole (near-field effect).These real data can be used, as an approximation, to process

the reflections and signals of the single-well imaging gath-ers for borehole seismic and acoustic purposes in the low-frequency approximation.Figure 12 shows the comparison between 10m offset single-

well signals obtained by vibroseis cross-correlation and vibro-seis deconvolution input data (P-component gathers) and thecorresponding amplitude spectra. A significant bandwidth im-provement can be observed in all the traces. This improvementis more evident in the shallower traces (those at higher eleva-tions), because the vibroseis deconvolution compensates forthe source signature but not for the attenuation effects due topropagation.The single-well imaging data were processed to remove di-

rect arrivals and enhance the upgoing wavefield to obtain thereflectivity in the borehole. The wavefield separation was per-formed by the use of median and f-k (frequency-wavenumber)filters. Because of the large amplitude of the direct arrivalsrelative to the reflections, some residual dipping artefacts are



Figure 10 Virtual signals. Single-well imaging gathers (virtual zero offset) tool: a) P-waves and b) SV-waves BP filtered 20300 Hz. Arrowsindicate the reflections coming from a lithological interface located at about 10 m below sea level. For display purposes the polarity of thesignals is reversed.

Figure 11 Single-well imaging gathers (P-component). a) Simulation of a virtual signal of a 10 m offset tool compared to b) corresponding zerooffset signal. The arrows indicate the reflection coming from an interface located at about 10 m below sea level.

still present after removal of the direct signals. However, theseresidual events can be distinguished from reflections in the fi-nal sections for the flatter moveout of the artefacts.The interferometric data interpretation was performed by

comparison with surface reflection seismic, borehole seismicand well logs data. Figure 13 shows the upgoing wavefield ofthe P-wave gather compared with the lithological profile andborehole sampling and the compressional velocity measuredby the sonic log. The geologic column results from the drillcutting analysis and well log interpretation. The moveout as-sociated with the virtual reflection events is in agreement withthe formation velocity and the reflection projections in depthare compared with seismic data obtained by a surface reflec-tion survey and by conventional borehole (VSP) acquisition,respectively. The strong signal at about 10 m elevation be-low sea level is detected by the interferometric data as wellas by the conventional surface and borehole data. This signal

corresponds to an interface between the conglomerate andnot-well-cemented gravel inside the Montello conglomerate.At this depth, an abrupt velocity decrease can be observed inthe sonic log data. The borehole interferometric result showsother reflections confirmed by the sonic log velocity vari-ations. The arrows in Fig. 13 indicate reflections at about75 m and 100 m, respectively. Both the signals are in cor-respondence of compressional velocity variations in the soniclog data.As for P-wave signals, we perform a comparison between

lithology, sonic log and interferometric data for the SV-waveconfiguration (Fig. 14). The P-wave reflections reported inFig. 13 at about 75 m and 10 m elevation are also observedin the SV-wave single-well imaging results (Fig. 14).The joint analysis performed with well lithology, sonic log

data, conventional surface- and borehole-seismic data con-firms that the interferometric approach is suitable to obtain



Figure 12 Virtual signals. Single-well imaging gathers obtained by vibroseis cross-correlation (top left panel) and vibroseis deconvolution (topright panel) and relative amplitude spectra (bottom panels).

reliable signals in the presence of complex overburden forshallow targets. The interferometric result shows a betterresolution than conventional surface- and borehole-seismicdata. The validation performed with different data suggeststhat the acquisition geometry adopted in this experiment issuitable to obtain a sufficient data coverage able to avoidartefacts in the interferometric results.In Fig. 15, P and S interval velocities measured by single-

well imaging gather (10 m offset) are plotted together withthe VP/VS ratio and compared with the sonic-log veloci-ties. Taking into account the different vertical resolution ofthese methods (also related to the available receiver inter-trace spacing in the well array), the results show goodagreement.

DISCUSS ION

In this work we use high-frequency seismic signals acquiredin a shallow-medium borehole and closely-spaced surface vi-brator sources to calculate single-well virtual gathers withand without vibroseis deconvolution. Even if it may be inprinciple possible to reduce the number of real sources andstill recover essential parts of the interferometric signal (vanManen et al. 2006), in this test we use dense spatial samplingfor the surface sources subject to variations in their emissionproperties due to local coupling conditions. In addition tocorrelation with the reference vibrator signal, we investigatethe processing approach by stacking the correlations of thedeconvolved vibrator traces in order to improve the



Figure 13 P-wave virtual single-well imaging upgoing wavefield (e) is compared with sonic log data (d), lithology obtained by the well masterlog (c), borehole (b) and surface (a) seismic data.

bandwidth in the interferometric signal (Figs 8 and 9). Thefrequency analysis shows that the bandwidth of the signalobtained above 180 Hz is still relevant, with improved S/Nin the virtual single well data obtained at shorter distancesfrom the boundary surface where the real sources are located(Fig. 12). This is not surprising because the vibroseis decon-volution improves the bandwidth in the real source signature,which even if affected by local noise and phase instabilitiesin the deconvolved signals obtained at each individual realsource point is rephased by the subsequent interferometrycorrelation and then averaged and stabilized by the interfer-ometry integration in the source space. However, note thatthis approach does not correct for the propagation effects inthe signature of the virtual signal and the results at differ-ent borehole depths differ for frequency attenuation effects

(Fig. 12), such as those due to absorption in the wave prop-agation. These results, obtained in a test well, confirm thepotential of high-frequency borehole interferometry for thepurposes of SWI with seismic sources in the short-mediumrange. It is envisaged that the method can be used in conjunc-tion with other interferometry-by-deconvolution approaches(see, for example, Vasconcelos et al. 2008a,b; Wapenaar, vander Neut and Ruigrok 2008) to compensate also for the prop-agation effects in the signal signature.

CONCLUSIONS

A seismic experiment was performed at the OGStest site to simulate single-well imaging wavefields byinterferometric processing of multi-component data recorded



Figure 14 S-wave virtual single-well imaging upgoing wavefield (c) is compared with sonic log data (b) and lithology obtained by the wellmaster log (a).

Figure 15 P (a) and S (b) interval virtual single-well imaging velocities are compared with sonic log data. The ratio VP/VS is shown in (c).

by a fixed borehole array. The aim of this survey was to ob-tain a data set with a frequency range of several hundredhertz usable for simulating single-well imaging acquisitionand processing, useful for a better planning of future bore-hole tests. The work we present here is an initial analysisof the data: further developments are foreseen by processingmulti-component data, including SH and comparison withborehole acoustic data acquired in the instrumented test site.The presence of a deep water table and of an overburden with

loose gravel suggested to use the interferometric approachto improve the knowledge of the geology in the well. As inthe case of hydrocarbon targets, this near-surface experimentalso allows seismic interferometry to obtain improved local(downhole) data in the presence of a complex overburden.Appropriate acquisition parameters were essential to obtainhigh-frequency signals, when using a 10400 Hz sweep vi-brator source. In particular, we selected very small surfacesource intervals to improve the signal quality with dense



spatial sampling. The target was to obtain a good approxi-mation of a complete coverage for interferometry (by surfaceillumination), which would provide a full reconstruction ofthe wavefields and amplitudes in the proximity (below) of thesources. The results were used to simulate single-well gathersin the low-frequency approximation. The single-well imag-ing data were processed and validated by lithological, soniclog and conventional surface- and borehole-seismic data. Themain geological interfaces in the sedimentary sequence weredetected by the interferometric approach, with a better reso-lution than the conventional seismic methods.Processing tests performed by using vibroseis deconvolution

before interferometry demonstrated that vibroseis deconvolu-tion is beneficial and the procedure is robust when we usevibroseis deconvolution signals to serve as input for virtualsignal processing.

ACKNOWLEDGEMENTS

We thank David F. Halliday, the associate editor and anony-mous reviewers for their fruitful comments and suggestionsthat helped to improve the manuscript. Thanks to the OGScrew for field acquisition support. Part of the basic processingwas performed by Seismic Unix.

REFERENCES

Baeten G.J.M. and Ziolkowski A.M. 1990. The Vibroseis Source.Elsevier.

Bakulin A. and Calvert R. 2004. Virtual source: New method forimaging and 4D below complex overburden. 74th SEG meeting,Denver, Colorado, USA, Expanded Abstracts, 24772480.

Bakulin A., Mateeva A., Calvert R., Jorgensen P. and Lopez J. 2007.Virtual shear source makes shear waves with air guns. Geophysics72(2), A7A11.

Brittle K.F., Lines L.R. and Dey A.K. 2001. Vibroseis deconvolution:A comparison of cross-correlation and frequency domain sweepdeconvolution. Geophysical Prospecting 49, 675686.

Calvert R.W. 2004. Seismic imaging a subsurface formation. USPatent 6,747,915.

Chabot L., Henley D.C., Brown R.J. and Bancroft J.C. 2001. Single-well imaging using the full waveform of an acoustic sonic. 71st SEGmeeting, San Antonia, Texas, USA, Expanded Abstracts, 420423.

Claerbout J.F. 1968. Synthesis of a layered medium from its acoustictransmission response. Geophysics 33, 264269.

Gaiser J.E., Vasconcelos I. and Ramkhelawan R. 2009. Elastic-wavefield interferometry Pseudo-source VSPs from 3D P-wave

vibrator data. 71st EAGE meeting, Amsterdam, the Netherlands,Expanded Abstracts.

Haldorsen J.B.U. and Borland W. 2008. Use of seismic vibratorsfor vertical seismic profiling: Considerations for the choice ofsweep. EAGE Vibroseis Workshop, Prague, Czech Republic, Octo-ber 2008, E04.

Haldorsen J.B.U., Miller D.E. and Walsh J.J. 1994. MultichannelWiener deconvolution of vertical seismic profiles. Geophysics 59,15001511.

Halliday D., Curtis A. and Kragh E. 2008. Seismic surface wavesin a suburban environment Active and passive interferometricmethods. The Leading Edge 27, 210218.

Hornby B.E. 1989. Imaging of near-borehole structure using full-waveform sonic data. Geophysics 54, 747757.

van Manen D.J., Curtis A. and Robertsson J.O. 2006. Interferometricmodelling of wave propagation in inhomogeneous elastic mediausing time-reversal and reciprocity. Geophysics 71, S147S160.

Mateeva A., Ferrandis J., Bakulin A., Jorgensen P., Gentry C. andLopez J. 2007. Steering virtual sources for salt and subsalt imaging.77th SEG meeting, San Antonio, Texas, USA, Expanded Abstracts,30443047.

Mehta K., Snieder R., Calvert R. and Sheiman J. 2008. Acquisition ge-ometry requirements for generating virtual-source data. The Lead-ing Edge 27, 620629.

Mewhort L., Bezdan S. and Jones M. 2002. Does it matter whatkind of vibroseis deconvolution is used? CSEG Geophysics 2002Conference, Expanded Abstracts, 14.

Poletto F., Corubolo P. and Comelli P. 2010. Drill-bit seismic inter-ferometry with and without pilot signals. Geophysical Prospecting58, 257265.

Poletto F., Petronio L., Farina B. and Schleifer A. 2008. Single WellImaging in cased borehole by interferometry. 70th EAGE meeting,Rome, Italy, Expanded Abstracts, P278.

Sallas J.J. 1984. Seismic vibrator control and the downgoing P-wave.Geophysics 49, 732740.

Snieder R., Wapenaar K. and Wegler U. 2007. Unified Greens func-tion retrieval by cross-correlation; connection with energy princi-ples. Physical Review E 75, 036103.

Vasconcelos I. and Snieder R. 2008a. Interferometry by Deconvolu-tion: Part 1 Theory for acoustic wave and numerical examples.Geophysics 73, S115129.

Vasconcelos I. and Snieder R. 2008b. Interferometry by Deconvolu-tion: Part 2 Theory for elastic waves and application to drill-bitseismic imaging. Geophysics 73, S129141.

Wapenaar K. and Fokkema J. 2006. Greens function representationfor seismic interferometry. Geophysics 71, SI33SI46.

Wapenaar K., Van Der Neut J. and Ruigrok E. 2008. Passive seismicinterferometry by multidimensional deconvolution.Geophysics 73,A51A56.

Zanferrari A., Poli M.E. and Rogledi S. 2002. The external thrust-belt of the eastern Southern Alps in Friuli (NE Italy).Memorie dellaSocieta` Geologica Italiana 54, 159162.


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