the first 3d/4-c ocean bottom seismic surveys in the

Bubbling mud volcanoes createhavoc for conventional seismicimages over the crest of the Azeri-Chirag-Gunashli (ACG) structurein the Caspian Sea. Layers oftrapped gas and shallow mudflows in the overburden sedi-ments leave areas of severelydegraded data quality at the crestof the reservoir structure. Fortun-ately, in 2002, the application ofthe first 3D four-component (3D/4-C) seismic in the Caspian hashelped lift the veil of mud andgas attenuation which previouslyobscured the details of the sub-surface reservoir.

Application of closely cou-pled innovations in both acqui-sition and processing have nowdelivered exceptional improve-ments in data quality, particu-larly in the case of the P-waveimages, which have significantlyreduced structural uncertainty inboth the critical, high well den-sity, central Azeri area, and theundeveloped Gunashli portion ofthe structure. Armed with animproved image of these reser-voirs, the ACG team is poised to accelerate production byavoiding both unplanned well sidetracks and expensive pilotholes.

Background. In 1995, a 3D grid of (conventional) towed-streamer seismic data was acquired which delineates the NW-SE trending ACG anticline. In the synclines, and part way upthe flanks of the structure, the signal-to-noise ratio (SNR) andtemporal bandwidth of this data set is good. However, insome central areas, near the crest, the target reflectors becomecompletely obscured. Figure 1 shows the shape of the anti-cline structure at Azeri with the central zone of poor SNR.Figure 1 also shows the scarps and mud volcanoes on the ren-dered bathymetry, and a data quality map detailing the extentof the poor data along the crest of the structure.

These low SNR zones are most likely caused by a combi-nation of factors including:

• P-wave absorption and attenuation through distributedgas in the overburden sediments

• poorly-consolidated sediments in the vicinity of the mudvolcano

• backscattered shot-generated noise from near-surface het-erogeneities

As a partial solution to these problems, a progressive suiteof imaging technologies was applied to the towed-streamerseismic, starting with poststack time migration in 1995 andending with Kirchhoff prestack depth migration in 2004. Eachtechnology yielded incremental image improvements; how-

The first 3D/4-C ocean bottom seismic surveys in the Caspian Sea:Acquisition design and processing strategyJACK BOUSKA and RODNEY JOHNSTON, BP E&P Technology, Sunbury, UK

910 THE LEADING EDGE SEPTEMBER 2005

Figure 1. Examples of Azeri towed-streamer data showing the structural shape, with steep dips, and areaswhere the signal is compromised over the structure crest due to seabed scarps, mud volcanoes, and near-surface distributed gas. Note the circular mud volcanoes features on the bathymetry plot.

Figure 2. Acquisition pattern for Azeri and Gunashli OBS surveys.

ever, neither the towed-streamer acquisition nor imagingenhancements has resolved the structural image within theareas of poor data quality.

The barriers that restrict additional improvements fortowed-streamer imaging are inherent in the actual design ofthe ACG towed-streamer data sets—namely, narrow-azimuth,60-fold recording which uses only hydrophone sensors thatare towed near the sea surface.

In the search for a solution to these problems, the initialbelief was that the multicomponent acquisition and subsequentconverted-wave (PS) seismic imaging would improve dataquality, through reduced gas-induced attenuation in the upgo-ing shear leg. Unfortunately, near-surface unconsolidated andfluidized mud flow sediments in the vicinity of the mud vol-canoes also attenuate and perturb the shear waves, restrict-ing the quality of the PS images.

The technology which has penetrated these barriers mosteffectively to date is the P-wave, (PZ sum) image derivedfrom high-fold, multiazimuth ocean bottom seismic (OBS)acquisition. OBS has a number of distinct advantages com-pared to towed-streamer data:

• The OBS sensors are deployed in quieter, stationary posi-tions on the seabed, providing improved sensitivity toground motion, and an extended low-frequency response.

• The OBS cables employ three orthogonal geophones, andone hydrophone, yielding improved multiple attenuationand P-wave SNR improvement via PZ summation for P-waves, as well as the opportunity to record upgoing con-verted shear-wave energy (C-waves).

• The OBS cables and air-gun sources are deployed inde-pendently, creating an uncoupled marine acquisition sys-tem which allows the design of a high-fold multiazimuthsurvey.

The first two items above could be considered as genericadvantages for any OBS survey, compared to towed-streamerseismic. However, the specific details embodied in the lastadvantage on the list, namely the acquisition design geome-try of sources and receivers, strongly impact the level to whichthese first two OBS advantages can be leveraged during pro-cessing.

In the case of the Caspian OBS surveys, it is the uniquesurvey geometry configurations which were tailored specifi-cally to match the subsurface imaging requirements that setthese surveys apart from traditional seabed seismic designs.The surveys were also designed to permit novel, and previ-ously untested, processing flows to be used during the seis-mic data processing stage.

Acquisition design. The ACG structure can be brieflydescribed as elongated anticlines with steep dips (up to 45°)on two sides, and poor data quality in the central core. To imagethese subsurface reservoirs, the OBS survey design mustaccommodate the following criteria:

• tight spatial sampling to capture the steep dips on theflanks

• high fold in the core to combat noise and P-wave attenu-ation

• large areal extent to record downdip migration apertureon flanks (3D surveys over reservoir structures that are bothsteep-and-deep are not cheap)

The third point is particularly important when a limitedbudget is available. These three requirements appear contra-dictory, because any OBS survey design which deploys both

dense sampling and high fold over a large area would be pro-hibitively expensive. The problem is compounded by theremote location of the Caspian Sea. This exaggerates the typ-ically high cost of 3D/4-C OBS acquisition, particularly whenthe recording equipment is restricted to a pair of 6-km activerecording cables. Budget constraints, coupled with the require-ment to acquire both high fold and densely sampled P-waveand C-wave surveys, restricted the size of preliminary surveydesigns.

Acost-effective solution was achieved by borrowing acqui-sition geometry innovations from heliportable land 3D sur-vey designs in the Rocky Mountains and the Andean thrustbelts. Receiver line interlacing and wide-patch acquisitionwere used to create a cost-effective design that satisfied allimaging requirements of the subsurface target, and provideda unique set of data which prompted continued innovationin data processing as well.

Apart from air-gun sources, it can be argued that OBS hasmore in common with land surveys than with towed-streamermarine seismic. Ocean-bottom seismic can be complicated by:

• seabed topography, and associated source and receiverelevation differences

• variable geophone coupling• converted-wave, near-surface, low-velocity layers and sta-

tic shifts• refraction first break picking and tomography• multiazimuth acquisition, variations in source-receiver

geometry• multiazimuth processing, including unusual noise and

multiple attenuation strategies

OBS multiazimuth acquisition is one of the most impor-tant differences, and warrants additional discussion. In typi-cal towed-streamer acquisition design, the streamer and sourceare towed in fixed positions behind the boat, resulting in asurvey containing a fairly narrow range of source-to-receiverazimuths. The fold of coverage for towed-streamer data is verysimilar to the generation of fold in 2D acquisition. Within agiven offset limit imposed by the processing mute, any attemptto increase fold by deploying additional sources or receiversis roughly equivalent to packing more raypaths into the samearea of the subsurface. These (nearly) replicated raypaths mayoffer some advantages for 2D dip filtering, but in order to gainthe best results, the secret to fold effectiveness hinges onexpanding the diversity of raypaths.

For a set of traces grouped in a common bin or conver-sion point gather, this raypath diversity is only accomplishedby covering new territory, or spreading the sources andreceivers over a wider surface area. Land acquisition schemesachieve this by using multiline aerially-large recording patches.

In OBS designs, the independence of source geometry andactive recording patch permits configuration of wide sourcepatch grids, which can be used to generate very high-fold lev-els, along with excellent distribution of raypath coverage inthe subsurface. The resultant high fold is also high quality (inthe sense of nonrepeated raypaths) so that SNR performanceis optimized. Additionally, this wide and uniform distribu-tion of active source and receiver stations on the surface pro-vides a wide range of illumination raypath angles andazimuths through the subsurface, with obvious benefits forprestack migration imaging.

Traditional OBS designs come in three distinct flavors: (1)inline narrow azimuth swath, (2) crossline wide azimuthorthogonal, and (3) node. The swath geometry uses parallelsource and receiver lines, and is usually also narrow azimuth(2D style fold), which may simplify converted-wave process-


ing. Orthogonal geometry has source lines at 90° to receiverlines, which gives good raypath distribution, and small binsizes, but can make high fold very expensive, and may sufferazimuth-dependant conversion point mis-positioning duringC-wave imaging. Node geometries contain widely distrib-uted receivers, and a relatively dense, uniform source grid.For node surveys, the source grid spacing defines the bin size,and the receiver density sets the fold.

The advantages of all three geometries can be combinedby using a wide-patch, uniform-source grid in conjunction withinline swath shooting over receiver cables. This is similar todesigning a swath survey with the flip-flop source gridextended laterally to cover most of the crossline maximumoffset. In this way, each receiver in the cable(s) can be treatedas single node in processing (with its own fully-sampled wide-patch source grid), which has notable benefits for noise atten-uation.

The dense receiver spacing along the cable direction (25-m group interval) will set the inline bin size for the imagingstep, which means that if the cables are deployed in the dip

direction, the source spacing inthe shot grid can be adjustedindependent of the small bin sizerequired for steep dip imaging.For the Azeri and Gunashli struc-tures, the plunge along strike hasslopes of less than 20°, and canbe adequately imaged with amuch larger bin size than in thedip direction. This gentle slopealong strike allows a flip-flopsource configuration of 75 � 75m, which generates a natural binsize of 12.5 m inline and 37.5 mcrossline (strike).

The broad 75 � 75 m spacingof the flip-flop source has obvi-ous benefits for reducing overallcost. Adjusting the overall shotpatch width to include the max-imum offset range around eachreceiver ensures high fold, withdiverse raypaths for imaging,and a true 3D receiver gather forenhanced processing. Figure 2shows a scale diagram of thereceiver cables (blue) and the gridof sources (red). The receiver lineseparation was 720 m. Therewere 26, 10.4-km flip-flop saillines (4 km total shot patchwidth). A yellow circle indicatesthe diameter of usable offsetrange in the mute at the targethorizon surrounding a typicalreceiver point in the cable, andhighlights the size of the 3D sin-gle-fold patch surrounding eachreceiver gather.

Even though the widely-spaced flip-flop shooting helpedreduce costs, it was impossibleto extend the high fold (whichwas needed in the structure’s cen-tral core) over the full surfaceextent. Rather than restrict theareal coverage, the fold distribu-

tion was intentionally tapered away from the central area,using a land seismic technique, known as receiver line inter-lacing.

To combat noise and weak signal in the core of the struc-ture, the line spacing needs to be quite tight; 360-m separa-tion in this case. At the structural crest the reservoir targethorizon is just over 2 km below the seabed, and the process-ing mute restricts offsets beyond 2200 m. Moving downdip,away from the crest and beyond the oil-water interface, thereservoir horizons are much deeper, and the processing mutecan be opened up to allow offsets up to 3500 m. Line spacingcan be relaxed for these deeper reflectors, and the larger max-imum useable offset compensates for this, keeping the foldrelatively unchanged. More importantly, the data quality inthe flanks and synclines is very much better than in the coreof the structure, so that the overall requirement for high foldcan be relaxed significantly in the downdip areas. Extendingthe source lines 3 km outbound off the end of the receiver linesdowndip also provides useable reflection data for additionalmigration aperture.

SEPTEMBER 2005 THE LEADING EDGE 913

Figure 3. Detail of receiver line interlacing used for Azeri (left) and Gunashli (right). Positions of individualtwo-line receiver patches are highlighted in geeen, blue and red.

Figure 4. Receiver gathers (left) showing offset limit increase with target depth. Fold models (right) showingfold levels increasing downdip as mute is opend up to allow more offsets for deeper targets. Note that themajority of stack fold on the final image comes from the small section of data highlighted in the yellow trian-gle on the first receiver gather.

Ray-trace modeling for Azeri indicated that receiversshould extend 6 km down slope on both sides of the crest (12km total line length to capture sufficient migration aperture).This value matched well with the available 6-km OBS cables.The use of a wide, regularly-sampled, source grid allows ade-quate P-wave and C-wave fold with fairly wide spacedreceiver lines of 720 m in the area of the flanks. However, theshallower, poor data quality near the crest requires muchhigher fold, which was only achievable by using a line inter-val of 360 m, half that of the flanks.

This gradational spatial sampling scheme was executedby deploying all of the two-line receiver patch layouts usingthe wider 720-m line spacing, so that the whole survey areahas a baseline spacing of 720 m. The central portion of AzeriField was then covered with an additional suite of identicalpatches, which were interlaced among the base grid, so thatthe crest of the structure has an effective line spacing of 360m (Figure 3, left).

The Gunashli structure is narrower than the Azeri struc-ture, and so requires less migration aperture extension in thedip direction (9 km versus 12 km) to image the reservoir atand below the waterline. This survey used a simpler form ofinterlacing (Figure 3, right). Here only two patches wereinterfingered to generate the variation in inline spacing from750 m to 350 m over the crest.

In the case of these structural targets, interlacing and theuse of a wider line spacing on the flanks implies that the foldis held more constant with stratigraphy, rather than the tra-ditional designs which attempt to hold fold constant withdepth. This effect is illustrated in Figure 4, which shows howthe downdip fold is partially maintained by additional offsetavailable within the mute for these deeper horizons.

The novel use of variable receiver line spacing in con-junction with fully sampled source grids in acquisition sup-ported additional innovations during the processing phase ofthe project.

Processing strategy. For efficiency, the acquisition of bothAzeri and Gunashli OBS surveys took place in a single sea-son, mid-2002, while the processing and imaging projectsspanned several subsequent years. The business unit’s reser-voir development plan included a staggered set of well plan-ning commitments which prompted the decision to progressthe data processing as a set of staged projects, with sequenceddeadlines for deliverables, as follows:

• To verify OBS effectiveness, 2D PS test lines (Azeri struc-ture) were processed in Baku, Azerbaijan during acquisi-tion.

• As a check on data quality and navigation/geometrymerge, full area PZ and PS brute stacks were generatedfor the Azeri structure.

• To meet an early well planning deadline for Azeri, a sub-set priority volume was selected and processed throughto PS and PZ prestack time migration.

• Immediately following the delivery of the priority vol-umes, processing of the full Azeri data set was re-initiated(starting back at PZ sum and vector fidelity) through toprestack time image volumes.

• At the point in the flow where the prestack time signalprocessing was complete for both PZ and PS, the data werealso passed for prestack depth imaging, and generationof a fast-track PZ prestack depth-migrated volume for thefull area.

• Following delivery of the fast-track prestack depth vol-ume, a full joint inversion, depth migration project wasrun to generate registered PZ and PS volumes.

• Following the completion of the Azeri time processing, thesame time and depth processing projects listed above werethen initiated for the Gunashli survey (with the priorityand fast-track products omitted).

The numerous repeated steps in the serial processing effortjust described satisfied the dual requirements of (1) appropriateinterpretation products at specific deadlines, and (2) adher-ing to the constraints of fixed processing resources. Moreimportantly, this serial approach significantly improved thequality of the final products via application of key learningsfrom the prior processing stages, consciously fed forward intosubsequent project phases. This serial scheme is somewhatanalogous to the familiar case where reprocessing older vin-tage data is seen to produce dramatic improvements.

In the case of the Azeri and Gunashli projects, the base-line and reprocessing steps were wrapped into a single pro-ject so key personnel from the contractor and client wereinvolved during acquisition, through processing and inter-pretation. This optimized the ability to understand and learnfrom the data, and ensure that any enhancements to the pro-cessing flow could be rapidly applied during the following


Figure 5. PZ receiver gathers from the Gunashli survey showing databefore (left) and after application of random noise attenuation, used toattenuate aliased portion of the coherent noise.

Figure 6. PZ brute stacks from the Gunashli survey showing data before(left) and after application of random noise attenuation.

round of processing-imaging. For instance, each subsequenttesting phase progressively built on the knowledge gainedfrom the previous work (on the same data set) so that a broaderrange of options could be tested, and compared against theearlier benchmark fast track, or priority volume.

Processing and its link with acquisition. Consistency of per-sonnel is important, and it is rare for geophysicists to beresponsible for both the acquisition design and QC/projectmanagement during the processing stage. When acquisitionand processing are treated as a single process, knowledge ofany innovations and/or compromises in the design can be car-ried seamlessly into the processing phase. Some of these arediscussed below. (Very simply, the processing was designedto preserve and exploit the available signal, temporally from

low to high frequencies and spa-tially, while removing all sourcesof coherent and incoherent noise.)

1) Noise attenuation. The inno-vative use of a wide patch flip-flop source grid was included inthe acquisition design so thatnoise attenuation techniquescould be applied to each 3D com-mon receiver gather (node-styleprocessing). Compromises suchas the coarse 75 � 75 m sourcegrid are often viewed as barriersto application of prestack 3D fil-tering (such as f-kxky dip filters).Due to the large spatial samplinginterval, the noise wave trains ina 3D common receiver gather areexcessively aliased. Methods bor-rowed from land thrust beltacquisition and processing wereused to turn this perceived bar-rier into an advantage. Thealiased frequencies of backscat-tered and direct arrival noise canbe effectively suppressed using3D prestack f-xy deconvolutionnoise attenuation. Followingapplication of NMO, the sourcesampling is adequate for the

reflection signal; however, the more steeply dipping noisesuffers aliasing in the 3D receiver gather. When 3D f-xy decon-volution is applied to this prestack 3D data set, the aliasedcomponents of the noise appear similar to random noise, andare effectively suppressed. Figure 5 shows an application ofrandom noise attenuation on a receiver gather from theGunashli survey. The confused noise energy seen in the back-ground of the data on the left is actually the aliased high-fre-quency portion of the coherent surface noise event. Afterapplication of random noise suppression, much of this appar-ent background noise is effectively reduced, as illustrated onthe same gather on the right side of Figure 5.

Even though this noise is aliased on the coarsely sampledreceiver gather, and appears random to this 3D f-xy decon-volution filter, when the noise is viewed in other, well-sam-pled domains, (such as a CDP stack volume), the coherentbackscatter and direct arrival energy is seen to “leak through”the stacking process, as illustrated on the stack shown on theleft side of Figure 6 (generated from the data in Figure 5). Thishigh-fold stack is adept at suppressing multiples, and randomenvironmental noise, but not as effective against the steeplydipping coherent energy. After application of prestack com-mon receiver domain f-xy deconvolution, the stack data shownon the right is observed to exhibit much less “fuzz,” indicat-ing the effectiveness of the random noise attenuation in attack-ing this aliased coherent noise.

Coherent noise can be intentionally aliased by sorting thedata to any other appropriate domain, such as common off-set. When the data are sorted to narrow-offset subvolumes,the signal remains coherent (independent of NMO), yet thebackscatter and direct arrival noise undergoes disorganizationvia subsampling, and appears aliased, which makes thisdomain appropriate for additional filtering using 3D prestackf-xy deconvolution. Care must be taken to ensure the data arefiltered as single fold within each subvolume, so that the crit-ical source and receiver x,y,z information is retained forprestack migration. Examples of common-offset random noise


Figure 7. Time slice images of Gunashli common offset volumes showing PZ data (left) and PS data (right)before (top) and after (bottom) application of single fold, prestack 3D f-xy deconvolution random noiseattenuation.

Figure 8. PS brute stacks from the Gunashli survey showing data afterthe first application of receiver domain pre-stack 3D f-xy deconvolutionnoise attenuation (left) and after application of random noise attenuation.

suppression are shown in Figure 7. These four images showtime slices through low-fold common offset volumes before(top) and after (bottom) the application of 3D prestack com-mon offset f-xy deconvolution noise suppression. In this exam-ple from the Gunashli survey, the random noise suppressionis shown for both the PZ data (left) and the PS converted-wavedata (right). Both PZ and PS data sets show marked improve-ment in continuity and SNR after the application of 3D prestackf-xy deconvolution.

This type of noise is not suppressed very well by eitherstack or the prestack migration processes. The aliased portionsof the energy are exaggerated by the migration operator, mak-ing the noise virtually impossible to attenuate post migration.Figure 8 (left) shows a PS stack volume which has had a sin-gle pass of 3D prestack common-receiver domain f-xy decon-volution noise suppression. The stack exhibits reasonablygood SNR after one pass of random noise attenuation; how-ever, the deeper reflectors are not continuous. Following appli-cation of automatic statics, and a second pass of 3D prestackcommon-offset f-xy deconvolution noise suppression (Figure8, right), the stack shows improved continuity and SNR, par-ticularly for the deeper target reflectors.

2) Statics. The wider line spacing, and wide source patchfeatures of the acquisition geometry facilitate applyingprocesses such as first break refraction tomography, and auto-matic reflection statics. Both processes rely on spatial averag-ing to iteratively solve for either the subsurface velocity profile,or surface-consistent static shifts. Narrow azimuth data setscan cause instability during the calculation of automatic sta-tics, because the lack of broad areal extent, in what is effec-tively a 2D shot or receiver gather, may induce local spatialbias. For wide-azimuth acquisition, the source and receivergathers cover a much larger area on the surface, whichimproves the statistical diversity in the solution. Refractiontomography also benefits from a broad range of source receiverazimuths, in much the same way that multiazimuth dataimprove prestack migration performance around complexstructures.

In both the Azeri and Gunashli OBS surveys, the hydro-phone first breaks were of sufficient quality to be machine pick-able. This permitted the option of using first break refractiontomography to estimate the P-wave near-surface velocitystructure. Refraction statics and now refraction tomographyare familiar processes for land data, and have also been usedon towed-streamer marine. However, in the marine case, theconfiguration of limited towed cable length combined withboth the source and receiver side of the raypath traversing thewater leg, often means that the useable range of refractions islimited. With the Azeri and Gunashli OBS, the hydrophonesare on the seabed, which allows better capture of the short-offset raypaths from the very shallowest layers, and the sourcelines exceeded 10 km, yielding maximum offsets beyond 6 kmalong the inline azimuth, which help stabilize the tomographicsolution to provide a reasonable velocity profile to depths of1200 m in the deeper sediments of the structure flanks. Anexample of the refraction-tomography-derived P-wave veloc-ity profile is shown in the top of Figure 9, in an area whichbisects the Azeri mud volcanoes. The correspondence of veloc-ity profile with the shallow reflection data (bottom) is seen tobe very good, with the isovelocity contours conforming to thedip of the seismic horizons along the flanks, and in the strikedirection.

The P-wave statics derived from the refraction model weretoo small in magnitude to affect the processed image. However,when the refraction model was scaled using a shallow VP/VSratio derived from poststack event registration, the resultingshear-wave receiver statics were of significant magnitude (and

positive benefit) to warrant their application in processing.The refraction tomography velocity field is also valuable

as a starting model for the depth migration. The spatial reso-lution and detail contained in the geometry of the refractiontomography near-surface velocity model exceeds that of thesmoother model derived from the OBS reflection data. The


Figure 9. Velocity model generated using P-wave refraction tomography(top) compared to the seismic data in the vicinity of the Azeri mud vol-cano.

Figure 10. 4-C prestack field records: (upper) common shot domain,(lower) common receiver domain. The arrows indicate sources of noise.Shear-like energy is visible on Z-component in the receiver domain.

wide line spacing of the OBS geometry does not contain near-offset traces between the receiver lines which restricts the spa-tial resolution of velocity analysis in the strike direction. Thisproblem can be alleviated by using refraction tomography.

The magnitudes of the P-wave refraction-derived veloci-ties are systematically about 10% higher than the velocitiesderived from the reflection data. The refractions travel hori-zontally and preferentially in the faster (deeper) subsurfacelayers and these combined effects of intrinsic anisotropy andlayering are difficult to separate. Instead, the refraction veloc-ities were simply scaled prior to incorporation in the depthmigration velocity model.

3) Vector fidelity. The surveys made use of the cable-basedNessie 4-C sensors with inline groups of nine elements over25 m. Establishing adequate vector fidelity, that is, the truerecording of the earth’s motion on each of the four compo-nents, is essential in any multicomponent data survey (whethergimballed or fixed-axis geophones are used). In cable-basedsystems, the surface area available for coupling to the seaflooris different for inline and crossline components. Group-form-ing exacerbates the resulting differences in response. As thesystem was known in advance and differences were observedin the field record components, particular attention was paidto correct for vector infidelity during processing.

Slower velocity events which contaminate the verticalcomponent geophone correspond well with primary shear-wave energy recorded on the crossline geophone (Figure 10).Analysis of shallow windows in the data, from a full azimuthof shot points over a range of offsets (provided by the widesource patch), allows orientation errors in the geophones tobe detected and corrected, receiver-by-receiver. A by-productof this analysis is a prediction (and removal) of any shear-waveenergy on the vertical component, ensuring the vectorresponses of hydrophone and geophone are more similar.This improves the efficiency of the PZ summation.

The inline and crossline geophone components are com-

bined to produce radial andtransverse components. The indi-vidual component responsesshould therefore be equal.Analysis of longer windows inthe data allows differences in geo-phone response (due to theequipment design) to be reducedthrough filtering or multichanneldeconvolution.

4) Multiples. The out-of-phaseghost operators present in thehydrophone and vertical-com-ponent geophone are exploitedthrough PZ summation in thecommon-receiver domain. Ideal-ly this combination is done in theplane-wave domain, where wavepropagation effects and interfaceproperties which are direction-ally dependent can be accountedfor correctly. However, due to thelarge shot spacing (75 m), it is notfeasible to transform the data toa plane-wave domain. Conse-quently, the familiar approach ofassuming 1D propagation wasadopted here, in the knowledgethat the furthest offsets from thewide shot patch are suboptimallycombined. (Water column rever-

berations generated on the source side are attacked withdeconvolution, applied to τ-p transformed shot gathers.)

The nonideal PZ summation is countered by the wider3D shot patch. The richness of medium-to-far offsets meansgreater velocity discrimination over the multiples so thatstacking is ultimately more effective in multiple suppression.

Note that the problem of multiples in towed-streamer datain this area is usually tackled through a surface-related mul-tiple attenuation approach (designed to remove all orders andtypes of multiples that have hit the sea surface). Althoughthe acquisition configuration in OBS is regular, and poten-tially more suited to wave-theoretical methods of multipleremoval, the cost of survey deployment in the Caspian is toohigh at this time to permit adequate sampling for such mul-tichannel 3D processing.

5) Depth migration. Borehole seismic (wireline and VSP)data were acquired in a number of Azeri wells specifically tosupport the OBS processing. This has greatest use and impactin depth imaging where the borehole data constrains thestructure to be closer to a “true earth” model.

Imaging in the time domain was (polar) anisotropic, usingcurved-ray Kirchhoff prestack time migration. Early attemptsto image the PS data isotropically were unsuccessful.However, the multiparameter velocity models required forthe PS imaging made parameter optimization challenging.

A pragmatic approach was adopted for the parameteri-zation of the depth migration. Where sufficient well infor-mation was available and the data supported the additionalcomplexity, the migration was polar anisotropic (Azeri).Where the data supported a simpler model (in the absenceof additional constraints), the migration was isotropic(Gunashli). This decision was driven largely by considera-tion of the PZ data whose data quality is more aerially reli-able than the PS data.

The methodology used in the depth migration was basedon the best available technology to image both modes of the


Figure 11. Azeri P-wave depth migration depth slices (top) and dip cross-sections (bottom) for the OBS data(left) and the 1995 vintage towed-streamer data (right).

OBS data:

• starting velocity model fromprior towed-streamer depthmigrations and refractiontomography

• both grid-based and layer-based reflection tomography

• joint/iterative inversion ofthe P-wave and C-wave(radial component of the P-to-S converted mode) data setsfor coupled P and S velocitymodels

• estimation of anisotropy pa-rameters from walkawayVSP

• anisotropic (where applica-ble) depth migration for bothPZ and PS data

• joint P-wave velocity modelbuilding using both OBS andtowed-streamer data setsover the Gunashli structure

The strategy used to updatethe P- and S-wave velocities in-volved strong interpreter bias toguide the solution, via interpre-tation of PZ and PS volumes along key horizons during eachiteration. The anisotropic parameters (Thomsen’s epsilon anddelta) were constrained by the borehole data. Velocities werefurther constrained by the assumption of equivalence in depthof the picked PZ and PS horizons, as well as the borehole infor-mation. Analysis of depth mis-ties between well markers andseismic provides further confidence on the convergence of thevelocity model to a true earth model.

The axis of the anisotropy was driven by the mis-tieanalysis; transverse isotropy with an axis normal to bedding(rather than vertical) provides a more consistent structuralshape, and agrees with more of the well data. This assump-tion is consistent with the concept that the anisotropy is com-paction-driven since the anisotropic layers in the modelwere deposited prior to any structural activity.

Results. The imaging results of the P-wave depth migra-tion are of much higher quality in the central zone of thestructure, compared to the previous towed-streamer depthimages (Figure 11). The PZ sum and other noise attenuationpreprocessing steps contribute significantly to the qualityof the PZ depth image, as do the inherent high fold and mul-tiazimuth attributes of the acquisition design. A wide rangeof source-receiver azimuths and offsets are particularly use-ful when undershooting near-surface low-velocity anomalies(trapped gas, mud, etc.) and when imaging circular fea-tures such as the mud volcano-induced slump visible in thecenter of the OBS depth slice on the top left of Figure 11.The results would most likely be even better with the appli-cation of the f-xy deconvolution techniques which did notform part of the Azeri processing scheme.

The image quality through the core of the structure alsobenefits significantly from the OBS bandwidth having arichness of low-frequency signal. At least three factors con-tribute to the emphasis on low frequencies: (1) the input sig-nal from the air guns is enhanced by having the receiverson the seabed (the ghost operator provides amplification atvery low frequencies which, depending on water depth,

can preferentially enhance a large part of the output energy);(2) the NMO stretch, associated with the predominance offar offsets, shifts the spectrum to lower frequencies; (3) thehigh-cut effect of the antialias filter in the migration. Thisadditional octave of low-frequency bandwidth contributessignificantly to the image in the core, as the longer wave-length P-waves in OBS suffer less attenuation when prop-agating through overburden gas compared to the outputbandwidth of the towed streamer data.

The advantages of the OBS acquisition are enhanced byapplication of anisotropic depth migration. Amplitudes aremore consistent on the steep north and south flanks of theanticline, as is the overall structural shape. However, resultsare not entirely dependant on that technology, as evidencedby the high quality PZ prestack time-migration images forGunashli (Figure 12). Here the time slice images (top) andthe cross-section images (bottom) illustrate that the SNR andcontinuity of the OBS prestack time migration (left) is supe-rior throughout the central area of the structure as comparedto the prestack depth migration (stretched back to time) ofthe towed-streamer data (right).

As mentioned previously, the converted-wave data suf-fer attenuation and disruption over most of the Azeri andGunashli survey areas, except in the northwest third of theGunashli OBS survey. The PS data quality is markedly bet-ter in this region, as shown on the comparison seismic sec-tions in Figure 13. Here the prestack time imaging of theOBS PS radial data (bottom) yields better SNR and conti-nuity, with more accurate overall structural shape than eitherthe OBS PZ (middle) or the towed streamer (top). While theOBS PZ (middle) is better quality than the towed-streamer(top), both time images suffer strong structural disruptiondue to the velocity delays caused by gas trapped in theoverburden. In this area, the converted-wave energy is notattenuated by near-surface unconsolidated sediments, andis also less affected by trapped gas, and so the image qualityis very good. The structural interpretation derived from thePS volume thus confirms the dome feature which appears dis-


Figure 12. Gunashli P-wave time slices (top) and dip cross-sections (bottom) for the OBS prestack time mi-gration volume (left) and 1995 vintage towed-streamer data depth migration, stretched back to time (right).

torted on the P-wave volumes.

Conclusions. Ocean bottom seismichas provided a breakthrough technicalsolution to the problems associatedwith the zones of poor seismic dataquality in the Azeri and Gunashli struc-tures. The key acquisition design inno-vations of receiver line interlacing anddeployment of a wide-patch, regularlysampled source grid allowed addi-tional novel techniques to be tested andapplied during processing and im-aging.

Clear understanding of the benefitsand limitations of the OBS acquisitionsystem and additional borehole datahave allowed optimized approaches tonoise attenuation, signal enhancement,velocity estimation, and prestack imag-ing. All of these factors have leveragedadditional value from the OBS datasets. Ocean-bottom seismic is now con-sidered the key technology for devel-oping these reservoirs. Additional workremains to understand the nature ofelastic wave propagation in the mudvolcano settings of the Caspian Sea.

Suggested reading. “Acquisition designof the first four-component 3D ocean bot-tom seismic in the Caspian” by Bouskaet al. (SEG 2004 Expanded Abstracts).“Azeri 4-C: Processing the first 3D OBSsurvey in the Caspian Sea” by Johnstonet al. (SEG 2004 Expanded Abstracts).“Reducing structural uncertainty on theAzeri Field using ocean bottom seismic:Offshore Azerbaijan” by Lyon et al. (SEG2004 Expanded Abstracts). TLE

Acknowledgments: BP operates ACG field onbehalf of the shareholders of the AzerbaijanInternational Oil Company (AIOC)—BP34.14%, Unocal 10.28%, Socar 10%, Inpex10%, Statoil 8.56%, ExxonMobil 8%, TPAO6.75%, Devon 5.63%, Itochu 3.92% andAmerada Hess 2.72%. The authors thank theAIOC shareholders for permission to publishthis paper and their input to the planning andexecution of the project. We thank our colleaguesin bp who have been involved in this project eitherdirectly or indirectly: Tom Lyon, DominicManley, John Howie, Sean Mohammed, DaveBuddery, Dave Howe, Leon Thomsen, MikeMueller, Dan Ebrom and Jan Kommedal. We alsoacknowledge the dedication and skill of thoseindividuals in Caspian Geophysical and WesternGeco who acquired and processed the OBS sur-vey—in particular Andy Ashby, RichardCrompton, Emma Luke, and Richard Walters.

Corresponding authors: [email protected] [email protected]


Figure 13. Gunashli seismic sections from the NW area of the OBS survey In this region of the sur-vey, the PS data quality (bottom) exceeds that of either the towed-streamer (top) or the PZ OBS(middle).

the first 3d/4-c ocean bottom seismic surveys in the

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