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Marine Seismic

1 Schlumberger.2 Apache North Sea Limited.3 Apache Corporation.* Corresponding author, E-mail: [email protected]

Towards improved time-lapse seismicrepetition accuracy by use of multi-measurement streamer reconstruction

Patrick Smith1*, Jason Thekkekara1 , Julie Branston1, Grant Byerley2, David Monk3 and Jeffrey Towart2 describe, with an example from the North Sea Forties field, how multi-measurement streamer technology enables the reconstruction of a monitor survey to the streamer locations and datum of a previous survey, thus improving time-lapse repeatability.

M onitoring producing hydrocarbon reservoirs by time-lapse (4D) seismic measurements has become routine, and it is widely accepted that accurate repetition of source and receiver locations between

surveys is key to time-lapse seismic data quality. Most marine time-lapse seismic surveys are acquired with towed streamer techniques. While the source locations of these surveys can be accurately repeated (Paulsen and Brown 2008), exact rep-etition of streamer locations can still be challenging.

Eiken et al. (2003) introduced the concept of interpolat-ing multi-streamer shot records from their acquired positions to the desired receiver locations. Duplicating shotpoints and interpolating to the same receiver locations for each survey resulted in well repeated time-lapse seismic data. Unfortunately, accurate interpolation required the use of such small crossline streamer separations that the technique was uneconomic for large-scale reservoir monitoring. Multi-domain interpolation techniques (e.g., Sharma et al., 2011) show promise, but to date have not eliminated the need to accurately repeat source and receiver locations.

The introduction of multi-measurement streamer technol-ogy (Robertsson et al., 2008) enabled wavefield reconstruction from data acquired with economically feasible streamer separations. Two or more multi-measurement surveys may be reconstructed at common locations, or a multi-measurement monitor survey may be reconstructed at the receiver locations of a previous conventional marine streamer dataset. This arti-cle describes the latter application using multi-measurement test lines acquired by Apache North Sea Limited over the Forties field in 2012 with WesternGeco’s IsoMetrix marine isometric seismic technology.

Reconstruction of multi-measurement marine streamer seismic dataThe multi-measurement streamer used by Apache contained

densely spaced hydrophones and accelerometers that record the pressure wavefield and the vertical and crossline com-ponents of the particle motion vector. Commercial surveys acquired in 2012 used eight multi-measurement streamers, typically with 75 m crossline streamer spacing. The streamers were towed at depths of between 15 and 20 m to minimise streamer noise.

Vassallo et al. (2012) showed how the generalized matching pursuit (GMP) algorithm can be used to recon-struct from each multistreamer record, shot-by-shot upgoing and downgoing pressure wavefields sampled on a dense 6.25 m inline by 6.25 m crossline grid. The streamer array records signal energy over an apparent velocity range of roughly -1480 to +1480 m/s, implying that the reconstructed wavefield supports spatially unaliased signal for temporal frequencies up to about 120 Hz. This is more than sufficient for most hydrocarbon reservoir imaging applications. The GMP algorithm outputs the reconstructed wavefields at a user-defined datum.

Repeating a conventional marine streamersurvey with multi-measurement streamer dataThe multi-measurement monitor survey should accurately repeat the source locations of the base survey. The streamer spread width must be sufficient to enable reconstruction of the base survey receiver locations, taking into account any feather mismatch that occurs during acquisition. GMP gener-ates densely gridded shot gathers for the up and downgoing pressure wavefields at the datum of the base survey. These are combined to create the total pressure wavefield, which is then interpolated to the X,Y co-ordinates of the receiver locations of the equivalent base survey shots. The process is shown schematically in Figure 1. The total wavefield gener-ated by this process should closely match that recorded by the baseline survey. Performing the reconstruction at the ini-

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source locations of the 2000 survey, and the acquisition parameters of the two surveys are summarized in Table 1 and Figure 3. The 50 m streamer spacing of the 2010 survey provides substantial oversampling of receiver locations relative to the 2000 survey and maximizes the chances of accurate receiver repetition. During processing, time-lapse binning was used to select the best repeated traces from the 2000 and 2010 surveys, and to discard the redundant ones.

Having used the Q-Marine point-receiver marine seismic system to acquire the 2010 monitor survey, Apache noted improved signal-to-noise ratio in the 6 to 10 Hz frequency range relative to previously acquired datasets. This was attributed to better spatial and temporal sampling, along with the lower noise floor delivered by digital group form-ing technology. The additional usable low frequency energy helped to better delineate the thicker turbidite channel sand complexes that define the Forties reservoir (Figure 4). Apache therefore decided, in 2012, to test whether acquisi-

tial stages of the processing flow ensures identical operation, between the baseline and monitor survey, of subsequent steps such as wavelet processing and demultiple.

Time-lapse survey positional repetition accuracy is typi-cally assessed by evaluating, for each time-lapse survey trace pair, the sum of the absolute distance between the source locations and the absolute distance between the receiver locations. This sum is commonly referred to as ΔS+ΔR. A multi-measurement monitor survey reduces ΔR to zero, and so should provide seismic data that closely matches the baseline survey. However the multi-measurement reconstruc-tion process is not error free (Eggenberger et al. in review) and becomes progressively less accurate as ΔD (the distance between the reconstructed output location and the nearest multi-measurement streamer) increases. This may, to some degree, offset the benefits of reducing ΔR to zero.

Time-lapse seismic monitoring of the Forties fieldThe Forties field was discovered by BP in 1970 and is located in the North Sea, about 175 km east of Aberdeen. Apache North Sea Limited bought the field in 2003, by which time production had declined from a peak of about 500,000 barrels of oil per day (bopd) in 1979 to about 35,000 bopd. Apache implemented an intensive re-evalu-ation and drilling programme that increased production to around 60,000 bopd by targeting further bypassed pay opportunities with infill wells. The black dots on Figure 2 represent the approximately 120 infill wells drilled since Apache began operating the field in 2003. Time-lapse seis-mic technology played a key role in de-risking those infill wells by clearly imaging areas where water has replaced oil in the reservoir (Rose et al., 2011). The red dots represent the 90 remaining infill targets in the current portfolio which are the focus of continued time-lapse monitoring efforts at Forties.

Towed streamer seismic surveys were acquired over Forties in 1988, 1995, 2000 and 2010, with all four surveys being co-processed in 2010. The 2010 dataset repeated the

Figure 1 Schematic representation of the recon-struction of a multi-measurement shot record. The pale blue lines represent multi-measurement streamers at 75 m spacing. The dotted lines represent the dense 6.25 x 6.25 m grid of traces generated by the GMP reconstruction process. The red lines represent the streamer locations of the previous baseline survey. The dense grid of traces is interpolated to the XY co-ordinates of the receiv-ers within the baseline survey streamers for this shot. The reconstruction accuracy is a function of ΔD (the distance between the reconstructed out-put location and the nearest multi-measurement streamer).

Figure 2 Outline of the Forties field, and locations of two multi-measurement test lines.

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ation of how the accuracy of the reconstructed data would vary with ΔD. The source array was identical to that used in 2010.

Each multi-measurement multi-streamer shot gather was reconstructed at the receiver locations and receiver datum of the equivalent multi-streamer shotpoint from the 2000 survey. Summation of the reconstructed up and down-going wavefields introduced a streamer ghost consistent with that of the 2000 data.

The reconstructed multi-measurement test line and the equivalent sail lines from the 2000 and 2010 surveys were processed through the flow described in Table 2. This flow is identical to that used during the 2010 time-lapse

tion with a multi-measurement streamer system could accurately repeat the previous surveys while simultaneously enabling the creation of data with broad spatial and tempo-ral bandwidths.

Forties 2012 multi-measurement marine streamer acquisition testTwo of the previous sail lines were reacquired in 2012, as shown in Figure 2. This article describes the results from line 1294 only. The streamer spread configuration, shown in Figure 3, was designed so that some of the previous streamer positions would be accurately repeated by multi-measure-ment streamers and others would not. This facilitated evalu-

Figure 3 Overview of the basic acquisition configurations for the Forties 2000 monitor survey, 2010 Q-Marine monitor survey and 2012 multi-measurement test lines.

Acquisition date 2000 2010 2012

Acquisition system Syntrak Q-Marine IsoMetrix

Sail line interval 300 300 n/a

No. of sources / separation (m) 2 / 50 2 / 50 2 / 50

Shotpoint interval (m) 12.5 (flip-flop) 12.5 (flip-flop) 12.5 (flip-flop)

Source volume (cu.in.) / depth (m) 3090 / 5 3147 / 6 3147 / 6

No. of streamers / separation (m) 6 / 100 11 / 50 8 / variable

Streamer length (m) / depth (m) 3000 / 7 3600 / 7 3000 / 18

Array formed group interval (m) 12.5 6.25 6.25

Table 1 Acquisition parameters of the 2000 and 2010 Forties monitor surveys and the 2012 multi-measurement test lines.

Figure 4 Seismic derived lithology volume without (left) and with (right) the additional 6-10 Hz low frequency signal gained from the 2010 Q-Marine survey high resolution processing. Wells with gamma ray logs are overlain and show the channel sands (yellow) that define the reservoir.

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Figure 5 shows schematically the midpoint coverage of regularized common offset 3D cubes produced from a single multi-streamer sail line of a 3D survey. The coverage of the longer offset cubes is displaced in the crossline direction as a result of streamer feathering. However, provided that the streamer feathering is not too great, at least one full-fold 3D inline can be created, as indicated by the blue line in Figure 5. The regularized 3D inline created from this single multi-streamer sail line is essentially identical to the 3D inline that would be created when regularizing the entire 3D survey. We were able to create seven full-fold 3D inlines (inlines 6609 to 6615) from the acquired datasets.

The recorded traces have non-regular midpoint loca-tions, offsets and azimuths, and there may be zero, one or more traces per 3D cell in each common offset dataset. The common offset regularization procedure reconstructs the recorded data onto a regular grid, with a single trace posi-tioned at the centre of each cell of each common offset cube. A nominal azimuth is assigned to all traces. Regularizing the baseline and monitor surveys to identical grids ensures, in the absence of overburden and reservoir heterogeneity, that time-lapse comparisons are not affected by differences in trace locations. Industry-standard time-lapse processing flows also assume that this is the case when heterogeneity is present. This is only partly true, which is why minimization of ΔS+ΔR during acquisition is so important.

Analysis strategyWe wanted to evaluate the benefit associated with reducing ΔR to zero, and to understand how reconstruction accuracy

co-processing with the exceptions that sail-line by sail-line residual time shifts were handled by the global matching operators and that each individual regularized 3D inline was 2D pre-stack depth migrated.

Step Processing

Reformat & data reduction Reformat, nav-seis merge, resample to 4 ms, truncate to 3000 m streamer length, tidal static corrections

Data conditioning Low-cut filter, tau-p first break removal, swell noise attenuation

Wavelet processing Calibrated marine source deconvolution (2010, 2012 only), deterministic zero phase conversion and debubbling

Data conditioning Receiver motion correction (applied to 2012 data at GMP stage)

Multiple and noise attenuation Deterministic water layer demultiple, shot & receiver domain tau-p dipfiltering. Anti-alias filter & trace drop to 12.5 m group interval (2010, 2012 only)

Wavelet processing Phase-only inverse-Q filtering

Binning & regularization Sort to 3D common offset cubes, expanded bin time-lapse binning & 3D azimuth moveout

Imaging 2D Kirchhoff pre-stack depth migration of individual 3D inlines

Stack Inner & outer mute, cmp stack

Survey matching Sail-line averaged frequency-variant global match filter, designed in the analysis window shown in Figure 6

Post-stack processing Exponential gain

Table 2 Processing flow applied to the 2000, 2010 and 2012 test lines.

Figure 5 Schematic diagram of the offset-variant midpoint coverage for a single regularized multi-streamer sail line. Provided the feather is not too great, at least one full-fold 3D inline can be selected from the regularized common offset cubes.

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The NRMS analysis window was positioned above the reservoir, and is marked by dotted black lines in the lower and middle panels of Figure 6. Each stacked trace was assigned a ΔS+ΔR value that was the average, over offset, of the ΔS+ΔR values of the traces comprising the corresponding common midpoint gathers. Offset-averaged ΔD values were computed in the same way.

ResultsThe lower panel of Figure 6 shows one of the stacked inlines. The panel above shows the difference, for this inline, between the 2000 and 2010 surveys, gained by +15 dB rela-tive to the stack. The high amplitudes on the difference data below the analysis window represent changes caused by oil production between 2000 and 2010. Elsewhere, high ampli-tude non-reservoir reflectors almost completely disappear during the differencing, indicating high-quality time-lapse seismic data. The remaining difference energy has a rather random appearance and careful examination reveals that the difference is noisier to the left of the section than to the right. The NRMS difference values are graphed in red in the top panel, and show an increase from about 10% on the right to about 15% on the left. Also graphed, in blue, is the offset-averaged ΔS+ΔR for this inline. There is a clear correlation between ΔS+ΔR and NRMS difference. This is confirmed by crossplotting NRMS against ΔS+ΔR for all of the inlines (the black points in Figure 7). A linear fit to these points sug-gests that the NRMS difference in the absence of source and receiver repositioning errors would be around 8%.

The lower panel of Figure 8 shows the same 2000-2010 difference data as seen in Figure 6. Above it is the equivalent 2000-2012 difference. The difference energy related to hydrocarbon production is quite similar between the two panels, but not identical – presumably due to reservoir changes between 2010 and 2012. The 2000 vs 2010 NRMS difference is graphed in blue and that for 2000 vs 2012 is graphed in red. The 2000 vs 2012 NRMS difference is lower than for 2000 vs 2010 on the left of the section, where the

affected time-lapse seismic data quality. We followed a two-step approach. We first used the 2000 vs 2010 comparison to define a relationship between NRMS difference (Kragh and Christie, 2001) and ΔS+ΔR. This enabled us to predict NRMS for ΔS+ΔR = 0. We then computed NRMS differ-ence for the 2000 vs 2012 comparison, where the 2012 multi-measurement data had been reconstructed at the receiver locations of the 2000 survey. Given exact source repetition, we would expect this NRMS value to equal the 2000 vs 2010 NRMS at ΔS+ΔR=0. Larger NRMS values would suggest that the GMP reconstruction was inducing non-repeatability.

Figure 6 The lower panel shows a representative stacked inline from the 2000 survey. The middle panel shows the difference between this stack and the equivalent from 2010. The dotted lines indicate the NRMS analy-sis window. The upper panel shows the NRMS difference (in red) and the offset-averaged ΔS+ΔR (in blue). Note the correspondence between the two curves, and to the increased noise levels on the left-hand side of the difference section.

Figure 7 Crossplot of NRMS difference versus ΔS+ΔR for all inlines of the 2000 vs 2010 com-parison (black points), and the associated linear fit. The NRMS values for the 2000 vs 2012 comparison are colour-coded by ΔD, with the average for all inlines marked by the magenta circle. The 2000 vs 2012 comparison has an offset-averaged ΔS of about 2 m, which is why the cloud of points is displaced to the right of the vertical axis.

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cal axis. The points are colour-coded by ΔD and show that, for small ΔD, the NRMS values are close to those predicted by the linear fit to the 2000 vs 2010 NRMS values. The points for larger ΔD lie above the line, showing that there is indeed a trade-off to be made between reconstruction accuracy and the benefits of reducing ΔR to zero.

This trade-off was quantified by crossplotting the 2000 vs 2012 NRMS differences against ΔD and performing a linear fit (left panel of Figure 9). The intercept of the fitted line occurs at about 8% NRMS, which is consistent with the intercept of the fitted line in Figure 7. In other words, for ΔD=0, the NRMS of the 2000 vs 2012 data is equivalent to that of 2000 vs 2010 when ΔS+ΔR=0. This suggests that the reconstruction, wavefield separation, and redatuming opera-tions performed by GMP do not induce significant variability at ΔD=0 and that we only need to concern ourselves with the variation of reconstruction accuracy with ΔD.

The GMP algorithm allows the user to weight the hydrophone, vertical vector, and crossline vector com-ponents during reconstruction, and these weights are usually set to 1.To evaluate the effect of the Y component on reconstruction accuracy, the crossline vector weight was set to zero and the above analysis repeated. The right panel of Figure 9 shows the result. The intercept of the line fit is just under 9%, suggesting that the Y component has little impact on the reconstruction accuracy for ΔD=0, as might be expected. However the gradient of the line is steeper, suggesting the availability of the Y component significantly improves reconstruction accuracy for larger reconstruction distances.

Designing a multi-measurement time-lapsemonitor surveyFigure 10 shows the offset-averaged ΔS+ΔR for the 2000 and 2010 Forties surveys. Roughly 50% of the survey has ΔS+ΔR less than 20 m and about 90% is less than 30 m. The equa-tion in Figure 7 was used to predict NRMS difference values

2000 vs 2010 ΔS+ΔR values were around 30 m, and the 2000-2012 difference seismic data is less noisy in this area. On the right, the NRMS difference values are about the same for both comparisons. This suggests that reducing ΔR to zero has a beneficial impact, but for small ΔS+ΔR the benefit is offset by reconstruction error. The coloured points in Figure 7 show the NRMS values for the 2000 vs 2012 comparison. Although ΔR is zero, ΔS is about 2 m, which is why the cloud of points is displaced to the right of the verti-

Figure 8 Comparison between the 2000-2010 and 2000-2012 differences.

Figure 9 Crossplots of NRMS difference versus off-set-averaged GMP reconstruction distance when the multi-measurement streamer Y component is used (left panel) and not used (right panel). The points are colour-coded by inline number. The filled diamonds represent the average value for each inline.

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feather there is a binary distribution of NRMS values with 67% of the survey having a value of 12% and the remainder having a value of 18%. Feather mismatch causes a more even distribution of offset-averaged ΔD, but the minimum value increases and the maximum value decreases. For 1.5 degree feather mismatch, roughly 33% of the survey is predicted to have an NRMS value of 13% with 100% of the survey having a value of less than 14% (overlain in blue on the left panel of Figure 11).

These results suggest that a 75 m streamer spacing multi-measurement repeat of the 2000 survey would have lower NRMS than a 50 m streamer separation conventional

for the survey and these were plotted as cumulative histo-grams, as shown in the left and right panels of Figure 11. Roughly 50% of the survey is expected to have NRMS less than 14%.

Now consider the repetition of the 2000 survey by a 75 m streamer spacing multi-measurement monitor survey. It is straightforward to predict the offset-averaged ΔD for the zero feather case (see Figure 12), and these values were converted into NRMS predictions using the equation in the left panel of Figure 9. The cumulative histogram of these predictions is overlain in beige on the 2000 versus 2010 cumulative histogram in the left panel of Figure 11. For zero

Figure 10 Offset-averaged ΔS+ΔR values for the 2000 vs 2010 comparison as a map (left panel) and a cumulative histogram (right).

Figure 11 Cumulative NRMS histogram for the 2000 vs 2010 survey (yellow bars), together with NRMS predictions for a zero feather and 1.5 degree feather multi-measurement monitor survey at 75 m streamer separation (left) and 50 m streamer separation (right).

Figure 12 Schematic diagram showing repetition of the 2010 survey by a 75 m streamer spacing multi-measurement monitor survey. The brown lines represent the locations of the sources and streamers of the 2000 survey. The green lines represent the locations of the multi-measurement survey. For zero feather, 67% of the survey would have offset-averaged ΔD=12.5 m and the remain-der would have ΔD=37.5 m. For 1.5 degree feather mismatch, the minimum offset-averaged recon-struction distance would increase to about 18 m and the maximum would reduce to just over 20 m.

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Forties 2013 monitor surveyIn early 2013 Apache had to decide whether to repeat the 2010 survey with the previously used acquisition technology, or whether to repeat it using multi-measurement technology. At that time, it was clear that a 75 m streamer separation multi-measurement monitor survey would provide results that were roughly equivalent to a 50 m conventional repeat. A vessel capable of deploying sufficient streamers to acquire a 50 m multi-measurement repeat was not available in the necessary timeframe. Apache felt that the risk associ-ated with a 75 m streamer separation multi-measurement repeat was too great and chose to acquire the 2013 Forties monitor survey with Q-Marine. Acquisition completed on 6 September 2013 with a mean ΔS+ΔR of 11 m at the far offset. At the time of writing (27 September), the processing is at the time-lapse binning stage, and we anticipate excellent repeatability between this and previous surveys.

DiscussionEggenberger et al. (in review) performed a similar analysis to that described above using limited offset data processed through a basic processing flow to a 2D stack. The slopes of their NRMS regression analyses are compared in Table 3 with those presented in this article. The intercept of their regressions was about 20% in all cases, which is substan-tially higher than the 8% documented here. Although part of this discrepancy may be due to differences in geological setting and acquisition parameters, the bulk of it is almost

repeat for roughly 50% of the survey, with the NRMS for the remaining 50% being somewhat higher. If we assume that the line-to-line variations in NRMS would be smoothed out during 3D processing, we can conclude that a 75 m streamer separation multi-measurement survey would have roughly the same repeatability as a 50 m conventional streamer repeat. Repeating the exercise for a 50 m streamer separation multi-measurement survey (right panel of Figure 11) predicts that significantly improved repeatability would be achieved relative to a 50 m streamer separation conventional survey.

Similarly we can predict the accuracy to which we could reconstruct the 2010 survey, as shown in Figure 13. At first glance, it would appear that a 75 m streamer spacing multi-measurement repeat would give consistently worse results than a 50 m separation conventional repeat. However, the combination of 50 m separation between sources and 50 m separation between streamers means that each 3D cell receives duplicate coverage from different source and streamer combinations. Selecting, during binning, the source and receiver combinations that minimize ΔD gives the his-togram marked by the black dotted line in the left panel of Figure 13. Once again it seems that a 75 m streamer separa-tion multi-measurement repeat of the 2010 survey would be of roughly equivalent quality to a 50 m conventional repeat. Reducing the multi-measurement streamer separation to 50 m should give consistently better results (right panel of Figure 13).

Figure 13 Estimated cumulative NRMS histogram for a 50 m streamer spacing conventional repeat of the 2010 survey (yellow bars), together with NRMS predic-tions for a zero feather and 1.5 degree feather multi-measurement monitor sur-vey at 75 m streamer separation (left) and 50 m streamer separation (right).

Eggenberger et al. Smith et al.

No GMP reconstruction 1.8 % m-1 of ΔS+ΔR 0.3 % m-1 of ΔS+ΔR

GMP using P, Z components 1.2 % m-1 of ΔD 0.3 % m-1 of ΔD

GMP using P, Y, Z components 0.6 % m-1 of ΔD 0.25 % m-1 of ΔD

Table 3 Comparison of the results of Eggenberger et al., (in review) with those presented in this article.

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Eiken, O., Haugen, G.U., Schonewille, M. and Duijndam, A. [2003]

A proven method for acquiring highly repeatable towed streamer

seismic data. Geophysics, 68 (4), 1303-1309.

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RMS and Predictability. 71st SEG Annual Meeting, Expanded

Abstracts, 20 (1), 1656-1659.

Paulsen, J.O. and Brown, G. [2008] Improved Marine 4D Repeatability

Using an Automated Vessel, Source and Receiver Positioning System.

70th EAGE Conference and Exhibition, Expanded Abstracts B028.

Robertsson, J., Moore, I., Vassallo, M., Özdemir, K., van Manen, D. and

Özbek, A. [2008] On the use of multicomponent streamer record-

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years on; continued success through the application of thorough

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certainly because our analyses have been performed on full offset data processed through a complete industry-standard time-lapse processing workflow.

Much of the noise observed on well repeated time-lapse difference datasets is related to imperfect repetition of wavefront distortion and scattering caused by overburden heterogeneity. Time-lapse processing workflows typically contain several steps to attenuate this scattered noise, and this, together with common offset regularization, could explain our smaller gradient for the result without GMP reconstruction. If so, it would appear that these noise attenu-ation processes can also help to mitigate the effects of recon-struction errors. Eggenberger’s results, on data including a strong crossline diffraction, suggest that the scattered energy is quite well reconstructed by GMP and this perhaps means that multi-measurement time-lapse seismic processing flows will require fewer noise attenuation steps. This in turn could improve the resolution of the time-lapse seismic datasets.

ConclusionsMulti-measurement streamer technology enables the recon-struction of a monitor survey to the exact streamer locations and datum of a previous survey at an early stage in the pro-cessing flow. The reconstructed data entering the processing flow is an accurate repeat of that previously acquired, and the processing flow can be expected to operate in a very similar manner. Multi-measurement streamer technology can thus provide backwards compatibility against existing sur-veys while enabling future monitoring with densely sampled data that has broad bandwidth in both the temporal and spatial dimensions.

However, the reconstruction process becomes progres-sively less accurate as the distance from the reconstructed point to the nearest multi-measurement streamer increases and, without careful survey design, this can offset some of the benefits of the reconstruction.

Analysis of the multi-measurement streamer test lines acquired by Apache over the Forties field clearly illustrates the above issues and demonstrates that the technology is applicable to time-lapse seismic monitoring of hydrocarbon reservoirs.

AcknowledgementsWe would like to thank Apache Corporation and WesternGeco for allowing us to present this work. We also thank the IsoMetrix commercialization team for its expert help during execution of the project.

ReferencesEggenberger, K., Christie, P., Vassallo, M., Özbek, A., Muyzert, E., van

Manen, D. J., and Kragh, E. [in review]. Fidelity and repeatability

of wavefields reconstructed from multi-component streamer data.

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