1. Waveform tomography images of velocity and inelastic attenuation from the Mallik 2002 crosshole seismic surveys
R. Gerhard Pratt1, F. Hou1, Klaus Bauer2, Michael Weber2
Thanks very much. In continuing with the analysis of the crosshole data that Klaus has just introduced you to, I am going to talk about some of the more detailed, high resoluion imaging we have carried out with the same data. As the title suggests, I am going to be showing you images not only of the distribution of the acoustic velocity, but also images of the distribution of the apparent inelastic attenuation.
I would also like to make it clear at the outset that this talk is about the analysis of the data from only one of the surveys – we have not yet processed the data to look for differences in the image due to production.
1Queen’s University in Kingston, Canada
2Geoforschungszentrum, Potsdam, Germany

2. Outline The Mallik crosshole survey Waveform pre-processing
Waveform tomography
Verification and interpretation
Conclusions
This is the structure of the talk I’ll be giving today: I’ll spend a short time reviewing the raw data, concentrating particularly on the amplitude effects we see in the data and their apparent association with gas hydrate. I’ll then spend the central part of my talk discussing the waveform tomography approach we use; since the technique makes use of the seismic waveforms there is a certain amount of preprocessing involved which I would like to describe, before showing you the waveform tomography results. Following this I will describe how we verify our tomographic images, and give you some preliminary interpretation. Finally I’ll draw your attention to some of the main conclusions, and indeed some of the open questions that remain to be resolved. 1

3. Outline The Mallik crosshole survey Waveform pre-processing
Waveform tomography
Verification and interpretation
Conclusions

4. Crosshole survey (Tomoseis Corporation, Houston)
As Klaus described, the survey was carried out by Tomoseis; here you can see the two Tomoseis trucks on site – The truck in the forground is located at the receiver well Mallik 4L-28, the other is located at the source well, Mallik 3L-38. The drill rig is at the location of the production well.

5. Crosshole survey parameters:
Vertical extent of target m
Source - receiver separation 90 m
Survey aperture +/- 55 o
Frequency Hz
Velocity km/s
Wavelength m
Source spacing m (2.5 ft)
Receiver spacing m (2.5 ft)
Total number of traces ≈ 100,000
Here some specifications for the crosshole survey: (read slide) 3

6. Common receiver - moving source gather
Zoom area
This is a sample common receiver gather, in the form we received the data from the contractor. The source is moved continuously during the survey while the receiver is fixed in depth. For this fan of data, the source was moved from about 1150 m (at the right), to 950 m in depth at the left. The receiver location is at the location of the flag, about halfway in between. The time window shown here is 300 ms long, you can see the first arrivals begin around 40 ms, with the arrival times increasing roughly hyperbolically as the source is either shallower than the receiver depth (at the left), or deeper (at the right).
You can also see that the data are somewhat dominated by the tube waves, slow, large amplitude guided waves that reverberate within the shot borehole.
The next image that I will show you is a zoom of the data within the area shown by this box. 2
Common receiver - moving source gather

7. Common receiver - moving source gather
Early (hydrate) arrival
Lower amplitudes, frequencies
Direct arrival
Lower amplitudes, frequencies
Tube waves (receiver hole)
Tube waves (shot hole)
When you zoom into the data, you can make out a lot more structure in the arrival patterns. The direct arrival, as mentioned earlier follows a roughly hyperbolic moveout with a minimum time located when the sources are roughly opposite the receiver in depth. You can make out an anomously early arrival at around 1080 m in depth, this corresponds to one of the hydrate zones, which are seismically fast due to the frozen nature of the hydrates. One of the most interesting things about these data is that not only do we observe a decrease in time for the gas hydrate arrivals, but we also have a marked decrease in both amplitude, and in the frequency content of the data. Its important to note that even when the source re-emerges below the hydrate zone, the amplitude and frequency content of the data remains anomalously low.
This pattern occurs both for the hydrate zone below the receiver, but also for the hydrates that are known to be present above the receiver at around 950 m depth.
As pointed out earlier, there are very obvious tube wave events: because these have a linear moveout in the receiver domain these must be arrivals that started out as tube waves in the shot hole, but were eventually converted to body waves in order to be recorded at the receiver location. What is perhaps not so obvious is that there are other strong events occuring after the first arrival that correspond to waves that initially travelled to the receiver hole as a body wave, but were then converted to a tube wave in the receiver hole. 5
Common receiver - moving source gather

8. Common Offset Gather (true amplitude)
Depth (m)
Depth (m)
800
1150 20
Early (hydrate) arrival
Channel waves
Direct arrival
Time (ms)
Tube waves
Another extremely useful display of crosshole data is the common angle gather, or common vertical offset gather. In this case the data correspond to all traces for which the sources and receivers were exactly opposite each other, so that the offset between the source-receiver pairs remains constant. The depth increases as before from left to right; you can see the late arrivals in the silty zones above the gas hydrates, the dramatic decrease in times in the fast hydrate zones as well as the dramatic decreases in amplitude and in frequency content. Within the low velocity silts sandwiched between the gas hydrate zones you can see that high amplitude, channel waves are generated that are essentially trapped modes travelling within a waveguide. Finally of course there is the presence of the tube waves, which also appear as linear events in this domain.
The weak amplitude of the hydrate arrivals ultimately is going to be the source of some of the difficulty in detecting changes in the hydrate arrivals within the production zone, which is about here. 6 60

9. Outline The Mallik crosshole survey Waveform pre-processing
Waveform tomography
Verification and interpretation
Conclusions

10. Waveform pre-processing
Tube wave removal (f-k filtering)
Low pass filtering (500, then 1000 Hz)
Spatial sub-sampling
Time windowing
Amplitude normalization (initial stages only)
This is the sequence of processes that were applied to the data in order to prepare them for waveform tomography: (Read slide) 7

11. Raw offset gathers 90 m vertical offset Zero vertical offset
What I would like to do now is take you through an illustration of all waveform processing stages that were applied. I am going to use these two common offset gathers to illustrate all the processing that was applied; later in the talk I will use the same gathers to compare the observations with the final synthetic data.
The offset gather on the left represents a large offset; the sources were about 90 m shallower than the receivers on this panel, and you see the evident reduction in signal to noise ratios for these large offsets. The signal level is dramatically absent in the gas hydrate regions. On the right is the zero offset gather, for which source and receiver depths are both equal. The amplitudes here are normalized trace by trace, so that the noise is much more evident on the large offset gather. 7
90 m vertical offset
Zero vertical offset

12. Offset gathers after tube wave removal
These are the same two offset gathers following removal of the tube waves. The clarity of the first arrivals has been significantly enhanced. 7
90 m vertical offset
Zero vertical offset

13. Offset gathers after low pass filter
Because we intend to process the data using only the lowest frequencies at first, the data were low pass filtered to remove all energy above 500 Hz. The purpose of this is to be able to carry out the initial waveform fitting of the lowest frequencies, which is both cheaper for computational purposes and also much more robust.
90 m vertical offset
Zero vertical offset

14. Offset gathers after spatial sub-samping
Following low pass filtering, the data were then sub-sampled spatially, retaining only traces from every 4th source and every 4th receiver position. 8
90 m vertical offset
Zero vertical offset

15. Offset gathers after time windowing
Finally, before input to the imaging algorithm, the data were windowed in time, retaining only the waveforms that arrive within a 40 ms time window following the first arrival pick.
90 m vertical offset
Zero vertical offset

16. Outline The Mallik crosshole survey Waveform pre-processing
Waveform tomography
Verification and interpretation
Conclusions
The waveform tomography approach we apply has been described in several papers in the literature, and its rather technical so I won’t bore you with a full description today. The process involves an iterative approach to fitting the waveform data. The process is implemented in the frequency domain, typically we fit a small number of frequencies at a time, moving progressively from low frequency to high frequency. As the name implies the technique tries to fit the waveform data, rather than simply the traveltime information. It’s a fairly computer intensive process, roughly equivalent to carrying out a prestack depth migration of the data at each iteration. 9

17. Starting model
Step 1: Projection of true co-ordinates onto best fitting 2D plane
Step 2: Anisotropic traveltime tomography
In order to work in a 2D plane, we first project the true, 3D borehole coordinates onto a 2D plane. Following the projection, we develop a starting model for the waveform tomography by applying traveltime tomography. As we shall see, the sediments are moderately anisotropic and this needs to be properly accounted for.
Lets begin an illustration of the starting velocity model from anisotropic traveltime tomography 10

18. Starting model: anisotropic traveltime tomography
These are the images we formed from the traveltime picks made on the data. The tomography procedure we use solves jointly for the horizontal and vertical velocity tomograms – the image on the right is the relative difference between the two images, showing that there is up to 10% anisotropy present in these sediments, that is the horizontal velocities are in places 10% faster than the vertical velocities.
At this stage it is already possible to see the major hydrate intervals on the images: anytime the colors rise above green on the colour bar this means a velocity of more than 3 km/s, which only happens in the hydrate layers. The intervening layers have a background velocity of around 2 to 2.5 km/s.
The starting velocity model for the waveform inversion was obtained by using the horizontal velocity values from this result. The anisotropy image was used as well; however since the waveform tomography is not currently capable of solving for anisotropy, this level of anisotropy was simply “frozen in” and never adjusted further. 11
Horizontal Vertical
Anisotropy

19. Waveform tomography: frequency strategy
Pass 1: Hz, ≈ 1D
Pass 2: Hz, 2D
Pass 3: Hz, 2D
Lets now look at the waveform tomography results. I am going to show you two progressions through the various stages: the first will be a set of images of the velocity distribution, the second will be a set of images of the distribution of the inelastic attenuation factor.
As this slide indicates, we control the process quite carefully, beginning with the lowest data frequencies and moving up to the higher frequencies (for which the algorithm is somewhat less stable). Our initial images will be heavily constrained to be approximately 1D, and later we relax these constraints to attempt to recover the lateral variations of acoustic properties within the layers. 11

20. Velocity models from waveform tomography
Starting model for waveform tomography
Pass Hz waveform tomography result
Pass Hz waveform tomography result
Final 1000 Hz waveform tomography result
Here are the velocity models obtained from the data using waveform tomography. Starting with the relatively featureless starting model, we progress through 1-D inversion using the 500 Hz data, a 2-D inversion from the 500 Hz data, and then to the final result using the data frequencies up to 1000 Hz. There are some areas in which the move to 1000 Hz yields a better resolved image of the layers, in particular the layered sediments between 1000 and 1060 m seem to be particularly well resolved.
By and large the gas hydrate layers don’t change very much as we move up to 1000 Hz. This is primarily because there simply is not very much energy at 1000 Hz that actually makes it through the hydrate layers. There does nevertheless appear to be a certain amount of focussing of the image showing some appreciable lateral variations in the velocities, particularly within the production zone just below 900 m. I’ll be returning to this point. 13

21. Attenuation models from waveform tomography
Pass 2 result 500 Hz
Pass 1 result 500 Hz
Starting attenuation model for waveform tomography
I would now like to show you a similar progression, this time illustrating the progressive imaging of the inelastic attenuation factor. First, let me use this display to point out the colour scale: we present the inelastic attenuation in units of 100 / Q, where Q is the inelastic, dimensionless “Quality factor”. Full scale (red) corresponds to very low Q values indeed; at 100/Q=30 Q is of the order of 3. At the other end of the scale, full black corresponds to infinite Q. Our starting model for attenuation was to use a homogeneous value of Q=200, which explains the near-black color of the image.
As we progress through the tomographic inversions, we see immediately that attenuation starts to rise in several zones, each of which corresponds roughly to one of the gas hydrate zones. As we relax the constraints that kept the image quasi 1-D, the image becomes quite noisy. We did not pursue the inversion of Q up to 1000 Hz, because the images were so noisy already at 500 Hz. Especially within the hydrates, the absence of strong signals left little to work with at 1000 Hz. 14

22. Outline The Mallik crosshole survey Waveform pre-processing
Waveform tomography
Verification and interpretation
Conclusions
Having obtained these images of the velocity and attenuation parameter, I’ll now address the important question of verification (that is, to what extent can we believe these images), before returning to the question of interpreting what the images might be telling us.

23. Waveform tomography verification
Comparison with sonic results in 5l-38
Time domain modelling
We will carry out this verification step do this in two ways: first by comparing both images with the sonic parameters recorded in the production well 5L-38. and secondly by comparing synthetic data generated in the final results with the original waveform data. 14

24. Waveform tomography velocities: comparison with sonic log in 5L-38
These are the waveform tomography velocities, in red, superimposed on the sonic log measurements from the production well. You’ll notice first of all there are two red curves: the dashed red curve are the crosshole velocities, but the solid red curves are the crosshole velocities after correcting for anisotropy to extract an equivalent vertical velocity to compare with the sonic log.
The comparison stands up quite well in many places, and particularly well in the region I pointed out earlier between 1000 and 1060 m, where there is very little gas hydrate saturation.
It is also evident that there are some mismatches, especially in the hydrate zones. In the deepest zone at 1070 m, the waveform tomography simply failed to find the low velocity zone in the middle of the hydrates. In fact, the places were the match is least impressive are all places in which the velocities are high, and the hydrate saturation level is high. The explanation is that the high levels of attenuation in the gas hydrates reduce the signal amplitudes to a level at which the inversions are very insensitive to the misfit. 15

25. Waveform tomography attenuation: comparison with sonic waveform analysis (Guerin et al, 2004) from 5L-38
This is a comparison which we have only recently obtained and we are reasonably pleased with. As you heard this morning Gilles Guerin has also extracted attenuation measurements from the full waveform sonic data. The attenuation images we have compare remarkably well with the sonic waveform results. Naturally the match is not as impressive as the match we observed for the velocities, and it is important to note that the amplitudes that control the attenuation images are rather difficult to control and model. Given that these are two very different estimation methods carried out a quite different frequencies it is really quite remarkable that they match as well as they do. And of course, as Gilles pointed out these high attenuation layers are associated with the high hydrate concentrations. 16

26. Synthetic offset gathers
90 m vertical offset
Zero vertical offset
These are now synthetic data, generated in the final velocity / attenuation tomographic results I just showed you, using the method of finite differences with a full visco-acoustic wave equation. These two gathers correspond to the same two offset gathers I showed you earlier, but it is important to note that we are now using a true amplitude display. These displays show the late arrivals in the fluid zones above the gas hydrates, and the high amplitude guided waves in the low velocity zones between the gas hydrate intervals. You can see the marked loss of amplitude of the waveforms in the zones corresponding to the gas hydrates. The loss of frequency content is less evident due to the weak amplitudes, but nevertheless the arrivals corresponding to the hydrate zones do experience a significant loss in frequency content. 17
Normalization: true amplitude

27. Normalization: true amplitude
Real offset gathers
90 m vertical offset
Zero vertical offset
This display shows the corresponding real data gathers. You can see the dramatic similarity of the two displays right away. The loss of amplitudes in the gas hydrate zones, the lost of high frequencies, the presence of large amplitude guided waves in the low velocity zones, etc. The correspondence of the synthetic data to the real data is very good indeed, although there are a few places were there are some discrepancies, most notably within this deepest hydrate zone here, where there appears to be a guided wave on the real data that does not show up on the synthetics. As pointed out a moment ago, this is exactly where the sonic velocity log shows a low velocity layer that is not picked up by the waveform tomography. 18
Normalization: true amplitude

28. Waveform tomography final results
Results at 5L-38
So lets return to these final images and see what we might be able to say. We now have superimposed the location of Mallik 5L-38 on these images so that you can make a visual comparison of the results. As pointed out earlier there are apparently significant variations in the velocities within the production zone itself, with the highest velocities occuring closer to 3L-38 than 4L-38. The velocity varies from about 3.8 km/s down to about 3.2 km/s within that layer over about a lateral distance of about 40 m. Some of the other layers show similar lateral velocities. Interestingly the deepest hydrates are apparently faster at 4L-38. In contrast, the slower layers that are so well imaged between 1000 m and 1065 m seem much more laterally continuous with only slight variations in velocity.
As pointed out earlier, there is a broad correlation between the high attenuation values and the high velocity hydrate layers. However the attenuation image is quite variable and difficult to interpret. If you look in detail at the apparent lateral variations in attenuation you can see the exact reverse effect, that the highest velocities within the production zone appear to be associated with rather low values of attenuation, and the somewhat lower velocities of the production zone towards 4L-38 are associated with higher attenuations. This lateral anticorrelation is also observed in the deepest hydrate zone. 20
Attenuation
Velocity
Attenuation

29. Outline The Mallik crosshole survey Waveform pre-processing
Waveform tomography
Verification and interpretation
Conclusions
This is the structure of the talk I’ll be giving today: I’ll start with a few introductory words about Gas hydrates, and, more specifically about the hydrates that are found in the Canadian arctic. I’ll then spend a little time describing the crosshole tomographic survey that was carried out at the site, following which I’ll move on to a description of the waveform tomography method that was used to process the data. This is split into two parts: the pre-processing of the data that was applied, and the actual results of the waveform tomography; the velocity and attenuation images themselves. Finally of course I’ll draw your attention to some conclusions.

30. Conclusions Velocity images are high resolution (3-4 m vertical scale)
Horizontal high velocity layers indicate hydrate saturation
Lateral velocity variations may reveal variable hydrate saturation
Attenuation images are more noisy, but broadly correlated with high seismic velocity
Attenuation/velocity images correlate with sonic results at Mallik 5L
Large attenuation within hydrates makes images less resolved, differences due to production will be partly obscured by attenuation effects
The data fit is best where the signal amplitudes are high (i.e., away from the hydrate zones!) 21

31. Open questions How is inelastic attenuation to be explained?
Is the attenuation consistent with surface seismics, VSP and/or core samples?
Can these properties be used as seismic attributes for hydrate detection?
Do these observations correspond to a general property of arctic gas hydrate zones?
Can this imaging approach detect the production?

32. Acknowledgements Steering committee of the Mallik 2002 well program
Partners of the Mallik 2002 well program:
Geological Survey of Canada
Japan National Oil Company
GeoForschungsZentrum Potsdam, Germany
United States Geological Survey
United States Department of Energy
India Ministry of Petroleum
Chevron/British Petroleum/Burlington joint venture group
International Scientific Continental Drilling Program
Michael Riedel for his challenging review of our paper!
Read slide