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Sonic Logs Need Troubleshooting

Seismic to Well Ties with Problematic Sonic Logs — Part 2

In last month's column we discussed the most common deleterious problems with sonic logs, and their effect on seismic ties were outlined. These included:

  • Cycle skips and noise.
  • Short logging runs, or gaps in sonic log coverage.
  • Relative pressure differences between the drilling fluid and the confining stress of the rocks around the wellbore.
  • Shale alteration (principally clay hydration from the drilling fluid).

This month we cover methods to correct these problems and calibrate the sonic log using the seismic data. Proper handling of sonic log problems can result in high quality informative ties that can be used in a variety of disciplines.

Solutions

Clearly, a key to being able to correct common problems with sonic logs requires the ability to replace questionable data with a reasonable estimate. This is important because if we replace bad data with an estimate that is poor, we may not have done much good with respect to the cumulative error, and may have added false reflectivity.

Other wireline data that have a good relationship to velocity include: density, resistivity, gamma ray and spontaneous potential.

Unfortunately:

  • The density tool has a very low tolerance to poor borehole conditions, and will likely not be useful.

  • Both the gamma ray and spontaneous potential curves are useful, but they tend to be rather bi-modal in their behavior (either sand or shale), and do not adequately capture the dynamic range of actual rock velocities.

The deep resistivity is neither affected by the near borehole environment (rugosity or invasion), nor is it bi-modal, making it the best candidate for the generation of pseudo sonic data and, in most cases, still has adequate vertical resolution to tie to seismic data.

Low Frequency Compaction Model

The sonic log exhibits a large low frequency component from burial compaction, which must be removed prior to modeling with other log data that do not have this same feature, such as the deep resistivity.

A fast and accurate way to model the low frequency component of a sonic log is to fit a polynomial to the entire curve.

Image Caption

Figure 1.
Raw sonic log from a continental basin with fitted polynomial showing low frequency burial trend. This trend can also be determined from VSP's, checkshot surveys and seismic stacking velocities.

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In last month's column we discussed the most common deleterious problems with sonic logs, and their effect on seismic ties were outlined. These included:

  • Cycle skips and noise.
  • Short logging runs, or gaps in sonic log coverage.
  • Relative pressure differences between the drilling fluid and the confining stress of the rocks around the wellbore.
  • Shale alteration (principally clay hydration from the drilling fluid).

This month we cover methods to correct these problems and calibrate the sonic log using the seismic data. Proper handling of sonic log problems can result in high quality informative ties that can be used in a variety of disciplines.

Solutions

Clearly, a key to being able to correct common problems with sonic logs requires the ability to replace questionable data with a reasonable estimate. This is important because if we replace bad data with an estimate that is poor, we may not have done much good with respect to the cumulative error, and may have added false reflectivity.

Other wireline data that have a good relationship to velocity include: density, resistivity, gamma ray and spontaneous potential.

Unfortunately:

  • The density tool has a very low tolerance to poor borehole conditions, and will likely not be useful.

  • Both the gamma ray and spontaneous potential curves are useful, but they tend to be rather bi-modal in their behavior (either sand or shale), and do not adequately capture the dynamic range of actual rock velocities.

The deep resistivity is neither affected by the near borehole environment (rugosity or invasion), nor is it bi-modal, making it the best candidate for the generation of pseudo sonic data and, in most cases, still has adequate vertical resolution to tie to seismic data.

Low Frequency Compaction Model

The sonic log exhibits a large low frequency component from burial compaction, which must be removed prior to modeling with other log data that do not have this same feature, such as the deep resistivity.

A fast and accurate way to model the low frequency component of a sonic log is to fit a polynomial to the entire curve.

Figure 1 shows a typical sonic log from a continental basin with the fitted polynomial on top. When we subtract this trend from the data, the resulting curve will be referred to as the "high pass sonic."

Check shot surveys, VSPs and seismic stacking velocities transformed to interval velocities also can be used to determine the low frequency velocity trend.

All we need to do to make a full pseudo sonic is to add the reflectivity from our model (based on resistivity or gamma ray data) to our burial trend.

Replacement Scheme

Our goal is to make a curve from the resistivity data that looks just like the high pass sonic.

In most cases, a Faust transform or neural net solution will fail to have the required accuracy for large vertical replacement intervals. When using these techniques, one often finds that far too much transit time is removed from the sonic log, especially in poorly constrained intervals of the model.

Since resistivity data are logarithmically distributed, and our high pass sonic is normally distributed, we must transform from resistivity to conductivity (reciprocal resistivity) before meaningful statistical work can be done. What we wish to do is examine the shape of the histogram of high pass sonic data compared to the shape of the conductivity histogram over the same interval.

Now, we will simply reshape the conductivity histogram to match the high pass sonic. This reshaping forces the asymmetrical shale-sand velocity response of the sonic log onto the conductivity data, thus making a pseudo sonic log. Zoning the well can improve the result, as the model will be forced to accommodate less geologic change (three to five zones should suffice).

Figure 2 shows the results of the redistribution.

Now we add the low frequency component back in (from our polynomial fit to the raw sonic) to obtain a full usable pseudo sonic log — and replacement of poor data now can be done with some confidence.

In compacted rocks, most of the problems described occur commonly in the shales and much less commonly in sands. Because sands have resistivity signatures that are highly dependant on hydrocarbon saturation, replacement of real sonic data in sands using a model based on the resistivity data should be done with care.

In cases where the sonic log is poor in a sandy interval, the gamma ray or spontaneous potential logs may be more suitable choices for modeling.

Shale Alteration

Desiccated shales can imbibe drilling fluid, thus producing an invaded zone. Within this invasion zone mechanical change occurs due to swelling of the shale. This may take the form of elastic swelling, or swelling with some fracturing. Subtle chemical alteration of the clay minerals may also occur. Both of these phenomena cause a reduction in apparent velocities as seen by the sonic tool.

Because it is difficult to directly determine invasion in shales using traditional resistivity analysis, we must try to develop an invasion indicator that we can use to correct the data.

If we cross plot interval transit time (high pass sonic) vs. conductivity in an interval that is believed to be invaded, we see a non-linear relationship (the fitted curve is a parabola).

Figure 3 shows such a cross plot. Note the data have been mirrored about the interval transit time axis for visual clarity.

If we assume that the parabolic behavior is related to an invasion profile (this is a good assumption because ray path bending in a layered media approaches a parabolic function), we can use the fitted parabola to correct the sonic data.

To do this, we simply scale the sonic data toward faster velocities using the fitted parabola. This correction alone can account for as much as 100 ms. of time in a 10,000-foot well. The correction is non-linear, thus its affect on the synthetic seismogram is not easy to predict.

We have found, however, that wells having had this correction applied tie to the seismic better over larger intervals with higher frequency, resulting in higher quality wavelet extractions.

Calibration to the Seismic Data

Now that we have a sonic log that has been treated with deterministic editing and corrections, we are ready to tie it to the seismic data.

Once the sonic log has been placed correctly in time with the seismic data, there are frequently small residual errors in the location of correlative events in time. If we can relate the observed errors to geologic packages and apply corrections only to those large intervals, we will not introduce harmful artifacts into our sonic log.

Figure 4 has raw and final synthetic seismograms from a sonic log that required a lot of data replacement (mostly between 8,000 and 11,000 feet).

Note the dramatically different character in the synthetics. While the raw version bares little resemblance to the seismic data, the final version ties quite nicely over the entire well. The drift curve in the far right track shows the difference in cumulative time between the raw and final corrected sonic logs.

The logging run numbers (R1, R2, R3) at the bottom of the well correspond to clear differences in final velocity calibration to the seismic. Separate runs may need to be treated differently due to tool and mud system changes.

Conclusions

  • Most sonic logs have problems that need to be addressed prior to tying to seismic data.

  • Due to the summing of errors in the sonic log, correction schemes need to be robust.

  • Building a good pseudo sonic log to substitute for poor or missing real sonic data is a must if we do not wish to introduce additional problems through non-deterministic editing.

  • Shale alteration can be empirically corrected, resulting in a superior tie to the seismic data.

  • The final calibration to the seismic data through drift analysis compensates for the effects of pressuring the near-wellbore environment with the drilling fluid.

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