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Advanced acoustic data analysis

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Processing acoustic data downhole as well as at the surface is necessary to transform the raw acoustic signals recorded by modern logging instruments into data suitable for interpretation and analysis. Data processing takes place:

  • During acquisition
  • In the logging tool itself
  • In the surface acquisition unit
  • In post-acquisition processing at computing centers

There are a variety of sources of noise in the downhole environment that contaminate the recorded acoustic signal:

  • Tool ("road") noise
  • Measurement error
  • Reflection and scattering from rough borehole or bed boundaries
  • Mode conversion
  • Interferences that occur in the downhole environment

The goal of acoustic-data processing is to minimize the data noise while maximizing the petrophysical information.[1] Data preprocessing reduces the influences of these sources, thus allowing extraction of the true formation signal.

Following the rapid theoretical advances in acoustic-wave propagation made during the 1980s and 1990s, significant advances in data processing provided improved quality in slowness measurements and enabled a number of new applications using Stoneley and dipole-shear wave in open and cased holes. The combined interpretation of Stoneley and dipole-shear acoustic measurements with NMR and borehole imaging enhances formation evaluation.

Slowness analysis

One of the primary goals in borehole acoustic logging is to obtain formation slowness from array measurements. Accurate slowness analysis is vital to most petrophysical, geophysical, and seismic applications. A variety of techniques are used for computing slowness from array tools (Table 1).

Semblace and Nth-root stacking

The two most commonly used techniques to determine slowness from borehole acoustic array are:

Both are cross-correlation, coherency techniques that compare signatures in an acoustic array and find the similarities that correspond to coherent wave types. Semblance has a direct physical interpretation, whereas Nth-root stacking is a purely mathematical solution. Although the semblance method is faster, the Nth-root stacking method is more tolerable to noise and ignores amplitude variations across the array and, in general, provides better results. Collecting the array data, either as receiver or transmitter (source) arrays (Fig. 1), enhances the slowness output from both of these techniques.

The primary application of the receiver- and transmitter-array-derived slowness curves is to provide compensation (DDBHC) to the monopole compressional and shear slowness for borehole irregularities such as washouts or cave-ins. Borehole compensation is achieved by averaging the slowness estimates from the receiver and transmitter arrays. The dipole-shear wave does not require borehole compensation because the flexural-shear wave is not as sensitive to the borehole geometry although it is often used to improve results in rough boreholes.

The objective of the semblance technique is to find the slowness that maximizes the coherence (time domain) among the wave power spectra over the receiver array. The Nth-root stacking technique is very similar to the semblance technique, except that the waveform amplitudes are modified in a different way to produce a so-called "pseudo" power spectrum instead of the true power spectrum.

Although the algorithms used in semblance and Nth-root stacking differ, the processing procedure is similar. A time window of fixed length is set up on each signature in the array. The windows are offset by a specified time interval on the successive signatures and a correlation is computed over these windows. The windows on all but the first signature are then stepped a certain distance out in time and another correlation value is computed. The process continues for the range of anticipated slowness in the well. This process is repeated as the window is progressively stepped on the first signature.

The group of correlation values obtained is known as a correlogram, in which the waveform coherence or correlation values either from semblance or Nth root method are displayed as a function of slowness and arrival time creating a 3-D surface. The correlation values in a range of arrival times are further combined to form a "combined correlogram," which is a projection of the 3-D surface on the slowness axis. The correlogram peak at each measured-depth level is used to obtain the Δt slowness for the wave type. These individual slowness values are then displayed as a continuous log curve. Filtering for the compressional wave or for the dispersive shear wave is normally required before using either method. As a quality check, the slowness results of semblance or Nth-root stack processing can also be plotted in the combined correlogram together with a "computed travel time" which is plotted against the waveform. The computed travel time represents the sum of the transit-time measurements from the transmitter to the receiver along with the mud travel time. Dipole shear-slowness processing must also take into account dispersion of the flexural wave.[8] Depending on the frequency of the flexural mode, a correction may be required to obtain shear-wave slowness.

Resolution enhancement

The aperture (vertical resolution) of array tools is typically 3.5 ft. This means that a bed must be at least that thickness to measure true acoustic slowness, although such tools can detect (resolve) beds down to 2-ft thickness. To meet the need for the higher resolution necessary for thin-bed evaluation, waveform-matching (multishot) processing techniques use the redundant information contained in overlapping receiver subarrays to improve vertical resolution (Fig. 1).[9] Recently introduced processing techniques reduce the aperture to 0.5 ft and thus achieve a true vertical resolution of 1.0 ft (Fig. 2).[7] However, reducing the aperture makes the measurements more susceptible to noise.

Attenuation analysis

Acoustic-wave attenuation correlates with a variety of petrophysical parameters, including[10][11][12][13]:

  • Formation lithology and pore-fluid permeability
  • Degree of fluid saturation
  • Fractures type

A recent study suggests that the combination of compressional- and shear-wave attenuation logs may provide a potential formation-evaluation tool (Figs. 8 and 9).[13]

Stoneley-wave analysis

The advanced use of Stoneley-wave analysis is best demonstrated through the following examples:


  1. Mari, J.-L., Glangeaud, F., and Coppens, F. 1999. Signal Processing for Geologists and Geophysicists, 1-458. Paris: Editions Technip.
  2. Neidell, N.S. and Taner, M.T. 1971. Semblance and Other Coherency Measures for Multichannel Data. Geophysics 36 (3): 482–497.
  3. Kimball, C.B. and Marzetta, T.M. 1984. Semblance Processing of Borehole Acoustic Array Data. Geophysics 49 (3): 274–281.
  4. Kimball, C.V. 1998. Shear Slowness Measurements by Dispersive Processing of the Borehole Flexural Mode. Geophysics 63 (2): 337–344.
  5. McFadden, P.L., Drummond, B.J., and Kravis, S. 1986. The Nth-Root Stack—Theory, Applications, and Examples. Geophysics 51 (10): 1,879–1,892.
  6. Kravis, S. 1980. The Nth Root Slant Stack—A New Method of Coherency Enhancement. First Break 8 (9): 339–344.
  7. 7.0 7.1 7.2 Zhang, T., Tang, X.M., and Patterson, D. 2000. Evaluation of Laminated Thin Beds in Formations Using High-Resolution Acoustic Slowness Logs, paper XX. Trans., 2000 Annual Logging Symposium, SPWLA, 1–14.
  8. Brie, A. et al. 1997. Shear Slowness Determination from Dipole Measurements, paper F. Trans., 1997 Annual Logging Symposium, SPWLA, 1–14.
  9. Hsu, K. and Chang, S.K. 1987. Multiple-Shot Processing of Array Sonic Waveforms for High Resolution Sonic Logs. Geophysics 52 (10): 1,376–1,390.
  10. Klimentos, T. and McCann, C. 1990. Relationships Between Compressional Wave Attenuation, Porosity, Clay Content, and Permeability in Sandstones. Geophysics 55(8): 998–1,014.
  11. Klimentos, T. 1995. Attenuation of P- and S-Waves as a Method of Distinguishing Gas from Oil and Water. Geophysics 60 (2): 447–458.
  12. Akbar, N., Dvorkin, J., and Nur, A. 1993. Relating P-Wave Attenuation to Permeability. Geophysics 58 (1): 20–29.
  13. 13.0 13.1 13.2 13.3 Market, J. et al. 2002. Processing and Quality Control of LWD Dipole Sonic Measurements, paper PP. Trans., 2002 Annual Logging Symposium, SPWLA, 1–14.

Noteworthy papers in OnePetro

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See also

Acoustic logging

Acoustic logging tools

Anisotropy analysis

Acoustic logging while drilling