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Permeability estimation with Stoneley waves

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Stoneley-wave velocity and attenuation are sensitive to formation and fracture permeability, particularly at low frequencies.[1][2][3][4] Stoneley-wave velocity decreases, and its attenuation increases, as permeability increases. Initial efforts (begun in the 1970s) to derive permeability information from Stoneley data were unsuccessful because neither the necessary low-frequency tools nor the appropriate processing methods had been developed. The parallel development of modern multipole array tools and sophisticated semblance- and inversion-processing methods enable computation of continuous profiles of formation permeability from monopole Stoneley-wave data.[5][6][7] Typically, these methods first model the nonpermeability effects using the elastic-wave theory and then relate differences between the modeled and the measured data to formation permeability.

Wave processing

One approach to Stoneley-wave processing is comprised of three parts[7][8](Fig. 1):

  • Slowness analysis
  • Reflectance mapping
  • Permeability estimation
  • Fig. 1 – Stoneley-derived-permeability processing flow (courtesy of Baker Atlas).

    Fig. 1 – Stoneley-derived-permeability processing flow (courtesy of Baker Atlas).

Stoneley-wave data are typically presented as both:

  • An interval transit time, Δtt
  • As a ratio of the amplitudes for the two receivers used

Both traces have been shown to correlate well with permeability changes and compare well with core data, when it is available. Stoneley-wave amplitude can be computed and used in conjunction with the slowness and the signatures to analyze dispersion and attenuation characteristics. Use of the attenuation (center-frequency shift) and dispersion (travel-time delay) provide good permeability indication and better quality control for permeability estimation.

Wave-separation processing minimizes the effects of nonpermeability-related influences (e.g., road noise and borehole scattering) and yields reflectance logs for the direct and reflected Stoneley-wave data. The center-frequency log for the reflected wave data characterizes the Stoneley-wave attenuation and can be used to indicate:

  • Fractures
  • Vugs
  • Bed boundaries

The center-frequency log for the direct (transmitted) data is used to estimate formation permeability. Knowledge of the formation-fluid properties (viscosity and compressibility from core or NMR) enables quantitative estimates. Without this information log-derived permeability estimates are only qualitative.

These models require sophisticated computer processing. A simplified, field-oriented technique based on Stoneley amplitude[9] has so far provided good results in ideal conditions and when calibrated to core or nuclear magnetic resonance (NMR) data.

Stoneley-derived formation permeability compares well with estimates obtained by nonacoustic methods, including core analysis and NMR logs (Fig. 2).[10][11] Additional improvement in permeability estimation is possible when anisotropy information is incorporated in the process.[12] Formation evaluation is further enhanced when these diverse measurements are integrated through joint interpretation.[13] As indicated by the plots in Fig. 2, calibration of log responses to core data would improve log-predicted values in noncored intervals.

  • Fig. 2 – Comparison of Stoneley-, NMR- and core-permeability profiles. The agreement of these fundamentally different measurements confirms the validity of each (courtesy of Baker Atlas).

    Fig. 2 – Comparison of Stoneley-, NMR- and core-permeability profiles. The agreement of these fundamentally different measurements confirms the validity of each (courtesy of Baker Atlas).

The Stoneley wave measures total permeability and NMR measures vuggy permeability. A comparison of these two measurements in carbonate formations makes it possible to evaluate the permeability contributions arising from fractures and vugs.[13] A combination of these two measurements calibrated using wireline formation-tester data enables improved permeability estimation in these reservoirs.[14]

References

  1. ↑ Paillet, F.L. and Cheng, C.H. 1991. Acoustic Waves in Boreholes, 1–264. Boca Raton, Florida: CRC Press.
  2. ↑ Burns, D.R. 1991. Predicting Relative and Absolute Variations of In-Situ Permeability from Full-Waveform Acoustic Logs. The Log Analyst 32 (3): 246–255.
  3. ↑ Tang, X.M., Cheng, C.H., and Toksoz, M.N. 1991. Dynamic Permeability and Borehole Stoneley Waves—A Simplified Biot-Rosenbaum Model. J. of the Acoustical Soc. of America 90 (3): 1,632–1646
  4. ↑ Tang, X., Cheng, C.H., and Toksoz, M.N. 1991. Stoneley-Wave Propagation in a Fluid Filled Borehole with a Vertical Fracture. Geophysics 56(4): 447–460. http://dx.doi.org/10.1190/1.1443062
  5. ↑ Saxena, V. 1994. Permeability Quantification from Borehole Stoneley Waves, paper T. Trans., 1994 Annual Logging Symposium, SPWLA, 1–22.
  6. ↑ Kimball, C.V. and Endo, T. 1998. Quantitative Stoneley Mobility Inversion, paper BH 1.1. Expanded Abstracts, 1998 Annual Meeting Technical Program, SEG, 252–255.
  7. ↑ 7.0 7.1 Tang, X.M. et al. 1997. Permeability from Borehole Acoustic Logs—An Overview with Recent Advances, paper BH 2.6. Expanded Abstracts, 1997 Annual Meeting Technical Program, SEG, 274–277.
  8. ↑ Sinha, A. et al. 1998. A New Method for Deriving Permeability from Borehole Stoneley Waves and Its Application in the North Mongas Field of Eastern Venezuela, paper P. Trans., 1998 Annual Logging Symposium, SPWLA, 1–12.
  9. ↑ Canady, W., Spooner, P., and Vasquez, R.B. 2005. Permeability Estimation From Stoneley Amplitude, Corrected for Borehole Geometry and Rugosity. Presented at the SPE Annual Technical Conference and Exhibition, Dallas, Texas, 9-12 October 2005. SPE-96598-MS. http://dx.doi.org/10.2118/96598-MS
  10. ↑ Geerits, T. et al. 1999. Comparison Between Stoneley, NMR, and Core-Derived Permeabilities, paper TT. Trans., 1999 Annual Logging Symposium, SPWLA, 1–14.
  11. ↑ Qobi, L., Kuijper, A.D., Tang, X.M. et al. 2000. Permeability Determination from Stoneley Waves in the Ara Group. Presented at the SPE Annual Technical Conference and Exhibition, Dallas, Texas, 1-4 October 2000. SPE-63140-MS. http://dx.doi.org/10.2118/63140-MS
  12. ↑ Tang, X.M., and Patterson, D. 2004. Estimating Formation Permeability and Anisotropy from Borehole Stoneley Waves, paper W. Trans ., 2004 Annual Logging Symposium, SPWLA, 1–14.
  13. ↑ 13.0 13.1 Tang, X.M., Altunbay, M., and Shorey, D. 1998. Joint Interpretation of Formation Permeability from Wireline Acoustic NMR, and Image Log Data, paper KK. Trans., 1998 Annual Logging Symposium, SPWLA, 1–13.
  14. ↑ Aladeddin, E., Tchambaz, M., and Al-Adani, N. 2004. Combining NMR and Stoneley Analysis for a Better Estimation of Permeability in Carbonate Reservoirs, paper E. Proceedings, 2004 Formation Evaluation Symposium of Japan, Society of Petrophysicists and Well Log Analysts, Japan Chapter, 7 p.

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See also

Acoustic logging

Permeability determination

Permeability estimation in tight gas reservoirs

Permeability estimation with NMR logging

PEH:Acoustic_Logging

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