1887
ASEG2007 - 19th Geophysical Conference
  • ISSN: 2202-0586
  • E-ISSN:

Abstract

Summary

Over the last decade, spectral decomposition techniques have greatly improved seismic interpretation in the exploration of hydrocarbons. Significant efforts have been directed to the development of such methods for tuning hydrocarbon reservoirs, thin beds and channel estimation.

Geological and geophysical changes of the earth formation manifest as discontinuities in a seismic trace. These discontinuities also called singularities thus carry most of the information in the trace. Statistical techniques are required to analyze these discontinuities in order to ascertain the variation in the fluid flow.

This paper discusses a technique for detecting reservoir homogeneity/heterogeneity based on singularity spectrum attributes. Singularity spectrum determined from multifractal analysis, can globally describe the singularity content of a signal. Obtaining the singularity spectrum of signals also helps in the identification and classification of different state changes in a signal.

The developed technique is applied on synthesized seismograms and two attributes of the singularity spectrum namely correlation dimension and width are determined. Results show that by using these attributes the heterogeneity or homogeneity of the reservoir can be evaluated. Such attributes can increase the reliability of suspected hydrocarbon zones; hence unravelling drilling uncertainties and is potentially useful for reservoir fluid flow modelling.

Loading

Article metrics loading...

/content/journals/10.1071/ASEG2007ab049
2007-12-01
2026-01-15
Loading full text...

Full text loading...

References

  1. Avijit C. and David O., 1995, Frequency-time decomposition of seismic data using wavelet-based methods: Geophysics, 60, 1906
  2. Antonia T., Conrad J.P., Jacopo G., 2005, Numerical methods for the estimation of multifractal singularity spectra on sampled data: a comparative study, Elsevier Science
  3. Barry R.L., and Kinsner W., 2004, Multifractal characterization for classification of network traffic: IEEE Electrical and Computer Engineering Canadian Conference Fraclab 2.03, Fractal Analysis Software by INRIA, http://fractales.inria.fr/html, accessed April 2006
  4. John P.C., Shengjie S., and Robert W.S., 2003, Instantaneous spectral analysis: Detection of low-frequency shadows associated with hydrocarbons: The Leading Edge 22, 120
  5. Ken Mela, Joh N.L., 2001, Correlation length and fractal dimension interpretation from seismic data using variograms and power spectra: Geophysics 66, 1372
  6. Mallat S. and Hwang W.L., 1992, Singularity detection and processing with wavelets: IEEE Transactions on Information theory
  7. Marianne R.D., and Michael A.G., 2006, Using spectral decomposition and coherence for reservoir delineation and fluid prediction in extensively explored region, SEG Expanded Abstracts 25, 690
  8. Maryam Mahsal, Ahmad Fadzil M.H, M Firdaus A. H., Ahmad Riza G., 2006, Multifractal Analysis of Seismic Data for Delineating Reservoir Fluids, 2nd International Research & Development Forum in Oil, Gas and Petrochemicals, organised by Universiti Teknologi PETRONAS and PETRONAS, Kuala Lumpur. 6th -7th December
  9. Sandhya Devi K.R., TX A.J., 2004, Wavelet Transforms and Hybrid Neural nets for improved pore fluid prediction and reservoir properties estimation, SEG Expanded Abstract 23, 1527
  10. Satish S., Partha S.R., Phil D.A., and John P.C., 2005, Spectral decomposition of seismic data with continuouswavelet transform: Geophysics 70, P19
  11. Staal, J., 1995, Characterizing the Irregularity of Measurements By Means Of the Wavelet Transform: PhD Thesis, Delft University of Technology
  12. SyntoolTM, Openworks Landmark Graphics Software, http://www.lgc.com/ Landmark/ integrated + solutions/ geophysicaltechnologies/syntoo/index.html, accessed June 2006
  13. Tatijana S., Borko D.S., 2006, Multifractal analysis of human retinal vessels: IEEE Transactions on Medical Imaging
  14. Westra R.L., 2002, The Analysis of Fractal Geometry using the Wavelet Transform with Application to Gene Expression Analysis: Proceeding of EUNITE Conference, Albufeira (Portugal), 19-21 September.
/content/journals/10.1071/ASEG2007ab049
Loading
This is a required field
Please enter a valid email address
Approval was a Success
Invalid data
An Error Occurred
Approval was partially successful, following selected items could not be processed due to error