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Discrimination between pressure and fluid saturation using direct non-linear inversion method: an application to time-la

This study explores the use of a direct non-linear inversion method to distinguish between pressure changes and fluid changes in reservoirs using time-lapse seismic data. The method is compared to conventional seismic attributes and tested using core data and well log data. The results show that the second-order approximation improves the predictions of shear modulus compared to Vp/Vs. The findings have implications for reservoir monitoring and management.

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Discrimination between pressure and fluid saturation using direct non-linear inversion method: an application to time-la

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  1. Discrimination between pressure and fluid saturation using direct non-linear inversion method: an application to time-lapse seismic data Haiyan Zhang, Arthur B. Weglein,Robert Keys, Douglas Foster and Simon Shaw M-OSRP Annual Meeting University of Houston May 10 –12, 2006

  2. Statement of the problem • Distinguishing pressure changes from reservoir fluid changes is difficult with conventional seismic time-lapse attributes. • Pressure changes or fluid changes? • shear modulussensitive topressure changes • Vp/Vs sensitive tofluid changes • A direct non-linear inversion method may be useful for accomplishing this goal.

  3. Introduction of the method Time-lapse seismic monitoring Inverse scattering series

  4. Outline • Comparing the first and second order algorithms in estimating shear modulus and Vp/Vs contrasts. • Core data tests (A. R. Gregory, 1976) • Heidrun well log data tests • Conclusions and Plan • Acknowledgements

  5. Core data tests • Fixing the fluid as 100% water saturation, while the pressure changes from 1000 to 9000psi. • Baseline: pressure = 5000psi • Monitor: other different pressures Fixing the pressure at 5000psi, while the fluid changes from 0 to 100 percent. • Baseline: 100% saturation • Monitor: other different water saturations

  6. Core data tests • Compare effects of pressure and fluid changes on the elastic properties. • Compare first order and second order approximations.

  7. Effects of pressure changes on the elastic properties Fluid fixed (100% water saturation) A. R. Gregory 1976 Baseline: 5000 psi Decrease in confining pressure Increase in confining pressure

  8. Effects of fluid changes on the elastic properties Pressure fixed (5000 psi) A. R. Gregory 1976 Baseline: 100% Sw

  9. Comparison of 1st and 2nd order approximation for pressure changes Fluid fixed (100% water saturation) A. R. Gregory 1976

  10. Comparison of 1st and 2nd order approximation for pressure changes Fluid fixed (100% water saturation) A. R. Gregory 1976

  11. Comparison of 1st and 2nd order approximation for fluid changes Pressure fixed (5000 psi) A. R. Gregory 1976

  12. Comparison of 1st and 2nd order approximation for fluid changes Pressure fixed (5000 psi) A. R. Gregory 1976

  13. Outline • Comparing the first and second order algorithms in estimating shear modulus and Vp/Vs contrasts. • Core data tests (A. R. Gregory, 1976) • Heidrun well log data tests • Conclusions and Plan • Acknowledgements

  14. Synthetic Modeling -- A-52 Baishali Roy (ConocoPhillips) Depth (m) 3150 50 % gas 50% water 90 % oil 10% water 100 % water Garn 3185 3200 90 % oil 10% water 20 % oil 80% water 100 % water Ile 3220 3245 Initial (1986) 2001 Wet

  15. Heidrun well log data tests • Compare effects of pressure and fluid changes on the elastic properties. • Compare first order and second order approximations.

  16. Heidrun well log data tests

  17. Heidrun well log data tests

  18. Heidrun well log data tests

  19. Heidrun well log data tests

  20. Observations • The second order approximation provides improvements in the earth property predictions. • In this well log data case, the second order approximation is more helpful for predicting shear modulus compared to Vp/Vs.

  21. Plan • Comparing the first and second order algorithms in estimating shear modulus and Vp/Vs contrasts. • Heidrun synthetic data • Real seismic data tests (Heidrun)

  22. Acknowledgements • Baishali Roy (ConocoPhillips) • Thanks to ConocoPhillips for providing the opportunity. • The M-OSRP sponsors are thanked for supporting this research.

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