1 / 26

Youli Quan & Jerry M. Harris Stanford University

Stochastic Seismic Inversion using Waveform and Traveltime Data and Its Application to Time-lapse Monitoring. Youli Quan & Jerry M. Harris Stanford University. November 12, 2008. Outline . Introduction Motivations Kalman filter Seismic inversion with EnKF

aliza
Download Presentation

Youli Quan & Jerry M. Harris Stanford University

An Image/Link below is provided (as is) to download presentation Download Policy: Content on the Website is provided to you AS IS for your information and personal use and may not be sold / licensed / shared on other websites without getting consent from its author. Content is provided to you AS IS for your information and personal use only. Download presentation by click this link. While downloading, if for some reason you are not able to download a presentation, the publisher may have deleted the file from their server. During download, if you can't get a presentation, the file might be deleted by the publisher.

E N D

Presentation Transcript

  1. Stochastic Seismic Inversion using Waveform and Traveltime Data and Its Application toTime-lapse Monitoring Youli Quan & Jerry M. Harris Stanford University November 12, 2008

  2. Outline • Introduction • Motivations • Kalman filter • Seismic inversion with EnKF • An example of CO2 storage monitoring • Conclusions

  3. Introduction • Seismic inversion recovers subsurface elastic properties (e.g., acoustic impedance and velocity) from seismic data. • Inversion using both waveform and traveltime improves the estimation of velocity. • Estimation of absolute velocity helps quantitative interpretation of seismic data.

  4. Images of seismic inversion may be more meaningful for interpretation. Impedance Traces Amplitude Traces Seismic Inversion

  5. Deterministic inversion methods normally need less computation. • Stochastic inversion uses more computing power but can integrate other information (e.g., sonic logs.) • Ensemble Kalman Filter (EnKF) is a stochastic method used in this study for seismic inversion.

  6. Motivations to Use EnKF • Seismic monitoring - To integrate time-lapse seismic data - Dynamic imaging • Reservoir characterization - Integration of sonic logs and seismic data

  7. Kalman Filter • Dynamic imaging • Integration of sonic logs and seismic data Kalman gain

  8. Seismic Inversion with EnKF • Define observation function • Create model & data ensembles using their probability distributions d – poststack seismic data in this case

  9. Define observation function Poststack Full waveform Convolution

  10. Create model ensemble

  11. Create data ensemble d

  12. Estimate model parameters with EnKF K can be simply calculated from the ensemble covariance and observation function It can handle large model and non-linear inverse This is a Monte Carlo approach

  13. An Example of CO2 Storage Monitoring CO2 Sequestration • CO2 sequestration provides a possible solution for reducing the green gas emission to the atmosphere. • For safety and operational reasons, we need to monitor the containment of the CO2 storage in the subsurface.

  14. Creation of Time-lapse Models • Find model parameters from unmineable coalbeds in Powder River Basin • Build a stationary geology model • Run flow simulation with GEM • Convert flow simulation results to time-lapse seismic velocity models

  15. Four time-lapse P-wave velocity modes created based on CO2 flow simulation in the coalbeds. A: time=0; B: time=3 months; C: time=1 year; D: time=3 years.

  16. A Simple Synthetic Test “Observed” data calculated by convolution

  17. Inversion with Waveform Data Inversion with Waveform and Travel Time Data Use constant initial model

  18. A Full Waveform Synthetic Test • Run FD for time-lapse Vp models derived from flow simulation. • Process complete shot gathers and get depth and time images. • Extract wavelet. • Use convolution as the modeling in the inversion. • Perform seismic inversion with EnKF. • Compare the inverted Vp with given models.

  19. Samples of the shot gathers calculated using the finite difference

  20. Time image Depth image Traveltime picks used for the inversion

  21. Time-lapse velocity models inverted using EnKF time=0 time=3 months time= 1 year time=3 years

  22. Vp differences between time-lapse models and base model True Inverted time=3 months time= 1 year time=3 years

  23. A comparison between true model and inverted model Solid black line: Ture model; Dash-dot blue line: inverted model; Dotted yellow line: Initial model; At distance x=500m

  24. A comparison between “observed” data and modeled data Solid line: “Observed” seismic trace Dotted line: Modeled seismic trace from inverted model

  25. Conclusions • The ensemble Kalman filter is a useful tool for stochastic seismic inversion, especially for dynamic inversion in seismic monitoring (field data tests will be done.) • Integrating travetime data into the inversion makes the estimation of absolute velocity possible. • Fast forward modeling and true amplitude processing are essential.

  26. Acknowledgements • We would like to thank the sponsors (ExxonMobil, General Electric, Schlumberger, and Toyota) of Global Climate & Energy Project at Stanford University for their support to this study. • Eduardo Santos, Yemi Arogunmati, and Tope Akinbehinje helped for the creation of time-lapse velocity models.

More Related