Transcript Slide 1
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 • An example of CO
2
storage monitoring
• Conclusions
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.
• Images of seismic inversion may be more meaningful for interpretation. 0 100 200 300 400 500 600 700 800 -0.2
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Amplitude Traces Seismic Inversion Impedance Traces
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• • • 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.
Motivations to Use EnKF
• Seismic monitoring - To integrate time-lapse seismic data - Dynamic imaging • Reservoir characterization - Integration of sonic logs and seismic data
Kalman Filter
• Dynamic imaging
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Seismic Inversion with EnKF
Define observation function
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Define observation function
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Full waveform Convolution
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Create model ensemble
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An Example of
CO 2
Storage Monitoring
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CO 2 Sequestration
CO 2 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 CO in the subsurface.
2 storage
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
200 400 600 800 0 200 400 600 Distance(m) 800 200 400 600 800 0 200 4000 600 800 Distance(m) 200 400 600 800 0 200 400 600 800 Distance(m) 200 400 600 800 0 200 400 600 Distance(m) 800 200 400 600 800 0 200 400 600 Distance(m) 800 200 400 600 800 0 200 400 600 Distance(m) 800 200 400 600 800 0 200 400 600 Distance(m) 800 200 150 100 50 5000 4000 3000
Four time-lapse P-wave velocity modes created based on CO 2 flow simulation in the coalbeds. A: time=0; B: time=3 months; C: time=1 year; D: time=3 years.
A Simple Synthetic Test
“Observed” data calculated by convolution 200 400 600 800 0 500 Distance(m) 200 400 600 800 1000 0 500 Distance(m) 200 400 600 800 1000 0 500 Distance(m) 200 400 600 800 1000 0 500 Distance(m) 1000
Inversion with Waveform Data Inversion with Waveform and Travel Time Data Use constant initial model
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.
Samples of the shot gathers calculated using the finite difference
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100 200 300 400 500 600 700 800 220 380 Distance(m) 580 780
Time image
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Traveltime picks used for the inversion
Reflector Depth (m) Time (sec) 270 1 0.1675
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Time-lapse velocity models inverted using EnKF
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time=0 time=3 months time= 1 year time=3 years
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Vp differences between time-lapse models and base model
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A comparison between true model and inverted model
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800 2000 3000 4000 Velocity (m/s) 5000 Solid black line: Ture model; Dash-dot blue line: inverted model; Dotted yellow line: Initial model; At distance x=500m
A comparison between “observed” data and modeled data
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Solid line: “Observed” seismic trace Dotted line: Modeled seismic trace from inverted model
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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.
Acknowledgements
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We would like to thank the sponsors (ExxonMobil, General Electric, Schlumberger, and Toyota
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Global Climate & Energy Project at Stanford of University for their support to this study. Eduardo Santos, Yemi Arogunmati, and Tope Akinbehinje helped for the creation of time-lapse velocity models.