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Statistical performance of CO 2 leakage detection using seismic travel time measurements

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  • Zan Wang
  • Mitchell J. Small

Abstract

Monitoring for possible CO 2 leakage is an important part of a safe and effective geological sequestration program. Seismic monitoring has been implemented in several pilot sequestration sites for site characterization and CO 2 leakage detection. This study evaluates the detection power of seismic wave travel time measurements and statistical tests at different CO 2 leakage rate levels. A simplified rock physics model is assumed for monitoring zones at sequestration sites and the effects of leakage‐induced changes in pressure and CO 2 saturation on P‐wave travel times are modeled. The empirical distributions of detection power using the P‐wave travel time for four regions in the permeability‐porosity input space at four leakage levels are obtained from the Monte Carlo uncertainty analysis with a stochastic response surface method. The detection power using the P‐wave travel time measurements and test alone is generally not high enough, unless the porosity and the permeability of the monitoring zone are high, and/or a long period of time has elapsed since the leakage occurred. For monitoring layers with lower permeability and porosity, measurements from other monitoring techniques will likely be needed to increase the probability that leakage events are detected and addressed in a timely manner. © 2015 Society of Chemical Industry and John Wiley & Sons, Ltd

Suggested Citation

  • Zan Wang & Mitchell J. Small, 2016. "Statistical performance of CO 2 leakage detection using seismic travel time measurements," Greenhouse Gases: Science and Technology, Blackwell Publishing, vol. 6(1), pages 55-69, February.
    • Handle: RePEc:wly:greenh:v:6:y:2016:i:1:p:55-69
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    File URL: http://hdl.handle.net/10.1002/ghg.1533
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    2. Chuanhe Lu & Yunwei Sun & Thomas A. Buscheck & Yue Hao & Joshua A. White & Laura Chiaramonte, 2012. "Uncertainty quantification of CO 2 leakage through a fault with multiphase and nonisothermal effects," Greenhouse Gases: Science and Technology, Blackwell Publishing, vol. 2(6), pages 445-459, December.
    3. Ya‐Mei Yang & Mitchell J. Small & Egemen O. Ogretim & Donald D. Gray & Arthur W. Wells & Grant S. Bromhal & Brian R. Strazisar, 2012. "A Bayesian belief network (BBN) for combining evidence from multiple CO 2 leak detection technologies," Greenhouse Gases: Science and Technology, Blackwell Publishing, vol. 2(3), pages 185-199, June.
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