Reservoir-parameter identification using minimum relativeentropy-based Bayesian inversion of seismic AVA and marine CSEMdata
Astochastic joint-inversion approach for estimatingreservoir-fluid saturations and porosity is proposed. The approachcouples seismic amplitude variation with angle (AVA) and marinecontrolled-source electromagnetic (CSEM) forward models into a Bayesianframework, which allows for integration of complementary information. Toobtain minimally subjective prior probabilities required for the Bayesianapproach, the principle of minimum relative entropy (MRE) is employed.Instead of single-value estimates provided by deterministic methods, theapproach gives a probability distribution for any unknown parameter ofinterest, such as reservoir-fluid saturations or porosity at variouslocations. The distribution means, modes, and confidence intervals can becalculated, providing a more complete understanding of the uncertainty inthe parameter estimates. The approach is demonstrated using synthetic andfield data sets. Results show that joint inversion using seismic and EMdata gives better estimates of reservoir parameters than estimates fromeither geophysical data set used in isolation. Moreover, a moreinformative prior leads to much narrower predictive intervals of thetarget parameters, with mean values of the posterior distributions closerto logged values.
- Research Organization:
- COLLABORATION - UCBerkeley
- DOE Contract Number:
- AC02-05CH11231
- OSTI ID:
- 920349
- Report Number(s):
- LBNL--60931; BnR: AC1005000
- Journal Information:
- Geophysics, Journal Name: Geophysics Journal Issue: 6 Vol. 71; ISSN GPYSA7; ISSN 0016-8033
- Country of Publication:
- United States
- Language:
- English
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