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A sampling-based Bayesian model for gas saturation estimationusing seismic AVA and marine CSEM data

Journal Article · · Geophysics
OSTI ID:923190
We develop a sampling-based Bayesian model to jointly invertseismic amplitude versus angles (AVA) and marine controlled-sourceelectromagnetic (CSEM) data for layered reservoir models. The porosityand fluid saturation in each layer of the reservoir, the seismic P- andS-wave velocity and density in the layers below and above the reservoir,and the electrical conductivity of the overburden are considered asrandom variables. Pre-stack seismic AVA data in a selected time windowand real and quadrature components of the recorded electrical field areconsidered as data. We use Markov chain Monte Carlo (MCMC) samplingmethods to obtain a large number of samples from the joint posteriordistribution function. Using those samples, we obtain not only estimatesof each unknown variable, but also its uncertainty information. Thedeveloped method is applied to both synthetic and field data to explorethe combined use of seismic AVA and EM data for gas saturationestimation. Results show that the developed method is effective for jointinversion, and the incorporation of CSEM data reduces uncertainty influid saturation estimation, when compared to results from inversion ofAVA data only.
Research Organization:
Ernest Orlando Lawrence Berkeley NationalLaboratory, Berkeley, CA (US)
Sponsoring Organization:
USDOE. Assistant Secretary for Fossil Energy.Petroleum
DOE Contract Number:
AC02-05CH11231
OSTI ID:
923190
Report Number(s):
LBNL--60249; BnR: AC1005000
Journal Information:
Geophysics, Journal Name: Geophysics Journal Issue: 2 Vol. 72; ISSN GPYSA7; ISSN 0016-8033
Country of Publication:
United States
Language:
English