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

Abstract

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.

Authors:
; ; ; ;
Publication Date:
Research Org.:
Ernest Orlando Lawrence Berkeley NationalLaboratory, Berkeley, CA (US)
Sponsoring Org.:
USDOE. Assistant Secretary for Fossil Energy.Petroleum
OSTI Identifier:
923190
Report Number(s):
LBNL-60249
Journal ID: ISSN 0016-8033; GPYSA7; R&D Project: G32402; BnR: AC1005000; TRN: US200804%%1020
DOE Contract Number:
DE-AC02-05CH11231
Resource Type:
Journal Article
Resource Relation:
Journal Name: Geophysics; Journal Volume: 72; Journal Issue: 2; Related Information: Journal Publication Date: Mar.-Apr.2007
Country of Publication:
United States
Language:
English
Subject:
54; AMPLITUDES; DISTRIBUTION FUNCTIONS; ELECTRIC CONDUCTIVITY; GAS SATURATION; OVERBURDEN; POROSITY; QUADRATURES; SAMPLING; SATURATION; VELOCITY

Citation Formats

Chen, Jinsong, Hoversten, Michael, Vasco, Don, Rubin, Yoram, and Hou,Zhangshuan. A sampling-based Bayesian model for gas saturation estimationusing seismic AVA and marine CSEM data. United States: N. p., 2006. Web.
Chen, Jinsong, Hoversten, Michael, Vasco, Don, Rubin, Yoram, & Hou,Zhangshuan. A sampling-based Bayesian model for gas saturation estimationusing seismic AVA and marine CSEM data. United States.
Chen, Jinsong, Hoversten, Michael, Vasco, Don, Rubin, Yoram, and Hou,Zhangshuan. Tue . "A sampling-based Bayesian model for gas saturation estimationusing seismic AVA and marine CSEM data". United States. doi:. https://www.osti.gov/servlets/purl/923190.
@article{osti_923190,
title = {A sampling-based Bayesian model for gas saturation estimationusing seismic AVA and marine CSEM data},
author = {Chen, Jinsong and Hoversten, Michael and Vasco, Don and Rubin, Yoram and Hou,Zhangshuan},
abstractNote = {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.},
doi = {},
journal = {Geophysics},
number = 2,
volume = 72,
place = {United States},
year = {Tue Apr 04 00:00:00 EDT 2006},
month = {Tue Apr 04 00:00:00 EDT 2006}
}