skip to main content
OSTI.GOV title logo U.S. Department of Energy
Office of Scientific and Technical Information

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}
}
  • Joint inversion of seismic AVA and CSEM data requires rock-physics relationships to link seismic attributes to electrical properties. Ideally, we can connect them through reservoir parameters (e.g., porosity and water saturation) by developing physical-based models, such as Gassmann’s equations and Archie’s law, using nearby borehole logs. This could be difficult in the exploration stage because information available is typically insufficient for choosing suitable rock-physics models and for subsequently obtaining reliable estimates of the associated parameters. The use of improper rock-physics models and the inaccuracy of the estimates of model parameters may cause misleading inversion results. Conversely, it is easy tomore » derive statistical relationships among seismic and electrical attributes and reservoir parameters from distant borehole logs. In this study, we develop a Bayesian model to jointly invert seismic AVA and CSEM data for reservoir parameter estimation using statistical rock-physics models; the spatial dependence of geophysical and reservoir parameters are carried out by lithotypes through Markov random fields. We apply the developed model to a synthetic case, which simulates a CO{sub 2} monitoring application. We derive statistical rock-physics relations from borehole logs at one location and estimate seismic P- and S-wave velocity ratio, acoustic impedance, density, electrical resistivity, lithotypes, porosity, and water saturation at three different locations by conditioning to seismic AVA and CSEM data. Comparison of the inversion results with their corresponding true values shows that the correlation-based statistical rock-physics models provide significant information for improving the joint inversion results.« less
  • 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 ismore » 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.« less
  • In this study we developed an efficient Bayesian inversion framework for interpreting marine seismic amplitude versus angle (AVA) and controlled source electromagnetic (CSEM) data for marine reservoir characterization. The framework uses a multi-chain Markov-chain Monte Carlo (MCMC) sampler, which is a hybrid of DiffeRential Evolution Adaptive Metropolis (DREAM) and Adaptive Metropolis (AM) samplers. The inversion framework is tested by estimating reservoir-fluid saturations and porosity based on marine seismic and CSEM data. The multi-chain MCMC is scalable in terms of the number of chains, and is useful for computationally demanding Bayesian model calibration in scientific and engineering problems. As a demonstration,more » the approach is used to efficiently and accurately estimate the porosity and saturations in a representative layered synthetic reservoir. The results indicate that the seismic AVA and CSEM joint inversion provides better estimation of reservoir saturations than the seismic AVA-only inversion, especially for the parameters in deep layers. The performance of the inversion approach for various levels of noise in observational data was evaluated – reasonable estimates can be obtained with noise levels up to 25%. Sampling efficiency due to the use of multiple chains was also checked and was found to have almost linear scalability.« less
  • In this paper we developed an efficient Bayesian inversion framework for interpreting marine seismic Amplitude Versus Angle and Controlled-Source Electromagnetic data for marine reservoir characterization. The framework uses a multi-chain Markov-chain Monte Carlo sampler, which is a hybrid of DiffeRential Evolution Adaptive Metropolis and Adaptive Metropolis samplers. The inversion framework is tested by estimating reservoir-fluid saturations and porosity based on marine seismic and Controlled-Source Electromagnetic data. The multi-chain Markov-chain Monte Carlo is scalable in terms of the number of chains, and is useful for computationally demanding Bayesian model calibration in scientific and engineering problems. As a demonstration, the approach ismore » used to efficiently and accurately estimate the porosity and saturations in a representative layered synthetic reservoir. The results indicate that the seismic Amplitude Versus Angle and Controlled-Source Electromagnetic joint inversion provides better estimation of reservoir saturations than the seismic Amplitude Versus Angle only inversion, especially for the parameters in deep layers. The performance of the inversion approach for various levels of noise in observational data was evaluated — reasonable estimates can be obtained with noise levels up to 25%. Sampling efficiency due to the use of multiple chains was also checked and was found to have almost linear scalability.« less
  • This study investigates the effects of uncertainty inrockphysics models on estimates of reservoir parameters from jointinversion of seismic AVA and CSEMdata. The reservoir parameters arerelated to electrical resistivity using Archie's law, and to seismicvelocity and density using the Xu-White model. To account for errors inthe rock-physics models, we use two methods to handle uncertainty: (1)the model outputs are random functions with modes or means given by themodel predictions, and (2) the parameters of the models are themselvesrandom variables. Using a stochastic framework and Markov Chain MonteCarlo methods, we obtain estimates of reservoir parameters as well as ofthe uncertainty in themore » estimates. Synthetic case studies show thatuncertainties in both rock-physics models and their associated parameterscan have significant effects on estimates of reservoir parameters. Ourmethod provides a means of quantifying how the uncertainty in theestimated reservoir parameters increases with increasing uncertainty inthe rock-physics model and in the model parameters. We find that in theexample we present, the estimation of water saturation is relatively lessaffected than is the estimation of clay content and porosity.« less