Bayesian inversion of seismic and electromagnetic data for marine gas reservoir characterization using multi-chain Markov chain Monte Carlo sampling

Ren, Huiying; Ray, Jaideep; Hou, Zhangshuan; Huang, Maoyi; Bao, Jie; Swiler, Laura

doi:10.1016/j.jappgeo.2017.10.004

Bayesian inversion of seismic and electromagnetic data for marine gas reservoir characterization using multi-chain Markov chain Monte Carlo sampling

Journal Article · Tue Oct 17 00:00:00 EDT 2017 · Journal of Applied Geophysics

DOI:https://doi.org/10.1016/j.jappgeo.2017.10.004· OSTI ID:1406375

Ren, Huiying ^[1]; Ray, Jaideep ^[2]; Hou, Zhangshuan ^[1]; Huang, Maoyi ^[1]; Bao, Jie ^[1]; Swiler, Laura ^[3]

Pacific Northwest National Lab. (PNNL), Richland, WA (United States)
Sandia National Lab. (SNL-CA), Livermore, CA (United States)
Sandia National Lab. (SNL-NM), Albuquerque, NM (United States)

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 is 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.

View Accepted Manuscript (DOE)

Research Organization:: Sandia National Laboratories (SNL-NM), Albuquerque, NM (United States)

Sponsoring Organization:: USDOE National Nuclear Security Administration (NNSA); USDOE Office of Science (SC), Advanced Scientific Computing Research (ASCR); USDOE Office of Science (SC), Advanced Scientific Computing Research (ASCR) (SC-21)

Grant/Contract Number:: AC04-94AL85000; NA0003525

OSTI ID:: 1406375

Alternate ID(s):: OSTI ID: 1549478
OSTI ID: 1416689
OSTI ID: 1490281

Report Number(s):: SAND--2017-11661J; PII: S0926985116303196

Journal Information:: Journal of Applied Geophysics, Journal Name: Journal of Applied Geophysics Journal Issue: C Vol. 147; ISSN 0926-9851

Publisher:: ElsevierCopyright Statement

Country of Publication:: United States

Language:: English

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58 GEOSCIENCES
Bayesian
Controlled-Source Electromagnetic
Joint inversion
Multi-chain Markov-chain Monte Carlo
Seismic Amplitude Versus Angle

Bayesian inversion of seismic and electromagnetic data for marine gas reservoir characterization using multi-chain Markov chain Monte Carlo sampling

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