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 · Fri Dec 01 04:00:00 EST 2017 · Journal of Applied Geophysics

DOI:https://doi.org/10.1016/j.jappgeo.2017.10.004· OSTI ID:1416689

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

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, 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.

Research Organization:: Pacific Northwest National Laboratory (PNNL), Richland, WA (US)

Sponsoring Organization:: USDOE

DOE Contract Number:: AC05-76RL01830

OSTI ID:: 1416689

Report Number(s):: PNNL-SA-121160; KJ0401000

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

Publisher:: Elsevier

Country of Publication:: United States

Language:: English

References (18)

Fast stochastic inversion of marine CSEM and seismic data with the Neighbourhood Algorithm Fliedner, Moritz M.; Treitel, Sven; Frenkel, Michael SEG Technical Program Expanded Abstracts 2011 https://doi.org/10.1190/1.3627715	conference	August 2011
Using Proper Divergence Functions to Evaluate Climate Models Thorarinsdottir, Thordis L.; Gneiting, Tilmann; Gissibl, Nadine SIAM/ASA Journal on Uncertainty Quantification, Vol. 1, Issue 1 https://doi.org/10.1137/130907550	journal	January 2013
Elasticity of high‐porosity sandstones: Theory for two North Sea data sets Dvorkin, Jack; Nur, Amos GEOPHYSICS, Vol. 61, Issue 5 https://doi.org/10.1190/1.1444059	journal	September 1996
Efficient MCMC for Climate Model Parameter Estimation: Parallel Adaptive Chains and Early Rejection Solonen, Antti; Ollinaho, Pirkka; Laine, Marko Bayesian Analysis, Vol. 7, Issue 3 https://doi.org/10.1214/12-BA724	journal	September 2012
Classification of hydrological parameter sensitivity and evaluation of parameter transferability across 431 US MOPEX basins Ren, Huiying; Hou, Zhangshuan; Huang, Maoyi Journal of Hydrology, Vol. 536 https://doi.org/10.1016/j.jhydrol.2016.02.042	journal	May 2016
Sensitivity of surface flux simulations to hydrologic parameters based on an uncertainty quantification framework applied to the Community Land Model: UNCERTAINTY QUANTIFICATION FOR CLM4 Hou, Zhangshuan; Huang, Maoyi; Leung, L. Ruby Journal of Geophysical Research: Atmospheres, Vol. 117, Issue D15 https://doi.org/10.1029/2012JD017521	journal	August 2012
The Electrical Resistivity Log as an Aid in Determining Some Reservoir Characteristics Archie, G. E. Transactions of the AIME, Vol. 146, Issue 01 https://doi.org/10.2118/942054-G	journal	December 1942
DRAM: Efficient adaptive MCMC Haario, Heikki; Laine, Marko; Mira, Antonietta Statistics and Computing, Vol. 16, Issue 4 https://doi.org/10.1007/s11222-006-9438-0	journal	December 2006
A Bayesian model for gas saturation estimation using marine seismic AVA and CSEM data Chen, Jinsong; Hoversten, G. Michael; Vasco, Donald GEOPHYSICS, Vol. 72, Issue 2 https://doi.org/10.1190/1.2435082	journal	March 2007
Strictly Proper Scoring Rules, Prediction, and Estimation Gneiting, Tilmann; Raftery, Adrian E. Journal of the American Statistical Association, Vol. 102, Issue 477 https://doi.org/10.1198/016214506000001437	journal	March 2007
Quasi-Monte Carlo methods and pseudo-random numbers Niederreiter, Harald Bulletin of the American Mathematical Society, Vol. 84, Issue 6 https://doi.org/10.1090/S0002-9904-1978-14532-7	journal	January 1978
Quantitative estimate of VTI parameters from AVA responses: Estimate of VTI parameters from AVA Plessix, René-Edouard; Bork, Jonathan Geophysical Prospecting, Vol. 48, Issue 1 https://doi.org/10.1046/j.1365-2478.2000.00175.x	journal	January 2000
Elastic Waves Through a Packing of Spheres Gassmann, Fritz GEOPHYSICS, Vol. 16, Issue 4 https://doi.org/10.1190/1.1437718	journal	October 1951
A simplification of the Zoeppritz equations Shuey, R. T. GEOPHYSICS, Vol. 50, Issue 4 https://doi.org/10.1190/1.1441936	journal	April 1985
Pressure and fluid saturation prediction in a multicomponent reservoir using combined seismic and electromagnetic imaging Hoversten, G. Michael; Gritto, Roland; Washbourne, John GEOPHYSICS, Vol. 68, Issue 5 https://doi.org/10.1190/1.1620632	journal	September 2003
Reservoir-parameter identification using minimum relative entropy-based Bayesian inversion of seismic AVA and marine CSEM data Hou, Zhangshuan; Rubin, Yoram; Hoversten, G. Michael GEOPHYSICS, Vol. 71, Issue 6 https://doi.org/10.1190/1.2348770	journal	November 2006
Accelerating Markov Chain Monte Carlo Simulation by Differential Evolution with Self-Adaptive Randomized Subspace Sampling Vrugt, J. A.; ter Braak, C. J. F.; Diks, C. G. H. International Journal of Nonlinear Sciences and Numerical Simulation, Vol. 10, Issue 3 https://doi.org/10.1515/IJNSNS.2009.10.3.273	journal	January 2009
Information on elastic parameters obtained from the amplitudes of reflected waves Dȩbski, Wojciech; Tarantola, Albert GEOPHYSICS, Vol. 60, Issue 5 https://doi.org/10.1190/1.1443877	journal	September 1995

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