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

Journal of Applied Geophysics

Volumn 147, Issue , 2017, Pages 68-80

  Ren, Huiying ^a   Ray, Jaideep ^b   Hou, Zhangshuan ^a   Huang, Maoyi ^a   Bao, Jie ^a   Swiler, Laura ^c  

^a PACIFIC NORTHWEST NATIONAL LABORATORY   (United States)

^b SANDIA NATIONAL LABORATORIES   (United States)

^c SANDIA NATIONAL LABORATORIES   (United States)

Author keywords

Bayesian; Controlled Source Electromagnetic; Joint inversion; Multi chain Markov chain Monte Carlo; Seismic Amplitude Versus Angle

Indexed keywords

BAYESIAN NETWORKS; CHAINS; EVOLUTIONARY ALGORITHMS; MARKOV PROCESSES; MONTE CARLO METHODS; PETROLEUM RESERVOIRS; POROSITY; SEISMOLOGY;

BAYESIAN; CONTROLED-SOURCE ELECTROMAGNETIC; CONTROLLED SOURCE; ELECTROMAGNETICS; JOINT INVERSION; MARKOV CHAIN MONTE CARLO; MARKOV CHAIN MONTE-CARLO; MULTI-CHAIN MARKOV-CHAIN MONTE CARLO; SEISMIC AMPLITUDE VERSUS ANGLE; SEISMIC AMPLITUDES;

OPTIMIZATION;

AMPLITUDE; BAYESIAN ANALYSIS; DATA INVERSION; ELECTROMAGNETIC FIELD; HYDROCARBON RESERVOIR; MARKOV CHAIN; MONTE CARLO ANALYSIS; RESERVOIR CHARACTERIZATION; SEISMIC DATA;

EID: 85033713903     PISSN: 09269851     EISSN: None     Source Type: Journal    
DOI: 10.1016/j.jappgeo.2017.10.004     Document Type: Article

References (26)

1. Aki, K., and Richards, P., 1980, Quantitative Seismology: Theory and Methods: Freeman and Company, v. 2.
2. Archie, G. E., 1942, The electrical resistivity log as an aid in determining some reservoir characteristics: Trans. AIME, v. 146, no. 01, p. 54–62.
3. Castagna, J.P., Backus, M., AVO analysis-tutorial and review: offset-dependent reflectivity. Theory and Practice of AVO Analysis, 1993, 3–36.
4. Chen, J., Hoversten, G.M., Vasco, D., Rubin, Y., Hou, Z., Joint inversion of seismic AVO and EM data for gas saturation estimation using a sampling-based stochastic model. Proceedings 2004 SEG Annual Meeting, 2004.
5. Chen, J., Hoversten, G. M., Vasco, D., Rubin, Y., and Hou, Z., 2007, A Bayesian model for gas saturation estimation using marine seismic AVA and CSEM data: Geophysics, v. 72, no. 2, p. WA85-WA95.
6. Dȩbski, W., and Tarantola, A., 1995, Information on elastic parameters obtained from the amplitudes of reflected waves: Geophysics, v. 60, no. 5, p. 1426–1436.
7. Du, Z., MacGregor, L.M., Reservoir characterization from joint inversion of marine CSEM and seismic AVA data using Genetic Algorithms: a case study based on the Luva gas field. Proceedings 2010 SEG Annual Meeting, 2010, Society of Exploration Geophysicists.
8. Dvorkin, J., and Nur, A., 1996, Elasticity of high-porosity sandstones: theory for two North Sea data sets: Geophysics, v. 61, no. 5, p. 1363–1370.
9. Fliedner, M., Treitel, S., Frenkel, M., and MacGregor, L., 2011, Fast stochastic inversion of marine CSEM and seismic data with the Neighbourhood Algorithm: 81th Ann. Inter-nat. Mtg. Soc. Expl. Geophys., Expanded Abstracts, v. 30, p. 2517–2522.
10. Gassmann, F., 1951, Elastic waves through a packing of spheres: Geophysics, v. 16, no. 4, p. 673–685.
11. Gneiting, T., and Raftery, A. E., 2007, Strictly proper scoring rules, prediction, and estimation: J. Am. Stat. Assoc., v. 102, no. 477, p. 359–378.
12. Haario, H., Laine, M., Mira, A., and Saksman, E., 2006, DRAM: efficient adaptive MCMC Stat. Comput., v. 16, no. 4, p. 339–354.
13. Hou, Z., Rubin, Y., Hoversten, G. M., Vasco, D., and Chen, J., 2006, Reservoir-parameter identification using minimum relative entropy-based Bayesian inversion of seismic AVA and marine CSEM data: Geophysics, v. 71, no. 6, p. O77–O88.
14. Hou, Z., Huang, M., Leung, L. R., Lin, G., and Ricciuto, D. M., 2012, Sensitivity of surface flux simulations to hydrologic parameters based on an uncertainty quantification framework applied to the Community Land Model: J. Geophys. Res. Atmos., v. 117, no. D15.
15. Hoversten, G. M., Gritto, R., Washbourne, J., and Daley, T., 2003, Pressure and fluid saturation prediction in a multicomponent reservoir using combined seismic and electromagnetic imaging: Geophysics, v. 68, no. 5, p. 1580–1591.
16. Lang, X., Grana, D., Bayesian rock physics inversion of acoustic and electrical properties for rock-fluid property estimation. Proceedings 2015 SEG Annual Meeting, 2015, Society of Exploration Geophysicists.
17. Liang, F., Liu, C., Carroll, R., Advanced Markov Chain Monte Carlo Methods: Learning from Past Samples. 2011, John Wiley & Sons.
18. Miotti, F.M., Giraud, J., Joint Inversion of Attributes. 2015 (Google Patents).
19. Niederreiter, H., 1978, Quasi-Monte Carlo methods and pseudo-random numbers: Bull. Am. Math. Soc., v. 84, no. 6, p. 957–1041.
20. Plessix, R. E., and Bork, J., 2000, Quantitative estimate of VTI parameters from AVA responses: Geophys. Prospect., v. 48, no. 1, p. 87–108.
21. Ren, H., Hou, Z., Huang, M., Bao, J., Sun, Y., Tesfa, T., and Leung, L. R., 2016, Classification of hydrological parameter sensitivity and evaluation of parameter transferability across 431 US MOPEX basins: J. Hydrol., v. 536, p. 92–108.
22. Shuey, R., 1985, A simplification of the Zoeppritz equations: Geophysics, v. 50, no. 4, p. 609–614.
23. Solonen, A., Ollinaho, P., Laine, M., Haario, H., Tamminen, J., and Järvinen, H., 2012, Efficient MCMC for climate model parameter estimation: parallel adaptive chains and early rejection: Bayesian Anal., v. 7, no. 3, p. 715–736.
24. Thorarinsdottir, T. L., Gneiting, T., and Gissibl, N., 2013, Using proper divergence functions to evaluate climate models: SIAM/ASA J. Uncert. Quant., v. 1, no. 1, p. 522–534.
25. Vrugt, J. A., Ter Braak, C., Diks, C., Robinson, B. A., Hyman, J. M., and Higdon, D., 2009, Accelerating Markov chain Monte Carlo simulation by differential evolution with self-adaptive randomized subspace sampling: Int. J. Nonl. Sci. Numer. Simul., v. 10, no. 3, p. 273–290.
26. Ward, S. H., and Hohmann, G. W., 1988, Electromagnetic theory for geophysical applications: Electromag. Meth. Appl. Geophys., v. 1, no. 3, p. 131–311.