Assimilation Of Microseismic Data Into Coupled Flow And Geomechanics Models

References

1. Aanonsen, S., Naevdal, G., Oliver, D. et al.
   2009. The ensemble Kalman filter in reservoir engineering-a review. SPE J14(3): 393–412. doi:10.2118/117274-PA.
   [Google Scholar]
2. BissellRC, VascoDW, AtbiM, HamdaniM, OkwelegbeM., GoldwaterMH.
   2011. A full field simulation of the In Salah gas production and CO2 storage project using a coupled geo-mechanical and thermal fluid Flow simulator. Energy Procedia4:3290–3297.
   [Google Scholar]
3. Chang, C., Zoback, MD. and Khaksar, A.
   2006. Empirical relations between rock strength and physical properties in sedimentary rocks. Journal of Petroleum Science and Engineering. 51(3–4), 223–237. doi:10.1016/j.petrol.2006.01.003
   [Google Scholar]
4. Chatterjee, R. and Mukhopadhyay, M.
   2001. Petrophysical and geomechanical properties of rocks from the oilfields of the Krishna-Godavari and Cauvery Basins, India. Bulletin of Engineering Geology and the Environment. 61(2), 169–178.
   [Google Scholar]
5. Elahi, S.H. and Jafarpour, B.
   2017. A distance transform for continuous parameterization of discrete geologic facies for subsurface flow model calibration, Water Resour. Res., 53, 8226–8249, doi: 10.1002/2016WR019853.
   https://doi.org/10.1002/2016WR019853 [Google Scholar]
6. Emerick, A. A. and Reynolds, A. C.
   2013. Ensemble smoother with multiple data assimilations. Computers and Geosciences553–15.
   [Google Scholar]
7. Evensen, G.
   2009. The ensemble Kalman filter for combined state and parameter estimation: Monte-Carlo techniques for data assimilation in large systems. IEEE Control Syst. Mag29(3) 83–104, doi:10.1109/MCS.2009.932223.
   https://doi.org/10.1109/ MCS.2009.932223 [Google Scholar]
8. Evensen, G.
   (2007), Data Assimilation: The Ensemble Kalman Filter, Springer, New York.
   [Google Scholar]
9. Eshkalak, M.O, and Mohaghegh, S.D., and Esmaili, S.
   2014. “Geomechanical Properties of Unconventional Shale Reservoirs,” Journal of Petroleum Engineering, vol. 2014, Article ID 961641, 10 pages. doi:10.1155/2014/961641
   https://doi.org/10.1155/2014/961641 [Google Scholar]
10. Gelb, A.
    1974. Applied Optimal Estimation, MIT Press, Cambridge, Mass.
    [Google Scholar]
11. Jafarpour, B., and McLaughlin, D.
    2009. Estimating channelized reservoir permeabilities with the ensemble Kalman filter: The importance of the ensemble design, SPE J., 14(2), 374–388.
    [Google Scholar]
12. Kalman, R. E.
    1960. A new approach to linear filtering and prediction problems, J. Basic Eng., 82 (1): 35–45. doi:10.1115/1.3662552
    https://doi.org/10.1115/1.3662552 [Google Scholar]
13. KidneyR L, ZimmerU and BoroumandN.
    2010. Impact of distance dependent location dispersion error on interpretation of microseismic event distributions Leading Edge29284–9.
    [Google Scholar]
14. Naevdal, G., Johnsen, L., Aanonsen, S. et al.
    2005. Reservoir monitoring and continuous model updating using ensemble Kalman filter, SPE J. 10(1): 66–74.
    [Google Scholar]
15. Osypov, K., O’Briain, M., Whitfield, P and et al.
    2011. Quantifying Structural Uncertainty in Anisotropic Model Building and Depth Imaging: Hild Case Study. 73rd EAGE Conference and Exhibition incorporating SPE EUROPEC 2011. DOI: 10.3997/2214‑4609.20149165.
    https://doi.org/10.3997/2214-4609.20149165 [Google Scholar]
16. RutqvistJ, VascoD, MyerL.
    2010. Coupled reservoir-geomechanical analysis of CO2 injection and ground deformations at In Salah, Algeria. Int J Greenh Gas Control4:225–230.
    [Google Scholar]
17. Scott, D.W.
    (1992), Multivariate Density Estimation, JohnWiley, New York.
    [Google Scholar]
18. Skjervheim, J. A., G.Evensen, S.Aanonsen, B. O.Ruud, and T. A.Johansen
    (2007), Incorporating 4D seismic data in reservoir simulation models using ensemble Kalman filter, SPE J. 12(3): 282–292, SPE-95789-PA.
    [Google Scholar]
19. Sone, H. and ZobackMD.
    2013. “Mechanical properties of shale-gas reservoir rocks — Part 1: Static and dynamic elastic properties and anisotropy.” GEOPHYSICS, 78(5), D381–D392. doi:10.1190/geo2013-0050.1
    [Google Scholar]
20. Stork, A.L., Verdon, J.P. and KendallJ.M.
    2014. Assessing the Effect of Velocity Model Accuracy on Microseismic Interpretation at the In Salah Carbon Capture and Storage Site. Energy Procedia. 63, 4385–4393. doi:10.1016/j.egypro.2014.11.473.
    [Google Scholar]
21. Tarrahi, M. and B.Jafarpour
    (2012), Inference of permeability distribution from injection-induced discrete microseismic events with kernel density estimation and ensemble Kalman filter, Water Resour. Res., 48, W10506, doi:10.1029/2012WR011920.
    https://doi.org/10.1029/2012WR011920 [Google Scholar]
22. VerdonJP, KendallJM, WhiteDJ, AngusDA.
    2011. Linking microseismic event observations with geomechanical models to minimize the risks of storing CO2 in geological formations. Earth Planet Sci Lett305:143–152.
    [Google Scholar]
23. Zhou, H., Zhang, W., and Zhang, J.
    2016. Downhole microseismic monitoring for low signal-to-noise ratio events. Journal of Geophysics and Engineering. 13(5).
    [Google Scholar]
24. ZhouR, HuangL, RutledgeJ.
    2010. Microseismic event location for monitoring CO2 injection using double-difference tomography. Lead Edge29:201–214.
    [Google Scholar]
25. ZobackMD.
    2010. The potential for triggered seismicity associated with geologic sequestration of CO2 in saline aquifers. American Geophysical Union (AGU), EOS Trans. AGU, 91(52), Fall Meeting, Suppl., Abstract NH11C-01.
    [Google Scholar]
26. Zorn, E, Hammack, R., Harbert, W. and Kumar, A.
    2017. “Geomechanical lithology-based analysis of microseismicity in organic shale sequences: A Pennsylvania Marcellus Shale example.” The Leading Edge, 36(10), 845–851.
    [Google Scholar]