1887

Abstract

Summary

The efficiency of location methods is crucial for real-time microseismic monitoring. Migration-based methods can detect and locate very weak microseismic events, but they generally require more processing time and memory space than traveltime inversion. We propose to improve the efficiency of microseismic imaging methods with the particle swarm optimization algorithm, which is a stochastic population-based nonlinear optimization algorithm that can rapidly converge toward the optimum. In this work, we utilize the diffraction stacking operator and the cross-correlation stacking operator with the particle swarm optimization algorithm. The results of numerical and field data examples show that the proposed method can accelerate the imaging process without requiring additional memory space.

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/content/papers/10.3997/2214-4609.201701260
2017-06-12
2024-04-26
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References

  1. Cui, Q.H, Wang, D.K, Liu, L.P. and Diao, R.
    [2015] Application of particle swarm optimization algorithm in correction of microseismic velocity model. 77th EAGE Conference & Exhibition, Extended Abstracts, We P5 03.
    [Google Scholar]
  2. Duncan, P. and Eisner, L.
    [2010] Reservoir characterization using surface microseismic monitoring. Geophysics, 75(5), 75A139–75A146.
    [Google Scholar]
  3. Gajewski, D, Anikiev, D, Kashtan, B, Tessmer, E. and Vanelle, C.
    [2007] Localization of seismic events by diffraction stacking. SEG Technical Program Expanded Abstracts 2007, 1287–1291.
    [Google Scholar]
  4. Kennedy, J. and Eberhart, R.
    [1995] Particle swarm optimization. Proceedings of IEEE International Conference on Neutral Networks, 1942–1948.
    [Google Scholar]
  5. Lagos, S.R, Sabbione, J.I. and Velis, D.R.
    [2014] Very fast simulated annealing and particle swarm optimization for microseismic event location. SEG Technical Program Expanded Abstracts 2014, 2188–2192.
    [Google Scholar]
  6. Schuster, G.T, Yu, J. and Sheng, J.
    [2004] Interferometric/daylight seismic imaging. Geophysical Journal International, 157, 838–852.
    [Google Scholar]
  7. Shaw, R. and Srivastava, S.
    [2007] Particle swarm optimization: A new tool to invert geophysical data. Geophysics, 72(2), F75–F83.
    [Google Scholar]
  8. Shi, Y.H. and Eberhart, R.
    [1998] A modified particle swarm optimizer. Proceedings of International Conference on Evolutionary Computation, 1945–1950.
    [Google Scholar]
  9. Steiner, B., Saenger, E.H. and Schmalholz, M.
    [2008] Time reverse modeling of low-frequency microtremors: Application to hydrocarbon reservoir localization. Geophysical Research Letters, 35, L03307.
    [Google Scholar]
  10. UrbancicT.I, ZantoutS. and McGillivray, P.
    [2006] Simultaneous inversion for velocity and passive microseismic event locations — A particle swarm optimization approach. First EAGE Passive Seismic Workshop, Extended Abstracts, A12.
    [Google Scholar]
  11. Xie, Y.J. and Gajewski, D.
    [2016] Automatic estimation of the 3D CRS attributes by a metaheuristic-based optimization. 78th EAGE Conference & Exhibition, Extended Abstracts, Th P6 09.
    [Google Scholar]
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