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FractVis: Visualizing Microseismic Events

  • Ahmed E. Mostafa
  • Sheelagh Carpendale
  • Emilio Vital Brazil
  • David Eaton
  • Ehud Sharlin
  • Mario Costa Sousa
Part of the Lecture Notes in Computer Science book series (LNCS, volume 8034)

Abstract

We present our efforts of applying information visualization techniques to the domain of microseismic monitoring. Microseismic monitoring is a crucial process for a number of tasks related to oil and gas reservoir development, e.g., optimizing hydraulic fracturing operations and heavy-oil stimulation. Microseismic data has many challenging features including high dimensionality and uncertainty. We present a brief introduction to the domain of microseismic monitoring, and derive a set of tasks and data abstractions that can establish common ground between microseismic monitoring domain experts and visualization researchers. We then present FractVis, a prototype for visual analysis of microseismic data, describing the ongoing process of iteratively refining FractVis through close collaboration and consultation with domain experts. FractVis is designed to offer microseismic monitoring experts with visual analytic tools that allow investigation of the 3D spatial distribution of microseismic events, time-varying analysis and interactive exploration of high-dimensional parameter spaces, extensively complementing the existing tools in their disposal.

Keywords

Hydraulic Fracture Domain Expert Microseismic Event Microseismic Monitoring Stimulate Reservoir Volume 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

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References

  1. 1.
    Norm Warpinski, P.: Microseismic monitoring: Inside and out. Journal of Petroleum Tech. 61, 80–85 (2009)Google Scholar
  2. 2.
    Daku, B., Salt, J., Sha, L.: An algorithm for locating microseismic events. In: CCECE 2004, vol. 4, pp. 2311–2314 (2004)Google Scholar
  3. 3.
    Ulrich, Z.: Calculating stimulated reservoir volume (srv) with consideration of uncertainties in microseismic-event locations. In: CURC 2011. SPE International (2011)Google Scholar
  4. 4.
    Höllt, T., Beyer, J., Gschwantner, F., Muigg, P., Doleisch, H., Heinemann, G., Hadwiger, M.: Interactive seismic interpretation with piecewise global energy minimization. In: PacificVis 2011, pp. 59–66 (2011)Google Scholar
  5. 5.
    Patel, D., Bruckner, S., Viola, I., Groller, E.: Seismic volume visualization for horizon extraction. In: PacificVis 2010, pp. 73–80 (2010)Google Scholar
  6. 6.
    Dopkin, D., James, H.: Trends in visualization for e&p operations. First Break 24 (2006)Google Scholar
  7. 7.
    Rusby, R.I.: The future of visualization: Vision 2020. WorldOil 229 (2008)Google Scholar
  8. 8.
    Elmqvist, N., Dragicevic, P., Fekete, J.D.: Rolling the dice: Multidimensional visual exploration using scatterplot matrix navigation. TVCG 14, 1148–1539 (2008)Google Scholar
  9. 9.
    Elmqvist, N., Stasko, J., Tsigas, P.: Datameadow: A visual canvas for analysis of large-scale multivariate data. In: VAST 2007, pp. 187–194 (2007)Google Scholar
  10. 10.
    Inselberg, A., Dimsdale, B.: Parallel coordinates: a tool for visualizing multi-dimensional geometry. In: VIS 1990, pp. 361–378. IEEE (1990)Google Scholar
  11. 11.
    Heinrich, J., Weiskopf, D.: State of the art of parallel coordinates. In: Eurographics Association (ed.) STAR Proceedings of Eurographics 2013, pp. 95–116 (2013)Google Scholar
  12. 12.
    Martin, A.R., Ward, M.O.: High dimensional brushing for interactive exploration of multivariate data. In: VIS 1995, pp. 271–278. IEEE (1995)Google Scholar
  13. 13.
    Peng, W., Ward, M.O., Rundensteiner, E.A.: Clutter reduction in multi-dimensional data visualization using dimension reordering. In: INFOVIS 2004, pp. 89–96. IEEE (2004)Google Scholar
  14. 14.
    Steed, C., Swan, J., Jankun-Kelly, T., Fitzpatrick, P.: Guided analysis of hurricane trends using statistical processes integrated with interactive parallel coordinates. In: VAST 2009, pp. 19–26 (2009)Google Scholar
  15. 15.
    Yuan, X., Guo, P., Xiao, H., Zhou, H., Qu, H.: Scattering points in parallel coordinates. TVCG 15, 1001–1008 (2009)Google Scholar
  16. 16.
    Ward, M.: Xmdvtool: integrating multiple methods for visualizing multivariate data. In: Visualization 1994, pp. 326–333 (1994)Google Scholar
  17. 17.
    Siirtola, H., Räihä, K.J.: Discussion: Interacting with parallel coordinates. Interact. Comp. 18, 1278–1309 (2006)CrossRefGoogle Scholar
  18. 18.
    Roberts, J.: State of the art: Coordinated multiple views in exploratory visualization. In: CMV 2007, pp. 61–71 (2007)Google Scholar
  19. 19.
    Bowman, I., Joshi, S., Van Horn, J.: Query-based coordinated multiple views with feature similarity space for visual analysis of mri repositories. In: VAST 2011, pp. 267–268 (2011)Google Scholar
  20. 20.
    Wang Baldonado, M.Q., Woodruff, A., Kuchinsky, A.: Guidelines for using multiple views in information visualization. In: AVI 2000, New York, pp. 110–119 (2000)Google Scholar
  21. 21.
    Andrienko, G., Andrienko, N.: Coordinated multiple views: a critical view. In: CMV 2007, pp. 72–74 (2007)Google Scholar
  22. 22.
    Holtzblatt, K., Jones, S.: Contextual inquiry: a participatory technique for system design, pp. 177–210. Lawrence Erlbaum Associates, Hillsdale (1993)Google Scholar
  23. 23.
    ESG Solutions hydraulic fracture mapping, https://www.esgsolutions.com/english/view.asp?x=741 (accessed: March 31, 2012)
  24. 24.
    Amorim, R., Boroumand, N., Vital Brazil, E., Hajizadeh, Y., Eaton, D., Costa Sousa, M.: Interactive sketch-based estimation of stimulated volume in unconventional reservoirs using microseismic data. In: Proceedings of 13th European Conference on the Mathematics of Oil Recovery, ECMOR XIII (2012)Google Scholar
  25. 25.
    Amar, R., Eagan, J., Stasko, J.: Low-level components of analytic activity in information visualization. In: INFOVIS 2005, pp. 111–117. IEEE (2005)Google Scholar
  26. 26.
    Borland, D., Taylor, R.: Rainbow color map (still) considered harmful. IEEE Comp. Graph. and Appl. 27, 14–17 (2007)CrossRefGoogle Scholar
  27. 27.
    Bier, E.A., Stone, M.C., Pier, K., Buxton, W., DeRose, T.D.: Toolglass and magic lenses: the see-through interface. In: SIGGRAPH 1993, pp. 73–80 (1993)Google Scholar
  28. 28.
    Holten, D., Van Wijk, J.J.: Evaluation of cluster identification performance for different pcp variants. Computer Graphics Forum 29, 793–802 (2010)CrossRefGoogle Scholar
  29. 29.
    Doleisch, H., Hauser, H.: Smooth brushing for focus+context visualization of simulation data in 3d. Journal of WSCG, 147–154 (2001)Google Scholar
  30. 30.
    Matkovic, K., Jelovic, M., Juric, J., Konyha, Z., Gracanin, D.: Interactive visual analysis and exploration of injection systems simulations. In: VIS 2005, pp. 391–398 (2005)Google Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2013

Authors and Affiliations

  • Ahmed E. Mostafa
    • 1
  • Sheelagh Carpendale
    • 1
  • Emilio Vital Brazil
    • 1
  • David Eaton
    • 2
  • Ehud Sharlin
    • 1
  • Mario Costa Sousa
    • 1
  1. 1.Department of Computer ScienceUniversity of CalgaryCanada
  2. 2.Department of GeoscienceUniversity of CalgaryCanada

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