1887

Abstract

Summary

In this paper, we introduced a new visual analytics framework to select a few representative models from an ensemble of geostatistical models that can represent the overall production uncertainty. To achieve this purpose, a new block based similarity metric is defined based on mutual information. This metric is computed based on static geological properties and helps to identify geological models with similar flow simulation results. In the next step, utilizing the computed similarity values, a customized multi attribute clustering algorithm is applied on a set of geological models. One model is selected from each cluster randomly that results in having a few representative geological models. The whole process is implemented in a visual analytics framework. The proposed workflow was exemplified using some datasets generated from various geostatistical facies realizations using different variogram correlation lengths. The results on the case studies show that our technique is 70% accurate and much more time efficient in comparison to the existent techniques. The method is being enhanced for more accurate clustering.

Loading

Article metrics loading...

/content/papers/10.3997/2214-4609.201601143
2016-05-31
2020-08-10
Loading full text...

Full text loading...

References

  1. Ballin, P., Journel, A., & Aziz, K.
    (1992). Prediction of uncertainty in reservoir performance forecast.
    [Google Scholar]
  2. Caers, J.
    (2011). Modeling Uncertainty in the Earth Sciences. John Wiley & Sons, Ltd.
    [Google Scholar]
  3. Correa, C. D., Chan, Y. H., & Ma, K. L.
    (2009, October). A framework for uncertainty-aware visual analytics. In Visual Analytics Science and Technology, 2009. VAST 2009. IEEE Symposium on (pp. 51–58). IEEE.
    [Google Scholar]
  4. Deutsch, C. V.
    (2006). A sequential indicator simulation program for categorical variables with point and block data: BlockSIS. Computers & Geosciences, 32(10), 1669–1681.
    [Google Scholar]
  5. Fenik, D. R., Nouri, A., & Deutsch, C. V.
    (2009, January). Criteria for ranking realizations in the investigation of SAGD reservoir performance. In Canadian International Petroleum Conference. Petroleum Society of Canada.
    [Google Scholar]
  6. France, S. L., & Carroll, J. D.
    (2011). Two-way multidimensional scaling: A review. Systems, Man, and Cybernetics, Part C: Applications and Reviews, IEEE Transactions on, 41(5), 644–661.
    [Google Scholar]
  7. Goshtasby, A. A.
    (2012). Image registration: Principles, tools and methods. Springer Science & Business Media.
    [Google Scholar]
  8. Idrobo, E. A., Choudhary, M. K., & Datta-Gupta, A.
    (2000, January). Swept volume calculations and ranking of geostatistical reservoir models using streamline simulation. In SPE/AAPG Western Regional Meeting. Society of Petroleum Engineers.
    [Google Scholar]
  9. IMEX, Three Phase, Black-Oil Reservoir Simulator
    (2015), http://www.cmgl.ca/software/imex2015
  10. Li, Z., & Floudas, C. A.
    (2014). Optimal scenario reduction framework based on distance of uncertainty distribution and output performance: I. Single reduction via mixed integer linear optimization. Computers & Chemical Engineering, 70, 50–66.
    [Google Scholar]
  11. Lin, D.
    (1998, July). An information-theoretic definition of similarity. In ICML (Vol. 98, pp. 296– 304).
    [Google Scholar]
  12. Scheidt, C., & Caers, J.
    (2009). Representing spatial uncertainty using distances and kernels. Mathematical Geosciences, 41(4), 397–419.
    [Google Scholar]
  13. Majdi Yazdi, M.
    (2014). Screening Geostatistical Realizations for SAGD Reservoir Simulation.
    [Google Scholar]
  14. Schöelkopf, B., Smola, A.
    (2002) Learning with kernels. MIT Press, Cambridge, 664 p
    [Google Scholar]
  15. Sun, G. D., Wu, Y. C., Liang, R. H., & Liu, S. X.
    (2013). A survey of visual analytics techniques and applications: State-of-the-art research and future challenges. Journal of Computer Science and Technology, 28(5), 852–867.
    [Google Scholar]
http://instance.metastore.ingenta.com/content/papers/10.3997/2214-4609.201601143
Loading
/content/papers/10.3997/2214-4609.201601143
Loading

Data & Media loading...

This is a required field
Please enter a valid email address
Approval was a Success
Invalid data
An Error Occurred
Approval was partially successful, following selected items could not be processed due to error