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 GM  Vol.3 No.4 , October 2013
Facies and Fracture Network Modeling by a Novel Image Processing Based Method
Abstract: A wide range of methods for geological reservoir modeling has been offered from which a few can reproduce complex geological settings, especially different facies and fracture networks. Multi Point Statistic (MPS) algorithms by applying image processing techniques and Artificial Intelligence (AI) concepts proved successful to model high-order relations from a visually and statistically explicit model, a training image. In this approach, the patterns of the final image (geological model) are obtained from a training image that defines a conceptual geological scenario for the reservoir by depicting relevant geological patterns expected to be found in the subsurface. The aim is then to reproduce these training patterns within the final image. This work presents a multiple grid filter based MPS algorithm to facies and fracture network images reconstruction. Processor is trained by training images (TIs) which are representative of a spatial phenomenon (fracture network, facies...). Results shown in this paper give visual appealing results for the reconstruction of complex structures. Computationally, it is fast and parsimonious in memory needs.
Cite this paper: P. Mohammadmoradi, "Facies and Fracture Network Modeling by a Novel Image Processing Based Method," Geomaterials, Vol. 3 No. 4, 2013, pp. 156-164. doi: 10.4236/gm.2013.34020.
References

[1]   G. Arpat and J. Caers, “A Multiple-Scale, Pattern-Based Approach to Sequential Simulation,” 7th International Geostatistics Congress. GEOSTAT 2004 Proceedings, Banff, October 2004.

[2]   S. Strebelle, “Conditional Simulation of Complex Geological Structures Using Multiple-Point Statistics,” Mathematical Geology, Vol. 34, No. 1, 2002, pp. 1-21.
http://dx.doi.org/10.1023/A:1014009426274

[3]   A. G. Journel and T. Zhang, “The Necessity of a Multiple-Point Prior Model,” Mathematical Geology, Vol. 38 No. 5, 2006, pp. 591-610.

[4]   J. Ortiz, “Characterization of High Order Correlation for Enhanced Indicator Simulation,” Ph.D. thesis, University of Alberta, Edmonton, 2003.

[5]   J. Boisvert, M. J. Pyrcz and C. V. Deutsch, “Multiple-Point Statistics for Training Image Selection,” Natural Resources Research, Vol. 16, No. 4. 2007.

[6]   K. G. Boogaart, “Some Theory for Multiple Point Statistics: Fitting, Checking and Optimally Exploiting the Training Image,” International Association for Mathematical Geology 11th International Congress, 2006.

[7]   F. Guardiano and M. Srivastava, “Multivariate Geostatistics: Beyond Bivariate Moments,” In: A. Soares, Ed., GeostatisticsTroia, Kluwer Academic Publications, Dordrecht, 1993, pp. 133-144.

[8]   S. Strebelle, “Sequential Simulation Drawing Structures from Training Images,” Ph.D. Thesis, Stanford University, 2000.

[9]   J. Straubhaar, P. Renard, G. Mariethoz, R. Froidevaux, and O. Besson, “An Improved Parallel Multiple-Point Algorithm Using a List Approach,” Mathematical Geosciences, Vol. 43, No. 3, 2011, pp. 305-328.

[10]   J. Caers, “GEOSTATISTICS: From Pattern Recognition to Pattern Reproduction,” Developments in Petroleum Science, Vol. 51, 2003, pp. 97-115.
http://dx.doi.org/10.1016/S0376-7361(03)80010-9

[11]   X. Y. Liu, C. Y. Zhang, Q. S. Liu and J. Birkholzer, “Multiple-Point Statistical Prediction on Fracture Networks at Yucca Mountain,” 2004.

[12]   M. J. Ronayne, S. M. Gorelick and J. Caers, “Identifying Discrete Geologic Structures That Produce Anomalous HyDraulic Response: An Inverse Modeling Approach,” Water Resources Research, Vol. 44, No. 8, 2008.
http://dx.doi.org/10.1029/2007WR006635

[13]   M. Stien, P. Abrahamsen, R. Hauge and O. Kolbjørnsen, “Modification of the Snesim Algorithm,” EAGE, Petroleum Geostatistics, 2007.

[14]   M. Huysmans and D. Alain, “Application of Multiple-point Geostatistics on Modelling Groundwater Flow and Transport in a Cross-Bedded Aquifer,” Hydrogeology Journal, Vol. 17, No. 8, 2009, pp. 1901-1911.

[15]   H. Okabe and J. Blunt, “Pore Space Reconstruction Using Multiple-Point Statistics,” Journal of Petroleum Science and Engineering, Vol. 46, No. 1-2, 2005, pp. 121-137.
http://dx.doi.org/10.1016/j.petrol.2004.08.002

[16]   T. Zhang, P. Switzer and A. Journel, “Filter-Based Classification of Training Image Patterns for Spatial Simulation,” Mathematical Geology, Vol. 38, No. 1, 2006, pp. 63-80. http://dx.doi.org/10.1007/s11004-005-9004-x

[17]   M. Honarkhah and J. Caers, “Stochastic Simulation of Patterns Using Distance-Based Pattern Modeling,” Mathematical Geosciences, Vol. 42, No. 5, 2010, pp. 487-517. http://dx.doi.org/10.1007/s11004-010-9276-7

[18]   M. Honarkhah and J. Caers, “Classifying Existing and Generating New Training Image Patterns in Kernel Space,” 21st SCRF Aliate Meeting, Stanford University, 2008.

[19]   S. Chatterjee, R. Dimitrakopoulos and H. Mustapha, “Dimensional Reduction of Pattern-Based Simulation Using Wavelet Analysis,” Mathematical Geosciences Work to Be Submitted, 2011.

[20]   P. Mohammadmoradi and M. Rasaei, “Modification of the FILTERSIM Algorithm for Unconditional Simulation of Complex Spatial Geological Structures,” Journal of Geomaterials, Vol. 2 No. 3, 2012, pp. 49-56.

[21]   C. E. Shannon, “A Mathematical Theory of Communication,” Bell System Technical Journal, Vol. 27, 1948. pp. 379-423,623-656.

 
 
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