Applied geostatistics for reservoir characterization: High Resolution Cells based model
DOI:
https://doi.org/10.29019/enfoqueute.v8n4.174Keywords:
Geostatistics, reservoir characterization, SGEMS, variogram, SISIMAbstract
Is it easy to use geostatistics for reservoir characterization or not? Is SGEMS able to reproduce reservoir geological characteristics in a 3D high resolution model? After years, Geostatistics has been allowing high resolution models generation to increase reservoir lithological detail. This work presents a straightforward methodology to create a facies model conditioned with the porosity model. Therefore, Open source geostatistics software was used to interpolate data. Rock type and porosity data, obtained from 26 well-logs of the Oriente Basin, were loaded in SGEMS. A high vertical and areal resolution 3D grid was built. Finally, 20 facies and porosity equiprobable models were generated. The model that better represents the reservoir characteristics was selected. The result is a high-resolution grid that represents the porosity conditioned to a facies model.
Downloads
References
Chiles, J. y Delfiner, P. (2012). Geostatistics_ Modeling Spatial Uncertainty. (2da Edición). New Jersey. Wiley Series.
González, R. y Reeves, S. (2007). Geostatistical Reservoir Characterization of the Canyon Formation, SACROC Unit, Permian Basin. Advanced Resources International. Report for U.S. Department of Energy.
Gringarten, E. y Deutsch C.V. (1999). Methodology for Variogram Interpretation and Modeling for Improved Reservoir Characterization. Society of Petroelum Engineers. Paper 56654. Disponible: https://www.onepetro.org/search/
Kelkar, M. y Pérez, G. (2002). Applied Geostatistics for Reservoir Characterization. Richardson. Society of Petroeum Engineers.
Ogbeiwi, P., Aladeitan, Y. y Udebhulu, D. (2017). An approach to waterflood optimization: case study of the reservoir X. Journal of Petroleum Exploration Production Technology, Disponible: https://DOI 10.1007/s13202-017-0368-5
Ozturk, C.A., Nasuf, E. (2002). Geostatistical assessment of rock zones for tunneling. Tunn Undergr Space Technol 17(3):275–285.
Remy, N., Boucher, A. y Wu, J. (2009). Applied Geostatistics with SGEMS. New York. Cambridge University Press: 139.
Syrjanen, P. and Loven, P. (1999). Geostatistics and Block Modelling in rock mechanics. 9th ISRM Congress. International Society of Rock Mechanics. Paris, France. 503-506.
Zhang, M.L., Zhang, Y.Z. and Yu, G.M. (2017) Applied Geostatisitcs Analysis for Reservoir Characterization Based on the SGeMS (Stanford Geostatistical Modeling Software). Open Journal of Yangtze Gas and Oil, 2, 45-66. Disponible: https://doi.org/10.4236/ojogas.2017.21004
Zhao, S., Zhou, Y., Wang, M., Xin, X. y Chen, F. (2014). Thickness, porosity, and permeability prediction: comparative studies and application of the geostatistical modeling in an Oil field. Environmental Systems Research, 3:7. Disponible: http://www.environmentalsystemsresearch.com/content/3/1/7
Downloads
Published
Issue
Section
License
The authors retain all copyrights ©.
- The authors retain their trademark and patent rights, as well as rights to any process or procedure described in the article.
- The authors retain the right to share, copy, distribute, perform, and publicly communicate the article published in Enfoque UTE (for example, post it in an institutional repository or publish it in a book), provided that acknowledgment of its initial publication in Enfoque UTE is given.
- The authors retain the right to publish their work at a later date, to use the article or any part of it (for example, a compilation of their work, lecture notes, a thesis, or for a book), provided that they indicate the source of publication (authors of the work, journal, volume, issue, and date).
