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Static modeling of the reservoir for estimate oil in place using the geostatistical method

    Hakimeh Amanipoor Affiliation

Abstract

Three-dimensional simulation using geostatistical methods in terms of the possibility of creating multiple realizations of the reservoir, in which heterogeneities and range of variables changes are well represented, is one of the most efficient methods to describe the reservoir and to prepare a 3D model of it and the results have been used as acceptable results in the calculations due to the high accuracy and the lack of smoothing effect in small changes compared to the results of Kriging estimation.


The initial volumetric tests of the Hendijan reservoir in southern Iran were carried out according to the construction model and the petrophysical model prepared by the software and according to the fluid contact levels, and the ratio of net thickness to total thickness in different reservoir zones. The calculations can be distinguished based on the zoning of the reservoir and also on the basis of type of facies. Accordingly, the average volume of fluid in place of the field is calculated in different horizons. The results of the simulation showed that the Ghar reservoir rock has gas and Sarvak Reservoir has the largest amount of oil in place.

Keyword : reservoir modeling, static model, geostatistics, cut off, oil in place

How to Cite
Amanipoor, H. (2019). Static modeling of the reservoir for estimate oil in place using the geostatistical method. Geodesy and Cartography, 45(4), 147-153. https://doi.org/10.3846/gac.2019.10386
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Dec 23, 2019
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References

Abdideh, M. (2014). Prediction of mud loss in reservoir rock by geostatistical method. Geomatics, Natural Hazards and Risk, 5, 41-55. https://doi.org/10.1080/19475705.2013.773944

Abdideh, M., & Abyat, M. (2012). A geostatistical approach for predicting the top producing formation in oil fields. Geodesy and Cartography, 38(3), 111-117. https://doi.org/10.3846/20296991.2012.728896

Abdideh, M., & Ameri, A. (2019). Cluster analysis of petrophysical and geological parameters for separating the electrofacies of a gas carbonate reservoir sequence. Natural Resources Research (in press). https://doi.org/10.1007/s11053-019-09533-1

Abdideh, M., & Bargahi, D. 2012. Designing a 3D model for the prediction of the top of formation in oil fields using geostatistical methods, Geocarto International, 27, 569-579. https://doi.org/10.1080/10106049.2012.662529

Chambers, R. L., Yarus, J. M., & Hird, K. B. (2000). Petroleum geostatistics for nongeostaticians. Leading Edge Journal, 19(6), 592. https://doi.org/10.1190/1.1438664

Corstange, R., Grunwald, S., & Lark, R. M. (2008). Inferences from fluctuations in the Local Variogram about the assumption of stationary in the variance. Geoderma, 143, 123-132. https://doi.org/10.1016/j.geoderma.2007.10.021

Davis, J. C. (2002). Statistics and data analysis in geology. John Wilry & Sons.

Deutsch, C. V. (2002). Geostatistical reservoir modeling. Oxford: Oxford University Press.

Farzadi, P., & Hesthammer, J. (2007). Diagnosis of the Upper Cretaceous paleokarst and turbidite systems from the Iranian Persian Gulf using volume-based multiple seismic attribute analysis and pattern recognition. Petroleum Geoscience, 13, 227-240. https://doi.org/10.1144/1354-079306-710

Farzadi, P. (2006, April). High resolution seismic stratigraphic analysis, an integrated approach to the subsurface geology of the SE Persian Gulf (PhD Thesis). Bergen University.

Felletti, F. (2004). Statistical modeling and validation of correlation in turbidites: an example from the tertiary Piedmont basin. Marine and Petroleum Geology Journal, 21, 23-39. https://doi.org/10.1016/j.marpetgeo.2003.11.006

Flugel, E. (2004). Microfacies of carbonate rocks. Analysis, interpretation and application. New York: Springer-Verlag. https://doi.org/10.1007/978-3-662-08726-8

Kelsall, J., & Wakefield, J. (2002). Modeling spatial variation in disease risk: A geostatistical approach. Journal of the American Statistical Association, 97, 692-701. https://doi.org/10.1198/016214502388618438

Kumar, S. P., Vijay, R., & MP, P. (2015). Geostatistical evaluation of groundwater quality distribution of Tonk district, Rajasthan. International Journal of Geomatics and Geosciences, 6(2), 1474-1485.

Log Interpretation Charts. (2005). Schlumberger Company Publication.

Olea, R. A. (2006). A six-step practical approach to semivariogram modeling. Springer-Verlag. https://doi.org/10.1007/s00477-005-0026-1

Sahin, A., Ghori, S. G., & Ali, A. Z. (1998). Geological controls of variograms in a Complex Carbonate Reservoir, Eastern Province, Saudi Arabia. Mathematical Geology, 30(3). https://doi.org/10.1023/A:1021780915406

Zare Khosh Eghbal, M., Ghazban, F., Sharifi, F., & Khosro Tehrani, K. (2011). Using geostatistics and GIS to heavy metal pollution zonation in Anzali wetland sediments. Journal of the Earth, 6(19), 33-49 (in Persian).