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Application of Sequential Indicator Simulation in Geological Study of X Oilfield in Zhujiangkou Basin

DOI: 10.4236/ojogas.2020.51002, PP. 16-25

Keywords: 3D Geological Modeling, Sequential Indicator, Indicator Kriging, Lithofacies, Heterogeneity, Smoothing Effect

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Abstract:

Sequential indicator simulation is a commonly used method for discrete variable simulation in 3D geological modeling and a widely used stochastic simulation method, which can be used not only for continuous variable simulation but also for discrete variable simulation. In this paper, the X Oilfield in the western South China Sea is taken as an example to compare the sequential indicator simulation method and the Indicator Kriging interpolation method. The results of the final comparison show that the results of the lithofacies model established by the Indicator Kriging deterministic interpolation method are overly smooth, and its coincidence rate with the geological statistical results is not high, thus cannot well reflect the heterogeneity of the underground reservoir, while the simulation results of the lithofacies model established by the sequential indicator stochastic simulation method can fit well with the statistical law of the well, which has eliminated the smoothing effect of Kriging interpolation, thus can better reflect the heterogeneity of the underground reservoir. Therefore, the sequential indicator simulation is more suitable for the characterization of sand bodies and the study of reservoir heterogeneity.

References

[1]  Deutsch, C.V. and Wang, L. (1996) Hierarchical Object-Based Stochastic Modeling of Fluvial Reservoirs. Mathematical Geology, 28, 857-880.
https://doi.org/10.1007/BF02066005
[2]  Li, Y., Zhang, Z., Hu, W. and Xiong, P. (2011) Study on Facies-Controlled Water-Flooding Model for the First Member of Shahejie Formation in Pucheng Oilfield, Dongpu Depression. Fault-Block Oil & Gas Field, 18, 505-507.
[3]  Wu, X., Li, S., Yin, Y., Zhu, Y., Xu, Z. and Xiong, R. (2009) Application of Facies-Controlled Stochastic Modeling in Heterogeneity Study. Fault-Block Oil & Gas Field, 16, 58-60.
[4]  Feng, G.Q., Li, Y., Lin, Z.H. and Wang, Y.M. (2001) Application of Sequential Indicator Simulation Method to Delineating Sedimentary Microfacies. Journal of Southwest Petroleum Institute, 23, 1-4.
[5]  Yin, Y., Feng, S. and Yin, T. (2012) Comparison Study on Modeling Method of Meandering River Reservoir. Fault-Block Oil & Gas Field, 19, 44-46.
[6]  Wang, Y.W., Zhang, J.M., Wang, M., Pan, B.Z., Xing, Y.J. and Shi, D.H. (2010) Simulation of Lithology and Porosity of Volcanic Rock Reservoir Based on Sequential Indicator Simulation. Journal of Jilin University, 40, 455-460.
[7]  Vries, L.M.D., Carrera, J., Falivene, O., Gratacós, O. and Slooten, L.J. (2009) Application of Multiple Point Geostatistics to Non-Stationary Images. Mathematical Geosciences, 41, 29-42.
https://doi.org/10.1007/s11004-008-9188-y
[8]  Nunes, R. and Almeida, J. (2010) Parallelization of Sequential Gaussian, Indicator and Direct Simulation Algorithms. Computers & Geosciences, 36, 1042-1052.
https://doi.org/10.1016/j.cageo.2010.03.005
[9]  Bastante, F.G., Ordóñez, C., Taboada, J. and Matías, J.M. (2008) Comparison of Indicator Kriging, Conditional Indicator Simulation and Multiple-Point Statistics Used to Model Slate Deposits. Engineering Geology, 98, 50-59.
https://doi.org/10.1016/j.enggeo.2008.01.006
[10]  Bu, F. and Zhang, Y. (2013) Modeling of High-Sinuosity Deep Water Turbidite Channel. Science & Technology Review, 31, 70-73.
[11]  Du, Q., Hou, J. and Lu, M. (1999) A Predictable Geologic Model of Sedimentary Facies and Sands. Acta Petrolei Sinica, 20, 45-50.
[12]  Journel, A.G., Gundeso, R., Gringarten, E. and Yao, T. (1998) Stochastic Modelling of a Fluvial Reservoir: A Comparative Review of Algorithms. Journal of Petroleum Science & Engineering, 21, 95-121.
https://doi.org/10.1016/S0920-4105(98)00044-8
[13]  Journel, A.G. and Isaaks, E.H. (1984) Conditional Indicator Simulation: Application to a Saskatchewan Uranium Deposit. Journal of the International Association for Mathematical Geology, 16, 685-718.
https://doi.org/10.1007/BF01033030
[14]  Juang, K.W., Chen, Y.S. and Lee, D.Y. (2003) Using Sequential Indicator Simulation to Assess the Uncertainty of Delineating Heavy-Metal Contaminated Soils. Environmental Pollution, 127, 229-238.
https://doi.org/10.1016/j.envpol.2003.07.001
[15]  Jiang, H., Wang, H., Li. J., Chen, S., Lin, Z., Fang, X. and Cai, J. (2009) Analysis on Sequence Formation Styles of Zhu-3 Depression in Zhujiangkou Basin. Marine Geology & Quaternary Geology, 29, 87-94.
https://doi.org/10.3724/SP.J.1140.2009.01087
[16]  Jiang, H., Wang, H., Xiao, J., Lin, Z., Lv, X. and Cai, J. (2008) Tectonic Inversion and Its Relationship with Hydrocarbon Accumulation in Zhu-3 Depression of Zhujiangkou Basin. Acta Petrolei Sinica, 29, 372-377.
https://doi.org/10.3724/SP.J.1140.2009.01087
[17]  Zhu, W., Li, M. and Wu, P. (1997) Petroleum System in Zhu-3 Depression of Zhujiangkou Basin. Petroleum Exploration & Development, 24, 21-23.
[18]  Wang, G.J., Zhao, L.M., Wei, L.I. and Zhao, G.L. (2005) Sensitivity of Variogram in Stochastic Modeling. Petroleum Exploration & Development, 32, 72-75.
[19]  Barabas, N., Goovaerts, P. and Adriaens, P. (2001) Geostatistical Assessment and Validation of Uncertainty for Three-Dimensional Dioxin Data from Sediments in an Estuarine River. Environmental Science & Technology, 35, 3294-3301.
https://doi.org/10.1021/es010568n
[20]  Journel, A. and Zhang, T. (2006) The Necessity of a Multiple-Point Prior Model. Mathematical Geology, 38, 591-610.
https://doi.org/10.1007/s11004-006-9031-2

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