The aim of geostatistical reservoir characterization is to utilize wide variety of data, in different scales and accuracies, to construct reservoir models which are able to represent geological heterogeneities and also quantifying uncertainties by producing numbers of equiprobable models. Since all geostatistical methods used in estimation of reservoir parameters are inaccurate, modeling of “estimation error” in form of uncertainty analysis is very important. In this paper, the definition of Sequential Gaussian Simulation has been reviewed and construction of stochastic models based on it has been discussed. Subsequently ranking and uncertainty quantification of those stochastically populated equiprobable models and sensitivity study of modeled properties have been presented. Consequently, the application of sensitivity analysis on stochastic models of reservoir horizons, petrophysical properties, and stochastic oil-water contacts, also their effect on reserve, clearly shows any alteration in the reservoir geometry has significant effect on the oil in place. The studied reservoir is located at carbonate sequences of Sarvak Formation, Zagros, Iran; it comprises three layers. The first one which is located beneath the cap rock contains the largest portion of the reserve and other layers just hold little oil. Simulations show that average porosity and water saturation of the reservoir is about 20% and 52%, respectively. 1. Introduction The first step in optimizing the use of explored resources is to define the reservoir, which has a determinant role in reservoir management [1]. Definition of a reservoir includes description of empty spaces and size of grains, porosity and permeability of reservoir, identification of facies, sedimentary environment, and description of basin [2]. Three-dimensional models provide the best mechanism for linking all the existing data [3]. Nowadays, efficient three-dimensional simulation is popular in all major oil companies and has become an essential part of normal exploration and production activities. To overcome the inherent two-dimensional limitation of paper, it is necessary to use defined three-dimensional data. Three-dimensional simulation of geological structures enables collection of all the existing data for a certain project in a united model, by means of which data can be analyzed in software environment [4]. There are several methods for estimation. In a general classification, they can be divided into geostatistical and classical methods. Classical methods are those using classical statistics for estimation,
References
[1]
M. Nikravesh, “Computational intelligence for geosciences and oil exploration,” in Forging New Frontiers: Fuzzy Pioneers I, vol. 66, pp. 267–332, California University Press, 2007.
[2]
B. Yeten and F. Gümrah, “The use of fractal geostatistics and artificial neural networks for carbonate reservoir characterization,” Transport in Porous Media, vol. 41, no. 2, pp. 173–195, 2000.
[3]
G. Zamora Valcarce, T. Zapata, A. Ansa, and G. Selva, “Three-dimensional structural modeling and its application for development of the El Portón field, Argentina,” AAPG Bulletin, vol. 90, no. 3, pp. 307–319, 2006.
[4]
R. R. Jones, K. J. W. McCaffrey, P. Clegg et al., “Integration of regional to outcrop digital data: 3D visualisation of multi-scale geological models,” Computers and Geosciences, vol. 35, no. 1, pp. 4–18, 2009.
[5]
R. Haining, Spatial Data Analysis: Theory and Practice, Cambrige University Press, Cambrige, UK, 2003.
[6]
J. W. Jennings Jr., F. J. Lucia, S. C. Ruppel, A. John, and G. Katherine, “3D modeling of startigraphically controlled petrophysical variability in the South Wasson Clear Fork reservoir,” in Proceedings of the SPE Annual Technical Conference and Exhibition, pp. 2209–2223, San Antonio, Tex, USA, October 2002.
[7]
O. Kaufmann and T. Martin, “3D geological modeling from boreholes, cross section and geological maps, application over former natural gas storages in coal mines,” Computers and Geosciences, vol. 34, pp. 278–290, 2008.
[8]
A. MacDonald and J. L. Tollesfrud, “3D reservoir Uncertainty modeling workflows, production and benefits,” Roxar, September 2008.
[9]
D. P. Hampson, J. S. Schuelke, and J. A. Quirein, “Use of multiattribute transforms to predict log properties from seismic data,” Geophysics, vol. 66, no. 1, pp. 220–236, 2001.
[10]
P. J. Hatchell, “Fault whispers: transmission distortions on prestack seismic reflection data,” Geophysics, vol. 65, no. 2, pp. 377–389, 2000.
[11]
K. Hirsche, J. Porter-Hirsche, L. Mewhort, and R. Davis, “The use and abuse of geostatistics,” Leading Edge, vol. 16, no. 3, pp. 253–260, 1997.
[12]
M. T. Olowokere, “Geostatistical modeling of interval velocity to quantifying hydrocarbon resource in multi-layer reservoir from TMB field, Niger delta,” International Journal of Physical Sciences, vol. 5, no. 12, pp. 1897–1907, 2010.
[13]
H. Motii, “Geology of Iran, Zagros Geology,” Geological Survey of Iran, 2009.
Schlumberger, “Petrel Introduction Course,” Shlumberger information solutions, 2006.
[16]
M. Abdideh and D. Bargahi, “Designing a 3D model for prediction the top of formation in oil fields using geostatistical methods,” Geocarto International Journal, vol. 27, pp. 569–579, 2012.
[17]
N. Cressie and D. M. Hawkins, “Robust estimation of the variogram,” Journal of the International Association for Mathematical Geology, vol. 12, no. 2, pp. 115–125, 1980.
[18]
R. Corstanje, S. Grunwald, and R. M. Lark, “Inferences from fluctuations in the local variogram about the assumption of stationarity in the variance,” Geoderma, vol. 143, no. 1-2, pp. 123–132, 2008.
[19]
Schlumberger, “Property Modeling Course,” Shlumberger information solutions, 2004.
[20]
L. Dean, “Reservoir engineering for geologists,” Part 3—Volumetric Estimation, Reservoir no. 11, pp. 21–23, 2007.
[21]
M. R. Lelliott, M. R. Cave, and G. P. Walthall, “A structured approach to the measurement of uncertainty in 3D geological models,” Quarterly Journal of Engineering Geology and Hydrogeology, vol. 42, no. 1, pp. 95–105, 2009.
[22]
I. Zabalza-Mezghani, E. Manceau, M. Feraille, and A. Jourdan, “Uncertainty management: from geological scenarios to production scheme optimization,” Journal of Petroleum Science and Engineering, vol. 44, no. 1-2, pp. 11–25, 2004.
[23]
J. F. Bueno, R. D. Drummond, A. C. Vidal, and S. S. Sancevero, “Constraining uncertainty in volumetric estimation: a case study from Namorado Field, Brazil,” Journal of Petroleum Science and Engineering, vol. 77, no. 2, pp. 200–208, 2011.
