Application of Grid-Free Geostatistical Simulation to a Large Oil-Sands Reservoir
- Yevgeniy Zagayevskiy (University of Alberta) | Clayton V. Deutsch (University of Alberta)
- Document ID
- Society of Petroleum Engineers
- SPE Reservoir Evaluation & Engineering
- Publication Date
- July 2016
- Document Type
- Journal Paper
- 367 - 381
- 2016.Society of Petroleum Engineers
- Gridless Geomodeling, Fourier Series Simulation, SAGD, Firebag Oil Sands, Model Consistency
- 2 in the last 30 days
- 450 since 2007
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Geostatistical simulation is performed for reservoir characterization to depict local variability in the modeled properties. The conventional simulation methods are implemented in a grid dependent manner that makes regridding of realizations, refinement of existing grids, and the simulation on irregular grids challenging. The grid-free-simulation (GFS) method has been recently developed for flexible reservoir characterization. The geostatistical realizations of a reservoir are expressed as an analytical function of the coordinates of the simulation locations and, thus, are infinitely resolvable. The resulting model is conditioned to primary scattered point-scale hard data and secondary exhaustively sampled block-scale soft data. The former data are sampled along wells, whereas the latter data are from seismic surveys. The GFS methodology is applied to the Firebag oil-sands thermal project operated in northern Alberta, Canada. The conditioning data are point-scale core measurements, log observations, and block-scale acoustic impedance (AI). The models of correlated porosity, permeability, and water saturation attributes are constructed on three different grids by facies and are consistent with each other. These models are intended for resource estimation, reserves estimation, and subsequent-flow simulation, respectively. The modeling results of the grid-independent simulation method are promising for industrial application to petroleum reservoir characterization.
|File Size||1 MB||Number of Pages||15|
Aziz, K. 1993. Reservoir Simulation Grids: Opportunities and Problems. J Pet Technol 45 (7): 658–663. SPE-25233-PA. http://dx.doi.org/10.2118/25233-PA.
Butler, R. M. 1991. Thermal Recovery of Oil and Bitumen. Upper Saddle River, New Jersey: Prentice Hall.
Chiles, J.-P. and Delfiner, P. 2012. Geostatistics: Modeling Spatial Uncertainty, second edition. New York City: John Wiley & Sons.
Deutsch, C. V. and Journel, A. G. 1998. GSLIB: Geostatistical Software Library and User’s Guide, second edition. New York City: Oxford University Press.
Ellis, D. V. and Singer, J. M. 2008. Well Logging for Earth Scientists, second edition. Dordrecht, The Netherlands: Springer.
geoSCOUT. 2014. geoLOGIC systems geoSCOUT, http://www.geologic.com/products-services/geoscout (accessed 28 August 2014).
Goovaerts, P. 1997. Geostatistics for Natural Resources Evaluation. New York City: Oxford University Press.
Journel, A. G. and Huijbregts, C. J. 1978. Mining Geostatistics. New York City: Academic Press.
Kumar, D. 2006. A Tutorial on Gassmann Fluid Substitution: Formulation, Algorithm and Matlab Code. Geohorizons 1: 4–12.
Pyrcz, M. J. and Deutsch, C. V. 2014. Geostatistical Reservoir Modeling: 2nd edition. New York City: Oxford University Press.
Schneider, C. L. and Grobe, M. 2013. Regional Cross-Sections of Devonian Stratigraphy in Northeastern Alberta (NTS 74D, E). Open File Report 2013-05, Alberta Energy Regulator and Alberta Geological Survey, Canada.
Srivastava, R. M. 1992. Reservoir Characterization with Probability Field Simulation. Presented at the SPE Annual Technical Conference and Exhibition, Washington, DC, 4–7 October. SPE-24753-MS. http://dx.doi.org/10.2118/24753-MS.
Suncor. 2013. Suncor Firebag 2013 ERCB Performance Presentation, Commercial Scheme Approval No. 8870, Report Period March 1, 2012 to February 28, 2013. Oral presentation given at the ERCB Performance Presentation, Calgary, 1–2 May.
Wackernagel, H., 2003. Multivariate Geostatistics, third edition. Berlin: Springer.
Zagayevskiy, Y. 2012. Constraining 3D Petroleum Reservoir Models to Petrophysical Data, Local Temperature Observations, and Gridded Seismic Attributes with the Ensemble Kalman Filter (EnKF). Master’s thesis, University of Alberta, Edmonton, Canada.
Zagayevskiy, Y. and Deutsch, C. V. 2015. Multivariate Grid-Free Geostatistical Simulation with Secondary Data. Stoch. Env. Risk A. 1–21. http://dx.doi.org/10.1007/s00477-015-1154-x.
Zagayevskiy, Y. and Deutsch, C. V. In press. Multivariate Geostatistical Grid-Free Simulation of Natural Phenomena. Math. Geosci.