Abstract
Traditional stochastic reservoir modeling, including object-based and pixel-based methods, cannot solve the problem of reproducing continuous and curvilinear reservoir objects. The paper first dives into the various stochastic modeling methods and extracts their merits, then proposes the skeleton-based multiple point geostatistics (SMPS) for the fluvial reservoir. The core idea is using the skeletons of reservoir objects to restrict the selection of data patterns. The skeleton-based multiple point geostatistics consists of two steps. First, predicting the channel skeleton (namely, channel centerline) by using the method in object-based modeling. The paper proposes a new method of search window to predict the skeleton. Then forecasting the distributions of reservoir objects using multiple point geostatistics with the restriction of channel skeleton. By the restriction of channel centerline, the selection of data events will be more reasonable and the realization will be achieved more really. The checks by the conceptual model and the real reservoir show that SMPS is much better than Sisim (sequential indicator simulation), Snesim (Single Normal Equation Simulation) and Simpat (simulation with patterns) in building the fluvial reservoir model. This new method will contribute to both the theoretical research of stochastic modeling and the oilfield developments of constructing highly precise reservoir geological models.
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References
Xu A N. Distribution patternof OOIP and remaining mobile oil in different types of sedimentary reservoir of China (in Chinese). J Oil Explor Dev, 1998, 25(5): 41–44
Haldorsen H H, Lake L W. A new approach to shale management in field-scale models. SPE 10976, 1982
Chessa A G. On the object-based method for simulation sandstone deposits. In: Christie M A ed. The Third European Conference on the Mathematics of Oil Recovery, 1992, June 17–19, Netherlands. Delft: Delft University Press, 1992. 67–78
Holden L. Modeling of fluvial reservoirs with object models. Math Geol, 1998, 30(5): 473–496
Deutsch C V, Wang L. Hierarchical object-based stochastic modeling of fluvial reservoirs. Math Geol, 1996, 28(7): 857–880
Journel A G. Stochastic modelling of a fluvial reservoir: a comparative review of algorithms. JPSE, 1998, 21(1): 95–121
Yin Y S, Wu S H, Qin Z Y. Reservoir modeling of subwater fan microfacies using reservoir hierarchical object-based modelling method in Pucheng Oilfield (in Chinese). J Chengdu Univ Technol (Sci & Technol Ed), 2006, 33(1): 53–57
Deutsch C V, Tran T T. FLUVSIM:a program for object-based stochastic modeling of fluvial depositional systems. Comp & Geosci, 2002, 28(4): 525–535
Guardiano F, Srivastava R M. Multivariate geostatistics: Beyond bivariate moments. In: Soares A, ed. Geostatistics-Troia. Dordrecht: Kluwer, 1993. 113–114
Wang L. Modeling complex reservoir geometries with multiple-point statistics. Math Geol, 1996, 29(7): 895–907
11Caers J, Journel A G. Stochastic reservoir simulation using neural networks trained on outcrop data. SPE 49026, 1998
Strebelle S. Conditional simulation of complex geological structure using multiple-point statistics. Math Geol, 2002, 34(1): 1–21
Strebelle S, Journel A G. Reservoir modeling using multiple-point statistics. SPE 71324, 2001
Strebelle S. Sequential simulation drawing structures from training images. Doctoral Dissertation. California: Stanford University, 2000
Liu Y, Journel A G. Improving sequential simulation with a structured path guided by information content. Math Geol, 2004, 36(8): 945–964
Caers J. Geostatistical reservoir modeling using statistical pattern recognition. JPSE, 2001, 29(3): 177–188
Caers J, Srinivasan. Statistical pattern recognition and geostatistical data integration. In: Wong P, Aminzadeh F, Nikravesh M, et al. eds. Soft Computing for Reservoir Characterization and Modeling. Heidelberg: Physica Verlag, 2001. 355–386
Caers J, Zhang T. Multiple-point geostatistics: a quantitative vehicle for integrating geologic analogs into multiple reservoir models. In: Grammer G M, Eberllt G P, eds. AAPG Memoir 80: Integration of Outcrop and Modern Analog Data in Reservoir, 2002. 383–394
Caers J, Avseth P, Mukerji T. Geostatistical integration of rock physics, seismic amplitudes and geological models in North-Sea turbidite systems. SPE 71321, 2001
Arpat B. Simulation with patterns. Doctor Dissertation. Stanford: Stanford University, 2005
Jones T A. Using flowpaths and vector fields in object-based modeling. Comp & Geosci, 2001, 27(1): 133–138
Jones T A. FP2VF: Fortran 90 program to generate a vector field from flowpaths. Comp & Geosci, 2003, 29(2): 209–214
Deutsch C V, Journel A G. GSLIB: Geostatistical Software Library and Users Guide. New York: Oxford University Press, 1998
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Supported by the National Natural Science Foundation of China (Grant Nos. 40572078, 40602013)
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Yin, Y., Wu, S., Zhang, C. et al. A reservoir skeleton-based multiple point geostatistics method. Sci. China Ser. D-Earth Sci. 52 (Suppl 1), 171–178 (2009). https://doi.org/10.1007/s11430-009-5004-x
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DOI: https://doi.org/10.1007/s11430-009-5004-x