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Geostatistical modeling for fine reservoir description of Wei2 block of Weicheng oilfield, Dongpu depression, China

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Abstract

The Wei2 block of Weicheng oilfield is characterized by complicated structure mainly caused by high degree of fault development. Multiple reservoir types are found in this block and the reservoir heterogeneity is severe. The oil and gas reservoirs have already stepped into the stagnant stage of a great water-cut degree together with a rapid production decline rate. Thereby, both stabilizing the oil and gas production and optimizing adjustment for further exploitation make it urgent for geomodelers to build a useful model to predict the inter-well parameters and the distribution of the remaining oil and gas. A three-dimensional geological model established with the help of stochastic modeling technique may provide a perfect window and carrier for fine structure interpretation and reservoir heterogeneity description, compared with a traditional two-dimensional model. Hence, based on stratigraphical layering points, significant surfaces and fault points as well seismic interpretation, an integrated structure model is developed. Using the truncated Gaussian simulation and taking the existing geological maps as references, the sedimentary microfacies model was successfully constructed. Through the use of sequential Gaussian simulation method and the facies-controlled modeling method, the reservoir physical properties are populated. Meanwhile, the comparison between facies-controlled and non-facies-controlled property models indicates that the former is more loyal to previous researching and the representation of heterogeneity is ideal. Finally, the ideas of sample density and reserves fitting are proposed to evaluate the practicability and accuracy of the property models.

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References

  • Allard D (1993) On the connectivity of two random set models: the truncated Gaussian and the Boolean. Geostatistics Tróia’92. Springer, Netherlands, pp 467–478

    Google Scholar 

  • Bao X, Li J, Li SH (2012) Application of truncated Gaussian simulation algorithm in architecture modeling of alluvial fan reservoir. Fault Block Oil Gas Field 19(3):316–318

    Google Scholar 

  • Borgomano JRF, Fournier F, Viseur S, Rijkels L (2009) Stratigraphic well correlations for 3-D static modeling of carbonate reservoirs. Am Assoc Pet Geol 92(6):789–824

    Google Scholar 

  • Brigaud B, Vincent B, Durlet C, Jean-François D, Jobard E, Pickard N, Yven B, Landrein P (2014) Characterization and origin of permeability–porosity heterogeneity in shallow-marine carbonates: from core scale to 3-D reservoir dimension (Middle Jurassic, Paris Basin, France). Mar Pet Geol 57:631–651

    Article  Google Scholar 

  • Célio M, Carvalho D, Viegas CP, José SD (2010) A new methodology to reduce uncertainties in reservoir simulation models using observed data and sampling techniques. J Pet Sci Eng 72(1–2):110–119

    Google Scholar 

  • Chen L, Huang SW, Lai ZW (1998) A new approach for stochastic simulation of sedimentary microfacies. Pet Explor Dev 25(6):74–80

    Google Scholar 

  • Damsleth E, Charlottle B (1992) A two-stage stochastic model applied to North Sea reservoir. J Pet Geol 44(4):402–408

    Google Scholar 

  • Deutsch CV (2002) Geostatistical reservoir modeling. Oxford University Press, New York, p 402

    Google Scholar 

  • Deutsch CV, Journel AG (1992) GSLIB: geostatistical software library and user’s guide. Oxford University Press, New York, pp 152–153

    Google Scholar 

  • Deutsch CV, Wang L (1996) Hierarchical object-based stochastic modeling of fluvial reservoirs. Math Geol 28(7):857–880

  • Ding F, Zhang JL, Wang JK, Liu T (2013a) The geostatistical modeling of a terminal fan in the lower second member of the Shahejie formation. Pet Sci Technol 31(18):1899–1907

    Article  Google Scholar 

  • Ding F, Zhang JL, Xie J, Li CL, Shi CQ, Zhang PH, Zhang M, Tang MM (2013b) Fine description of structure and sedimentary microfacies of Li32 block of Lijin oilfield, Dongying depression, China. Arab J Geosci 7(5):1693–1704

    Google Scholar 

  • Emery X, Cornejo J (2010) Truncated Gaussian simulation of discrete-valued, ordinal coregionalized variables. Comput Geosci 36(10):1325–1338

    Article  Google Scholar 

  • Ehrlich R, Crabtree SJ, Kennedy SK, Cannon RL(1984) Petrographic image analysis I, analysis of reservoir pore complexes. Sediment Petrology 54:1365–1376

  • Falivene O, Arbués P, Gardiner A, Pickup G, Muñoz JA, Cabrera L (2006) Best practice stochastic facies modeling from a channel-fill turbidite sandstone analog (the Quarry Outcrop, Eocene Ainsa Basin, Northeast Spain). AAPG Bull 90(7):1003–1029

    Article  Google Scholar 

  • Guan SW, Chen ZX, Li BL (2010) Discussion on the character and interpretation model of Kelasu deep structures in the Kuqa area. Pet Explor Dev 37(5):531–536

    Article  Google Scholar 

  • Haldorsen H, Damsleth E (1990) Stochastic modeling. J Pet Technol 42(4):404–412

    Article  Google Scholar 

  • Hatampour A, Ghiasi-Freez J, Soleimanpour I (2014) Prediction of flow units in heterogeneous carbonate reservoir using intelligently derived formula: case study in an Iranian reservoir. Arab J Sci Eng 39(7):5459–5473

    Article  Google Scholar 

  • Jiang ZX (2003) Sedimentary. Petroleum Industry Press, Beijing, pp 78–86 (In Chinese)

    Google Scholar 

  • Journel AG, Isaaks E (1984) Conditional indicator simulation application to a Saskatchewan Uranium deposit. Math Geol 16(7):686–718

    Google Scholar 

  • Journel AG, Gundeso R, Gringarten E, Yao T (1998) Stochastic modeling of a fluvial reservoir: a comparative review of algorithms. J Pet Sci Eng 21:95–121

    Article  Google Scholar 

  • Kaydani H, Mohebbi A, Eftekhari M (2014) Permeability estimation in heterogeneous oil reservoirs by multi-gene genetic programming algorithm. J Pet Sci Eng 123:201–206

    Article  Google Scholar 

  • Keogh KJ, Martinius AW, Osland R (2007) The development of fluvial stochastic modeling in the Norwegian oil industry: A historical review, subsurface implementation and future directions. Sedimentary Geology 202:249–268

  • Kohshour IO, Ahmadi M, Hanks C (2014) Integrated geologic modeling and reservoir simulation of Umiat: a frozen shallow oil accumulation in national petroleum reserve of Alaska. J Unconv Oil Gas Resour 6(2014):4–27

    Article  Google Scholar 

  • Li YH, Zhang SM, Song GF (2000) Developmental technical policies of gas-cap gas reservoir in block Wei. J Jianghan Pet Inst 22(4):58–60

    Google Scholar 

  • Li SH, Zhang CM, Yin YS (2003) Several stochastic modeling methods for fluvial reservoirs. J Xi’an Pet Inst (Nat Sci Ed) 18(5):10–16

    Google Scholar 

  • Masoudi R, Halim MHA, Karkooti H, Othman M (2011) On the concept and challenges of water saturation determination and modeling in carbonate reservoirs, SPE International Petroleum Technology Conference, ISBN 978-1-61399-148-0

  • Nazarpour A, Shadizadeh SR, Zargar G (2014) Geostatistical modeling of spatial distribution of porosity in the Asmari reservoir of Mansuri oil field in Iran. Pet Sci Technol 32(11):1274–1282

    Article  Google Scholar 

  • Qi LS, Carr TR, Goldstein RH (2007) Geostatistical three-dimensional modeling of oolite shoals, St. Louis Limestone, southwest Kansas. Am Assoc Pet Geol 91(1):69–96

    Google Scholar 

  • Qiu YN (1991) Geological models of petroleum reservoir Acta Pet. Sin 12(4):55–62

  • Sarkar K, Vishal V, Singh TN (2012) An empirical correlation of index geomechanical parameters with the compressional wave velocity. Geotech Geol Eng 30:469–479

    Article  Google Scholar 

  • Seifert D, Jensen JL (2000) Object and pixel-based reservoir modeling of a braided fluvial reservoir. Math Geol 32:581–603

    Article  Google Scholar 

  • Smith R, Møllern N (2003) Sedimentology and reservoir modeling of the Ormen Lange field, mid Norway. Mar Pet Geol 20:601–613

    Article  Google Scholar 

  • Spilsbury-Schakel JA (2006) Quality control of static reservoir models: Asia Pacific Oil & Gas Conference and Exhibition. SPE101875:1–6

  • Vishal V, Singh L, Pradhan SP, Singh TN, Ranjith PG (2013) Numerical modeling of Gondwana coal seams in India as coalbed methane reservoirs substituted for carbon dioxide sequestration. Energy 49:384–394

    Article  Google Scholar 

  • Vishal V, Singh TN, Ranjith PG (2015) Influence of sorption time in CO2-ECBM process in Indian coals using coupled numerical simulation. Fuel 139:51–58

    Article  Google Scholar 

  • Visher GS (1969) Grain size distributions and depositional process. J Sediment Petrol 39:1074–1106

    Google Scholar 

  • Yarus JM, Chambers RL (2006) Practical geostatistics—an armchair overview for petroleum reservoir engineers. J Pet Technol 58(11):78–88

    Article  Google Scholar 

  • Zhang D, Wang J, Li M (2001) Characterization of block Wei 2 reservoir with gas-cap and bottom-water. J Jianghan Pet Inst 23(4):45–47

    Google Scholar 

  • Zhang SJ, Shao LY, Song J (2008) Application of facies controlled modeling technology to the fault-block A11 in A’nan Oilfield. Pet Explor Dev 35(3):355–361

    Article  Google Scholar 

  • Zhou CC, Wang YJ, Zhou FM (2006) Modeling and application of the lithofacies-controlled primary pore reservoir of proximal sandstone. Pet Explor Dev 33(5):553–557

Download references

Acknowledgments

This paper is supported by the Specialized Research Fund for the Doctoral Program of Higher Education (20110003110014) and Shandong province postdoctoral innovation project special funds (no. 201306069). The authors thank SINOPEC Zhongyuan Petroleum Exploitation Bureau Geoscience Institute for supplying researching data and offering convenience for observing the cores.

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Correspondence to Jinliang Zhang.

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Liu, L., Zhang, J., Wang, J. et al. Geostatistical modeling for fine reservoir description of Wei2 block of Weicheng oilfield, Dongpu depression, China. Arab J Geosci 8, 9101–9115 (2015). https://doi.org/10.1007/s12517-015-1924-2

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  • DOI: https://doi.org/10.1007/s12517-015-1924-2

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