叠前地质统计学反演技术在复杂储层量化预测中的应用

doi:10.11743/ogg20130618

Abstract

Abstract: The deterministic inversion technology and the post-stack geo-statistics inversion technology have been widely adopted in prediction of tight clastic reservoirs.However,they are not very effective in prediction of thin reservoirs with complex lithologic structure and wave impedance.The combination of the simultaneous inversion technology and the pre-stack geo-statistics inversion technology is effectively applied in the quantitative prediction of such reservoirs.The application of these technologies can not only improve the vertical and horizontal resolution but also can recognize effective re-servoirs even when the impedances of surrounding rocks and reservoirs are overlapped.Although the pre-stack simulta-neous inversion technology can extract P-wave impedance,S-wave impedance,v_P/v_S and density,and can recognize reservoirs with complex impedance,it has limited vertical resolution due to the limitation of frequency band.Stochastic mode-ling-based pre-stack geo-statistics inversion technology can effectively integrate geological,logging and 3D seismic data,greatly improve both vertical and lateral resolutions,thus can realize fine description of reservoirs.Based on deterministic pre-stack inversion,this technology can effectively identify thin reservoirs and thin interbedded reservoirs.It has been successfully applied to the prediction of reservoirs in the 4^th Member of Xujiahe Fm in Xinchang gas field of western Sichuan Basin, a continental tight clastic gas field.

Key words: resolution, deterministic inversion, stochastic simulation, pre-stack geo-statistics inversion, complex reservoir, quantitative prediction of reservoirs

CLC Number:

TE122.2

Qian Yugui, Ye Tairan, Zhang Shihua, Wang Yan, Zhan Xin, Yin Yingzi. Application of pre-stack geo-statistics inversion technology in quantitative prediction of complex reservoirs[J]. Oil & Gas Geology, 2013, 34(6): 834-840.

References

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Application of pre-stack geo-statistics inversion technology in quantitative prediction of complex reservoirs

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