Reservoir model building with multi-data fusion

被引：0
|
作者：
Xia J. [1 ]
Zhang Y. [1 ]
Lü D. [1 ]
机构：
[1] Geophysical Research Institute, Shengli Oilfield Branch Co, SINOPEC, Dongying, 257015, Shandong
来源：
Shiyou Diqiu Wuli Kantan/Oil Geophysical Prospecting | 2016年 / 51卷 / 02期
关键词：
3D seismic; Bayesian-sequential Gaussian simulation; Cross-well seismic; Logging; Multi-data fusion; Reservoir model building;
D O I：
10.13810/j.cnki.issn.1000-7210.2016.02.019
中图分类号：
学科分类号：
摘要：
Based on Bayesian theory, reservoir model building with multi-data fusion takes advantages of logging, 3D seismic and cross-well seismic data to improve the accuracy of reservoir model building. This approach provides a new way for fine reservoir description in the Chinese eastern mature oilfields. The key algorithm of the new approach is Bayesian-sequential Gaussian simulation, which utilizes small amount of high resolution logging and cross-well seismic data as condition data to realizes seismic attribute constraint. This algorithm takes advantages of logging data high vertical resolution and seismic data high horizontal resolution. We build a porosity model of Block K71 with this method. It is found out that in this model cross-well tendency is well matched with constrained data, and vertical resolution is greatly improved. This illustrates advantages of the approach. Applications show that the proposed approach can effectively enhance reservoir prediction. © 2016, Science Press. All right reserved.
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页码：350 / 354
页数：4
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