Porosity Inversion under the Sequence Stratigraphy Framework

Wei Yang; Ming Yi Hu

doi:10.4028/www.scientific.net/AMR.989-994.1424

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HomeAdvanced Materials ResearchAdvanced Materials Research Vols. 989-994Porosity Inversion under the Sequence Stratigraphy...

Porosity Inversion under the Sequence Stratigraphy Framework

Abstract:

Carbonate reef reservoir buried deeply, show great heterogeneity, Therefore, effective reservoir prediction is the key to the biohermal reservoir. In practical research, based on of the single well sequence stratigraphic framework, combined with these sequence stratigraphic framework and seismic section, found the reservoir development area in the longitudinal, which sq1-HST and sq3-HST is the main period of reservoir development, and then through the seismic Seismic multiple-attribute inversion, predicted porosity of system tracts of reservoir development in the sequence stratigraphic framework. Porosity inversion results shown, Changxing formation reservoir is mainly distributed in the central and north-central of Jiannan area.

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Periodical:

Advanced Materials Research (Volumes 989-994)

Pages:

1424-1427

DOI:

https://doi.org/10.4028/www.scientific.net/AMR.989-994.1424

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Online since:

July 2014

Authors:

Wei Yang*, Ming Yi Hu

Keywords:

Neural Network (NN), Porosity, REEF, Seismic Multiple Attributes, Sequence Stratigraphy

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References

[1] Jing Peng-gui. Reservoir prediction technology of reef-flat facics in northeast Sichuan province and its application. GPP, 2007, 46(4): 363-369.

Google Scholar

[2] Vail P R. Seismic stratigraphy interpretation using sequence stratigraphy, Part 1: Seismic Stratigraphy Interpretation Procedure. in Rally A W, ed, atlas of Seismic Stratigraphy; AAPG Studies in Geology, 1987, 27(1): 1-10.

DOI: 10.1306/bf9ab166-0eb6-11d7-8643000102c1865d

Google Scholar

[3] Ling Yun, Sun D-sheng, Gao Jun et al. Interpretation of depositional bodies in the parasequcnce sets from 3-D seismic data. GPP, 2006, 44(6): 568-578.

Google Scholar

[4] Russell B H. The application of multivariate statistics and neural networks to the prediction of reservoir parameters using seismic attributes[D]. Calgary: Department of Geology and Geophysics, Canada, (2004).

Google Scholar

[5] Liu S, Guo X S, Ma Z J, et al. Seismic response characteristic and hydrocarbon exploration perspertivc of reef flat fades[J]. Geophysical Prospecting for Petroleum, 2006, 45(5): 452-458.

Google Scholar

[6] Cai Hanpeng, He Zhenhua, Huang Deji. Study on method for prediction of coral shoal facies oil-gas reservoir[J]. Oil Geophysical Prospecting, 2008, 43(6): 685-688.

Google Scholar