Volume 6, Issue 5, September 2018, Page: 88-95
Application of Reservoir-Predicting Technique to Thin Sandstones in Coal-Bearing Strata in Block K
Liu Leisong, Geological Research Center, Bureau of Geophysical Prospect, Zhuozhou, China
Chen Zhigang, Geological Research Center, Bureau of Geophysical Prospect, Zhuozhou, China
Chen Jie, Geological Research Center, Bureau of Geophysical Prospect, Zhuozhou, China
Ma Hui, Geological Research Center, Bureau of Geophysical Prospect, Zhuozhou, China
Sun Xing, Geological Research Center, Bureau of Geophysical Prospect, Zhuozhou, China
Wang Yuzhu, Geological Research Center, Bureau of Geophysical Prospect, Zhuozhou, China
Han Yuchun, Geological Research Center, Bureau of Geophysical Prospect, Zhuozhou, China
Received: May 30, 2018;       Accepted: Aug. 9, 2018;       Published: Sep. 10, 2018
DOI: 10.11648/j.ogce.20180605.12      View  348      Downloads  20
Abstract
In Block K of South Turgay Basin in central Kazakhstan, the development of target, Aibalin, is controlled by the boundary of graben (especially rift-type stratigraphy-lithology assemblage). The Aibalin Fm is mainly developed with delta and lakeshore swamp facies, and composed of grey sandstone, siltstone, shale and coal-bearing strata, with extensive carbonized vegetal debris. Moreover, it contains thin and horizontally-variable reservoirs. Coal beds affect seismic survey greatly. Because of the influence of tuning effect in seismic data, thin sandstone reservoir distribution and physical properties cannot be reflected accurately in seismic data. Meanwhile, thin sandstone reservoir cannot be effectively predicted through seismic-based conventional inversion methods and processes. In this paper, a new prediction process for thin sandstone reservoir in this block is proposed, contributing to the effective prediction of thin sandstone reservoir distribution and physical properties. Firstly, sensitive parameters for lithology interpretation are defined and lithology interpretation template was established, through comprehensive analysis of drilling, logging and seismic data. Secondly, seismic wave impedance Bayes inversion genetic algorithm and cloud transform gamma attribute prediction technique are used to derive wave impedance and gamma data volume. Finally, the wave impedance and gamma data volume are combined with lithology interpretation template to predict the physical properties of the reservoirs.
Keywords
Coal Measure Strata, Sensitive Parameter, Genetic Method, Cloud Transform
To cite this article
Liu Leisong, Chen Zhigang, Chen Jie, Ma Hui, Sun Xing, Wang Yuzhu, Han Yuchun, Application of Reservoir-Predicting Technique to Thin Sandstones in Coal-Bearing Strata in Block K, International Journal of Oil, Gas and Coal Engineering. Vol. 6, No. 5, 2018, pp. 88-95. doi: 10.11648/j.ogce.20180605.12
Copyright
Copyright © 2018 Authors retain the copyright of this article.
This article is an open access article distributed under the Creative Commons Attribution License (http://creativecommons.org/licenses/by/4.0/) which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
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