Application of Reservoir-Predicting Technique to Thin Sandstones in Coal-Bearing Strata in Block K
International Journal of Oil, Gas and Coal Engineering
Volume 6, Issue 5, September 2018, Pages: 88-95
Received: May 30, 2018;
Accepted: Aug. 9, 2018;
Published: Sep. 10, 2018
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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
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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.
Coal Measure Strata, Sensitive Parameter, Genetic Method, Cloud Transform
To cite this article
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.
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.
Shi Juye, Fan Tailiang, et al. Several Typical Seismic Facies in South Turgay Basin and the Geological Meaning. Science Technology and Engineering, 2015, 15(Supplement 34): 133-138.
Yan Geng, Kong Linghong. Lithological trap identification and description technology under sequence stratigraphy framework—Kazakhstan South Turgay Basin Example. Geophysical Prospecting, 2013, 51(Supplement 1): 139-145.
Hu Meilan. Application of Recursive Inversion in STRATA. Petrochemical Industry Application, 2013, 32(Supplement 11): 53-55.
Zhang Junhua, Hou Jing, et al. Theory Annotation and Application of Trace Integration Attribute in The Prediction of Thin Channel Sand Body. Progress in geophysics. 2018, 33(Supplement 1): 326-333.
Fang Yuan, Zhang Fengqi, et al. Generalized Linear Joint PP-PS Inversion Based on Two Constraints. Applied Geophysics. 2016, 13(Supplement 1): 103-115.
Cheng Jianhua, ScottE. Parker, et al. A Second-Order Semi-Implict of Method for Bybrid Simulation. Journal of Computational Physics. 2013, Volume 245, 15 July:364-375.
Du Zeyuan, Wu Guochen, et al. Full Waveform Inversion Based on Well Logging Data Constraint. OGP. 2017, 52(Supplement 6): 1184-1192.
Lan Tian, Gui Zhixian, et al. Improved Particle Swarm Impedance Inversion. Fault-Block Oil & field. 2016, 23(Supplement 2): 176-180.
Qin Jingxin, Hao Tianyao, et al. The Density Interface Inversion Method of Improved Adaptive Simulated Annealing. Progress in Geophysics. 2014, 29(Supplement 5): 2060-2065.
Li Juanjuan, Cui Ruofei, et al. Coalfield Seismic Inversion Using Probabilistic Neural Network. Progress in Geophysics. 2012, 27(Supplement 2): 715-721.
Zhang Jin, An Zhenfang, et al. Elastic Impedance Inversion Based on Chaos and Colony Algorithm. Geophysical Prospecting for Petroleum. 2015, 54(Supplement 6): 716-723.
Yin Bin, Hu Xiangyun. Overview of Nonlinear Inversion Using Bayesian Method. Progress in Geophysics. 2016, 31(Supplement 3): 1027-1032.
Liu Leisong, Gao Jun, et al. Application of Cloud Transforms in Seismic Reservoir Prediction, SEG Houston 2013 Annual Meeting:2480-2484.
Zhang Yucun, Xu Fei, et al. Noise Cancellation Algorithm Method Combinated Priori Probability with Curve Probability Threshold Segmentation. China Mechanical Engineering, 2017, 28(Supplement 8): 936-945.
Xie Fei, Ding Wenlong, et al. Petrophysical Properties of Continental Shale Reservoir and The Influence Factors of Its Gas Contents. Science Technology and Engineering. 2017, 17(Supplement 5): 20-28.
Zhu Qidan, Jin Liqiu, et al. Improved algorithm of minimal error threshold division method. Opto-Electronic Engineering, 2010, 37(Supplement 7): 107-113.
Fu Bin, Li Daoguo, et al. Review and prospect on research of cloud model. Application Research of Computers, 2011, 28(Supplement 2): 420-426.
Yang Zhixiao, Fan Yanfeng. Cloud mapping and membership cloud of mapping. Application Research of Computers, 2012, 29(Supplement 2): 553-556.
Shu Xiao, Song Yongkang, et al. The application of fine variogram analysis in geological statistical modeling. Nei Jiang Science & Technology, 2013(Supplement 10): 74-75.