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赵钊, 王身建, 张延充, 李金芝. 川东地区长兴组生物礁有利相带预测技术研究[J]. 海洋石油, 2014, 34(1): 41-45. DOI: 10.3969/j.issn.1008-2336.2014.01.041
引用本文: 赵钊, 王身建, 张延充, 李金芝. 川东地区长兴组生物礁有利相带预测技术研究[J]. 海洋石油, 2014, 34(1): 41-45. DOI: 10.3969/j.issn.1008-2336.2014.01.041
ZHAO Zhao, WANG Shenjian, ZHANG Yanchong, LI Jinzhi. Study on the Technology for Prediction of the Favorable Reef Facies Belt in Changxing Formation in the East of Sichuan[J]. Offshore oil, 2014, 34(1): 41-45. DOI: 10.3969/j.issn.1008-2336.2014.01.041
Citation: ZHAO Zhao, WANG Shenjian, ZHANG Yanchong, LI Jinzhi. Study on the Technology for Prediction of the Favorable Reef Facies Belt in Changxing Formation in the East of Sichuan[J]. Offshore oil, 2014, 34(1): 41-45. DOI: 10.3969/j.issn.1008-2336.2014.01.041

川东地区长兴组生物礁有利相带预测技术研究

Study on the Technology for Prediction of the Favorable Reef Facies Belt in Changxing Formation in the East of Sichuan

  • 摘要: 川东地区长兴组生物礁为四川盆地海相碳酸盐岩主要含气层,生物礁有利相带预测技术的研究对生物礁储层的勘探生产有重要的指导意义。通过对川东地区长兴组生物礁有利相带预测技术的研究所得到的结果表明:目前地震属性分析技术和时差分析技术为生物礁有利相带预测中最常用和最有效的关键技术;层拉平分析技术为生物礁有利相带预测的辅助技术,能近似反映古沉积环境;地震谱分解技术能够精细地刻画相带边界,但是对资料信噪比要求较高。

     

    Abstract: The reefs of Changxing Formation in the East of Sichuan are the main gas bearing layers of marine carbonate rocks in Sichuan Basin. Therefore, it is very significant to conduct study on the techniques for prediction of favorable reef facies belt dur-ing exploration and production of the reef reservoir. Through analysis of the techniques for prediction of the favorable reef facies in Changxing Formation in the East of Sichuan, it is revealed that the technique of seismic attribute analysis and the technique of step out time analysis are the key techniques, being the most commonly used and most effective techniques for prediction of favorable reef facies belt. The technique of layer flattening analysis is an assisting technique, which can be used to study approximately the an-cient sedimentary environment. The technique of seismic spectrum decomposition can be used for depicting precisely the belt bound-ary, but the requirements for signal-to-noise ratio (SNR) of seismic data is high.

     

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