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东营凹陷牛庄洼陷超压特征及预测模型 被引量:12

Characteristics and Prediction Model of the Overpressures in the Niuzhuang Sag of Dongying Depression
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摘要 牛庄洼陷是东营凹陷的一个典型超压单元,明确超压分布特征需要研究泥岩超压的预测模型。基于测井和钻杆测试(DST)资料,分析了牛庄洼陷现今压力特征和3口典型井泥岩超压段的测井响应特征。分析表明,牛庄洼陷超压主要发育在2100m以下的沙河街组第三段和第四段,超压泥岩段的测井响应特征呈现多样化,主要表现在视电阻率测井响应的不唯一性,这种不唯一性可能主要与特殊岩性如油页岩等有关。采用Eaton公式利用泥岩声波时差换算出泥岩压力,建立了泥岩计算压力与储层实测压力之间的关系模型,即泥岩超压预测模型,通过超压砂、泥岩声波时差异常幅值的对比分析了该预测模型的可靠性,并开展了误差分析。研究认为,利用泥岩声波测井资料,通过该模型能够预测牛庄洼陷泥岩超压层空间分布的大致轮廓。 Niuzhuang Sag is a typical overpressured unit in Dongying Depression and so it is necessary to establish a prediction model of the shales in order to understand its spatial distribution characteristics. This paper studies the characteristics of the present-day overpressure and well-log response within the overpressured shale section from three typical wells based on well-log data, combined with the data of drill stem test (DST). Correlation analysis shows that the overpressures in the Niuzhuang Sag are mainly developed in Shahejie-3 Member and Shahejie-4 Member, with the top burial depth of overpressured zone measuring about 2 100 m. The diversified responses of overpressured shales, mostly presented by the non-uniqueness of resistivity log response, are considered to be related to the special lithologies, such as oil shales. A model of the prediction for calculating overpressures in the shales, a relationship between the measured pressures and the pressures conversed from sonic log using the Eaton's formula, is established. Moreover, the reliability of this model has been discussed by the comparison between the sonic transit times of sandstones and shales, and the error analysis is further analyzed. It is considered that the spatial distribution of the overpresses within shales will be profiled roughly by using the prediction model with the sonic log data of shales in the Niuzhuang Sag.
出处 《地质科技情报》 CAS CSCD 北大核心 2009年第4期34-40,共7页 Geological Science and Technology Information
基金 国家专项基金项目"断陷盆地油气成藏动力系统与驱动机制研究"(2008ZX05000-006)
关键词 超压 预测模型 牛庄洼陷 东营凹陷 overpressure predicted model Niuzhuang Sag Dongying Depression
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