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Using image logs to identify fluid types in tight carbonate reservoirs via apparent formation water resistivity spectrum

机译:使用图像日志通过表观形成水电阻率光谱识别紧密碳酸盐储层中的流体类型

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Hydrocarbon-bearing zone identification of tight reservoirs plays a crucial role in hydrocarbon exploration and development. However, tight carbonate reservoirs are generally characterized by complex lithology, low porosity, strong heterogeneity, and weak fluid logging responses, which present a major challenge to detect hydrocarbon-bearing zones using conventional methods. To address this challenge, this paper develops a new method defined as R(wa )spectrum, taking advantage of the high vertical resolution and borehole coverage of the image logs. The R-wa spectra can be derived from the histogram distribution of the apparent resistivity values of formation water (R-wa) in a sliding window, combining the Simandoux equation and the triple-porosity model. The results show that a very broad R-wa spectrum with long tails corresponds to hydrocarbon-bearing zones, and the peak shape of it is flat. In contrast, the narrow R-wa spectrum corresponds to water-bearing zones, and the peak shape of it is a sharp spike. Furthermore, two R-wa spectrum parameters, namely AVERAGE (reflects the size of main peak values) and VARIANCE (reflects shape changes of the R-wa spectra) are defined to quantificationally detect hydrocarbon-bearing zones. For oil zones in Yingxi field, the AVERAGE had a value higher than 10 and the VARIANCE was higher than 10. And for water zones, the AVERAGE had a value lower than 4.5 and the VARIANCE was lower than 5. The resistivity values decrease linearly with the increase in pyrite content of the reservoirs of high pyrite content, which disturbs the response to resistivity values from different fluid types (oil, gas or water). Hence, this method is suitable for the formation of no or little pyrite contents (lower than 0.2), and the case where the salinity value of drilling mud is similar to that of formation water. Moreover, the method has been validated by production test data, providing unusual perspectives on the identification of fluid types in tight carbonate reservoirs.
机译:余储层的碳氢化合物区识别在碳氢化合物勘探和发展中起着至关重要的作用。然而,紧密碳酸盐储存器通常以复杂的岩性,低孔隙率,强的异质性和弱流体测井反应为特征,这提出了使用常规方法检测烃区的主要挑战。为了解决这一挑战,本文开发了一种定义为R(WA)频谱的新方法,利用了图像日志的高垂直分辨率和钻孔覆盖范围。 R-WA光谱可以从滑动窗口中的形成水(R-WA)的表观电阻率值的直方图分布来源,组合Simandoux方程和三孔隙度模型。结果表明,具有长尾部的非常宽的R-WA光谱对应于含烃区,其峰值是平的。相反,窄的R-WA光谱对应于含水区,并且它的峰值形状是尖锐的尖峰。此外,两个R-WA频谱参数,即平均值(反映主峰值的尺寸)和方差(反射R-WA光谱的形状变化)以定量检测碳氢化合物区域。对于榕树领域的石油区,平均值高于10,方差高于10.对于水区,平均值低于4.5,方差低于5.电阻率值与差异降低高硫铁矿含量的储层含量的增加,这使得响应不同流体类型(油,气体或水)的响应。因此,该方法适用于形成NO或小的硫铁矿内容物(低于0.2),以及钻井泥浆的盐度值类似于地层水的情况。此外,该方法已经通过生产测试数据验证,提供了对碳酸盐储层中流体类型的不寻常的视角。

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