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吴进波. MRGC方法在储层分类及渗透率精细评价中的应用[J]. 海洋石油, 2017, 37(1): 49-53. DOI: 10.3969/j.issn.1008-2336.2017.01.049
引用本文: 吴进波. MRGC方法在储层分类及渗透率精细评价中的应用[J]. 海洋石油, 2017, 37(1): 49-53. DOI: 10.3969/j.issn.1008-2336.2017.01.049
WU Jinbo. Application of Reservoir Classification and Permeability Elaborate Evaluation with MRGC Method[J]. Offshore oil, 2017, 37(1): 49-53. DOI: 10.3969/j.issn.1008-2336.2017.01.049
Citation: WU Jinbo. Application of Reservoir Classification and Permeability Elaborate Evaluation with MRGC Method[J]. Offshore oil, 2017, 37(1): 49-53. DOI: 10.3969/j.issn.1008-2336.2017.01.049

MRGC方法在储层分类及渗透率精细评价中的应用

Application of Reservoir Classification and Permeability Elaborate Evaluation with MRGC Method

  • 摘要: 常规储层渗透率评价方法,忽视了砂层内部不同流动单元渗流特征的差异,导致渗透率评价精度误差较大。此文依据渗流力学原理,从修正的Kozeny-Carman方程入手,利用三个能综合反映不同孔喉特征的储集物性参数将储层分为六类,建立了以流动单元指数FZI值的分类标准,通过公式推导提出了以FZI值为斜率划分流动单元的新思路。并基于图像多分辨率聚类分析方法(MRGC)建立测井曲线数据与岩心FZI值的外推使用模型。通过分析不同类型储层孔隙度与渗透率的相关性,分别建立了不同流动单元的渗透率精细评价模型。研究表明,与传统的回归方法相比,计算精度明显提高,从方法理论和可操作方面具有较强实用性和区域推广应用性。

     

    Abstract: The conventional reservoir permeability evaluation methods often ignore the difference in seepage characteristics for different flowing units within sand layer, resulting in large precision error during permeability evaluation. In this paper, on the basis of seepage mechanics principle, from the Kozeny-Carman equation of correction, we divide the reservoir into six types by using three reservoir physical parameters which can comprehensively reflect the different pore and throat characteristics of reservoir, and establish classification standard with the flow unit index FZI value. Derived by formula, the new idea of dividing flow unit with FZI value as slope was proposed. With image multi resolution clustering analysis method (MRGC), an extrapolate model was established with well log data and FZI of core values. Through analyzing the correlation of reservoir porosity and permeability for different reservoir types, fine permeability evaluation model for different flow units are established. The study results show that the calculation precision is improved obviously compared with the traditional regression method, with stronger practicability and popularized application from the aspects of theory and operational method.

     

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