基于贝叶斯分类的图像分析方法在孔隙结构参数表征中的应用——以姬塬油田长9油层组为例

Application of image analysis based on Bayesian classification in characterization of pore structure parameters:A case study of Chang9 oil layer in Jiyuan Oilfield

致密砂岩储层具有低孔、低渗透及非均质性强的特点,造成其孔隙结构复杂。深入研究孔隙结构参数对提高低渗透储层的油气采收率、改善储层开发效果具有重要意义。岩石铸体薄片分析是研究孔隙结构最基本的方法,但其通过人工鉴定,随机误差较大且耗时费力。为充分挖掘岩石铸体薄片中丰富的孔隙结构参数信息,选取姬塬油田长9油层组6个岩石铸体薄片样本,采用基于RGB彩色空间的贝叶斯分类方法,根据得到的高信噪比孔隙-骨架二值化图像进行孔隙提取,并通过统计学方法获得样品的孔隙直径、孔隙形状因子和孔隙度。基于贝叶斯分类的图像分析方法计算的孔隙度与实测的孔隙度和渗透率呈较好的线性关系;与压汞法测试的结果对比,二者也具有较高的相关性,相关系数超过0.8。因此,该方法可以得到较准确的孔隙结构参数,提升了岩石铸体薄片图像分析的效率,是针对致密砂岩储层孔隙结构参数表征的有效方法。

The characteristics of low porosity,low permeability and strong heterogeneity in tight sandstone reservoirs make the pore structure of rocks complicated.Intensive study on pore structure parameters is of great significance to improve oil and gas recovery and reservoir development for the low permeability reservoirs.The rock thin section analysis is the most basic way to analyze the pore structure.The method is a manual approach,which has the shortage of large random error and time-consuming.In order to fully exploit the abundant information of the pore structure in the rock thin section,six samples from Chang9oil layer in Jiyuan Oilfield were selected to obtain pore structure parameters based on the pore extracted from binarization image with high SNR of pore-skeleton.Through the Bayesian classification method based on the RGB color space model,the parameters such as pore,pore shape factors and porosity were obtained through statistical methods.The calculated porosity by method of image analysis based on Bayesian classification is in linear agreement with the measured porosity and permeability.At the same time,we can concluded that there is a high correlation coefficient(above0.8)between the pore structure parameters obtained by the method above and the mercury penetration.The calculated results show that this method could obtain more accurate pore structure parameters,which improves the efficiency of rock image analysis.This method provides an effective approach for the characterization of the pore structure in the tight sandstone reservoir.