用多尺度形态学方法实现成像测井电导率图像的缝洞参数表征

裂缝和溶蚀孔洞为缝洞型储层提供了主要的储集空间和渗流通道,其分布的随机性和复杂性严重影响了对缝洞储层的定量评价。基于高覆盖率和高分辨率的电成像测井数据,采用多尺度数学形态学方法提取电导率图像中的缝洞孔隙度谱。选择不同尺度和形状的结构元素,构造不同种类的形态学滤波算子,实现井壁裂缝和溶蚀孔洞的电导率异常边缘检测。针对缝洞异常的边缘检测结果,用椭圆形函数拟合溶蚀孔洞,用多项式插值函数拟合裂缝边缘,继而提取缝洞参数并获得缝洞孔隙度谱。实验结果表明,用多尺度数学形态学方法对电导率图像的边缘检测有效地实现了缝洞的自动识别,验证了该方法计算缝洞孔隙度谱的准确性。

Characterization of reservoir fracture and vug parameters by conductivity image of FMI based on multi-scale mathematical morphology method

Fractures and solution pores provide the main reservoir space and seepage channels for fracture-vug reservoirs. However, due to their random and complex spatial distribution, it is difficult to obtain quantitative reservoir evaluation. Based on the formation microscanner image (FMI) data which has both complete coverage and high resolution, a multi-scale mathematical morphology method was proposed to derive the fracture-vug porosity spectrum from conductivity images. In order to implement the edge detection of the conductivity anomalies that are caused by fracture and solution pore, the structure elements of different scale and configurations were selected to construct various kinds of morphological filtering operators. The edges of fractures were fitted by a polynomial interpolation function while the edges of vug were fitted by an elliptic function. Furthermore, fracture-vug parameters and porosity spectrum were derived from the fitted edges. The results show that the edge detection based on this multi-scale mathematical morphology method can recognize fractures and solution pores automatically, which provides an accurate method for fracture-vug porosity spectrum in FMI processing.