基于电成像测井的碳酸盐岩储层孔隙结构识别新方法

A Novel Identification Method of Pore Structure in Carbonate Reservoirs Based on Electric Imaging Logging

碳酸盐岩储层储集空间复杂,孔隙结构多样,难以准确计算其储层参数。为了实现对碳酸盐岩储层孔隙结构的精细评价,提出一种基于电成像测井的储层孔隙结构识别新方法,实现裂缝与溶蚀孔洞自动识别。针对一维电扣数据,利用自适应形态滤波,实现随机噪声压制和低频地层层理识别,达到增强电成像图中裂缝与溶蚀孔洞的目的。针对二维电成像图,利用主成分标记连通域算法计算形态学属性参数,构建综合裂缝指数,实现裂缝与溶蚀孔洞自动分离。多口井的实测数据结果表明,该方法分离的裂缝与溶蚀孔洞子图像最大程度地保持了它们的原始形态,很好地描述了储集空间的分布,并为精细评价复杂碳酸盐岩储层提供算法支持。

Carbonates always have complex reservoir space and diverse pore structures,so it is difficult to accurately calculate the reservois parameters.In order to the fine evaluation on pore structure in carbonate reservoirs,a novel identification method of pore structure in carbonate reservoirs is proposed to automatically identify fractures and corrusion vugs from the electric imaging logging.For the one-dimensional original conductivity measured by button electrode plates,the adaptive morphological filtering is used to eliminate random noise and identify low frequency stratification,so as to enhance the information of fractures and vugs in the electric logging images.For the two-dimensional electric logging images,the principal component labeling connected region algorithm is applied to calculate the morphological attributes and construct the comprehensive fracture index,which realized the automatic separation of fractures and corrosion vugs.Through testing the measured data of several wells,the results show that the sub-images of separated fractures and corrosion vugs maintain their original morphology to the greatest extent.The novel method can not only accurately describe the distribution of reservoir space,but also provide the algorithm support for the fine evaluation of complex carbonate reservoirs.