一种基于电成像测井图像孔隙度自动提取计算新方法
A New Method of Porosity Automatic Extraction and Calculation Based on Electrical Imaging Logging Image

致密碳酸盐岩储层孔隙结构复杂，发育类型多，孔隙度难以精确预测。通过对电成像测井图像进行处理，采用图像处理阈值分割技术对图像采集时的偏差进行校正，并提出一种新的计算方法提取图像灰度值，并通过灰度值与常规测井电性参数的相关性来对不同算法的优度进行评估，最后根据灰度值与实测孔隙度存在的相关性建立回归模型对某区段致密碳酸盐岩孔隙度进行预测。研究结果表明，采用散点拟合法针对致密碳酸盐岩结构非均质性较强、储层类型多样等特点具有较强的适应性，对孔隙度进行提取优于其他算法。该方法不仅预测更加精确致密，也能高效的完成孔隙度解释工作，为致密碳酸盐岩储层孔隙度预测探索了一条新的途径。
The pore structure of tight carbonate reservoir is complex and there are many types of development, so it is difficult to accurately predict the porosity. Through the processing of electrical imaging logging image, the image processing threshold segmentation technology is used to correct the deviation of image acquisition, and a new calculation method is proposed to extract the gray value of image, and the advantages of different algorithms are evaluated by the correlation between gray value and conventional logging electrical parameters. Finally, the regression model is established according to the correlation between gray value and measured porosity. The model is used to predict the porosity of tight carbonate rocks in a section. The results show that the scatter fitting method has strong adaptability to the characteristics of strong structural heterogeneity and diverse reservoir types of tight carbonate rocks, and it is superior to other algorithms in porosity extraction. This method can not only predict more accurately and compactly, but also complete porosity interpretation efficiently, which provides a new way for porosity prediction of tight carbonate reservoir.