Comparison of Edge Detection Algorithms for Automatic Identification of Fractures in Hydrocarbon Reservoirs with Image Logs

Considering the effect of fractures in increasing hydrocarbon recovery, the study of reservoir rock fractures is of particular importance. Fractures are one of the most important fluid flow paths in carbonate reservoirs. Image logs provide the ability to detect fractures and other geological features and reservoir layers. In this study, two approaches were used to detect fractures using FMI image log in two wells A and B located in one of oilfields in southwest of Iran. In the first stage, the correction and processing of the FMI raw data were carried out to identify the number and position of fractures, as well as the dip, extension, classification, and density of fractures. In the second step, by considering that the fractures possess the edges in the FMI images, various edge detection filters such as Prewitt, Canny, Roberts, LOG, Zero-cross and Sobel were applied on the image data, and then, their performances for identification of fractures were compared. Finally, the automatic identification of fractures was done by applying the Hough transform algorithm and the results showed that Canny algorithm was the best option to perform Hough transformation. The comparison of the efficiency of the above-mentioned edge detection filters for identification of fractures, and more importantly, the automatic identification of fractures using the Hough transform algorithm can be considered as the novelty of this research work.