Exploring the potential of extracting seismic attributes from image logs for enhanced fracture characterization

Image logs aid in accurate fracture network characterization by providing detailed information about natural fractures in reservoirs. Moreover, larger-scale fracture mapping and detection can be accomplished by extracting seismic attributes offering important insights into the distribution of reservoir fractures. Through the extraction of seismic attributes from image logs the current study offers a novel method for locating naturally occurring fractures in a carbonate reservoir of southwest Iran. Extending the application of seismic attributes to image log data is made possible for the first time in this study by using data from electrical and ultrasonic image logs converted from DLIS to SEGY format. After converting the format of the image logs, seismic attributes were extracted and assessed for possibility of enhanced image log interpretation. To examine and compare the results of various attributes, a total of 23 structural, stratigraphic, and signal processing attributes were derived. The newly tested attributes, including the iso-frequency component, RMS amplitude, and variance (edge method), showed improved performance in identifying fractures. However, the iso-frequency component attribute was found to be very powerful in emphasizing the fractures among the different attributes examined. The findings of this study show how seismic attributes can be used to detect the natural fractures based on image logs in a reservoir resulting in more accurate fracture detection and modeling.