Abstract
This study examines the connections between various fracture indicators and production data with an example from one of the giant fields in the Middle East producing complex fractured carbonate lithologies. The field under study hosts two reservoirs with a long development and production history, including carbonates from the Asmari and Bangestan Formations. A fracture intensity map was generated based on the interpretation of image logs from 28 wells drilled within the field. Mud loss data were collected and mapped based on the geostatistical Gaussian Random Function Simulation (GRFS) algorithm. Maximum curvature maps were generated based on Asmari structural surface maps. Comparing the results shows a good agreement between the curvature map, fault distribution model, mud loss map, fracture intensity map, and productivity index. The results of image log interpretations led to the identification of four classes of open fractures, including major open fractures, medium open fractures, minor open fractures, and hairline fractures. Using the azimuth and dip data of the four fracture sets mentioned above, the fracture intensity log was generated as a continuous log for each well with available image log data. For this purpose, the fracture intensity log and a continuous fracture network (CFN) model were generated. The continuous fracture network model was used to generate a 3D discrete fracture network (DFN) for the Asmari Formation. Finally, a 3D upscaled model of fracture dip and azimuth, fracture porosity, fracture permeability, fracture length, fracture aperture, and the sigma parameter (the connectivity index between matrix and fracture) were obtained. The results of this study can illuminate the modeling of intricate reservoirs and the associated production challenges, providing insights not only during the initial production phase but also in the application of advanced oil recovery methods, such as thermal recovery.