8eefdfbd-365f-4a09-b0d5-9d1d66de7de720231214050630164wseas:wseasmdt@crossref.orgMDT DepositEARTH SCIENCES AND HUMAN CONSTRUCTIONS2944-90062944-915410.37394/232024https://wseas.com/journals/eshc/312023312023310.37394/232024.2022.3https://wseas.com/journals/eshc/2023.phpZohrehMovahedSchlumberger, Kuala Lumpur, MALAYSIAMeisamAshrafAhawz Oil and Gas Research Department, IRANAli AsgharMovahedUniversity of Bergen, NORWAYCharacterizing fracture properties in naturally fractured reservoirs poses a significant challenge. While welltesting remains valuable, it often fails to provide precise descriptions of these properties. Bridging this gap requires the integration of geological expertise to enhance fracture assessment. This study addresses the limitations of well-test analysis and explores the application of Conventional Image Logs in structural, fracture, and geomechanical analysis. However, effectively combining these applications with well-test analysis on a field scale reveals a substantial knowledge gap. A critical challenge in this context is the absence of a defined procedure for calculating the variable "σ," a crucial parameter for simulating fractured carbonate reservoirs using image log fracture density. Integrating geological knowledge is essential to reduce uncertainties associated with well-test analysis and provide more accurate characterizations of fracture properties. Image log data processing emerges as a valuable avenue for gaining insights into the static attributes of naturally fractured reservoirs. This study focuses on Characterizing fractures using data from ten image logs and Developing a more accurate simulation model through the interpretation of images, with a particular emphasis on OBM imaging. The main goals of this fracture study revolve around establishing correlations between fracture densities well by well within the simulation and enhancing the accuracy of the simulation model by incorporating fracture data from image logs. Borehole imaging tools such as FMI/FMS and OBMI-UBI play a pivotal role in identifying significant structural features, including faults, fractures, and bedding. Fine-tuning fracture parameters during the history matching process, while potentially time-consuming, significantly impacts other historical match parameters. Consequently, the reliability of reservoir simulation results, predictions, and recovery enhancement strategies hinges on the precision of fracture properties and their distribution within the model. Recent advances in interpretation techniques have expanded the horizons of image interpretation, enabling the creation of more accurate simulation models for fractured reservoirs using fracture data obtained from image logs. The overarching goal of this project is to comprehensively evaluate a fractured reservoir field by integrating data from ten individual wells.1214202312142023991189https://wseas.com/journals/eshc/2023/a18eshc-006(2023).pdf10.37394/232024.2023.3.9https://wseas.com/journals/eshc/2023/a18eshc-006(2023).pdf10.1007/s12517-015-1951-zMovahed, Z., Junin, Amiri Bakhtiary, H., Safarkhanlou, Z., Movahed, A., Alizadeh, M. (2015). Identification of a Sealing Fault in the Asmari Reservoir Using FMI and RFT in an Iranian Naturally Fractured Oil Field. Arabian Journal of Geoscience, Volume 8, Issue 12, Pages 10919-10936. 10.3997/2214-4609.20145945Eynollahi, A. (2009). Microfacies and Sedimentary Environment of the Asmari Formation in Lali Oil Field, NW Masjed-eSoleyman. 10.2523/iptc-14399-msChokthanyawat, S., Daungkaew S., Athichanagorn, S. (2012). Well, Productivity Prediction for Laminated Reservoir Using Borehole Electrical Image Logs. 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