Document Type : Original Research Paper
Authors
1 M.Sc., School of Mining Engineering, University of Tehran, Tehran, Iran
2 Professor, School of Mining Engineering, University of Tehran, Tehran, Iran
3 Ph.D. Student, School of Electrical Engineering, Amirkabir University of Technology, Tehran, Iran
4 AssociateProfessor, School of Electrical & Computer Engineering, University of Tehran, Tehran, Iran
5 AssociateProfessor, Faculty of Mining Engineering, Geophysics and Petroleum Engineering, Shahrood University of Technology, Shahrood, Iran
6 Ph.D. Student, Institute of Petroleum Engineering, University of Tehran, Tehran, Iran
Abstract
In the oilreservoirsof the ZagrosBasin, fractures play a major role in hydrocarbon migration and production. Borehole image log is a powerful tool to study and identify fractures around the wells. These logs provide critical information about orientation, depth and type of natural fractures. Since thereis noaccuratealgorithmfor automaticidentification of fracture parametersonimage logs of the carbonatereservoirsin Iran, interpretation of theselogsisoftendone manually. This process may become erroneous if the interpreter is not sufficiently experienced. Aimed at automatic detecting of fractures in image logs, this paper presents a new implemented method, which is based upon image processingandoptimization techniques,as well as Artificial Bee Colony Algorithm. According to this approach, points related to fractures arefirst extracted from images using classification methods. Then, the Artificial Bee Colony Algorithmis used to determine the number, depth, dip and dip directionof fractureson extracted points. The proposed method is performed on FMS image log ofonewell located in an oilfield in southernIran. Results areshownindensity log, rose diagramandstereogramfor the identified fractures, and the obtained resultsshow efficiency of the proposedmethod.
Keywords
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