عنوان مقاله [English]
نویسندگان [English]چکیده [English]
Many of the problems faced in engineering and science can be effectively modeled mathematically. However, in constructing these models many assumptions have to be made which are often not true in the real world.For some applications, the sets that will have to be defined are easily identifiable. For other applications, they will have to be determined by knowledge acquired from an expert or group of experts. Once the names of fuzzy sets have been established, one must consider their associated membership functions. Development of this idea has led to many successful implementations of fuzzy logic systems, also called Fuzzy Inference Systems (FIS). A Fuzzy Inference System is a system that uses fuzzy sets to make decisions or draw conclusions.
The approach adopted for acquiring the shape of any particular membership function is often depend on the application. In some applications, membership functions must be selected directly by a `statistical' approach or by an automatic generation of shapes. The determination of membership functions can be categorized as being either manual or automatic. The manual approaches rely only on the experience of an expert. All of the 'manual' approaches suffer from the fact that they rely on subjective interpretation of words.
A new indirect fracture detection technique called Fuzzy Logic Integrated System (FLIS) from well logs is presented in this paper. The FLIS can be widely used for fracture detection with high precision in comparison with image logs in zones of interest. This method is very suitable for multiple well logs, where changes in the log- shapes are affected by the fractures. Therefore, the above method should be used correctly. Fuzzy membership of the log data serves also as an indicator for the classification of results and provides valuable information concerning the reliability of the fracture zones.
The procedures of executing the fuzzy logic are as follows: First, based on the RockLog program (Ghassem Alaskari, 2005), the well log data on each zone of interest are analyzed and plotted in the log format. Second, anisotropic parameters necessary for the evaluation of highly fractured zones from image logs are determined and compared with the full data set. Third, using FLIS algorithm written for this purpose, fractures can be identified in each zone of interest. Fourth, the comparison between the results given in the third step with the core samples at the same intervals (the fracture density and fracture types) in each zone can be identified.
The above procedure has been used successfully for determining fractured reservoir zones in the South-Pars field from an open-hole well log data. A comparison between core samples and image logs was done for the same intervals detected by this technique. As described earlier, a fuzzy set is fully defined by its membership function. How best to determine the membership function is the main question that needs to be addressed. The degree of applicability of this technique is checked by image logs and core samples for the same region, where a full well data was available.