Porosity Classification of Carbonate Rocks with Fuzzy Logic Methods by Using Well Log Data

Intelligent computing approaches are recently utilized for interpretation tasks In well-logging. This is mainly because of the necessity of processing well logs where no complete data containing e.g. core data are available. In this case, we use fuzzy logic for classifying the porosity types by using the well log data of the studied field located in the Iranian offshore of Persian Gulf. The classification of porosity types was based on known classification of log data belonged to wells nO.1 and no.2 into primary, cavernous and micro-fractures porosity classes. Each fuzzy class was regarded as a union of several fuzzy granules that each granule was also obtained by the intersection of correspondent membership functions. The analysis of the achieved results reveal that the fuzzy logic approach we developed can compensate for the absence of exact information with maintaining accuracy of data analysis and decreasing costs at the same time.