Publications

Knowledge of reservoir rock porosity and permeability are essential elements of competetent reservoir simulation and management. Vertical and horizontal heterogeneities are critical components of reservoir characterization and are among the key input parameters into three-dimensional geological and flow simulation models. A previous study based upon Keshtkar (2010) has been carried out to predict permability and rocktype of a well in a gas field in Iran using fuzzy C-means clustering method (FCM). [A critical review of that paper will be presented in this paper according to the use of fuzzy C-means clustering method.] By using another method of fuzzy clustering, fuzzy subtractive clustering algorithm (FS), the authors attempt to present a formula to predict carbonate rock type. The result will be compared to the results of applying fuzzy C-means clustering method. FS is used when there is no clear idea how many clusters there should be for a given data set. This method, however, strictly depends on the value of accepted ratio, rejected ratio, influence range and squash. The fuzzy subtractive method, theoretically, has the effect of reducing the number of computations significantly, making it linearly proportional to the number of input data instead of being exponentially proportional to its dimension. The result of rock type determination using this method resulted in circle clustering. Hence, this method would be suitable for heterogeneity grouping rather than equation-based trend of porosity-permeability grouping. Keywords: Rock type, Fuzzy subtractive algorithm, Carbonate rock