The first and probably the most important part of any ore reserve feasibility study are the simulation and estimation of the ore deposit. The uncertainty associated with physical and geometrical parameters of ore deposits and consequently the risk of mineral exploration stage could be quantified using geostatistical simulations. These measures are fundamental in determining the precision of financial and economical assessments normally made for the entire mine life. The main objective of current study is to evaluate the estimated reserve of Jian (Bavanat) vein type copper using different geostatistical methods including ordinary kriging, indicator kriging and sequential Gaussian fractal simulation (SGFract) through comparing the established grade- tonnage curves. Jian vein copper deposit is located close to the Jian village which is about 185‌km northeast of Shiraz in Fars province. The mineralization has occurred in two main high grade copper veins with 2-10 meters width and about 400 meters along their strikes. To observe the unbiasedness property required by all geostatistical ore reserve estimators, the deployed core sample data should be composited to the optimum length size, forming an equal probability sampling space. Therefore in the first step of current study, the available dataset were composited and it was declared that its probability distribution still represent the original raw sample data distribution function. Also the dataset were statistically tested for normal distribution and the results showed the presence of a strong skewness and since in most linear geostatistical estimation methods, having normal distribution is a necessity, the data were transformed to their normal scores. Through plotting Cu grades over the coordinate axis, it was found that the copper grade does not show any significant trend along main coordinate axis. The main directions of anisotropic axis were detected using 3D covariance maps. The geometrical anisotropy ranges and precise rotation angels were determined through plotting directional and non-directional variograms. Using cross validation technique, in the final pre-processing step, the optimum block size for all 3D ordinary kriging, 3D Median indicator kriging and 3D sequential Gaussian fractal simulations was determined to be 5*4*2 meters with a sub cell discretization of 3*3*3 in the X, Y and Z directions respectively. In an attempt for using multiple indicator kriging technique, the indicator variograms for seven different cutoffs were computed. Unfortunately, these variograms couldn't reflect the actual spatial structure of the deposit due to the lack of enough sample data, and therefore it was decided to use median indicator kriging as the best possible substitute. Based on the fact that fractal simulation algorithms are powerful methods for modelling vein type deposits, a Sequential Gaussian Fractal Simulation algorithm named SGFract was modified and employed for 3D reserve estimation of Jian deposit. By comparing the Grade – tonnage curves obtained by using the above mentioned geostatistical methods, it was concluded that the fractal simulation method outperforms the ordinary and indicator kriging methods in modeling this deposit structurally. The results of fractal method have a better agreement with the known geology of the Jian vein type copper deposit in almost all elevations. The other significant advantage of the fractal method over the ordinary and indicator kriging is the ability to extrapolate the vein structure in the regions where no drilling data are available. Also the comparison of copper grade-tonnage curves showed that the model which is based on the fractal algorithm have more correspondence with the reality of the Jian copper vein deposit, whereas the median indicator kriging has under-estimated the veins grade due to using low cutoff grade value.