Accurate in-situ oil reserve estimation is one of the main goals in developing reservoirs in petroleum upstream industry. To achieve such goals the newly developed algorithms named under multiple-point geostatistics (MPG) is becoming industry standard among petroleum geoscientists. The main advantage of.applying MPG is to integrate all possible and available data for simulating unsampled nodes thorough the reservoir spatial extent. The main purpose of this study is to reconstruct the spatial distribution of petrophysical parameters from one of the well known Iranian south oil fields using modern geostatistical tools specially MPG algorithms in order to maximize the correspondence of geological patterns existing in related hard and soft data in the estimation process. Therefore, at the first step, using 32 wells taken from one of oil reservoir of south IRAN oil fields, the lithologic model was constructed through conventional Sequntial Indicator Simulation algorithm (SISIM) and Single Normal Equation Simulation of Multiple-point algorithm (SNESIM). Comparison between reproduced lithologic models obtained from SNESIM realizations are better reconstruction of actual well core data. Next the Direct Sequential Simulation algorithm (DSSIM) was used to recover the spatial distribution of petrophysical parameters. The E-type estimation of DSSIM realizations were used as Training Images (TI) for Filter-based Simulation algorithm (FILTERSIM). In the second step the petrophysical parameters were estimated through adopting two different approaches: 1- in the first approach the SNESIM E-type model was inputed as soft data to the FILTERSIM algorithm and 2- in the second approach the SISIM E-type Model was set as Soft data for FILTERSIM algorithm. Also petrophysical parameters was simulated on the whole reservoir domain through Sequential Gaussian Simulation 2-point algorithm (SGSIM). To assess the performance of the above mentioned approach in simulating lithology and petrophysical parameters the E-Type estimation of simulated values out of 50 realizations were compared to that of actual core sample data from preset aside 4 wells as validation data. The results showed that the second approach outperformed the first giving better correlation between the observed and predicted values. This result is believed to be caused by imposing the geological constraints and in particular lithological distribution throughout reservoir. Finally the in-situ oil reserve is estimated using petrophysical parameters estimated out of both approaches giving 8.29 and 6.44 billion barrels of oil reserve. Based on better agreement between known geology and simulated SNESIM approach it is believed that the 8.29 billion