Abstarct: Determination of porosity distribution is important to hydrocarbon supply computing, facies analysis, optimized planning for field expansion and decrease in drilling risk and cost. Regression and neural networks are of prevalent methods used to analysis multi attributes. High drilling costs prevents the interpreter from making absolute decision about determining promising exploratory areas by the application of merely one single method. That’s why in this study we introduce a new analysis method named similarity map, in addition to porosity distribution prediction using previously mentioned methods. This statistical method ease the interpreter’s task of analyzing tens of seismic attributes by summarizing whole information in one single map. This map, shows the resemblance of the seismic response of each region of the whole studding area with respect to a selected location in the field. In this paper seismic inversion has been conducted in order to estimate the porosity distribution baxsed on acoustic impedance within the studied area. Ultimately, we obtain a porosity map of given area. The inputs of the similarity analysis include a set of uncorrelated seismic attribute maps. Four different attribute volumes were generated then used in the study: instantaneous amplitude, instantaneous phase, instantaneous frequency, and acoustic impedance and the well “08-08” (high producing well) were chosen as the reference well and the similarity between the area and the reference point was mapped. The comparison between the results obtained from “similarity map” and “porosity map” in reservoir zone presents a convincing correlation between the two maps found through different methods each having specific information for the interpreters. Similarity map being a confirmation on validity of porosity map, leaded us to propose a new point for the following optimum point of drilling at about 45th inline and 55th xline