The Hassi Messaoud field is very thick sandstone reservoir covering an area of 2000 square km. The producing layer which occurs at an average depth of 3300 m, is characterized by very variable petrophysical properties. The properties impacting on productivity remain unpredictable despite the knowledge accumulated from than 900 wells continuously cored. Cambrians formations, deposited over the infra Cambrian basement, consist of sandstone, quartzite and conglomerates. These deposits are best know in this field, where they constitute the reservoir designated by: R3, R2, Ra and Ri units. Considering the complexity of the inferred sedimentary depositional model of the Cambrian and the large number of petrophysic and petrography data the statistics cannot solve the problem of the variations required in Hassi Messaoud field. In a first step we propose to study in a new manner petrography and petrophysic by using the families of evolutions of the various parameters to understand the distributions and in the second step to integrate the numerous statistics tools which can answer to the geological questions. In fact, we have used a statistical technique that seems to be the best for characterization and quantification of the reservoir parameters. This study focused on the determination of cut off values for the porosity and the permeability and on observation of the mutual relationships between all other parameters of interest. One of the widely used procedures in the earth science is the discriminate function. We will consist it at lengh for two reasons: it is powerful statistical tool, and discrimination can be regarded as either a univariate problem related to the statistical tests we have just discussed. In this case, we have collected two suites of well parameters (petrographic, petrophysic) between product well and unproduct well. The problem is to find the linear combination of these variables which produces the maximum difference between the two previously defined groups. If we find a function that produces a significant difference we can use it to allocate new samples of unknown origin to one of the two original groups

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