In this paper, we present a novel method of data integration based on the permanence of ratio hypothesis. In order to model the conditional probability, it would be convenient if the information from each data source can be assessed independently in order to find P(A|B) and P(A|C), and then these joint probabilities are merged to calculate P(A|B,C) accounting for the redundancy between different data sources. We propose a methodology for calculating the redundancy between different sources of information. Our formulation is based on the information from each data modeled using a mixture of Gaussian assumption indicative of the multiple facies or categories of rock properties observed in the reservoir. We implemented the proposed methodology to characterize a carbonate reservior in the Gulf of Mexico. The available data sets were drill cutting data, core data, well log measurements and 3D seismic volume. We used core data to calibrate log measurements to lithofacies. Then, we merged the probability maps of lithofacies using permanence of ratio hypothesis and generated multiple realization by Monte-Carlo sampling from the probability maps. The modeling resulted in identification of reservoir regions that have higher proportion of dolomitized grainstones that might be suitable drilling targets.