Conventional geostatistics aims at creating models of heterogeneity and uncertainty in static rock properties such as facies, porosity and permeability. This is appropriate for providing input to flow simulations. There are times, however, when no flow simulation is going to be performed and we would like to directly predict reservoir flow characteristics. Different techniques are required when the aim is to directly create maps of the (uncertainty in) production potential. This paper summarizes a technique for this purpose. The petroleum industry is reliant on many types of geological and geophysical information to predict reservoir performance. This data covers different areas, provides data on different scales and is variably correlated to the production characteristics we are trying to predict. Statistical techniques can be used to summarize the relationships between the variables, however, they do not account for spatial correlation. Geostatistical techniques incorporate spatial structure but these techniques are cumbersome in the presence of many secondary variables. We propose that all seeondary data be merged statistically by a multivariate Gaussian approach into a single variable that contains all of the secondary variable, information. This would provide a likelihood distribution. The spatial distribution of each variable by itself is mapped independently of the secondary variable information, which provides a prior distribution. The likelihoods and priors are merged to provide an updated posterior distribution. We describe the methodology and show an example application.