Porosity and permeability are the most difficult properties to determine in subsurface reservoir characterization. The difficulty of estimating them arising from the fact that porosity and permeability may vary significantly over the reservoir volume, and can only be sampled at well location. Secondly, the porosity values are commonly evaluated from the well log data, which are usually available from most wells in the reservoir, but permeability values, which are generally determined from core analysis, are not usually available. The aim of this study is: First, to develop correlations between the core and the well log data which can be used to estimate permeability in uncored wells, these correlations enable to estimate reservoir permeability at the "flow unit" scale. Second, generate spatial distributions of reservoir properties (porosity and permeability). These distributions of reservoir properties are the basis for a geological model that can be used to perform reservoir modeling and reservoir management tasks. The Alternating Conditional Expectation (ACE) technique has been used and tested in this study. ACE is classified as non-parametric method against the parametric methods which are represented by the traditional multiple regression. A comparison between these two methods shows the superiority of the ACE method correlations for four wells in an Iraqi oil field. General correlations for unit (a) and (b) are also presented. These correlations can be used to estimate permeability in uncored wells with a good approximation
The estimation process is one of the pillars of the statistical inference process as well as the hypothesis test, and the assessment is based on the collection of information and conclusions about the teacher or the community's teachers on the basis of the result
