Abstract:The reservoir classification and evaluation is an important topic of study, and in order to make reservoir classification more accurate and reasonable, in this paper, the method of Q cluster analysis combined with Bayesian discrimination algorithm is adopted. First, various reservoir parameters are optimized globally, including sandstone thickness, porosity, permeability, carbonate content, shale content and others, with the mathematical statistics method to assemble the parameters, and the algorithm of Q cluster analysis to do the reservoir classification. On this basis, Bayesian discrimination algorithm is adopted to establish the discriminant relationship between reservoir parameters and reservoir classification and evaluation, together with the discriminant function of reservoir classification and evaluation, based on which, the target beds of non-coring wells are classified and evaluated. Examples show that the method of Q cluster analysis combined with Bayes discrimination algorithm is effective to achieve reservoir classification and evaluation.