A new consistent characterisation method has been developed to describe the complex composition of petroleum mixtures in terms of the molecular type and homologous series. The petroleum mixture is conceived as a matrix in which the rows represent carbon numbers, while each column represents a homologous series. The concentration of each individual component in the matrix can be measured using modern analytical tools such as gas chromatography (GC), high-performance liquid chromatography (HPLC), mass spectrometry (MS), field ionisation mass spectrometry (FIMS), sulphur chemiluminescence detection (SCD), etc. To evaluate the impacts of crude composition and refining chemistry on the composition and quality of refinery products, a novel method is proposed to predict the properties of petroleum mixtures based on the compositional information contained in the matrix. In this method, molecular structure-property correlations have been developed first to predict the boiling point and density of the molecular type homologous series in the matrix with high accuracy. Then the ASTM distillation curve and bulk density of the petroleum mixtures can be calculated with an assumed mixing rule. To predict other properties such as critical constants, freezing point, cetane number, pour point, cloud point, etc., well-tested correlations based on the distillation curve and bulk density are used along with the compositional information in the matrix. In addition, gasoline octane number can be predicted from molecular composition-based correlations. A simple but accurate method is also proposed to predict the molecular composition of a new feed through blending of fully characterised petroleum mixtures, thus expensive and time-consuming experimental analyses can be spared. The consistent molecular level characterisation of petroleum mixtures has enabled the development of refinery reaction and separation models based on the underlying process chemistry and thermodynamic principles. In addition, with the molecular information provided by the new characterisation, more efficient optimisation and integration can be conducted in the context of overall refinery.