A method (30, 40) for estimating a relative concentration for each of two or more chemical components in a hydrocarbon of interest, the method (30, 40) characterized by: performing a measurement for each physical property of a plurality of physical properties of the hydrocarbon of interest using a sensor to provide a value for each different physical property being measured; and estimating, by a processor, the chemical composition of the hydrocarbon of interest by using a correlation prediction function for each chemical component in the chemical composition in terms of the different physical properties being measured; wherein the hydrocarbon of interest is crude oil; wherein the two or more chemical components comprise methane, ethane, propane, butane, pentane, hexane, or heptane and higher (C7+). wherein the prediction function comprises a prediction function for each of the two or more chemical components; and wherein the prediction functions were generated by: obtaining a plurality of measurements of values of different physical properties of samples of hydrocarbons at reservoir temperatures and pressures, each sample having a known chemical composition, to serve as a training set; generating a plurality of composite independent variables comprising two or more variables corresponding to the physical properties of the samples being measured; and performing a regression on the training set for dependent variables representing the chemical composition of the hydrocarbons in terms of the composite independent variables so as to develop the correlation prediction function that uses measured values of the different physical properties as input to predict the chemical composition of a sample being evaluated downhole. performing a measurement for each physical property of a plurality of physical properties of the hydrocarbon of interest using a sensor to provide a value for each different physical property being measured; and estimating, by a processor, the chemical composition of the hydrocarbon of interest by using a correlation prediction function for each chemical component in the chemical composition in terms of the different physical properties being measured; wherein the hydrocarbon of interest is crude oil; wherein the two or more chemical components comprise methane, ethane, propane, butane, pentane, hexane, or heptane and higher (C7+). wherein the prediction function comprises a prediction function for each of the two or more chemical components; and wherein the prediction functions were generated by: obtaining a plurality of measurements of values of different physical properties of samples of hydrocarbons at reservoir temperatures and pressures, each sample having a known chemical composition, to serve as a training set; generating a plurality of composite independent variables comprising two or more variables corresponding to the physical properties of the samples being measured; and performing a regression on the training set for dependent variables representing the chemical composition of the hydrocarbons in terms of the composite independent variables so as to develop the correlation prediction function that uses measured values of the different physical properties as input to predict the chemical composition of a sample being evaluated downhole. obtaining a plurality of measurements of values of different physical properties of samples of hydrocarbons at reservoir temperatures and pressures, each sample having a known chemical composition, to serve as a training set; generating a plurality of composite independent variables comprising two or more variables corresponding to the physical properties of the samples being measured; and performing a regression on the training set for dependent variables representing the chemical composition of the hydrocarbons in terms of the composite independent variables so as to develop the correlation prediction function that uses measured values of the different physical properties as input to predict the chemical composition of a sample being evaluated downhole.