Analytical Profiling and Latent-Variable Analysis of Multivariate Data in Petroleum Geochemistry

Various applications and facets of multivariate data analysis applied to the solution of petroleum geochemical problems are described. First, some important techniques for the analysis of multivariate geochemical data are briefly discussed. Principal component, partial-least-squares regression, marker and target projections are defined within the same formal scheme of latent-variable projections. Applications of these techniques to find solutions to important geochemical problems or to gain insight into geochemical processes in certain systems of petroleum geochemistry are then discussed in detail. These applications show that latent-variable analysis represents an efficient and important tool in petroleum geochemical problem solving.