OPERATIONAL PROCESS CLASSIFICATION IN PETROLEUM RESERVOIRS BASED ON APPI (+) FT-ICR MS AND CHEMOMETRIC PROFILING

This study evaluated 88 crude oil samples collected using two distinct operational methods (TFR and PVT) from the same well, using APPI(+) FT-ICR MS for detailed compositional analysis. Chemometric techniques such as PLS-DA and OPSDA were applied to classify the samples based on classical petroleomic indices and optimized molecular variables. The investigation revealed clear chemical differences between the operational types, with high classification accuracy, especially for HC, N, NO, and S classes. The methodology showed strong potential to support production understanding and decision-making in the petroleum industry. Results highlight the relevance of APPI(+) ionization combined with robust machine learning models for reservoir geochemistry.