Crude oil mixture resolution by FT-ICR MS and multivariate modeling: quantitative source attribution considering different thermal maturities

Abstract

Quantitative determination of the relative contributions from multiple sources in mixed source crude oils has consistently been posed as a significant challenge in petroleum geochemistry. In this context, this work explores the evaluation of crude oil mixtures using Fourier transform ion cyclotron resonance mass spectrometry (FT-ICR MS) with positive atmospheric pressure photoionization (APPI (+)). Two mixture designs (Plans 1 and 2) were evaluated, involving lacustrine and marine endmembers with moderate and high thermal maturities. Classical petroleomics characterization was combined with multivariate curve resolution-alternating least-squares (MCR-ALS) and partial least-squares (PLS) regression to investigate compositional behavior across these systems. In Plan 1, which involved mixtures of oils with distinct thermal maturity, substantial differences were observed in molecular class distributions. Plan 2, composed of highly mature lacustrine and marine oils, showed more subtle, yet detectable, differences, confirming the role of thermal evolution in shaping class profiles. MCR-ALS successfully recovered pure spectral profiles for both crude oil origins in each design, with Pearson correlation coefficients exceeding 0.97. PLS regression proved to be a robust tool for estimating the lacustrine content in the mixtures. For Plan 1, the model using only hydrocarbon (HC) class variables achieved the best performance, while in Plan 2, the model based on nitrogen-, oxygen-, and sulfur-containing (NOS) compounds provided the most accurate predictions. This study demonstrated the application of different chemometric tools and approaches to understand the behavior of crude oil mixtures with different origins and thermal maturity.