Kerogen Type Classification in Hydrocarbon Source Rocks Using Hyperspectral Data and Machine Learning

Kerogen type in source rocks is directly related to its hydrocarbon generation potential. Its determination is often carried out with destructive methods. This study presents a non-destructive technique as an alternative to determine kerogen type using hyperspectral data and machine learning techniques. To present the technique, models were training using Support Vector Machines, K Nearest Neighbors, and Random Forest classifiers on spectral data collected in rock samples acquired from Taubaté Basin, Brazil, of an outcrop with high hydrocarbon generation potential. The models were trained and evaluated using spectral signatures measured with a spectroradiometer and the results were also tested on hyperspectral images of the samples. The experiments described here achieved accuracy above 0.8 with precision and recall above 0.62 and 0.8, respectively, for every kerogen type, indicating the soundness of the classification.