A Predictive Model for Maceral Discrimination by Means of Raman Spectra on Dispersed Organic Matter: A Case Study from the Carpathian Fold-and-Thrust Belt (Ukraine)

• Type: Article in International Scientific Journal
• Date: 2021

Authors

Schito, A (Author) Other; Guedes, A (Author) FCUP; B. Valentim (Author) FCUP; Sassarini, AV (Author) Other; Corrado, S (Author) Other

Journal

Title: Geosciences

Vol. 11

Final page: 213

ISSN: 2076-3263

Publisher: MDPI

Indexing

ISI Web of Knowledge - 3 Citations

Scopus - 4 Citations

Other information

Authenticus ID: P-00T-XWG

DOI: 10.3390/geosciences11050213

Abstract (EN): In this study, we propose a predictive model for maceral discrimination based on Raman spectroscopic analyses of dispersed organic matter. Raman micro-spectroscopy was coupled with optical and Rock-Eval pyrolysis analyses on a set of seven samples collected from Mesozoic and Cenozoic successions of the Outer sector of the Carpathian fold and thrust belt. Organic petrography and Rock-Eval pyrolysis evidence a type II/III kerogen with complex organofacies composed by the coal maceral groups of the vitrinite, inertinite, and liptinite, while thermal maturity lies at the onset of the oil window spanning between 0.42 and 0.61 R-o%. Micro-Raman analyses were performed, on approximately 30-100 spectra per sample but only for relatively few fragments was it possible to perform an optical classification according to their macerals group. A multivariate statistical analysis of the identified vitrinite and inertinite spectra allows to define the variability of the organofacies and develop a predictive PLS-DA model for the identification of vitrinite from Raman spectra. Following the first attempts made in the last years, this work outlines how machine learning techniques have become a useful support for classical petrography analyses in thermal maturity assessment.

Language: English

Type (Professor's evaluation): Scientific

No. of pages: 19