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)

• Tipo: Artigo em Revista Científica Internacional
• Data: 2021

Autores

Schito, A (Autor) Outra; Guedes, A (Autor) FCUP; B. Valentim (Autor) FCUP; Sassarini, AV (Autor) Outra; Corrado, S (Autor) Outra

Revista

Título: Geosciences

Vol. 11

Página Final: 213

ISSN: 2076-3263

Editora: MDPI

Indexação

ISI Web of Knowledge - 3 Citações

Scopus - 4 Citações

Outras Informações

ID Authenticus: 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.

Idioma: Inglês

Tipo (Avaliação Docente): Científica

Nº de páginas: 19