Organic Matter Maturity Profile of a Well Case Study by Combination of Raman Spectroscopy and Principal Component Analysis–Partial Least Squares Regression (PCA–PLS) Chemometric Methods

Abstract

Raman spectroscopy is largely informative about organic matter (OM) chemical composition and structure and can therefore be applied to evaluate the thermal maturity of coals, carbonaceous materials and kerogens, i.e., their degree of evolution during burial heating. The evaluation of OM maturity is commonly performed by band-fitting followed by the measure of suitable spectral parameters (typically band separations and area ratios). However, this procedure can introduce some subjectivity both in the number and line-shape of the fitting bands and in the definition and selection of the most meaningful spectroscopic parameters. Here, a principal component analysis–partial least squares regression (PCA–PLS) chemometric approach for the treatment of spectra is presented, that is intrinsically unaffected by such arbitrariness. In fact, the total spectrum is analyzed in order to extract the spectroscopic ranges of maximum variance in a multivariate approach. In addition, being automated, the treatment is well-suited to huge sets, as those commonly collected to appropriately and reliably evaluate the chemical–physical and geological variables in an exploration well. As a case study the maturity profile (maximum paleotemperature) of OM from a well drilled in the Lower Congo basin (offshore Angola) is evaluated, both by spectral parameters and by PCA–PLS analysis, with results in good agreement on the basis of the prediction error. A set of coals is adopted as a reference to predict the reflectance in oil (%R o) profile from the Raman data. The comparison of the calculated %R o values with the experimental ones, presenting, in this case, some suppressed values, shows that the Raman analysis is not affected by underestimation of maturity as the vitrinite reflectance may be.

Micro-Raman Spectroscopy, a Powerful Technique Allowing Sure Identification and Complete Characterization of Asbestiform Minerals

“…On chrysotile, in the 4000-1100 cm −1 spectral range, different bands ascribing to carbonaceous materials were detected, Figure 4f. In particular the band at 1118 cm −1 may be ascribed to symmetric stretching of C-O-C bonds, the band at 1273 cm −1 to disordered C-H, the band at 1340 cm −1 produced by D vibrations, the band at 1613 cm −1 by G + D' vibrations, and the band at 1690 cm −1 due to C=O bonds [31,32]. With the laser wavelength used, 632.8 nm, the OHvibrations were detected only on amosite, bands at 3618 and 3637 cm -1 , anthophyllite at 3666 and 3671 cm −1 , actinolite at 3643 and 3660 cm −1 , and chrysotile, bands at 3688 and 3699 cm −1 .…”

Progress of Raman spectroscopic investigations on the structure and properties of coal

“…Jubb et al [140] pointed out that for low-mature organic matter, Raman spectra show a high degree of heterogeneity, which requires caution when trying to accurately estimate the thermal maturity of organic matter by Raman spectroscopy. In order to avoid the subjectivity of the operator during the Raman spectral curve-fitting process, Lupoi et al, [141,142] Bonoldi et al, [143] and Schito et al [144] combined Raman spectroscopy and chemometric methods to predict the maturity of organic matter. The method is unaffected by the subjectivity of Raman spectral curve-fitting and parameter selection.…”

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)

“…In the last years, an increasing interest in the application of Raman spectroscopy for thermal maturity assessment in catagenesis and metagenesis [24,26,27,29,31,[34][35][36][37][38] has been observed. The majority of articles focused on the analysis of single macerals, mainly vitrinite in coals [24,26,27], whereas other ones on bulk kerogen [29,34,38]. Nevertheless, only a few authors dealt with the heterogeneity that can characterize organic facies [23,32] or even a single organoclast [37,39].…”

“…Most of the works dealing with Raman spectra of kerogen use a classic band fitting approach that in many cases can result in biased results, as recently outlined [36,40]. Thus, different automatic approaches have been proposed based on simplified band deconvolution [31,38,41] or on a multivariate analysis chemometric scheme [34,36] for the spectra analysis, proposing a challenging approach for geological studies. In particular, a principal component analysis (PCA)-partial least square (PLS)-based multivariate analysis has been recently proposed [34,36] to correlate predictive parameters from PLS regression against vitrinite reflectance.…”

“…Thus, different automatic approaches have been proposed based on simplified band deconvolution [31,38,41] or on a multivariate analysis chemometric scheme [34,36] for the spectra analysis, proposing a challenging approach for geological studies. In particular, a principal component analysis (PCA)-partial least square (PLS)-based multivariate analysis has been recently proposed [34,36] to correlate predictive parameters from PLS regression against vitrinite reflectance. Moreover, Schito et al [32] demonstrated how a partial least square discrimination analysis (PLS-DA) allows to define and predict the differences between vitrinite and sporomorphs on the base of their Raman spectrum for a given thermal maturity degree.…”