Vibrational spectroscopy assessment of kerogen maturity in organic-rich source rocks

Bonoldi, Lucia; Paolo, Lea Di; Flego, C.

doi:10.1016/j.vibspec.2016.08.014

Cited by 33 publications

(37 citation statements)

References 15 publications

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“…The quality of the fit is visually assessed by evaluating the residual spectrum remaining after the peaks have been fit to the experimental data, and numerically by the χ 2 metric. The literature illustrating this technique highlights the use of different numbers of peaks to effectively reproduce the original Raman data (Quirico et al, 2005;Bonoldi et al, 2016). In this study, two, five, six, and seven-peak fits were employed to evaluate how the selection of the D, G, and ancillary peaks would affect the linear regressions.…”

Section: Data Processingmentioning

confidence: 99%

“…Vibrational spectroscopy has been evaluated as a potential technique for predicting the thermal maturity of carbonaceous samples (Spotl et al, 1998;Ferrari and Robertson, 2000;Beyssac et al, 2002;Quirico et al, 2005;Rahl et al, 2005;Guedes et al, 2010Guedes et al, , 2012Hinrichs et al, 2014;Wilkins et al, 2014;Zhou et al, 2014;Chen et al, 2015;Bonoldi et al, 2016;Lünsdorf, 2016;Lünsdorf and Lünsdorf, 2016;Sauerer et al, 2017;Schito et al, 2017;Schmidt et al, 2017). Most of this research has focused on Raman spectroscopy, an analytical tool that provides fast, non-destructive analyses, requires little-to-no sample preparation, and can grant both qualitative and quantitative information about the analyte.…”

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confidence: 99%

“…While studies comparing Raman spectral parameters to intrinsic properties like thermal maturity have uncovered some basic trends, they have employed a multitude of different experimental and spectral processing techniques to develop the correlations (Spotl et al, 1998;Quirico et al, 2003Quirico et al, , 2005Rahl et al, 2005;Guedes et al, 2010Guedes et al, , 2012Hinrichs et al, 2014;Lünsdorf et al, 2014;Wilkins et al, 2014;Zhou et al, 2014;Bonoldi et al, 2016;Lünsdorf, 2016;Lünsdorf and Lünsdorf, 2016;Schito et al, 2017). These methods differed in the number of samples evaluated, the acquisition parameters used to generate the data (excitation wavelength, spectral resolution, acquisition time, number of unique and random points measured per sample), data processing (i.e., baseline correction) and peak-fitting metrics (number of peaks, wavenumber, maximum peak width).…”

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confidence: 99%

“…In general, the authors found that samples must have a VRo > 1% for accurate linear regressions. Several studies have evaluated samples with VRo < 1%, with some results showing reasonable correlations, and others exhibiting higher error with decreases in VRo% (Keleman and Fang, 2001;Hinrichs et al, 2014;Wilkins et al, 2014;Bonoldi et al, 2016;Lünsdorf, 2016;Schito et al, 2017). Correlations of Raman spectral parameters with VRo values ranging from 0.3 to 1.5% provided coefficients of determination (R 2 ) of >0.90 when fit with power equations (Schito et al, 2017).…”

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confidence: 99%

“…The authors used multiple linear regression (MLR) to develop a model that showed good correlation and accuracy (R 2 = 0.89) when applied to samples dedicated for validating the MLR model. Bonoldi et al (2016) evaluated both Raman and IR spectroscopy to compare the two techniques for developing thermal maturity relationships between Raman or IR parameters and samples having VRo values between 0.2 and 2.7%. While the sensitivity of the method decreased when the VRo < 1.0%, the study found that IR seemed better suited for analyzing low maturity samples.…”

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confidence: 99%

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Assessment of Thermal Maturity Trends in Devonian–Mississippian Source Rocks Using Raman Spectroscopy: Limitations of Peak-Fitting Method

et al. 2017

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The thermal maturity of shale is often measured by vitrinite reflectance (VRo). VRo measurements for the Devonian-Mississippian black shale source rocks evaluated herein predicted thermal immaturity in areas where associated reservoir rocks are oil-producing. This limitation of the VRo method led to the current evaluation of Raman spectroscopy as a suitable alternative for developing correlations between thermal maturity and Raman spectra. In this study, Raman spectra of Devonian-Mississippian black shale source rocks were regressed against measured VRo or sample-depth. Attempts were made to develop quantitative correlations of thermal maturity. Using sample-depth as a proxy for thermal maturity is not without limitations as thermal maturity as a function of depth depends on thermal gradient, which can vary through time, subsidence rate, uplift, lack of uplift, and faulting. Correlations between Raman data and vitrinite reflectance or sample-depth were quantified by peak-fitting the spectra. Various peak-fitting procedures were evaluated to determine the effects of the number of peaks and maximum peak widths on correlations between spectral metrics and thermal maturity. Correlations between D-frequency, G-band full width at half maximum (FWHM), and band separation between the G-and D-peaks and thermal maturity provided some degree of linearity throughout most peak-fitting assessments; however, these correlations and those calculated from the G-frequency, D/G FWHM ratio, and D/G peak area ratio also revealed a strong dependence on peak-fitting processes. This dependency on spectral analysis techniques raises questions about the validity of peak-fitting, particularly given the amount of subjective analyst involvement necessary to reconstruct spectra. This research shows how user interpretation and extrapolation affected the comparability of different samples, the accuracy of generated trends, and therefore, the potential of the Raman spectral method to become an industry benchmark as a thermal maturity probe. A Raman method devoid of extensive operator interaction and data manipulation is quintessential for creating a standard method.

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Section: Data Processingmentioning

confidence: 99%

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confidence: 99%

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confidence: 99%

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Assessment of Thermal Maturity Trends in Devonian–Mississippian Source Rocks Using Raman Spectroscopy: Limitations of Peak-Fitting Method

et al. 2017

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

Xia

Wang

et al. 2020

J Raman Spectroscopy

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Raman spectroscopy is a nondestructive and in situ analytical technique that could provide information on the chemical structure and structural ordering of carbonaceous materials. Based on the E2g symmetric stretching vibration mode in the aromatic layers (G band, ~1,580 cm−1) and the defect structure in graphite (D band, ~1,350 cm−1), Raman spectroscopy has been used extensively to characterize the structural features of carbonaceous matters since 1970. Coal is a complex organic compound made up mainly by carbon showing characteristic Raman bands. This article reviews the application of Raman spectroscopy for coal structure characterization under room temperature, to determine its chemical structure, crystallite size, coal rank, and combustion reactivity. Future research for collecting Raman spectra during coal pyrolysis at high temperatures is also discussed. The combination of high‐temperature hot stage with Raman spectroscopy technology allows direct collection of Raman spectra at high temperature.

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The maturity of Silurian Longmaxi shale in Jiaoshiba, Sichuan Basin: as revealed by laser Raman spectroscopy

Lin

Zhang

et al. 2022

J Petrol Explor Prod Technol

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Accurate estimates of the thermal maturity of organic matter are important in studies of shale oil and gas, as these data are directly related to the genesis type of shale gas, adsorption capacity of shale and formation of organic pores in organic-rich shale. Longmaxi shale is a major shale gas exploration target in South China and a typical overmature shale gas play. Because Longmaxi shale is Silurian marine strata without vitrinites, it is difficult to determine its maturity accurately. In order to evaluate the maturity of the Longmaxi shale exactly, in this paper, solid bitumen reflectance, basin modeling and laser Raman spectroscopy analyses of solid bitumen were carried out on the shale. The solid bitumen reflectance of Longmaxi shale is range of 2.77–4.14% with a mean of 3.5%, and the corresponding equivalent vitrinite reflectance (EqVRo) is 2.6%; additional information about the maturation is provided by the basin modeling with the constraint of the maximum paleo-geothermal about 210–220 °C which was indicated by thermo-acoustic emission measurement, fluid inclusion thermometry and equivalent vitrinite reflectance. In addition, the Raman was applied to Longmaxi shale in Jiaoshiba firstly, and the Raman inter-peak interval between peak G (graphitic band) and peak D (disordered band) is range of 270–279 cm−1, which suggests that equivalent vitrinite reflectance is about 2.67% and is consistent with the results of solid bitumen reflectance, infrared spectrum and basin simulation. Approximate conclusions have been drawn from these different methods, and the thermal evolution of Longmaxi shale in Jiaoshhiba area, the largest shale gas field in China, was estimated at approximately 2.5–2.7%, which means that it has reached the overmature dry gas stage. Overall, the findings also imply that Raman spectra of pyrobitumen can be used effectively to evaluate the maturation of the marine shale without vitrinites.

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Vibrational spectroscopy assessment of kerogen maturity in organic-rich source rocks

Cited by 33 publications

References 15 publications

Assessment of Thermal Maturity Trends in Devonian–Mississippian Source Rocks Using Raman Spectroscopy: Limitations of Peak-Fitting Method

Assessment of Thermal Maturity Trends in Devonian–Mississippian Source Rocks Using Raman Spectroscopy: Limitations of Peak-Fitting Method

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

The maturity of Silurian Longmaxi shale in Jiaoshiba, Sichuan Basin: as revealed by laser Raman spectroscopy

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