Critical insights from Rock-Eval analysis of vitrains☆
Introduction
Rock-Eval, an open-system temperature-programmed pyrolysis technique, has found widespread application in the last several decades for geochemical screening of potential hydrocarbon plays such as conventional petroleum systems, unconventional shale gas, shale oil, coal bed methane systems (Sykes and Snowdon, 2002; Jarvie, 2012a, Jarvie, 2012b). Based on the data from this programmed-pyrolysis technique, several ‘classic’ guidelines have been put forward by some workers to aid geochemical interpretations (see Espitalié et al., 1977; Peters, 1986; Peters and Cassa, 1994). Recent years have also seen the application of this technique in other areas such as for typing organic-matter in soils and recent sediments, and for soil contamination studies etc. (Poot et al., 2009; Sebag et al., 2016). The technique is simple, quick, requiring small sample quantities and producing reliable data such as amount of free hydrocarbons present within the sample (S1 curve), hydrocarbons generated from cracking of kerogen (S2 curve), Tmax (thermal maturity proxy, calculated from temperature-peak of S2 curve), total organic carbon (TOC), hydrogen index (HI; (S2/TOC)*100; indicates the type of kerogen present within the sample), etc. However, researches have also highlighted certain pitfalls about the data generated, such as suppression of Tmax in samples with high resinite concentration (Snowdon, 1995; Dembicki Jr., 2009). Recent works have also assessed the impact of analytical protocols on the quality of data generated from ‘routine’ Rock-Eval analysis of shales and coals. For example, Carvajal-Ortiz and Gentzis (2015) stressed on the importance of the shape of S2 curves for reliable data generation while assessing shale plays. For type I-II kerogen-bearing shales having petroleum generation potential, with increasing sample weights, Flame Ionization Detector (FID) saturation was observed with resulting broadening of S2 pyrograms and generation of less reliable data. Similarly, for overmature shales they documented the asymmetric nature of the S2 curves, leading to generation of less reliable Tmax values. On the other hand, for coals and type III-IV carbonaceous shales, Hazra et al., 2017, Hazra et al., 2019 stressed on the importance of S4 oxidation graphics and documented the problems with using higher sample weights, leading to undercounting of residual carbon (RC) and total organic carbon (TOC). All these indicate the importance of critical cross-examination of the nature of S2 and S4 curves by analysts and interpreters, before the data is reported.
This work looks at some fundamental issues that affect the S2 curve for coals and thereby helps in understanding the processes operative during the Rock-Eval analysis of coals. Although initially this technique was applied extensively for assessing hydrocarbon potential of rocks with dispersed organic matter, following the landmark work of Sykes and Snowdon (2002) on petroleum generation capability of coaly source rocks, the technique has been extensively applied for coal deposits to understand their oil and/or generation potential (Wilkins and George, 2002; Petersen, 2006; Petersen and Nytoft, 2006; Akande et al., 2007; Varma et al., 2015; Karayiğit et al., 2018).
For a set of manually isolated vitrains from Mvb and Lvb coals, we observe unusual spiky and uneven nature of the S2 pyrograms, in contrast to perfectly smooth curves for HvbA coals. Vitrain lithotype in coal are the bright bands which are homogeneous in nature with vitreous to sub-vitreous luster, marked by presence of concoidal fractures to even fractures, and are generally derived from the bark of large plants. The spikes are interpreted to form from the release of gases or bubbles bursting from the melt during the pyrolysis stage.
Section snippets
Samples and sample characterization
One coal sample was collected from each of five different open cast mines of Damodar Valley basin, India. Vitrain bands were manually isolated from the bulk coal samples and both the bulk coal and vitrain bands were analyzed separately. Proximate analysis and CSN (crucible swelling number) of the bulk coals and manually isolated vitrain samples were conducted according to ASTM D3172 (2013) and ASTM D 720 (1999).
Rock-Eval
Samples crushed to 212 μm size were analyzed using the Rock-Eval 6 apparatus at the
Sample characterization
Results of proximate analysis and CSN determination are given in Table 1. Ash yield data show that samples are all low ash (<20%). The hand-picked vitrains have consistently lower ashes than the bulk coals, which is to be expected. Moisture data shows two discrete samples sets, one around 2% moisture (SAL, TIS and BNK samples) and the other around 10% moisture (SNB and PAK samples), suggesting a lower rank (higher moisture) and higher rank (lower moisture) sample groupings. Volatile matter on
Conclusions
Previous work of Carvajal-Ortiz and Gentzis (2015) stressed on the importance of the shape of S2 curves and its relation with sample weights and FID saturation. In this work we show the influence of melt-formation and subsequent bursting of gaseous bubbles from molten-mass during pyrolysis of coking coals resulting in formation of spiky S2 curves from Rock-Eval.
The present study documents the nature of S2 curves from Rock-Eval analysis of manually isolated vitrain bands from coking and
Contribution of authors
- 1.
Bodhisatwa Hazra: Conceptualization; Investigation; Methodology; Writing - original draft; Funding acquisition; Visualization.
- 2.
Deependra Pratap Singh: Methodology; Investigation; Resources; Software; Data curation.
- 3.
Peter J Crosdale: Supervision; Validation; Writing - review & editing.
- 4.
Vivek Singh: Formal analysis; Software; Resources.
- 5.
Pradeep K Singh: Writing - review & editing; Supervision; Project administration.
- 6.
Monalisa Ganguly: Methodology; Investigation.
- 7.
Prasenjeet Chakraborty: Resources;
Declaration of Competing Interest
I, Dr. Bodhisatwa Hazra, on behalf of all the co-authors of this manuscript, would hereby like to declare that there is no conflict of interest related to this manuscript.
Acknowledgements
The Director CSIR-CIMFR is thankfully acknowledged for giving permission to publish this work, and for awarding B. Hazra the CSIR-CIMFR in-house research grant (Project No.: MLP-93/2019-20), the funds of which were utilized to purchase the Rock-Eval 6 device at CSIR-CIMFR and conduct the research.
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