Accepted Manuscript RaMM (Raman maturity method) study of samples used in an interlaboratory exercise on a standard test method for determination of vitrinite reflectance on dispersed organic matter in rocks Ronald W.T. Wilkins, Neil Sherwood, Zhongsheng Li PII:
S0264-8172(17)30511-1
DOI:
10.1016/j.marpetgeo.2017.12.030
Reference:
JMPG 3185
To appear in:
Marine and Petroleum Geology
Received Date: 12 July 2017 Revised Date:
20 December 2017
Accepted Date: 22 December 2017
Please cite this article as: Wilkins, R.W.T., Sherwood, N., Li, Z., RaMM (Raman maturity method) study of samples used in an interlaboratory exercise on a standard test method for determination of vitrinite reflectance on dispersed organic matter in rocks, Marine and Petroleum Geology (2018), doi: 10.1016/ j.marpetgeo.2017.12.030. This is a PDF file of an unedited manuscript that has been accepted for publication. As a service to our customers we are providing this early version of the manuscript. The manuscript will undergo copyediting, typesetting, and review of the resulting proof before it is published in its final form. Please note that during the production process errors may be discovered which could affect the content, and all legal disclaimers that apply to the journal pertain.
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RaMM (Raman maturity method) study of samples used in an interlaboratory exercise on a standard test method for determination of vitrinite reflectance on dispersed organic matter in rocks
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Ronald W. T. Wilkins, Neil Sherwood* and Zhongsheng Li
[email protected],
[email protected],
[email protected] CSIRO Energy, P.O. Box 52, North Ryde, NSW 1670, Australia
* Corresponding author:
[email protected]/
[email protected]
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Keywords: Raman spectroscopy, thermal maturity, vitrinite reflectance, petroleum source rocks, gas shales, vitrinite, inertinite, solid bitumen
ACCEPTED MANUSCRIPT Abstract RaMM (Raman maturity method) is a thermal maturity tool for humic macerals in coals or as dispersed organic matter (DOM), expressed in equivalent vitrinite reflectance (EqVR). The
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EqVR values are based on a recently established calibration according to multi-linear regressions of selected parameters from Raman spectra of vitrinite and inertinite in a suite of Australian coals with vitrinite reflectance (VR) in the range 0.4–2.5%. The study
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contributes to the application of Raman spectroscopy for thermal maturity determinations as applicable to the petroleum exploration industry and researched by many groups
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worldwide.
A suite of samples to apply the technique was made available from an interlaboratory round robin exercise on application of ASTM D7708, a method for determination of VR of DOM in sedimentary rocks. This exercise involved the distribution of two sets of six unknown rock
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samples to all participating laboratories. Collated VR results from some samples of the first set, having high maturities and low organic matter (OM) contents, showed a disturbingly
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high degree of scatter. Although general agreement exists between RaMM determinations and the average of ‘mean random’ VR values determined at the various laboratories, some
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important differences emerged for some samples in the range EqVR = 1.2 – 1.8%. On RaMM diagrams, which involve plots of EqVRs derived from the two regression equations defining the method, the pattern of distribution of vitrinite and inertinite changes with thermal maturity for ‘normal’ coals and with OM composition in subhydrous versus perhydrous coals. Most of the round robin samples contained solid bitumens having reflectances close to those of the associated vitrinite. Measurements taken from solid bitumen were avoided as
ACCEPTED MANUSCRIPT much as possible, but because the RaMM study was done in air medium, some contamination of the humic maceral data was inevitable. Many of the solid bitumens and other potentially complicating components such as Tasmanites-related alginite can be identified on the RaMM diagram and eliminated from RaMM results for lower maturity
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samples (EqVR < 1.4%) whereas solid bitumen and humic macerals appear to provide similar EqVR for higher maturity samples (EqVR > 1.8%).
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The humic DOM of marine samples having EqVR > 1.0% has spectral properties that differ from those of humic macerals of the same maturity in terrestrially deposited rocks; this
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could possibly explain any discrepancies between RaMM EqVR and VR data for the marine rocks.
The presence of contaminating solid bitumen with reflectance similar to that of vitrinite, is the main complicating factor for thermal maturity evaluation using either VR or RaMM
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analyses of gas shales and tight oil reservoir rocks. To enable the best results from RaMM studies of these types of rocks, a preliminary study of the solid bitumen component should
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be carried out to determine its field on the RaMM diagram with the object of eliminating
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contaminating solid bitumen data from EqVR calculations.
ACCEPTED MANUSCRIPT 1. Introduction In Hackley et al. (2015), vitrinite reflectance (VR) results on a selected suite of North American shales were reported, following the completion of a round robin exercise to test
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the reproducibility of applying ASTM D7708 (ASTM, 2014) for the microscopical determination of the reflectance of vitrinite dispersed in sedimentary rocks. Subsamples of six unknown rocks, selected to cover a range of thermal maturity, and organic matter (OM)
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type and abundance such as are likely to be encountered in practice, were distributed to 28 participating laboratories in 15 countries. Basic characterization of the samples —Rock-Eval
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pyrolysis, total organic carbon (TOC), X-ray diffraction (XRD) mineralogy of low temperature ash residues—was carried out by the U.S. Geological Survey. Participating laboratories were asked to prepare the samples according to their usual procedures. In the resulting publication (Hackley et al., 2015), the formations, locations and ages of the samples were
extensive discussion.
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revealed, petrographic descriptions given, and the VR results evaluated for precision with
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Subsequently, a further suite of six unknown samples from gas shale and tight oil plays were provided to a similar number of participating laboratories for VR determination using the
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ASTM D7708 methods (ASTM, 2014). The results are presently undergoing evaluation before publication. In the meantime, the twelve rock samples used in these two exercises were analysed using RaMM (Raman maturity method) for determination of thermal maturity of OM in rocks (Wilkins et al., 2014, 2015), and the results of our examination of the samples are given in the present paper. RaMM is based on multi-linear regression analysis of selected parameters derived from the Raman spectra of humic macerals in coals or as dispersed organic matter (DOM), compared
ACCEPTED MANUSCRIPT against a calibration suite of Australian coals; the calibration has been also applied to Ruhr coals (Germany) and other Australian coals. In the past decade there has been an upsurge of interest in the application of Raman spectroscopy in geological investigations and the field has been thoroughly reviewed in two recent papers to which the reader is referred for an
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overview (Hinrichs et al., 2014; Lunsdorf, 2016). For the use of Raman spectroscopy in
thermal maturity studies, attention has mainly been focussed on spectra from vitrinite, or
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what is assumed to be vitrinite, in the DOM of sedimentary rocks. Less attention has been paid to the complications to spectral data caused in particular by measuring inertinite and
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solid bitumen. Both of these can be notoriously difficult to accurately distinguish from vitrinite, especially if the OM is in low abundance (<1% TOC) and finely disseminated, or the solid bitumen component is in high abundance. These complications commonly result in considerable scatter in VR results on test samples by expert analysts from different
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laboratories (e.g. Dembicki, 1984; Lo, 1992; Borrego et al., 2006; Mendonca Filho et al., 2010; Araujo et al., 2014; Hackley et al., 2015). In some samples where vitrinite is rare or absent, the temptation exists to take measurements on atypical grains. Some analysts make
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a clear distinction between primary (indigenous) and reworked (recycled) vitrinite (e.g. Lo,
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1992; Nzoussi-Mbassani et al., 2005) or different types of vitrinite (e.g. Buiskool-Toxopeus, 1983). Recycled vitrinite may have a reflectance considerably higher than the indigenous vitrinite and be indistinguishable from primary inertinite should it be present, especially if the grains are finely disseminated. For the purpose of this investigation, we refer to all humic macerals with a reflectance greater than vitrinite as inertinite. Poor sample preparation technique can also contribute to low analytical precision, but correct vitrinite identification remains the major challenge for many samples. In addition, there is the problem of vitrinite reflectance suppression or enhancement related to differences in H/C of
ACCEPTED MANUSCRIPT isometamorphic vitrinites (Newman and Newman, 1982; Diessel, 1990, 1992; Diessel and Gammidge, 1998; Wilkins et al., 1992; Wilkins and George, 2002; George et al., 1994). There are strong indications that RaMM can contribute to the solution of all of these problems
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(see Wilkins et al., 2014, 2015). 2. Materials and methods
The studied samples consist of a suite of shales, some of which are calcareous or bituminous
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or both. They are listed in Table 1 with details on formation, location and age. All sample information including mean random VR data from participants in the interlaboratory
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exercise on the first six samples as given in Hackley et al. (2015) was known before the RaMM analyses were carried out, but for the second suite of six samples (samples 7 to 12 in Table 1) the mean random VR results of other participants were unknown to us, until the RaMM results were evaluated. RaMM (by Wilkins) and VR (by Sherwood and Li)
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determinations were carried out independently and not compared until all results were complete. As the final analysis of the VR data from the interlaboratory exercise is
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unavailable at present, we have compared VR values determined in our laboratory with the RaMM results; VR analyses were also carried out at CSIRO on two of the samples from the
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first suite that had a particularly large variation in VR from participating laboratories, and the associated overall mean VR values differed significantly from the RaMM results (Rodiles, Pearsall; Table 2). General petrographic descriptions of rocks from formations yielding the second suite of samples are given in Hackley and Cardott (2016). Information on the methods of VR determination for the reference coals used in the present study are given in Wilkins et al. (2014, 2015).
ACCEPTED MANUSCRIPT Table 1 Sample information Location
Period/epoch
1
Green River
Colorado, USA
Eocene
2
Boquillas
Texas, USA
Upper Cretaceous
3
Ohio/Huron
Virginia, USA
Devonian
4
Rodiles
Asturias, Spain
5
Pottsville
Alabama, USA
6
Pearsall
Texas, USA
Lower Cretaceous
7
Marcellus
West Virginia, USA
Middle Devonian
8
Haynesville
9
Eagle Ford
10
Barnett
11
Bakken
12
Woodford
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Formation/Member
Jurassic
Carboniferous
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Sample number
Texas, USA
Jurassic
Texas, USA
Upper Cretaceous
Texas, USA
Mississippian
North Dakota, USA
DevonianMississippian DevonianMississippian
Texas, USA
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The samples, received in the form of granules of crushed rock, were mounted in epoxy resin
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and given a high quality polish according to Australian Standard AS 2856.1-2000. The blocks were first used for RaMM analyses because these are carried out in air medium. Subsequently, mean random VR was determined under oil immersion following the ASTM D7708 standard test guidelines. Raman spectra were generally obtained from about 20-30 grains of vitrinite and/or inertinite and solid bitumen if present, with results for each maceral type recorded separately. For some of the gas shales it was difficult to achieve the desired numbers of
ACCEPTED MANUSCRIPT measurements from vitrinite because of a dominance of solid bitumen. Although maceral identification for the reference coals was straightforward, identifications for DOM, being made in air, and without a polariser or rotatable stage to check bireflectance, are less accurate than normally achieved during petrographic determinations of DOM in an oil
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medium with a polariser. Vitrinites were identified by lower reflectance, simple linear
boundaries and elongation parallel to bedding in comparison to inertinites which have
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higher reflectance, commonly together with arcuate boundaries or remnant cell structures. Liptinites were delineated mainly on the basis of their thin form, and relatively low
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reflectance accompanied by high luminescence (commonly termed fluorescence) in the lower maturity range. Our Raman microprobe does not have the facility for epi-fluorescence illumination so references to fluorescence in this paper refer to the slope of the background of the Raman spectrum. For the Raman study, this was less a study of solid bitumen than its
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avoidance. Except for some determinations to check on the effect of possible misidentifications on the humic maceral results, grains with anastomosing outlines and moulding against crystal terminations and vug fillings were avoided in the analyses. Myers
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et al. (2017) proposed a method of using Raman spectroscopy to distinguish solid bitumens
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and vitrinite in shales, though being technically advanced, it seems unlikely to become a routine tool in commercial laboratories. General systematic changes in Raman spectra and their backgrounds, especially the nature of decreasing fluorescence of vitrinites and inertinites with increasing reflectance in any given sample (e.g. Diessel, 1992; Newman, 1997; Newman et al., 2000; Wilkins et al., 2002) provide a valuable supplementary check on discriminating vitrinite from inertinite. The RaMM analyses were carried out following the method described in detail by Wilkins et al. (2014, 2015). In brief, the CSIRO instrument is a modular Horiba Jobin Yvon optical fibre
ACCEPTED MANUSCRIPT Raman microprobe with a CCD detector, equipped with a 488 nm Melles Griot solid state laser. With the laser power set at 0.45 mW at the surface of the petrographic block, the beam is focussed to a ~2 µm diameter spot on the maceral surface using a 50x objective. Acquisition time for each spectrum was 20 seconds. A joystick controlled X-Y stage
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facilitated rapid and systematic scanning of the blocks. Software scripts controlling
instrumental functions enabled the collection of spectral data to be made quickly and efficiently. A 600 line grating with the monochromator centred at 1500 cm-1, giving a
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spectral range of 500 to 2500 cm-1, covered the first order vibrational spectrum of carbon,
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with sufficient background to allow a fair estimation on the baseline which is stripped from the spectra during data processing. The technique requires the spectra to be fitted by two Lorentzian curves, approximating the so-called G and D bands (Tuinstra and Koenig, 1970), and a linear baseline. We cannot overemphasise, in agreement with Quirico et al. (2005),
for the calibration.
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that all acquisitions must be carried out under the same strictly controlled conditions used
In the application of RaMM, an equivalent VRo random (RaMM EqVR) is calculated from
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Raman spectral parameters of vitrinite and/or inertinite using multilinear regressions based
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on a calibration suite of reference coals (Wilkins et al., 2014, 2015) examined under the same specified standard instrumental operating conditions. Two equations are required to determine EqVR: RaMM (1), applicable to the EqVR range 0.4 – 1.0% and RaMM (2) for EqVR 1.25 – 2.5%, with a zone of overlap within which results from both equations are averaged (EqVR 1.0-1.25). As noted in Wilkins et al. (2015), the RaMM (1) equation is,
ACCEPTED MANUSCRIPT EqVR% = −6.384 + 5.429 log(vG−vD) + 0.863 log ‘b’ + 0.832 log saddle index – 2.677 log FWHM G – 0.661 log FWHM D,
and the RaMM (2) equation is, EqVR% = −52.3363 + 13.1992 log(vG-vD) − 0.7964 log ‘b’ + 8.1121 log FWHM D + 15.3255 log
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HG/HD,
where vG − vD is the separation of the positions of the G and D bands in cm-1, ‘b’ is the slope
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of the linear spectral background, the saddle index is the ratio of the height of the G band to the minimum within the saddle between the overlapping G and D bands, FWHM G and
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FWHM D are the band widths at half height of the G and D bands respectively and HG/HD is the ratio of heights of the G and D bands.
In practice, the protocol requires that for samples where RaMM (1) gives a mean value of 0.4-1.0%, this value is selected as the EqVR; for those where RaMM (2) gives a mean value
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>1.25% it is used as the EqVR, and if the equations yield values between 1% and 1.25% the EqVR is represented either by a simple average of the two values as in this paper, or as a
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weighted average suggested by Wang and Li (2016). If the ‘b’ value is <1 the data point is excluded (e.g. for some fusinites; see Wilkins et al., 2014, 2015).
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It is convenient to display individual RaMM EqVR results in the form of a diagram of calculated values of RaMM (1) EqVR vs RaMM (2) EqVR. The main features are illustrated by the idealised diagram (Fig. 1). In the RaMM diagram, vitrinite and inertinite having the same origin and geological history are best considered as a single population because they are related either by the RaMM (1) or RaMM (2) equations according to the level of thermal maturity. In the region in which RaMM (1) EqVR is appropriate, the individual values of EqVR (vitrinite + inertinite) are strongly constrained about their mean by equation (1). In this
ACCEPTED MANUSCRIPT range, the values of RaMM (2) EqVR are poorly constrained. There is much overlap of vitrinite and inertinite fields in the range VR = 0.4 – 0.6% but with increasing maturity (VR = 0.8 – 1.2%) the expression of the vitrinite + inertinite population on the RaMM diagram becomes a quasi-horizontal band in which inertinite typically plots to the right and vitrinite
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to the left. A recycled vitrinite + inertinite population would plot as a second quasi-
horizontal band at this level of thermal maturity. In the region appropriate to RaMM (2)
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EqVR, individual values of EqVR (vitrinite + inertinite) are fixed about the mean by equation (2) and RaMM (1) EqVR values are poorly constrained. This results in a quasi-vertical band
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for the vitrinite + inertinite population which is not so obvious in the diagram because of the compression of the vertical axis. Typically, vitrinite occupies the lower and inertinite the upper part of this band as shown in Fig. 1. A 1:1 line across the RaMM diagram aids in visualising the data when comparing samples.
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Although the fields of vitrinite and inertinite on Fig. 1 are hypothetical, based on observations on many samples, they are not without significance as we will later discuss. Occasional points of one maceral within the field of another could represent slightly
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divergent composition, or misidentified grains. In practice, possible mis-identification of
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vitrinite versus inertinite makes no difference to the RaMM EqVR because all data from both are included in the calculations. Standard deviations for the mean RaMM EqVRs may be high compared to conventional VR on the same sample, but it should be remembered that the standard deviation for VR determination on DOM, is influenced by the analyst in the act of interpreting what will, or will not be accepted as vitrinite versus inertinite. Such a selection is not made by the analyst in a routine RaMM study. In other words, an advantage of RaMM is that information is used from both vitrinite and inertinite, solving the problem associated with their delineation, and possibly also with vitrinite reflectance suppression/enhancement
ACCEPTED MANUSCRIPT (see section 4.2), but has implications for precision of results. Similar to VR, the range of RaMM EqVR values may be affected by the inclusion of some measurements on reworked vitrinite and/or reworked inertinite, and solid bitumen. Liptinites such as Tasmanites-related alginite, and solid bitumen are not part of the calibration suite and would normally be
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avoided in analysis; in many cases they may be distinguishable from the humic macerals by their distinctive ‘RaMM EqVR’ values even if they have similar reflectances to the associated
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vitrinite. 3. Results
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Table 2 provides a summary of mean RaMM EqVR results together with the corresponding average mean random VRs as determined in the first of the interlaboratory exercises (Hackley et al., 2015), and CSIRO mean random VR results where available. More details on
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the CSIRO study including solid bitumen reflectance are given in Table 3. Fig. 2 shows RaMM diagrams for three (Boquillas, Pottsville, Huron) of the first suite of six samples. RaMM EqVR results for these three samples are similar to the average of mean
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random VR values for all exercise participants taken together (see Hackley et al., 2015). RaMM results on the Green River Formation sample (not shown) are considered unreliable
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because the laser beam severely damaged the huminite, which was the only available humic maceral to measure. All that can be inferred from the spectra of this sample is that RaMM EqVR is <0.4%, and the huminite has a high fluorescence. The Pottsville sample contains more clearly defined humic maceral populations. We have derived our EqVR value, using the RaMM (1) equation, for this sample from all vitrinite and inertinite data excluding the allochthonous coal fragments. RaMM results on the Huron sample are discussed in more detail below (Section 4.4).
ACCEPTED MANUSCRIPT RaMM results for six gas shale and tight oil play samples are shown in Fig. 3 (Marcellus, Haynesville and Eagle Ford) and Fig. 4 (Barnett, Bakken, Woodford), and Fig. 5 displays CSIRO reflectance histograms. RaMM results on the Bakken and Woodford samples are discussed in more detail below (Section 4.4). In Fig. 6, RaMM results on the problematic
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Rodiles and Pearsall samples are compared with mean random VR results from participating laboratories in the first exercise (Hackley et al., 2015). RaMM EqVR results on the Rodiles
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sample (VR 1.25%, EqVR 1.51%) have a tight distribution whereas the Pearsall (VR 1.58%, EqVR 1.74%) sample results (Fig. 6) are more diffuse, although neither show clear evidence
corresponding measured VR values.
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of multiple populations of OM. Both Rodiles and Pearsall EqVR values are higher than their
For the four higher maturity samples (Marcellus, Haynesville, Eagle Ford and Barnett), we consider there is reasonable agreement between mean measured VR and mean RaMM
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EqVR considering the level of difficulty posed by the samples for both VR and RaMM measurements. For example, Fig. 5 indicates the complexity due to the overlapping reflectances of solid bitumen and vitrinite. Results from RaMM with VR approaching 2.5%
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are expected to be low by 0.1-0.2% absolute (Wilkins et al., 2015) such that the agreement
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between CSIRO VR and RaMM EqVR for the Marcellus sample in particular, is improved.
ACCEPTED MANUSCRIPT Table 2. RaMM EqVR and VR results on samples used in interlaboratory tests of the ASTM standard method D7708 for determination of VR for DOM (Hackley et al., 2015, in part). Sample
Formation
VR random%
VR SD (n)
RaMM
RaMM SD
EqVR%
(n)
Green River
0.31
0.04
<0.401
—
2.
Boquillas
0.50
0.07
0.541
0.19 (27)
3.
Huron
0.80
0.12
0.871
0.19 (38)
4.
Rodiles
1.18,
0.15,
1.512
0.14 (32)
1.25ᵟ
0.08 (31)ᵟ 0.06
1.001
0.13 (31)
1.742
0.18 (21)
0.11 (30)ᵟ
2.142
0.17 (7)
0.15 (32)ᵟ
2.032
0.19 (16)
0.97
6.
Pearsall
1.53, 1.58ᵟ
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Pottsville
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5.
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1.
0.19,
0.07 (31)ᵟ
Marcellus
2.41ᵟ
8.
Haynesville
2.18ᵟ
9.
Eagle Ford
10.
Barnett
11.
Bakken
Note:
0.11 (30)ᵟ
1.992
0.14 (17)
1.83ᵟ
0.09 (25)ᵟ
1.952
0.13 (13)
0.92ᵟ
0.04 (32)ᵟ
1.16 (1.101,
0.131,0.092
1.222)
(6)
1.372
0.25 (11)
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2.14ᵟ
Woodford
1.43ᵟ
0.09 (32)ᵟ
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12.
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7.
The VR values and associated standard deviations (SD) given in the table for the first six samples are for the total results from the different laboratories (Hackley et al., 2015). ᵟIndividual sample determinations from the CSIRO study. 1
2
Mean EqVR determined from RaMM equation (1); from RaMM equation (2).
‘n’ for VR is the number of vitrinite measurements and ‘n’ for RaMM is the number of vitrinite + inertinite measurements.
ACCEPTED MANUSCRIPT Table 3. A summary of random vitrinite and solid bitumen reflectances for samples analysed by CSIRO as part of the current study Random Vitrinite Reflectance
Solid Bitumen Reflectance
Mean
Range
n
SD
Mean Min
4
Rodiles
1.25
1.13-1.40
32
0.08
1.03
6
Pearsall
1.58
1.48-1.73
31
0.07
1.51
7
Marcellus
2.41
2.21-2.63
30
0.11
2.01
8
Haynesville
2.18
1.97-2.54
32
0.15
9
Eagle Ford
2.14
1.92-2.34
30
0.11
10
Barnett
1.83
1.65-2.04
25
11
Bakken
0.92
0.84-1.02
12
Woodford
1.43
1.28-1.62
Max
n
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Formation
SD
32
0.08
1.22 1.68
20
0.14
1.27 2.70
32
0.41
1.89
1.73 1.99
30
0.08
2.10
1.90 2.33
22
0.12
0.09
2.01
1.81 2.23
20
0.11
32
0.04
0.75
0.55 0.87
31
0.09
32
0.09
1.34
1.22 1.45
20
0.08
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1.13 1.40
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Sample
Note:
Min=minimum value measured; Max=maximum value measured; n=number of readings, SD=standard deviation.
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Measurements taken on grains in random orientations with unpolarised reflected light. Vitrinite reflectance values may include some readings from solid bitumen and vice versa because of some
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overlapping optical properties with vitrinite.
4. Discussion
4.1 Methods
There are two approaches to the use of Raman spectroscopy in the geological sciences, which we may call phenomenological and analytical. The former seeks to describe rather than explain and it is appropriate to the practical application of Raman spectral data to the solution of geological problems such as thermal maturity determination, and the
ACCEPTED MANUSCRIPT identification of mineral species. The analytical approach on the other hand is appropriate to the investigation of structural and chemical changes that occur at the molecular level as a consequence of the reactivity of geological materials within the earth or experimentally in
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the laboratory. Similar to VR analyses, the approach of RaMM is phenomenological insofar as reflectance and Raman spectral data are accepted at face value in working towards a geologically useful
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outcome. As a putative thermal maturity tool for use in petroleum exploration, the focus of RaMM has been on the development of a simple, objective and rapid technique for
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obtaining data of a similar quality as for the VR technique—which remains the most important thermal maturity tool for industrial application in coal utilization and petroleum exploration—without some of its drawbacks.
There is some difference between the requirements of thermal maturity tools based on
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vitrinite alone and those such as RaMM, based on more, humic macerals. The Raman spectrum of vitrinite, though intriguingly rich in information, appears to provide much the
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same thermal maturity information as VR, and it remains to be fully demonstrated that it can solve complex problems such as those we attempt to solve in this paper, namely
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misidentification of the vitrinite population in the presence of inertinite and/or solid bitumen, and correcting for VR suppression or enhancement effects—while at the same time being comparable with VR in rapidity and cost. The correct delineation of the vitrinite population, which is generally routine for coal analyses but often problematical for DOM, is equally vital in any method based on vitrinite alone. For the simple Raman approach, the fluorescence background to the spectrum of vitrinite is a distraction to be properly disposed of before the analysis of the Raman spectrum commences. RaMM, however, requires in
ACCEPTED MANUSCRIPT addition to Raman parameters, a fluorescence parameter which is taken from the background (represented as ‘b’ in the RaMM EqVR equations above) and included in the calculations for EqVR. It is a complex parameter which includes contributions from fluorescence intensity and its changes over time of excitation, both of which are known to
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have empirical relationships with thermal maturity of macerals (Teichmüller, 1982;
Ottenjann, 1988; Diessel, 1992, Figs. 5.26, 5.27; Wilkins et al., 1992, 1998, 2002). Even
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within a spectral acquisition time of 20 seconds very important changes to fluorescence intensity of macerals may occur and this is a major reason why experiments must be carried
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out under strictly-controlled conditions, if information on the spectral background information is to be utilised. It is the addition of the fluorescence parameter to parameters derived from the Raman spectrum itself that enables RaMM to include information from the humic macerals in the equations from which EqVR is derived.
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To achieve the best expression of the fluorescence parameter for RaMM, requires careful consideration of the excitation wavelength. We were guided by the fact that much information is known about the fluorescence alteration behaviour of macerals using the
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488 nm line of the argon ion laser for excitation (Wilkins et al., 1992, 1998, 2002). It is
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convenient that small solid state lasers are now available for this wavelength. The thermal maturity range of major interest with respect to oil and gas generation (VR 0.5 – 2.5%) is accessible when RaMM is calibrated with 488 nm radiation. The basic method could be used with other excitation sources, but eventually a standard procedure similar to the ASTM D7708 method for VR determination, in which measurements are made at a specified wavelength, would need to be established and agreed upon to enable and encourage interlaboratory testing.
ACCEPTED MANUSCRIPT Another methodological consideration is the choice of curve fitting procedure applied for processing spectra. Because RaMM has been developed for routine practical application, the curve fitting procedure needs to be robust, rapid and universally applicable to a range of macerals in the vitrinite and inertinite groups—in practice chiefly all telovitrinite,
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detrovitrinite, gelovitrinite, semifusinite, macrinite and inertodetrinite, from low to high maturity. Most fusinites are rejected by the adopted analytical procedure (see discussion
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below) but usually they are an uncommon component of DOM assemblages.
General changes in the Raman spectrum of vitrinite that occur with increasing thermal
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maturity are now well known viz. decrease in width at half height of the two major bands, G and D and their increasing separation, both leading to a deepening of the well between them and to their eventual separation, and the gradually increasing dominance of the Gband towards graphitization. The rise and demise of several bands of lesser intensity
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revealed by more intensive analysis of the spectra adds useful complexity to the consideration of the evolutionary sequence of maturity of OM (Pasteris and Wopenka, 1991; Hu et al., 1993; Kelemen and Fang, 2001; Beyssac et al., 2002; Jehlinka et al., 2003;
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Quirico et al., 2005; Guedes et al., 2005; Rahl et al., 2005; Sadezky et al., 2005; Li et al.,
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2006; Li, 2007; Marshall et al., 2007; Zeng and Wu, 2007; Quirico et al., 2009; Marques et al., 2009; Guedes et al., 2010; Kwiecinska et al., 2010; Morga, 2011a, b; Romero-Sarmiento et al., 2014; Hinrichs et al., 2014; Wang and Li, 2016; Schito et al., 2017). There is a disturbing lack of consensus among different laboratories however, both in reported experimental conditions, and on the most appropriate curve fitting procedure, making it extremely difficult to reconcile results between laboratories. We are in accord with Myers et al. (2017) that an international working group is urgently required to come to agreement on standard conditions for the Raman spectroscopic study of OM in rocks.
ACCEPTED MANUSCRIPT The shapes of the Raman bands are deeply Lorentzian in nature (Sadezky et al., 2005). To keep the spectral processing practical and straightforward, we adopt the simplest method of deconvolution using Lorentzian curve-fitting for the broadly defined D and G bands with a linear background, reasoning that any systematic inadequacies in the curve fitting
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procedure will largely be corrected out because comparison is made with macerals of
reference coals with the same characteristics and that respond in the same manner to the
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fitting procedure. Essentially, this is the curve fitting procedure adopted by Liu et al. (2013) and Hinrichs et al. (2014) in their broad surveys of Raman spectra of vitrinites in coals
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ranging from lignites to meta-anthracites, and it seems to produce adequate results for our phenomenological approach. One should note that RaMM makes a comparison with the spectra of a suite of macerals as DOM with a suite of macerals from the reference coals and only indirectly with VR. Popular curve fitting procedures for the first order Raman bands of
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carbons usually specify a fixed number of bands (see references above) but Lünsdorf et al. (2014), Lünsdorf (2016) and Lünsdorf and Lünsdorf (2016) use an unconstrained number of functions to fit the Raman spectra, and a third order polynomial to fit the background. The
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results appear very convincing for vitrinite, but even a polynomial fit to the background
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does not always give plausible results for the wide range of macerals and degree of maturity considered in RaMM. The more complex and complete fitting procedures involving up to as many as ten Raman bands, by researchers working on macerals (see references quoted above), seem more appropriate to intensive investigation of changes at the molecular level to provide better understanding of the many anomalies in comprehension of the thermal maturation of OM (i.e. an analytical rather than a phenomenological approach). Inertinite (Guedes et al., 2010; Morga, 2011a, b) and solid bitumen (Jehlicka et al., 2003; Court et al., 2007; Liu et al., 2013; Zhou et al., 2014; Grobe et al., 2016; Schmidt at al., 2017)
ACCEPTED MANUSCRIPT have not been as well studied as vitrinite by Raman spectroscopy, but it is clear that although spectral parameters at the same rank differ, the sequence of changes with thermal maturity for both macerals are broadly similar to those for vitrinite. The inaccurate distinction of vitrinite from inertinite and solid bitumen, and possibly even mineral
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components such as carbonates, chalcedony and iron oxides having the same reflectance as some vitrinite, can create a serious problem in VR determination as shown, for example, in
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the spread of results from the Rodiles and Pearsall samples in the interlaboratory study of the ASTM D7708 method (Hackley et al., 2015; see also our Fig. 6).
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In the range of EqVR 0.4– 1.0% the proportion of vitrinite to inertinite macerals measured using RaMM, matters little because no systematic difference is apparent between RaMM (1) results of vitrinite and inertinite. The same observation holds for the RaMM (2) results in the range of EqVR 1.25–2.5%. In the region of overlap of the two regression equations (EqVR
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1.00–1.25%), however, the mean RaMM EqVR is affected by the proportion of vitrinites and inertinites measured. For initial calibration of the method, vitrinite comprised about a third of the measured points for the RaMM (1) equation whereas in the calibration of the RaMM
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(2) equation the vitrinite proportion was about half. For best estimation of EqVR in the
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region of overlap, vitrinite should be 30 – 50% of the total humic macerals measured. As mentioned above, the CSIRO VR analyses were done as ‘random measurements’ with non-polarised light and no stage rotation. Because of the high degree of anisotropy for vitrinite in gas mature or overmature rocks however, ‘maximum’ reflectance readings in plane polarised light with stage rotation, provide a more precise measurement of thermal maturity with a smaller standard deviation (SD) than for ‘random’ measurements (see discussion on bireflectance in Taylor et al., 1998). Determination of mean maximum vitrinite
ACCEPTED MANUSCRIPT reflectance is therefore the preferred technique used in our laboratory for samples having maturities beyond the conventionally defined oil window (VR>1.3%). In addition, evaluation of bireflectance, is an effective method for discriminating macerals, because bireflectance
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characteristics vary between the different maceral groups. Although correlation between VR and RaMM EqVR determined from vitrinite spectra alone is straightforward for coal because VR can be accurately determined on the telovitrinite
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component, the greater dispersion of VR results for DOM from different laboratories makes the accuracy of alternative techniques difficult to establish. It invites the question what is
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the ‘true’ mean random VR of DOM in a given rock sample? The average of mean random VR results on DOM from a large number of independent analysts is not the ‘agreed mean random VR’, especially if there is a long low reflectance tail on the distribution. The mode, or the average of results of selected analysts who are highly experienced in processing of
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rocks of that particular type, age and geographical area, could offer more reliable estimations of the ‘true’ VR. Furthermore, as noted in Wilkins et al. (2014, 2015) and further investigated below using the new RaMM diagram, VR and RaMM EqVR will not be the same
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for samples in which VR suppression or enhancement is involved because of internal
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corrections for these anomalies in the RaMM technique. 4.2 VR suppression / enhancement
It is possible that some of the discrepancies observed between VR and RaMM EqVR originate in compositional anomalies in the humic macerals associated with marine influence. Examples showing RaMM plots of normal (orthohydrous) coals from seams with terrestrial roof rocks are shown in Fig. 7. In the range of lower to intermediate maturity where the RaMM (1) equation is applicable, the general form (Fig. 1) of the array of vitrinite
ACCEPTED MANUSCRIPT and inertinite points is linear and horizontal with vitrinite mostly falling on the left and inertinite mostly falling on the right of the field divided by the 1: 1 line on the diagram (e.g. Bulli, VR = 1.14%, Fig. 7; Pottsville, VR = 0.97%, Fig. 2). In the higher maturity range appropriate to the RaMM (2) equation (EqVR = 1.25-2.5%), the field compacts into a more
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equi-dimensional array as shown by the plots for Norwich Park (VR = 1.58%), and Baralaba (VR = 2.02%) coals (Fig. 7). This reflects the gradual convergence of chemical and optical
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properties of macerals with increasing maturity (e.g. van Krevelen, 1993; Smith and Cook, 1980; Smith and Smith, 2007).
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For comparison, in Fig. 8, RaMM results are presented for three coal samples with a wide range of measured VR, although according to VR on orthohydrous vitrinites of their associated ‘normal’ coals, they have similar ranks. Two samples are from the marine-roofed Greta seam of the Hunter Valley, NSW, Australia, containing perhydrous vitrinite (see Fig. 9).
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The highest mean VR for a coal from the sampled section of the ~2 m Greta seam is 0.68% (Diessel, 1992 Fig. 8.11) with decreasing VR towards the marine roof rocks. The two samples we have examined were sampled from near the base (10; VR = 0.64%), and near the top (14;
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VR = 0.50%) of the seam. They are compared with a coal (10A; VR = 0.90%) from the
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megaseam of the Dartbrook mine, NSW, Australia, which contains subhydrous vitrinite (see Fig. 9), and is interpreted to be derived from a non-bedded rooted soil (Diessel and Gammidge, 1998; Wilkins et al., 2002). Bedded ’normal’ coals elsewhere in the Dartbrook mine have VRs of 0.65-0.68% (Diessel and Gammidge, 1998). The measured VRs on these three ‘anomalous’ coals range from 0.50% to 0.90%, yet the coals of normal composition with which they are associated all have VR values of about 0.7%. The relationship between increase in atomic H/C and degree of VR suppression for
ACCEPTED MANUSCRIPT Greta coals has been investigated by George et al. (1994). A similar study of New Zealand Brunner coals with marine-deposited roof rocks (Newman and Newman, 1982) also demonstrated a correlation of high H/C with VR suppression, although the causes of the anomalies may be more complex (Fermont, 1988; Veld and Fermont, 1990; Wenger and
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Baker, 1987). H and C values for several samples from a section of the Greta seam (Diessel et al., 1992 Fig. 8.7) and the Dartbrook coal sample (Wilkins et al., 2002) are shown on a
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modified Seyler chart in Fig. 9, where perhydrous and subhydrous coals plot respectively above and below the normal or bright coal band in the chart.
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Sherwood et al. (1999) and Zhong et al. (2000) have demonstrated the relationship between laser-induced fluorescence and hydrogen richness. In the RaMM diagrams (Fig. 8) for the Greta coals, the vitrinite points migrate to the left of the field (i.e. lower EqVR RaMM (2)) with higher fluorescence and greater degree of VR suppression; in fact for Greta 14 the
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background fluorescence is too high to record a Raman spectrum under standard experimental conditions, and vitrinite data would plot outside the field to the left of the diagram. Therefore EqVR must be determined from the inertinite spectra alone, which are
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otherwise normal. For the subhydrous coal with VR enhancement (Dartbrook 10A, Fig. 8),
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vitrinite points move to the right of the 1:1 line (i.e. higher EqVR RaMM (2)) and plot among the inertinites. By reference to the RaMM (1) equation it would appear that for the perhydrous coal, the negative contributions of Raman parameters (describing the G and D bands) for vitrinite to the determined EqVR value, is balanced by a positive contribution from the anomalously high fluorescence background (described by ‘b’ in the equation). If the fluorescence was not considered the EqVR would be more similar to the suppressed VR value. On the other hand, the positive contribution by the Raman parameters of the vitrinite of the subhydrous coal to the RaMM (1) equation is balanced by the negative contribution
ACCEPTED MANUSCRIPT of the abnormally low fluorescence background. Inertinite in these coals will be affected by the same considerations but as the fluorescence is lower, the effect on the calculated EqVR values is smaller.
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4.3 Review of the RaMM diagrams Indicating more variability in the composition of the individual macerals, none of the
samples give the tight cluster of points usually found for coals of similar maturity. Although
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the scatter of points on the RaMM diagrams of both the marine Boquillas and terrestrial Pottsville samples (Fig. 2) is larger than for coals of equivalent VR, there is no clear evidence
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of VR suppression effects such as for the Greta coals, and the mean VR values from the interlaboratory study are similar to the mean EqVRs (Table 2). The results from these shale samples may be complicated by oxidation, both being from outcrops. Solid bitumen filling tests of foraminifera (Hackley et al., 2015) in the Boquillas sample was easily avoided, and
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solid bitumen is absent in the Pottsville sample, which contains more clearly defined humic maceral populations; these have been distinguished as inertinite, vitrinite stringers, ‘vitrinite
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DOM’ (in which the grains are more equidimensional), and ‘coal vitrinite and inertinite’ occurring within rare angular multi-maceral fragments in the shale. The EqVR value for this
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sample, using the RaMM (1) equation, is derived from all vitrinite and inertinite data excluding the allochthonous coal fragments. Especially where vitrinite is rare and solid bitumen abundant, some contamination of the nominated vitrinite population is inevitable. In the preliminary search for the lowest reflecting humic maceral to define the vitrinite, it is even possible that solid bitumen may become the reference against which higher reflecting macerals are compared.
ACCEPTED MANUSCRIPT In the RaMM diagram for the Huron sample (Fig. 2), Tasmanites-related alginite plots in a distinct field. The scatter of points for grains identified as humic macerals however seems too large for a single population and in particular, a cluster of points identified as vitrinite (outlined in Fig. 2) is problematic because, at this level of thermal maturity, vitrinite typically
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plots to the left of the 1:1 line. Only with a subhydrous composition (cf. Dartbrook 10A, Fig. 8) could the points in this restricted area represent vitrinite, for which the mean RaMM
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EqVR would be 0.69%, but there is no supporting evidence. It is more likely, as the sample contains abundant solid bitumen with a reflectance close to vitrinite (Hackley et al., 2015),
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that this group represents contaminant solid bitumen. If the group is eliminated from the calculations, EqVR from the remaining points increases to 0.97%. With the probable solid bitumen removed, remaining points are still highly scattered and a second population consisting mainly of inertinite is suggested. It is also possible that some of the nominated
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humic maceral grains along the Tasmanites-related trend are misidentified small Tasmanites-related alginite fragments, in part because characteristic internal structures cannot be seen in air medium. Their removal would lower the estimated EqVR. In view of
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the uncertainties of interpretation, perhaps the best estimation of EqVR (0.87%) for this
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sample may be that obtained from all originally nominated humic maceral grains. DOM in the Bakken and Woodford samples yields RaMM diagrams unlike those of any coal we have examined. The Bakken sample (Fig. 4) gives an example of how two humic maceral populations of different origin may appear on the RaMM diagram. The mainly vitrinite field, Group 1, has a mean EqVR read from the ordinate of 0.70% (RaMM (1) being <1.0%). Because RaMM (1) EqVR for Group (2) is >1.0% and <1.25%, the EqVR is determined as 1.16% derived from averaging RaMM (1) and RaMM (2) values (i.e. 1.10%, 1.22%). Some solid bitumen clearly identified on textural features occupies a distinct third field. The lower
ACCEPTED MANUSCRIPT maturity Group 1 could be interpreted as the indigenous population, and the higher maturity Group 2, identified mainly as inertinite, could be a recycled humic maceral population.
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However, this interpretation is unlikely to be correct. For both the Bakken and Huron samples, which have similar levels of thermal maturity, the lower reflecting Group 1 falls in the field normally occupied by inertinite at this maturity. Similar to the Huron sample as
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discussed above, the most likely explanation is that this field represents a second population of solid bitumen. In this interpretation, Group 2, with RaMM EqVR = 1.16%, is the sole
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humic maceral population. Due to the small number and dispersion of data points, the determination is of low accuracy and somewhat different from the CSIRO mean random VR value of 0.92%.
The Woodford sample (Fig. 4) has a RaMM diagram similar to that for the Bakken sample, at
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first sight involving two vitrinite populations. In view of the abundant solid bitumen in the Woodford sample and the real possibility for misidentifications, Group 1, containing more
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equidimensional grains (with an EqVR of about 1.0% on the supposition it is vitrinite), more likely represents a solid bitumen population. This identification, however, cannot be made
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with as much certainty as the similar groups in the Bakken and Huron samples because its field on the diagram, straddling the 1:1 line, is possible for vitrinite. Group 2 contains some larger linear or lath-shaped grains which, checked in oil medium show bireflectance and appear to be vitrinite. If Group 2 is accepted as the vitrinite population, RaMM EqVR obtained from equation (2) is 1.37%. RaMM results for the problematic Rodiles and Pearsall samples from the first suite of samples are shown in Fig. 6 together with the VR results from participating laboratories in
ACCEPTED MANUSCRIPT the round robin exercise (Hackley et al., 2015). They are of particular interest, both because of their analytical difficulty as illustrated by the considerable scatter in results reported from different laboratories in the VR study (Hackley et al., 2015, Figs. 4E, 4F, reproduced in our Fig. 6), and because the mean RaMM EqVR results are widely discrepant with the average of
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mean random VR values of participating laboratories (Table 2). For the Rodiles sample, the mean random VR values for individual laboratories range from 0.82-1.54% with an average
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of 1.16%; the mean RaMM EqVR is 1.51%. Similarly, for the Pearsall sample, the mean
random VR of participating laboratories ranges from 1.03-1.82%, with an average of 1.53%
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(though the mode is higher), whereas the RaMM EqVR is 1.74%. Because of the wide scatter in results, VR was also measured as part of the current study. Although the differences between CSIRO VR and RaMM EqVR determinations on Rodiles and Pearsall samples are less than for the average values for all laboratories participating in the exercise, differences
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remain.
Solid bitumen is present in both of these samples (Fig. 5) but there are no distinct fields of data points that could be interpreted as solid bitumen, misidentified as vitrinite (cf. Huron,
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Bakken, Woodford samples). The data points representing vitrinite and inertinite on the
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Rodiles RaMM diagram occupy a relatively tight field for DOM, with no compelling evidence of multiple populations (Fig. 6; CSIRO VR = 1.25%). With its compact scatter of points, this RaMM diagram has greater similarity to that of the Norwich Park coal (Fig, 7, VR = 1.58%) than for that of the ‘closer-in-VR’ Bulli coal (Fig. 7, VR 1.14%), which shows the linear, horizontal scatter of data points typical of coals having EqVRs up to about 1.25%. The major difference between the Norwich Park and Rodiles sample data is the higher EqVR RaMM (1) values derived from the vitrinite in the Rodiles sample. The same feature (see Section 4.5
ACCEPTED MANUSCRIPT below) is shown in the Pearsall RaMM diagram (Fig. 6) as compared with Baralaba (VR=2.02%) and Norwich Park (VR= 1.58%) diagrams (Fig. 7). The Rodiles marl sample, taken from a seashore outcrop displays much evidence of
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weathering (Hackley et al., 2015), and is reported to contain multiple populations of recycled vitrinite and low reflecting semifusinite, though the humic component only
represents a very small proportion of the total OM (Borrego et al., 1996; Hackley et al.,
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2015). Marchioni (1983) recorded a decrease in VR of 0.15% absolute due to outcrop
oxidation of a fresh coal with Romax of 1.2%. Lo and Cardott (1995) compared weathering
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effects of OM in Upper McAlester coal and Woodford shale and found the lowering of VR, which occurred in both cases, to be greater in coal than in the shale. It is unknown if all laboratories who reported on the Rodiles sample have removed what were identified as recycled vitrinites before calculating mean VR. As an example of the effects of recycled
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vitrinite on VR, Houseknecht and Matthews (1985) considered that the removal of obviously recycled vitrinite measurements for Carboniferous shales of the Ouachita Mountains would reduce reflectance means by 0.1 to 0.3% absolute. The fluorescence parameter used in the
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RaMM calculations, although compromised for weathered surfaces, is usually unaffected for
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DOM within shaly rocks (Wilkins et al., 1998) though it is doubtful if this would also hold for extensively weathered samples. At this level of thermal maturity the expected effect of a decrease in fluorescence due to oxidation would be, as seen from the RaMM (2) equation (Section 2), an increase in the calculated EqVR. Taken together, all of these factors do not seem to us to be sufficient explanations for the large discrepancy between RaMM EqVR and VR for this sample, although they may play a role.
ACCEPTED MANUSCRIPT Of the gas shale and tight oil suite of samples, there is little on which to comment for the higher maturity samples with VR>1.8%. Where data are sufficient, the RaMM diagrams appear unremarkable (e.g. Eagle Ford, Barnett; Figs. 3, 4), though with a greater dispersion of data than for coals of similar maturity (Fig. 7). Whereas Eagle Ford and Barnett formation
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samples give a range of EqVR of ~0.6% each, compared to ~0.4% for the VR, both EqVR and VR from the Haynesville and Marcellus formation samples (Fig. 3) give a wider range, and for
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these samples the possibility of multiple populations is in play. However, there is little
evidence from these four gas shale samples that the RaMM EqVR results from solid bitumen
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would be distinguishable from the associated humic macerals with a larger collection of data. This is consistent with the closing gap between solid bitumen and vitrinite reflectance at higher maturity observed by other authors (Robert, 1988; Landis and Castano, 1995; Schoenherr et al., 2007; Ferreiro Mahlmann and Le Bayon, 2016; Wei et al., 2016). It is
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stressed that an equivalent solid bitumen reflectance, EqBR%, cannot be deduced from the RaMM results because solid bitumen data were not included in the reference calibration suite.
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4.4 Terrestrial vs. Marine DOM
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The discrepancies between RaMM EqVR and VR for the Rodiles, Pearsall and Bakken samples are difficult to explain because we have not been able to source critical calibration material, such as marine-influenced coals in which VR suppression is likely at this level of thermal maturity (viz. VR 1.2- 1.7%), such that consideration can be made of the high vitrinite fluorescence relative to normal coals in this range, as recorded for a suite of Ruhr coals with marine roof rocks by Diessel (1990).
ACCEPTED MANUSCRIPT As we have noted above, displacements of the vitrinite field relative to inertinite on RaMM diagrams, related to compositional differences, are horizontal for the lower maturity range (VR < 1%); movements of the vitrinite field relative to inertinite for the higher maturity range (VR > 1.25%) are vertical. Throughout this higher maturity range, vitrinite in terrestrial
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rocks occupies the lower to middle part of the humic maceral array in RaMM diagrams. This investigation is expanded in Fig. 10, which shows mean EqVR RaMM (1) values for
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vitrinite of terrestrial and marine rocks having EqVR values >~1%. For rocks of this thermal maturity these values are not indicative of EqVR because rather the RaMM (2) equation is
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applicable, but RaMM (1) is used in the figure to show the effects of depositional environment on the RaMM spectral characteristics of vitrinite, as referred to above for vitrinite and inertinite. The terrestrial samples comprise eight of our reference Australian Permian coal samples, seven Ruhr Carboniferous coals (see Wilkins et al., 2015) and the
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Pottsville shale; the marine samples comprise six gas shale and tight oil samples of the 2016 interlaboratory ASTM D7708 exercise, plus the Rodiles and Pearsall samples (Hackley et al., 2015). The low maturity Boquillas and Huron marine samples cannot be compared on the
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same basis because they fall in the region (EqVR < 1.0%) where RaMM (1) is constrained by
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thermal maturity. For the terrestrial samples, all mean EqVR RaMM (1) for vitrinite are within a well-defined horizontal trend between EqVR 0.9 and 1.1%. For all marine shales, however, mean RaMM (1) EqVR values plot above this trend in a field which currently is poorly defined because of the few available data. There are several possibilities to explain these rather surprising observations. The first is the possibility of experimental inconsistencies because coal and gas shales were examined in different projects at different times. However, protocols exist to maintain experimental
ACCEPTED MANUSCRIPT conditions over time as best as possible and repeats on several of the coal samples gave very similar results to those previously recorded. Secondly, some designated ‘vitrinite’ could be mis-identified solid bitumen. Solid bitumen is difficult enough to distinguish from vitrinite examined in plane polarised light under oil immersion, and with our Raman microprobe the
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identifications are based only on form, brightness, and the shape of the Raman spectrum, the examinations being done in air, in non-polarised light. Both the Rodiles and Pearsall
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samples however have void-filling bitumens (Hackley et al., 2015) and they were avoided, but some grains that mimic vitrinite in textural characteristics could have been included. In
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any case, solid bitumens we identified in the higher maturity gas shales seem to respond to RaMM in much the same way as the humic macerals, as far as our data allow comparison. RaMM (1) values for inertinites of marine, compared to terrestrial samples, are also increased though not to the same extent.
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The vertical displacements of the vitrinite field on RaMM diagrams could be considered the higher maturity equivalent of the horizontal displacements of vitrinite in the perhydrous and subhydrous Greta and Dartbrook coals at moderate maturity levels. Fluorescence intensity
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measured at 650 nm of both vitrinite and inertinite in orthohydrous coals peaks at about VR
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1-1.2 % and is almost extinguished at VR 2% (Diessel, 1990). The fluorescence intensities for vitrinites of the Rodiles and Pearson samples, judged from the backgrounds to the Raman spectra, are about twice that of ‘normal’ coals of similar VR. This observation would suggest a VR suppression effect for the Rodiles and Pearson samples, an effect that would diminish approaching a VR of 2%, leaving the EqVR of many gas shale samples little affected. However, there is at present very little direct evidence to support this suggestion.
ACCEPTED MANUSCRIPT We can conclude, for at least the suite of samples shown in Fig. 10, there are some differences in the chemistry and/or molecular structure that distinguish the marine from the terrestrial vitrinites. The unresolved question is, what effect do these differences have on
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their VR and RaMM EqVR? 5. Conclusions
In this study RaMM has been applied to a range of rock samples, which in terms of
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geological age, thermal maturity, total organic carbon, and degree of bituminization are such as would be encountered in a commercial laboratory. Although our RaMM analyses
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were carried out rapidly, at the rate of 2-3 samples per day, it was possible to obtain results that for the most part compared reasonably well with available VR data, especially considering the complexities involved for many of the samples, as indicated by the wide spread in VR values determined by the different laboratories participating in the first VR
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round-robin exercise (Hackley et al., 2015). Notable and unresolved discrepancies between VR and RaMM EqVR were found for three marine rock samples (Rodiles, Pearsall, Bakken) in
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the range VR = 0.9 -1.8%. These discrepancies may be related to observed, consistently higher values of EqVR (1) for vitrinites of marine sedimentary rocks relative to terrestrial
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vitrinites in samples with EqVR >1%, at least in the sample suite available. It is clear that complex samples containing possible reworked humic matter populations and/or abundant solid bitumen require more intensive examination than was given in our exploratory study. RaMM was designed for the determination of EqVR on humic macerals in any proportion. As maceral misidentification may have important consequences for the derivation of RaMM EqVR values, inclusion of other OM in the suite of data, in particular solid bitumen and liptinite such as Tasmanites-related alginite needed to be avoided. For shale samples heavily
ACCEPTED MANUSCRIPT impregnated with solid bitumen having about the same reflectance as vitrinite, discrimination was often difficult as the samples were studied in air medium. The RaMM plot of EqVR (1) vs EqVR (2) derived from the two equations that define the technique, was introduced to test for different OM populations in samples in which they may be suspected
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from organic petrological study, but unproven. The homogeneity of the OM population
could be confirmed, or different populations defined, by their groupings on the plot. The
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definition of fields on the RaMM diagram occupied by vitrinite and inertinite of normal coals at different levels of thermal maturity was explored, and a study initiated on how these
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fields change with perhydrous or subhydrous character of the macerals. How a fuller understanding of maceral fields could lead to a better discrimination of humic macerals and solid bitumens is illustrated by RaMM results on the Bakken and Woodford samples in this study. Nevertheless, similar as for conventional VR analyses, misidentified vitrinite in
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samples containing abundant solid bitumen remains a problem for some samples, and a preliminary examination of such samples is recommended to establish the solid bitumen field on RaMM diagrams before measurements are taken on the humic macerals. Samples
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containing complex assemblages including possible reworked OM, could benefit by
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subsequent examination in oil medium to resolve possible alternative interpretations. The study has clarified the benefits and limitations of applying RaMM for thermal maturity determination on DOM in challenging samples. Minerals with similar reflectance to vitrinite cannot be confused with OM because their spectra are distinct. The discrimination of vitrinite and inertinite is of little importance because EqVR results are obtained by calculations from data combined from both. In complex samples, the RaMM diagram aids in eliminating misidentified macerals. The major disadvantage of RaMM is that the samples are examined in air medium, and without a polarizer, such that maceral discrimination is not
ACCEPTED MANUSCRIPT as accurate as for samples conventionally examined in oil medium with a polarizer. Although RaMM applied to samples of low complexity requires only a passing identification of the measured macerals, it could be a more powerful tool in the hands of a skilled organic
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petrologist, where there are multiple populations of OM.
6. Acknowledgements
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This study was made possible through the courtesy of Paul Hackley of the US Geological
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Survey who provided materials, data and encouragement for our study. We are pleased to acknowledge Roger Boudou and Min Wang for their considerable contributions in establishing the CSIRO Raman facility and computational methods for the RaMM technique. We also thank Walter Pickel for samples and discussion and Paul Marvig for his usual high
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quality sample preparations. We also appreciate Paul Hackley and Xiao Xianming as reviewers of the paper who provided valuable feedback that led to significant improvements. This research did not receive any specific grant from funding agencies in the
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public, commercial, or not-for-profit sectors. 7. References
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Araujo, C. V., Borrego, A. G., Cardott, B., Das Chagas, R. B. A., Flores, D., Gonçalves, P., Hackley, P. C., Hower, J. C., Kern, M. L., Kus, J., Mastalerz, M., Mendonça Filho, J. G., de Oliveira Mendonça, J., Menezes, T. R., Newman, J., Suarez-Ruiz, I., Da Silva, F. S., de Souza, I. V., 2014. Petrographic maturity parameters of a Devonian shale maturation series, Appalachian Basin, USA. ICCP Thermal Indices Working Group interlaboratory exercise. International Journal of Coal Geology 130, 89-101. ASTM D7708-14, 2014. Standard Test Method for Microscopical Determination of the Reflectance of Vitrinite Dispersed in Sedimentary Rocks Australian Standard AS 2856-2000.1. Preparation of coal samples for incident light microscopy. Standards Association of Australia, 6pp.
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parameters and reflectance of solid bitumen. International Journal of Coal Geology 121, 19-25.
ACCEPTED MANUSCRIPT Figure captions Fig. 1 Idealised RaMM diagram showing vitrinite and inertinite fields for DOM, for samples having VRs = 0.45, 0.9 and 2.0%. See text for definition of axes. Arrows indicate how EqVR is read for the low maturity range 0.4 - 1.0% and high maturity range 1.25 – 2.5%. In the
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intermediate range, values are averaged. Fig. 2 RaMM diagrams for Boquillas, Huron and Pottsville formation samples. Arrows show determinations of EqVR. Outlined points in the Huron diagram are possibly misidentified solid bitumen.
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Fig. 3 RaMM diagrams for Marcellus, Haynesville and Eagle Ford formation samples. Arrows show determinations of EqVR.
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Fig. 4 RaMM diagrams for Barnett, Bakken and Woodford formation samples. Arrows show determinations of EqVR. For the Bakken and Woodford samples, Group 1 is interpreted as misidentified solid bitumen and Group 2 the humic maceral population. See discussion in text.
Fig. 5 Reflectance histograms for all samples analysed petrographically as part of the
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present study, showing mean random vitrinite reflectance values for each sample and relationships of the various maceral reflectances; bitumen refers to ‘solid bitumen’. Fig. 6 RaMM diagrams for Rodiles and Pearsall formation samples and histograms of mean
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random vitrinite reflectance results of participating laboratories in the ASTM D7708 round robin exercise (after Hackley et al., 2015, Figs 4E, 4F). Arrows show determinations of EqVR.
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Fig. 7 RaMM diagrams for Norwich Park, Bulli and Baralaba Permian coal samples from eastern Australia. Arrows show determinations of EqVR. Fig. 8 RaMM diagrams for perhydrous Greta 10, Greta 14, and subhydrous Dartbrook 10A Permian coal samples from eastern Australia. Arrows show determinations of EqVR. Fig. 9 Modified Seyler Chart plot of coal samples from the Greta Seam, Hunter Valley, New South Wales plotting in the perhydrous field above the normal or bright coal band compared with the Dartbrook coal (10A) plotting in the sub-hydrous field below this band (after Fig. 8.7 in Diessel, 1992).
ACCEPTED MANUSCRIPT Fig. 10 Plot of mean RaMM (1) EqVR values against mean random vitrinite reflectances for terrestrial and marine rocks with EqVR values >~1%. For the marine rocks, VR values are used from the present study where available; others represent means of values from all the
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round robin laboratories (Hackley et al., 2015).
ACCEPTED MANUSCRIPT Figure. 1 RaMM Diagram inertinite
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Average RaMM (1) EqVR%
Terrestrial vs Marine Vitrinite Terrestrial Coals Marine DOM Terrestrial Coals Trend Marine DOM Trend
ACCEPTED MANUSCRIPT Figure. 2 Sample 2: Boquillas Formation EqVR 0.54%, VR 0.50% inertinite
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Sample 3: Huron Formation EqVR 0.87%, VR 0.80% vitrinite
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Sample 7: Marcellus EqVR 2.14%, VR 2.41% inertinite
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Sample 8: Haynesville EqVR 2.03%, VR 2.18%
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ACCEPTED MANUSCRIPT Figure 4 Sample 10: Barnett EqVR 1.95%, VR 1.83% inertinite
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Sample 11: Bakken EqVR 1.16%, VR 0.92% inertinite
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Bitumen Bitumen
Sample Formation, VR=1.25% Sample 4: 7: Rodiles Marcellus Formation; VR=2.41%
Inertinite Inertinite
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Sample 6: Pearsall Formation, VR=1.58%
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ACCEPTED MANUSCRIPT Sample 8: Haynesville Formation, VR=2.18%
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Sample 9: Eagle Ford Formation, VR=2.14%
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Sample 10: Barnett Formation, VR=1.83%
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Sample 11: Bakken Formation, VR=0.92%
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Sample 12: Woodford Formation, VR=1.41%
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Figure 6.
EqVR=1.51%, CSIRO VR=1.25% inertinite
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Bulli Seam Coal: EqVR 1.13%, VR 1.14% inertinite
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1.5 1.0 0.5 0.0 0.5
1.0 1.5 EqVR% RaMM (2)
2.0
inertinite
1.5 1.0 0.5 0.0 0.0
0.5
vitrinite
TE D
EqVR% RaMM (1)
2.0
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Norwich Park Coal: EqVR 1.54%, VR 1.58%
1.0
2.5
SC
0.0
1.5
2.0
2.5
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EqVR% RaMM (2)
Baralaba Coal Measures: EqVR 1.94%, VR 2.02% inertinite
vitrinite
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EqVR% RaMM (1)
2.0 1.5 1.0 0.5 0.0
0.0
0.5
1.0
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EqVR% RaMM (1)
2.0
1.5
EqVR% RaMM (2)
2.0
2.5
ACCEPTED MANUSCRIPT Figure 8.
Greta Coal Seam (10): EqVR 0.72%, VR 0.64% inertinite
vitrinite
1.0
1.5
1.5
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EqVR% RaMM (1)
2.0
1.0 0.5 0.0 0.5
2.0
EqVR% RaMM (2)
inertinite
1.5 1.0 0.5 0.0 0.0
0.5
TE D
EqVR% RaMM (1)
2.0
M AN U
Greta Coal Seam (14): EqVR 0.73%, VR 0.50%
1.0
2.5
SC
0.0
1.5
2.0
2.5
EP
EqVR% RaMM (2)
Dartbrook Coal Mine (10A): EqVR 0.70%, VR 0.90% vitrinite
AC C
inertinite
EqVR RaMM (1)
2.0 1.5 1.0 0.5 0.0
0.0
0.5
1.0
1.5
EqVR RaMM (2)
2.0
2.5
ACCEPTED MANUSCRIPT
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Figure 9.
ACCEPTED MANUSCRIPT
RaMM study of samples used in an interlaboratory exercise on a standard test method for determination of vitrinite reflectance on dispersed organic matter in rocks
RI PT
Ronald W. T. Wilkins, Neil Sherwood* and Zhongsheng Li
Highlights
Demonstrating ‘RaMM’ for maturity analyses of clastic rocks
•
Samples range in RaMM equivalent vitrinite reflectance (EqVR) from ~0.55-2.15%
•
RaMM data in general agreement with average VR results from the participating labs
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Some differences between EqVR and VR due to multiple organic populations
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Also due to differences in nature of marine and terrestrial humic organic matter
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•