Oil shales contain abundant immature organic matter and are a potential unconventional petroleum resource. Prior studies have used visible/shortwave infrared imaging spectroscopy to map surface exposures of deposits from satellite and airborne platforms and image cores in the laboratory. Here, we work at an intermediate, outcrop-scale, testing the ability of field-based imaging spectroscopy to identify oil shale strata and characterize the depositional environments that led to enrichment of organic matter in sedimentary rocks within the Green River Formation, Utah, USA. The oil shale layers as well as carbonates, phyllosilicates, gypsum, hydrated silica, and ferric oxides are identified in discrete lithologic units and successfully mapped in the images, showing a transition from siliciclastic to carbonate- and organic-rich rocks consistent with previous stratigraphic studies conducted with geological fieldwork

Birgenheier, Lauren P.

Ehlmann, Bethany L.

Green, Robert O.

Greenberger, Rebecca N.

Jewell, Paul W.

Rebecca N. Greenberger

Bethany L. Ehlmann

Paul W. Jewell

Lauren P. Birgenheier

Robert O. Green

Crossref

Detection of organic-rich oil shales of the green river formation, Utah, with ground-based imaging spectroscopy

Caltech Authors - Main

DETECTION OF ORGANIC-RICH OIL SHALES OF THE GREEN RIVER FORMATION, 
UTAH, WITH GROUND-BASED IMAGING SPECTROSCOPY 
 
Rebecca N. Greenberger1, Bethany L. Ehlmann1,2, Paul W. Jewell3, Lauren P. Birgenheier3, Robert O. 
Green1 
 
1Jet Propulsion Laboratory, California Institute of Technology, 4800 Oak Grove Drive, Pasadena, CA 
91109, USA, Rebecca.N.Greenberger@jpl.nasa.gov 
2California Institute of Technology, Div. of Geological and Planetary Sci.,1200 E. California Blvd, 
Pasadena, CA 91125, USA 
3University of Utah, Dept. of Geology and Geophysics, 115 S. 1460 East, Salt Lake City, UT 84112 
 
ABSTRACT 
 
Oil shales contain abundant immature organic matter and are 
a potential unconventional petroleum resource. Prior studies 
have used visible/shortwave infrared imaging spectroscopy 
to map surface exposures of deposits from satellite and 
airborne platforms and image cores in the laboratory. Here, 
we work at an intermediate, outcrop-scale, testing the ability 
of field-based imaging spectroscopy to identify oil shale 
strata and characterize the depositional environments that 
led to enrichment of organic matter in sedimentary rocks 
within the Green River Formation, Utah, USA. The oil shale 
layers as well as carbonates, phyllosilicates, gypsum, 
hydrated silica, and ferric oxides are identified in discrete 
lithologic units and successfully mapped in the images, 
showing a transition from siliciclastic to carbonate- and 
organic-rich rocks consistent with previous stratigraphic 
studies conducted with geological fieldwork. 
 
Index Terms— imaging spectroscopy, hyperspectral, 
oil shale, Green River Formation, sequence stratigraphy 
 
1. INTRODUCTION 
 
Oil shales form in unique depositional environments and 
contain thermally immature organics that can be processed 
to produce usable oil resources [1], [2]. The largest known 
deposit of oil shales is located in the Eocene-age Green 
River Formation of eastern Utah, Wyoming, and western 
Colorado [1]. In the portion of the Formation within the 
Uinta Basin, Utah, in-place resources are estimated to be 
1.32 trillion barrels of oil [3]. 
 The organics within oil shales have distinct 
visible/shortwave infrared (VSWIR) reflectance properties 
that allow them to be identified within matrices of mineral 
constituents in rocks and sediments [4], [5]. Imaging 
spectroscopy has been previously used to evaluate potential 
oil resources from airborne and satellite platforms [6]–[8] 
and in the laboratory [9], [10]. A newly emerging geologic 
technique is ground-based imaging spectroscopy, which 
permits rapid identification and mapping of compositional 
units at outcrop scale with a resolution of a few centimeters 
[11]. Here, we show the results of a study using imaging 
spectroscopy in the field to map sedimentary rock strata, 
including oil shales, of the Green River Formation near 
Vernal, Utah, USA. The goals of this work are to determine 
the utility of this technique to identify and map sedimentary 
rock lithologies, discriminate the presence and quality of oil 
shales, and understand the environmental conditions leading 
to formation of organic-rich strata. 
 
2. GEOLOGICAL SETTING 
 
The sedimentary rocks of the Green River Formation were 
deposited in tectonically-formed lakes during the middle 
Eocene [1], [2] and are at least 2100 feet thick [12]. 
Broadly, as lake level rose through time, siliciclastic input 
decreased, resulting in preservation of organic matter [2]. 
The most organic-rich portion of the Green River Formation 
in the Uinta basin, the Mahogany zone, formed at this time, 
and the organic matter present is largely algal in origin [13]–
[15]. Two sedimentary sequences comprising portions of the 
Green River Formation were investigated. The first was an 
outcrop ~150 m high known informally as the “Condo” 
section near Evacuation Creek located at 39° 50.109’ N, 
109° 7.111’ W [2]. This section contains alternations 
between siliciclastic-rich and carbonate- and oil shale-rich 
intervals and has been described in detail by [2], providing a 
geologic framework for interpretation of the imaging 
spectroscopy data. Visible at the top of the section are 
several darker layers of the Mahogany zone, which contains 
the richest oil shale beds in the basin.  A Mahogany zone 
outcrop was investigated close-up at 39° 50.76’ N, 109° 
7.42’ W. 
 
3. METHODS 
 
Images were acquired with the California Institute of 
Technology imaging spectrometer system, built by Headwall 
Photonics, Inc. This system contains visible-near infrared 
(VNIR) and shortwave infrared (SWIR) sensors co-
boresighted on an optical bench for simultaneous acquisition 
of spectral imaging data spanning 0.4 to 2.6 µm. The VNIR 
sensor is a 2560x2160 pixel CMOS array, which samples 
wavelengths 0.4-1.0 µm at 1.625 nm intervals with 5 nm 
spectral resolution. The SWIR sensor is a 640x512 pixel 
Stirling-cooled MCT focal plane array that covers 
wavelengths 0.97-2.60 µm with 6 nm spectral resolution and 
spectral sampling. This pushbroom imaging spectrometer is 
mounted atop a tripod and a rotational stage to scan an 
outcrop line-by-line and create an image. The typical frame 
rates are 10-40 ms, requiring <5 min. for a 180° panorama. 
 After acquisition, images were calibrated to reflectance. 
First, a dark current measurement taken in the field was 
subtracted. Next, a flat field correction was performed using 
multiplicative factors derived from laboratory measurements 
of an integrating sphere. These corrections removed spectral 
and spatial non-uniformities. Corrections for atmospheric 
scattering and absorption and conversion to reflectance were 
performed either using the empirical line method [16], [17], 
derived from the averaged spectra of three Labsphere 
calibration targets in the scene (approximate reflectances of 
99%, 20%, and 5%) or using a dark object subtraction [18] 
and multiplicative correction using the 99% calibration 
panel. Finally, observed offsets between the VNIR and 
SWIR detectors, which likely result from slight variations in 
detector response or errors in co-registration, were removed 
using a multiplicative factor, scaling SWIR reflectance at 
969.3 nm to VNIR reflectance at 968.5 nm.  
 Spectral parameters [19] were calculated to map 
absorption features that result from specific molecular 
vibrations or electronic transitions within mineral structures. 
Combinations of spectral parameters [20] were then used to 
map specific mineralogies. Vegetation was identified and 
masked using a linear unmixing algorithm, and shadows 
were masked where possible using band ratios to highlight 
artifacts in atmospheric absorption bands that generally were 
highest in the darkest shadows. A linear unmixing algorithm 
was attempted to map lithologies but could not distinguish 
fine shifts in the center wavelengths of spectral features 
indicative of different compositional units and was 
overwhelmed by differences in slope and iron oxide 
associations. Combinations of spectral parameters tuned to 
the features of interest proved most successful at 
distinguishing the lithologies present here. 
 
 
Figure 1. a) Reflectance spectra of some lithologies present at 
the investigated outcrops labeled with the spectral 
interpretation, band centers (in µm), and band assignments. 
Zones of atmospheric absorption have been partially masked, 
and the VNIR portion of each spectrum was smoothed with a 
moving average. b) Library spectra from the USGS library [24]. 
Band assignments are from [5], [21], [23], [25]. 
4. RESULTS 
 
The following lithologies were identified through 
spectroscopy (Fig. 1): gypsum, mixed Fe/Mg/Al-bearing 
phyllosilicates including smectites and illite, Fe3+-oxides, 
calcite, dolomite, Si-OH bearing materials, and lithologies 
with high organic content (oil shales) (e.g., [21]–[23]). 
Mineral identifications through spectroscopy are consistent 
with the literature from prior geologic mapping [2] with the 
exception of gypsum, which was not identified previously 
and seems to occur as weathering products or in enriched 
lenses. The oil shales (bottom spectrum in Fig. 1a) have 
spectra characterized by an absorption feature at 1.725 µm 
and a steep drop in reflectance followed by multiple 
absorption features at 2.30 µm. The relatively strong feature 
at 1.725 µm has previously been attributed to overlapping C-
 
Figure 2. a) Approximate true color composite image of a portion of an image of the Mahogany formation. b) Approximate true color 
image with the strength of the 1.725 µm absorption feature that indicates organic material overlain. Data are only shown where there 
were not strong indications of other minerals (e.g., gypsum).  Vegetation, the darkest shadows, and the calibration targets have been 
masked in the BD1725 overlay.  
 
 
Figure 3. a) Approximate true color composite image of Condo section sedimentary strata. The dark layers near the top are Mahogany 
Formation. b) Spectral parameter image with legend shown to the right. Vegetation has been masked. c) Stratigraphy of the outcrop 
inferred from imaging spectroscopy with colors corresponding to the legend and (b). Gypsum occurs in enriched lenses and/or 
weathering products. d) Stratigraphy from [2] created with traditional sedimentology field methods. Blue zones have higher organic 
content, and yellow zones have lower organic content. See [2] for additional information about colors and symbols. Dashed lines show 
approximate correlations between (c) and (d) strata. 
H stretching overtones and combination tones [5]. The 
absorption features longward of 2.30 µm are due to 
combinations of C-H bending and stretching fundamentals 
[5]. Absorptions at ~1.7 µm and ~2.3 µm also occur in 
gypsum and carbonates or Fe/Mg phyllosilicates, 
respectively; however, the spectral signature of organics can 
be separated by the co-occurrence of the features, the 
slightly different central position of the 1.725 µm feature 
(vs. 1.75 µm in gypsum), and a strongly downward sloping 
reflectance in VNIR wavelengths. 
Most of the in situ rocks of the Mahogany zone have 
spectra dominated by absorption features due to organics 
(Fig. 2), though occasional layers or ledges of other phases, 
mostly carbonates (calcite and dolomite), gypsum, and 
mixed Fe/Mg/Al-bearing clays, are present. Mapping the 
strength of the 1.725 µm C-H absorption feature shows 
layers with higher inferred organic content (reds and yellows 
in Fig. 2b). Mapping the larger-scale Condo outcrop with its 
>100 m of sedimentary rocks exposed in the section (Fig. 3), 
quartz- and silica-rich lithologies such as sandstone or 
aluminum phyllosilicate-rich mudstones generally dominate 
spectrally in the bottom portion of the outcrop whereas 
carbonate-rich units (limestone and dolostone) and organic-
rich oil shales characterize the upper portion.  
 
5. DISCUSSION 
 
These ground-based imaging spectroscopy analyses fill a 
gap in scale between orbital and laboratory imaging 
spectroscopy measurements of similar materials [6]–[10] 
and provide the advantage of mapping the types of vertical 
exposures commonly seen in field settings that are difficult 
or impossible to map from airborne platforms. The inferred 
stratigraphy from imaging spectroscopy here (Fig. 3c) 
compares well with the sequence stratigraphy work of [2] 
using traditional sedimentary and core analysis techniques 
(Fig. 3d) and adds additional data on the variation in 
mineralogy of the strata around the organic-rich zones. In 
addition, the depositional environments and changing 
environmental conditions that led to the preservation of 
abundant organic matter can be inferred from these data by 
analyses of the sequence stratigraphy of siliciclastic- and 
carbonate-rich facies associated with the oil shales. At a 
broad scale, there is a transition from spectra consistent with 
siliciclastic material likely fluvial in origin to carbonates and 
organics (lacustrine), while at higher resolution alternations 
between siliciclastic and carbonate layers are present. These 
results are consistent with interpretations by [2] of a 
deepening lake in the Uinta Basin lowering siliciclastic input 
and leading to the generation and preservation of carbonate 
and organic-rich rocks higher in the section. 
 
6. CONCLUSIONS 
 
Imaging spectroscopy of Green River Formation 
sedimentary rocks containing oil shale strata successfully 
identifies and maps organic-rich oil shales as well as 
carbonates, phyllosilicates, gypsum, and ferric oxide 
minerals and provides the ability to do sequence 
stratigraphy. Mapping locations of oil shales helps remotely 
identify this potential resource, and lithologic discrimination 
provides insights into the creation of these organic-rich 
shales. Collectively, the data indicate ground-based imaging 
spectroscopy is a powerful new tool for sedimentary 
sequence stratigraphy on Earth and other planets (e.g., 
Mars). 
 
7. ACKNOWLEDGMENTS 
 
We thank Jen Morris and Julia Howe for field assistance. 
RNG was supported by an appointment to the NASA 
Postdoctoral Program at the Jet Propulsion Laboratory, 
California Institute of Technology, administered by 
Universities Space Research Association under contract with 
NASA, and PJW and LPB were partially supported by a 
2014-2015 grant from the Utah Geological Survey. A NASA 
Planetary Major Equipment grant (NNX13AG74G) and 
Rose Hills Foundation grant provided partial support to BLE 
for purchase of the imaging spectrometer system. © 2016. 
All rights reserved. 
 
8. REFERENCES 
 
[1] J. R. Dyni, Geology and resources of some world oil-shale 
deposits. US Dept. of the Interior, USGS Scientific Investigations 
Report 2005-5294, 2006. 
 
[2] M. J. Rosenberg, L. P. Birgenheier, and M. D. V. Berg, 
“Facies, Stratigraphic Architecture, and Lake Evolution of the Oil 
Shale Bearing Green River Formation, Eastern Uinta Basin, Utah,” 
in Stratigraphy and Paleolimnology of the Green River Formation, 
Western USA, M. E. Smith and A. R. Carroll, Eds. Springer 
Netherlands, 2015, pp. 211–249. 
 
[3] USGS Oil Shale Assessment Team, “Oil Shale resources of 
the Uinta Basin, Utah and Colorado,” US Geol. Surv. Digit. Data 
Ser. DDS–69–BB 7 Chapters, 2010. 
 
[4] E. A. Cloutis, M. J. Gaffey, and T. F. Moslow, 
“Characterization of minerals in oil sands by reflectance 
spectroscopy,” Fuel, vol. 74, no. 6, pp. 874–879, Jun. 1995. 
 
[5] E. A. Cloutis, “Spectral Reflectance Properties of 
Hydrocarbons: Remote-Sensing Implications,” Science, vol. 245, 
no. 4914, pp. 165–168, Jul. 1989. 
 
[6] B. Hörig, F. Kühn, F. Oschütz, and F. Lehmann, “HyMap 
hyperspectral remote sensing to detect hydrocarbons,” Int. J. 
Remote Sens., vol. 22, no. 8, pp. 1413–1422, May 2001. 
 
[7] F. Kühn, K. Oppermann, and B. Hörig, “Hydrocarbon Index – 
an algorithm for hyperspectral detection of hydrocarbons,” Int. J. 
Remote Sens., vol. 25, no. 12, pp. 2467–2473, Jun. 2004. 
 
[8] D. Xu, G. Ni, T. Jiang, L. Jiang, and M. Chi, “Integration of 
field work and hyperspectral data for oil and gas exploration,” in 
Geoscience and Remote Sensing Symposium, 2007. IGARSS 2007. 
IEEE International, 2007, pp. 3194–3197. 
 
[9] M. Speta, B. Rivard, J. Feng, M. Lipsett, and M. Gingras, 
“Hyperspectral imaging for the determination of bitumen content 
in Athabasca oil sands core samples,” AAPG Bull., vol. 99, no. 7, 
pp. 1245–1259, Jul. 2015. 
 
[10] M. Speta, B. Rivard, J. Feng, M. Lipsett, and M. Gingras, 
“Hyperspectral imaging for the characterization of athabasca oil 
sands drill core,” in Geoscience and Remote Sensing Symposium 
(IGARSS), 2013 IEEE International, 2013, pp. 2184–2187. 
 
[11] R. N. Greenberger, J. F. Mustard, B. L. Ehlmann, D. L. 
Blaney, E. A. Cloutis, J. H. Wilson, R. O. Green, and A. A. 
Fraeman, “Imaging spectroscopy of geological samples and 
outcrops: Novel insights from microns to meters,” GSA Today, vol. 
25, no. 12, pp. 4–10, 2015. 
 
[12] W. B. Cashion, “Geology and Fuel Resources of the Green 
River Formation, Southeastern Uinta Basin, Utah and Colorado,” 
USGS Prof. Pap., vol. 548, Jan. 1967. 
 
[13] J. R. Dyni, “Oil-shale resources of the Mahogany Zone in 
eastern Uinta Basin, Uintah County, Utah,” U.S. Geological 
Survey, USGS Numbered Series 91-285, 1991. 
 
[14] M. D. Vanden Berg, “Basin-wide evaluation of the 
Uppermost Green River Formation’s Oil-Shale Resource, Uinta 
Basin, Utah and Colorado,” Utah Geological Survey Special Study 
128, 2008. 
 
[15] L. P. Birgenheier and M. D. Vanden Berg, “Core-based 
Integrated Sedimentologic, Stratigraphic, and Geochemical 
Analysis of the Oil Shale Bearing Green River Formation, Uinta 
Basin, Utah,” US DOE, Oil & Natural Gas Technology Topical 
Report, Apr. 2011. 
 
[16] R. J. Aspinall, W. A. Marcus, and J. W. Boardman, 
“Considerations in collecting, processing, and analysing high 
spatial resolution hyperspectral data for environmental 
investigations,” J. Geogr. Syst., vol. 4, no. 1, pp. 15–29, Mar. 
2002. 
 
[17] J. E. Conel, R. O. Green, G. Vane, C. J. Bruegge, R. E. Alley, 
and B. J. Curtiss, “AIS-2 radiometry and a comparison of methods 
for the recovery of ground reflectance,” 1987. 
 
[18] P. S. Chavez, “An improved dark-object subtraction 
technique for atmospheric scattering correction of multispectral 
data,” Remote Sens. Environ., vol. 24, no. 3, pp. 459–479, Apr. 
1988. 
 
[19] R. N. Clark and T. L. Roush, “Reflectance Spectroscopy: 
Quantitative Analysis Techniques for Remote Sensing 
Applications,” J. Geophys. Res., vol. 89, no. B7, p. PP. 6329–
6340, 1984. 
 
[20] R. N. Greenberger, J. F. Mustard, E. A. Cloutis, P. Mann, J. 
H. Wilson, R. L. Flemming, K. M. Robertson, M. R. Salvatore, 
and C. S. Edwards, “Hydrothermal alteration and diagenesis of 
terrestrial lacustrine pillow basalts: coordination of hyperspectral 
imaging with laboratory measurements,” Geochim. Cosmochim. 
Acta, vol. 171, pp. 174–200, 2015. 
 
[21] G. R. Hunt and J. W. Salisbury, “Visible and near infrared 
spectra of minerals and rocks. II. Carbonates,” Mod. Geol., vol. 2, 
pp. 23–30, 1971. 
 
[22] G. R. Hunt and R. P. Ashley, “Spectra of altered rocks in the 
visible and near infrared,” Econ. Geol., vol. 74, no. 7, pp. 1613–
1629, Nov. 1979. 
 
[23] R. N. Clark, T. V. V. King, M. Klejwa, G. A. Swayze, and N. 
Vergo, “High Spectral Resolution Reflectance Spectroscopy of 
Minerals,” J. Geophys. Res., vol. 95, no. B8, pp. 12653–12680, 
1990. 
 
[24] R. N. Clark, G. A. Swayze, R. Wise, E. Livo, T. Hoefen, R. 
Kokaly, and S. J. Sutley, “USGS digital spectral library splib06a: 
US Geological Survey, data series 231. 2007. 
 
[25] E. A. Cloutis, F. C. Hawthorne, S. A. Mertzman, K. Krenn, 
M. A. Craig, D. Marcino, M. Methot, J. Strong, J. F. Mustard, D. 
L. Blaney, J. F. Bell III, and F. Vilas, “Detection and 
discrimination of sulfate minerals using reflectance spectroscopy,” 
Icarus, vol. 184, no. 1, pp. 121–157, Sep. 2006. 
 


Detection of Organic-Rich Oil Shales of the Green River Formation, Utah, with Ground-Based Imaging Spectroscopy

https://authors.library.caltech.edu/70147/1/WHISPERS_2016_paper_93.pdf

Detection of Organic-Rich Oil Shales of the Green River Formation, Utah, with Ground-Based Imaging Spectroscopy

Abstract

Similar works

Full text

Available Versions

Crossref

Caltech Authors - Main