Article pubs.acs.org/EF
Detailed Description of Oil Shale Organic and Mineralogical Heterogeneity via Fourier Transform Infrared Microscopy Kathryn E. Washburn,*,† Justin E. Birdwell,‡ Michael Foster,† and Fernando Gutierrez† †
Ingrain, Incorporated, 3733 Westheimer Road, Houston, Texas 77027, United States United States Geological Survey, Denver Federal Center, Box 25046, MS 977, Denver, Colorado 80225, United States
‡
ABSTRACT: Mineralogical and geochemical information on reservoir and source rocks is necessary to assess and produce from petroleum systems. The standard methods in the petroleum industry for obtaining these properties are bulk measurements on homogenized, generally crushed, and pulverized rock samples and can take from hours to days to perform. New methods using Fourier transform infrared (FTIR) spectroscopy have been developed to more rapidly obtain information on mineralogy and geochemistry. However, these methods are also typically performed on bulk, homogenized samples. We present a new approach to rock sample characterization incorporating multivariate analysis and FTIR microscopy to provide non-destructive, spatially resolved mineralogy and geochemistry on whole rock samples. We are able to predict bulk mineralogy and organic carbon content within the same margin of error as standard characterization techniques, including X-ray diffraction (XRD) and total organic carbon (TOC) analysis. Validation of the method was performed using two oil shale samples from the Green River Formation in the Piceance Basin with differing sedimentary structures. One sample represents laminated Green River oil shales, and the other is representative of oil shale breccia. The FTIR microscopy results on the oil shales agree with XRD and LECO TOC data from the homogenized samples but also give additional detail regarding sample heterogeneity by providing information on the distribution of mineral phases and organic content. While measurements for this study were performed on oil shales, the method could also be applied to other geological samples, such as other mudrocks, complex carbonates, and soils. pyrolysis.4 These are performed by heating small amounts of pulverized rock to high temperatures (300−1000 °C) and observing the products using a flame ionization detector for pyrolysis methods (hydrocarbons) or an infrared cell for combustion methods (CO and CO2). Samples often need to be pretreated with hydrochloric acid to remove carbonates from the rock matrix; otherwise, when the sample is heated, the inorganic carbon present will breakdown and lead to overestimation of TOC. Transmission Fourier transform infrared (FTIR) spectroscopy has been used for many years to assess mineralogy.5−9 While results are frequently quite good, pellet preparation is exacting, time-consuming, and prone to problems, such as cracking or cloudiness. Diffuse reflectance FTIR spectroscopy (DRIFTS) does not require that samples be made into pellets but still requires use of a diluent, such as potassium bromide (KBr), for high-quality reflectance results that conform to Beer’s law.10 Sample preparation for DRIFTS measurements is still somewhat onerous. To avoid distortion because of grain size effects, the samples need to be pulverized to a uniform size below 5 μm. DRIFTS does have the advantage that, because no physical contact needs to be made with the analyte, the technique can be automated and performed on numerous previously prepared samples. Attenuated total reflectance (ATR)−FTIR is gaining popularity as an alternative infrared spectral acquisition mode.11,12 ATR− FTIR has been used in a wide range of fields from geology12−14 to pharmacuticals.15−17 Unlike transmission and DRIFTS, ATR can
1. INTRODUCTION Petroleum production in the past decade has shifted from a focus on “conventional” reservoirs with high porosity and permeability to “unconventional” or continuous resources. Some of the most successful of these unconventional resource plays are dominated by mudrocks, often described as gas and tight-oil shales by industry, that have low porosity and extremely low permeability. For mudrock reservoirs, much of the petroleum resource is stored in porosity within the organic matter.1 As such, production from wells is usually positively correlated with organic content. Therefore, geochemical evaluation of the formation is needed to determine the most organic-rich sections for well placement. These reservoirs also tend to be highly heterogeneous.2 In addition, because the permeability of the rock matrix is so low, unlike conventional resources, hydraulic fracturing is required for production to increase accessible surface area to the well bore. Mineralogy is important to assess the brittleness and, hence, how difficult it will be to fracture the rock. Carbonate and quartz regions fracture well, while clay-rich, particularly smectite-rich, intervals tend to fracture poorly. The most common method for examining mineralogy is X-ray diffraction (XRD). Monochromatic X-rays are used to irradiate the sample, and crystalline constituents will scatter these X-rays at different characteristic angles that can be measured and used to identify the mineral phases present. To perform clay speciation, the samples need to be treated and then heated to align the clay particles. Amorphous materials, such as organic matter, are not identifiable because they do not scatter the X-rays at distinct wavelengths.3 The organic matter content is mostly assessed by combustion methods, such as total organic carbon (TOC) analysis or programmed © XXXX American Chemical Society
Received: April 15, 2015 Revised: May 22, 2015
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DOI: 10.1021/acs.energyfuels.5b00807 Energy Fuels XXXX, XXX, XXX−XXX
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Figure 1. Measured mineralogy and TOC versus predicted values from the cross-validated PLSR results for the calibration set.
quality of the DRIFTS spectra tends to be much lower than that of spectra acquired using ATR. As such, we limit ourselves to the ATR acquisition mode here. Previous research has used spatially resolved FTIR to create three component mineralogy maps.18 Another work applied micro-FTIR methods to characterize mudrock samples.19 However, this study was limited in scope and did not provide detailed spatial mineralogy and TOC information or detailed statistical data relating individual measurements to the bulk. In addition, the calibration set for the model was created using artificial mixtures, which have been shown to perform poorly when used to predict the composition of natural samples.12
be run on neat samples with no need for dilution. However, traditional ATR still requires crushing and homogenization of the sample, adding additional preparation work and losing spatial and heterogeneity information. Because the sample needs to be placed on the internal reflectance element (IRE), the anvil manually pressed down and then lifted at the end of the measurement, and the IRE and stage cleared of the sample and cleaned, the traditional form of this technique is not readily amenable to automation. In contrast to these techniques, FTIR microscopy does not require the samples to be pulverized. This allows for localized measurements of mineralogy and organic content and does not destroy samples, which are frequently limited in number and quantity. In addition, geological samples, mudrocks in particular, are frequently heterogeneous on very small scales. Therefore, selecting a suitable sampling approach for traditional, bulk characterization methods is very important to obtain representative values, and different results can be observed for samples collected within very short distances of each other. The incorporation of automated ATR sampling with FTIR microscopy provides several advantages. Because the operator does not need to manually place the sample or operate the anvil for each measurement, multiple measurements across a sample can be performed rapidly and precisely. It is also possible to automate the method for application to multiple samples. The diffuse reflectance acquisition mode can be used to make FTIR microscopy measurements as well. However, this approach requires that surfaces be highly polished, and even then, the
2. EXPERIMENTAL SECTION Measurements were performed using a Bruker Lumos FTIR microscope in the ATR mode (Bruker Optics, Billerica, MA). The spot size for all measurements was 125 × 125 μm. The spectral window ranged from 400 to 4000 cm−1, with a resolution of 4 cm−1. FTIR spectra were baseline-corrected and area-normalized before use in model development or property prediction; no other preprocessing was performed. A calibration data set consisting of 96 rock samples from several tight-oil and gas-shale formations around the world with thermal maturities ranging from just before the early oil window to the wet gas window (vitrinite reflectance values of 0.5−1.5% Ro) was used to correlate infrared spectra to mineralogy and organic content using a partial leastsquares regression (PLSR) tool in the R programming language. Calibration set samples included a combination of pelletized samples of pulverized rock, end trims from cores and outcrop samples, and broken core fragments. A total of 16 spots were measured on each calibration sample, and the resulting spectra were averaged to produce a single B
DOI: 10.1021/acs.energyfuels.5b00807 Energy Fuels XXXX, XXX, XXX−XXX
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Table 1. AAD Values for the Predicted FTIR Results versus Measured Values, XRD Mineralogy and TOC Content Values (wt %) for the APM and BOS Samples, and Averaged Mineralogy and TOC Values from Micro-FTIR Predictions and Standard Deviations for the APM and BOS Oil Shale Samplesa FTIR calibration model
a
phase
calcite
dolomite
quartz
AAD (wt %)
3.3
2.1
4.5
phase
calcite
dolomite
weight percent
5.6
37.7
phase
calcite
dolomite
weight percent
5.3
40.4
feldspar
illite
1.6 4.0 APM oil shale XRD results quartz 11.3 BOS XRD results quartz
calcite
dolomite
quartz
2.4 5.7
37.0 12.9
13.4 9.7
feldspar
phase
calcite
dolomite
quartz
feldspar
weight percent standard deviation
7.1 7.5
37.2 7.9
18.1 13.1
3.0 3.4
kaolinite
smectite
1.8
1.7
2.6
illite
TOC
analcime
26.9
10.0
19.2
8.5
feldspar
phase
chlorite
1.2
feldspar
17.4 14.3 APM oil shale FTIR results
weight percent standard deviation
TOC
illite
TOC
fluorapatite
analcime
7.1
16.0
8.0
7.5
illite
TOC
chlorite
kaolinite
smectite
5.2 20.2 5.1 4.5 BOS FTIR results
20.4 15.1
0.2 0.9
3.5 4.7
17.9 5.8
illite
TOC
chlorite
kaolinite
smectite
17.9 3.2
17.4 8.9
0.4 1.1
0.5 1.8
15.6 6.0
Trace quantities of pyrite were detected by XRD but are not shown here.
Figure 2. White light photo of the APM oil shale (Mahogany zone; left) and the BOS (R-6 zone; right). Grid and linear measurement areas are highlighted on the images. spectrum for the sample. These averaged spectra were used for prediction of mineralogy and organic content. Mineralogy of the calibration samples was determined using XRD with clay speciation. Total organic carbon (TOC) content was measured using the LECO method. Predicted TOC values based on infrared spectra used spectral regions associated with aliphatic and aromatic carbon C−H stretches. Predictions for mineralogy used the entire spectral range. As we predict for the majority of the minerals present in the samples, the sum of the predicted minerals is close to unity, but because of the possibility of the presence of small amounts of minerals not predicted in the calibration set (e.g., pyrite) and errors of prediction, the results may not exactly sum to 1. Therefore, for consistency, the total predicted minerals are normalized to unity. Cross-validations of the resulting models were performed using the “leave one out” (LOO) method. This involves creating a PLSR model while withholding one sample from the set and then using the truncated model to predict the property of interest for the withheld sample. This is repeated for all samples in the calibration set, and the LOO-predicted values are compared to the measured values. Crossvalidation of the samples is important to avoid overfitting, which results in models that provide a good correlation but have limited predictive power.
Figure 3. Distribution of mineralogy along the length (53 mm or 53 000 μm) of the laminated APM sample with a 250 μm spacing between measurements. Validation of the mineralogy and TOC models was performed on spectra collected from two oil shale samples from the Eocene Green River Formation in the Piceance Basin of northwestern Colorado. The samples were cut using a thin section saw and then polished with a grinder using magnetized diamond pads. The first sample is from the C
DOI: 10.1021/acs.energyfuels.5b00807 Energy Fuels XXXX, XXX, XXX−XXX
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Figure 4. Histograms showing the range of values for TOC and constituent mineral phases present in the APM oil shale sample. The red dashed lines indicate the average value determined from all infrared-predicted values, and the solid line indicates measured bulk sample values. The absence of a solid line indicates that the mineral phase was not detected by XRD. Anvil Points Mine (APM) and is typical of laminated, Mahogany zone oil shale. The second is a brecciated or blebby oil shale20 (BOS) collected from an outcrop of the R-6 zone21 near Rifle, CO. A total of 212 spectral measurements were collected, spaced 250 μm across the laminae of the APM sample. Because of the smaller sample size and lack of obvious laminae, 210 measurements were taken across the surface of the BOS sample in a 7 × 30 grid with 250 × 250 μm spacing. A second run with the APM sample selected a representative area and used the same grid spacing as was applied to the brecciated sample for comparison.
to bitumen, oil, and gas. The aromatic carbon that dominates in thermally mature samples exhibits its most prominent peaks in the same region of the spectra as the broad peak for carbonate CO stretches (1100−1600 cm−1). The aromatic carbon is detectable only as a perturbation to this peak when substantial carbonate content is present. The PLSR approach can use these perturbations in the FTIR spectra, but reliability of prediction is lower than for samples of lower thermal maturity, which rely on the more distinct C−H stretches of aliphatic moieties (2800− 3000 cm−1). However, this does not appear to be a major issue for this method up to the early gas window. There is a possibility that prediction error may be a significant problem for more mature samples. White light photos of the APM and BOS samples are shown in Figure 2. The laminations of the APM can be clearly seen with the unaided eye and are typically ∼0.1 mm thick. The average composition of both the APM and BOS oil shale samples are shown in Table 1. The XRD compositions are on an organic-free basis. Figure 3 shows the composition of the APM along the length of the sample (sum of components normalized to 100%). The color variations in the laminae noted in the white light photo are reflected by changes in mineralogy along the length of the sample. Lighter colored lamina contained lower TOC contents and more calcite than the darker areas. Figures 4 and 5 show the
3. RESULTS AND DISCUSSION Figure 1 shows the results of the cross-validation of the PLSR model on the calibration samples. The average absolute deviations (AAD) for the different constituents based on the crossvalidated model results are shown in Table 1. Given the range of sample types used in the calibration set, the ATR−FTIR method appears to be robust against major sensitivity to surface preparation. All mineral deviations are below 5%, which is in line with expected measurement errors for XRD (±5%). The results for predicting TOC are also good, showing margins of error typical of current analytical methods. Some previous FTIR results have shown problems predicting TOC for high maturity samples.22 The intense aliphatic peak associated with thermally immature organic matter decreases in intensity as the kerogen is converted D
DOI: 10.1021/acs.energyfuels.5b00807 Energy Fuels XXXX, XXX, XXX−XXX
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Figure 5. Histograms displaying the range of each constituent present for the BOS sample. The red dashed lines indicate the average value determined from all infrared-predicted values, and the solid line indicates measured bulk sample values. The absence of a solid line indicates that the mineral phase was not detected by XRD.
diverse possible mineral constituents in mudrocks and their spectral similarity. The estimated TOC values for the samples determined by averaging the micro-FTIR predictions were also in line with the bulk TOC measurements. There were concerns that the model might overpredict TOC because the majority of the samples in the calibration model consist of type II kerogen with lower hydrogen content than the Green River Formation oil shales, which contain primarily type I kerogen. However, this does not appear to have been a problem, indicating a robust model was obtained using the calibration set. It was observed that, despite general agreement between the averaged FTIR results and the XRD and TOC data (Table 1), there are large standard deviations for most minerals and organic content. We also see a large difference in the standard deviation describing TOC between samples; the standard deviation for TOC in the brecciated sample was half that of the laminated sample, providing important insights into the extent of sample heterogeneity. This underscores the importance of representative sampling when performing bulk measurements. This can also be used to help understand the reasonable expected variation within a reservoir. If substantial variations in organic richness at the sub-millimeter to millimeter scale prove relevant to understanding mudrocks associated with unconventional petroleum resources, a bulk measurement will not be as useful as the micro-FTIR approach. Large variations in organic matter and
distributions of TOC and mineral content values for the APM sample (same data as shown in Figure 3) and BOS (on the basis of grid measurements) samples, respectively. Roughly similar mineral distributions were determined for both samples, while the BOS sample showed a narrower TOC distribution than the APM oil shale. Overall, the composited mineralogies calculated from the micro-FTIR compared well to the bulk XRD results. Notable exceptions include the prediction of illite, which is consistently higher when estimated by micro-FTIR than what was measured by XRD and the apparent assignment of some of the feldspar signal to smectite. Both of these oil shales are from the Parachute Creek Member of the Green River Formation in Piceance Basin. Rocks from this interval generally do not contain smectite and often contain a relatively uncommon mudrock mineral, analcime. Analcime is a zeolite with an elemental composition similar to clays and feldspars and infrared spectral properties similar to illite. The predictions from the FTIR results for illite are similar to the combined values of illite and analcime from XRD. Given that analcime is somewhat uncommon in other mudrocks, we do not expect this to be an issue in general, but unique phases in other mudrock formations may require unit-specific calibrations if those minerals are operationally or economically important. Overall, the clay prediction results on shale samples are improved in comparison to previous models,12 but highlight the challenges in performing clay and feldspar speciation via FTIR given the E
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Figure 6. Predictive capability between TOC and mineralogy for the APM and BOS. APM, R2 = 0.72; BOS, R2 = 0.41.
Figure 7. Mineral and TOC content distribution plots for the laminated APM (Mahogany zone) oil shale. All color contours indicate weight percent. Mineralogy values are on an organic-free basis.
trend, it is less prominent than for the laminated APM oil shale. This is reasonable, because the breccia contains a mixture of material from both marginal and offshore depositional environments because of mass movements within the paleolake.25 In contrast, the organic matter and minerals were deposited and lithified together in the laminated sample. While the mineralogy− TOC trends here are not universally applicable to other reservoirs, the method could be used to develop basin-specific models to help improve interpretation of logging measurements. Mineralogy can frequently be estimated downhole using γ-ray spectroscopy,26 but estimating TOC, which consists of low atomic number elements not measured by this method, is more challenging. Models could
mineral content within an interval may lead to more variability in production rates between wells, and therefore, a better understanding of this variability could facilitate improvement of probabilistic production models.23,24 To determine if trends in mineralogical variation were related to variation in TOC content, a PLSR correlation was performed to relate TOC to mineralogy. The results are shown in Figure 6. In the laminated sample, a good correlation was obtained when relating mineralogy to TOC. The model shows a positive relationship between organic matter content with quartz and illite and a negative correlation with dolomite. However, for the brecciated sample, although there appears to be a possible weak F
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Figure 8. Mineral and TOC content distribution plots for the BOS sample (R-6 zone). All color contours indicate weight percent. Mineralogy values are on an organic-free basis.
marginal and offshore sediments as well as the presence of mineral clasts transported with unlithified material.
be developed in the laboratory and then applied to the mineralogical logs to help provide estimates of TOC in the formation. Determining the relationships between mineralogy and organic matter at a sub-millimeter level can also help improve understanding and models of reservoir depositional environments. Figures 7 and 8 summarize micro-FTIR-predicted TOC and selected mineral content distributions for the grid of measurements made on the APM and BOS samples, respectively. The laminated structure of the APM sample is most clearly demonstrated by the TOC and dolomite distributions, which show alternating high and low concentrations along the y axis, perpendicular to the bedding plane. In general, laminae with higher dolomite content (40−70 wt %) had lower concentrations of organic matter (TOC ∼ 10−20 wt %) than those containing less dolomite. The laminae or “varves” in the Mahogany zone and other laminated Green River oil shales have been attributed to seasonal cycles,27 which could account for the fluctuation in organic and mineral contents. The BOS sample shows a more random distribution of mineralogical and organic contents, consistent with the more complex depositional history of this rock. The United States Geological Survey (USGS) recently reported volume percent distributions of laminated and brecciated oil shale in the Piceance Basin based on detailed core descriptions,21 with the results showing that oil shale breccias represent a substantial portion of the Green River Formation oil shale resource in that basin. These breccias are typically concentrated in the rich oil shale intervals below the Mahogany zone25 and are attributed to the collapse of unlithified to partially lithified sediments from marginal shelves into the basin center.28 The distribution of organic and mineral contents in the BOS sample may be due to mixing of unconsolidated
4. CONCLUSION FTIR microscopy provides a rapid, non-destructive, and spatially resolved method for determining mineralogy and organic content within shale samples. Mineralogy and TOC can be predicted to within margins of error similar to those of the current accepted industry standard techniques. By performing numerous measurements, information can be obtained on sample heterogeneity that is lost when conducting traditional bulk measurements on pulverized material. The results presented here emphasize the importance of representative sampling in mudrocks, because seemingly homogeneous samples can contain significant variability in composition that may be relevant to unconventional resource system properties, such as porosity, permeability, and petroleum storage potential. Correlations between mineralogy and TOC determined in the laboratory using the approach outlined here may be useful to the development of models that can be applied to well log data on mineralogy to provide an estimate of TOC along the borehole.
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AUTHOR INFORMATION
Corresponding Author
*Telephone: 713-993-9795. E-mail:
[email protected]. Notes
Disclaimer: Any use of trade, product, or firm names is for descriptive purposes only and does not imply endorsement by the U.S. Government. The authors declare no competing financial interest. G
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(25) Johnson, R. C.; Mercier, T. J.; Brownfield, M. E.; Pantea, M. P.; Self, J. G. U.S. Geological Survey: Reston, VA, 2010; U.S. Geological Survey Digital Data Series DDS-69-Y. (26) Freedman, R.; Herron, S.; Anand, V.; Herron, M. M.; May, D. H.; Rose, D. A. Proceedings of the Society of Petroleum Engineers Annual Technical Conference and Exhibition; Amsterdam, Netherlands, Oct 27− 29, 2014; SPE-170722-MS. (27) Bradley, W. H. U.S. Geological Survey: Reston, VA, 1931; Professional Paper 168, p 58. (28) Dyni, J. R.; Hawkins, J. E. Geology 1981, 9, 235−238.
ACKNOWLEDGMENTS The authors thank Michael Lewan (USGS, emeritus) and Ronald Johnson (USGS, Denver, CO) for assistance with sample acquisition.
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REFERENCES
(1) Driskill, B.; Walls, J.; Sinclair, S. W.; DeVito, J. AAPG Mem. 2013, 102, 115−136. (2) Passey, Q. R.; Bochacs, K. M.; Esch, W. L.; Klimentidis, R.; Sinha, S. Proceedings of the International Oil and Gas Conference and Exhibition in China; Beijing, China, June 8−10, 2010; SPE-131350-MS. (3) He, B. B. Two-Dimensional X-ray Diffraction; John Wiley & Sons: Chichester, U.K., 2011. (4) Bordenave, M. L. Applied Petroleum Geochemistry; Edition Technips: Paris, France, 1993. (5) Harville, D. G.; Freeman, D. L. Proceedings of the Society of Petroleum Engineers Annual Technical Conference and Exhibition; Houston, TX, Oct 2−5, 1988; SPE-18120-MS. (6) Ruessink, B. H.; Harville, D. G. Proceedings of the Society of Petroleum Engineers Formation Damage Control Symposium; Lafayette, LA, Feb 26−27, 1992; SPE-23828-MS. (7) Matteson, A.; Herron, M. M. Proceedings of the Society of Core Analysts Annual Technical Conference; Houston, TX, Aug 9−11, 1993; SCA-9308. (8) Herron, M. M.; Matteson, A.; Gustavson, G. Proceedings of the Society of Core Analysts Annual Technical Conference; Calgary, Alberta, Canada, Sept 7−10, 1997; SCA-9729. (9) Ballard, B. D. Proceedings of the Society of Petroleum Engineers Annual Technical Conference and Exhibition; Anaheim, CA, Nov 11−14, 2007; SPE-113023-STU. (10) Griffiths, P. R.; Haseth, J. A. Fourier Transform Infrared Spectroscopy, 2nd ed.; Wiley-Interscience: Hoboken, NJ, 2007. (11) Washburn, K. E.; Birdwell, J. E. Org. Geochem. 2013, 63, 1−7. (12) Müller, C. M.; Pejcic, B.; Esteban, L.; Piane, C. D.; Raven, M.; Mizaikoff, B. Sci. Rep. 2014, 4, 6764. (13) Klinkenberg, M.; Dohrmann, R.; Kaufhold, S.; Stanjek, H. Appl. Clay Sci. 2006, 33, 195−206. (14) Palayangoda, S.; Nguyen, Q. Oil Shale 2012, 29, 344−356. (15) Lewiner, F.; Klein, J.; Puel, F.; Fevotte, G. Chem. Eng. Sci. 2001, 56, 2069−2084. (16) Doki, N.; Seki, H.; Takano, K.; Asatani, H.; Yokata, M.; Kubota, N. Cryst. Growth Des. 2004, 4, 949−953. (17) Fujiwara, M.; Nagy, Z.; Chew, J.; Braatz, R. J. Process Control 2005, 15, 493−504. (18) Liang, K. K.; Supp, M.; Murphy, W. F. Scanning 1990, 12, 168− 178. (19) Chen, Y.; Furmann, A.; Mastalerz, M.; Schimmelmann, A. Fuel 2014, 116, 538−549. (20) Johnson, R. C.; Birdwell, J. E.; Brownfield, M. E.; Mercier, T. J. U.S. Geological Survey: Reston, VA, 2015; Open-File Report 20151044, p 40. (21) Cashion, W. B.; Donnell, J. R. U.S. Geol. Surv. Bull. 1974, 1394-G, 9p. (22) Herron, M. M.; Loan, M. E.; Charsky, A. M.; Herron, S. L.; Pomerantz, A. E.; Polyakov, M. Proceedings of the Society of Petrophysicists and Well Log Analysts 55th Annual Logging Symposium; Abu Dhabi, United Arab Emirates, May 18−22, 2014; SPWLA-2014-QQQQ. (23) Kabir, C. S.; Chawathe, A.; Jenkins, S. W. D.; Olayomi, A. J.; Aigbe, C.; Faparusi, D. B. Proceedings of the Society of Petroleum Engineers Annual Technical Conference and Exhibition; San Antonio, TX, Sept 29− Oct 2, 2002; SPE-77564-MS. (24) Dong, Z.; Holditch, S.; McVay, D.; Ayers, W. B.; Lee, W. J.; Morales, E. Proceedings of the Society of Petroleum Engineers/European Association of Geoscientists and Engineers European Unconventional Resources Conference and Exhibition; Vienna, Austria, Feb 25−27, 2015; SPE-167768-MS. H
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