Article pubs.acs.org/EF
Multivariate Analysis Relating Oil Shale Geochemical Properties to NMR Relaxometry Justin E. Birdwell*,† and Kathryn E. Washburn‡,§ †
U.S. Geological Survey, Denver Federal Center, Box 25046 MS 977, Denver, Colorado 80225, United States Weatherford Laboratories, 8845 Fallbrook Drive, Houston, Texas 77064, United States
‡
S Supporting Information *
ABSTRACT: Low-field nuclear magnetic resonance (NMR) relaxometry has been used to provide insight into shale composition by separating relaxation responses from the various hydrogen-bearing phases present in shales in a noninvasive way. Previous low-field NMR work using solid-echo methods provided qualitative information on organic constituents associated with raw and pyrolyzed oil shale samples, but uncertainty in the interpretation of longitudinal-transverse (T1−T2) relaxometry correlation results indicated further study was required. Qualitative confirmation of peaks attributed to kerogen in oil shale was achieved by comparing T1−T2 correlation measurements made on oil shale samples to measurements made on kerogen isolated from those shales. Quantitative relationships between T1−T2 correlation data and organic geochemical properties of raw and pyrolyzed oil shales were determined using partial least-squares regression (PLSR). Relaxometry results were also compared to infrared spectra, and the results not only provided further confidence in the organic matter peak interpretations but also confirmed attribution of T1−T2 peaks to clay hydroxyls. In addition, PLSR analysis was applied to correlate relaxometry data to trace element concentrations with good success. The results of this work show that NMR relaxometry measurements using the solid-echo approach produce T1−T2 peak distributions that correlate well with geochemical properties of raw and pyrolyzed oil shales. in the rock is overwhelmingly associated with the pore fluids. However, unconventional source and reservoir rocks often contain less fluid content and significant amounts of hydrogen in organic solids or hydrated minerals. This makes interpretation of the hydrogen-bearing phases in an unconventional resource rock from a one-dimensional (1-D) measurement ambiguous. To gain additional resolution, two-dimensional measurements are often performed on shales. The most common method for traditional reservoirs is a diffusion-transverse (D−T2) relaxation correlation measurement.4 Because oil, water, and gas all have very different diffusivities, this difference can be used to help identify the fluid types present in the sample. Unfortunately, diffusion measurements are usually hindered in shales because of the extremely small pore sizes, although researchers have had some success in observing the diffusion of gas in tight shales.5 Instead, T1−T2 correlations have been used to obtain more information on constituents in shale samples.1,3,6,7 Solids and liquids have very different behaviors in the relationship between T1 and T2 relaxation rates. Liquids have T1 and T2 times that are similar, whereas T1 times for solids are significantly longer than their corresponding T2 relaxation times.8 Conventional transverse relaxation measurements are performed using a technique called a spin echo.9 The spin echo can refocus time-invariant magnetic field inhomogeneities due to chemical shift, underlying field heterogeneity, and heteronuclear dipolar coupling. However, in organic-rich shales,
1. INTRODUCTION Recent studies have shown that nuclear magnetic resonance (NMR) relaxometry has potential as a method for investigating solid and viscous organic phases present in organic-rich source and reservoir rocks, particularly shales.1−3 However, interpretation of the peak distributions in relaxometry data collected on shale samples is complicated by the presence of multiple hydrogen-rich phases, including organic matter (e.g., solid kerogen, highly viscous bitumen, and in some cases oil) and water (e.g., mobile pore water and immobile clay-bound water). Previous work has shown qualitative correspondence between integrated peaks in NMR data and geochemical parameters related to shale organic matter content, but only the most basic quantitative analysis was attempted.2 Relaxometry methods provide limited but valuable information on geological samples. In particular, fluid saturation and pore structure information can be obtained by taking advantage of the two principal relaxation mechanisms. The first is longitudinal relaxation (T1), where, following excitation, nuclei return to alignment with an applied magnetic field. The second mechanism is transverse relaxation (T2) and occurs when excited nuclei, which precess around the applied magnetic field due to intrinsic angular momentum, go from a highly ordered precession in unison to a disordered, equilibrium state. For fluids within a porous material, the structure and composition of the matrix will significantly influence these relaxation rates, so the T1 and T2 relaxation time distributions are useful parameters for characterizing fluids in porous media. Transverse relaxation measurements are the most common NMR measurements made on petroleum reservoir rocks and core samples. For conventional reservoirs, the hydrogen present © 2015 American Chemical Society
Received: December 16, 2014 Revised: February 16, 2015 Published: March 10, 2015 2234
DOI: 10.1021/ef502828k Energy Fuels 2015, 29, 2234−2243
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reasonable period of time (minutes to months). Although oil shales do not contain liquid petroleum in the raw state, they may contain some bitumen and water and serve as useful testing materials for investigating the natural source rock maturation process. Understanding the physical and geochemical properties of a reservoir through different stages of thermal maturation is important for resource assessment. However, finding stratigraphically equivalent samples with similar mineralogy and organic richness of differing thermal maturity within a basin is very difficult, and even an approximate burial history of the samples is often unknown. Artificial maturation allows for direct experimentation with samples of essentially identical composition under controlled conditions. Methods have been developed for mimicking thermal maturation of source rocks to simulate natural processes15,16 or thermal processing to simulate oil shale retorting operations.17−20 Aliquots of the oil shales were subjected to three different laboratory pyrolysis methods: (1) Fischer assay (FA),20 (2) hydrous pyrolysis (HP),15,16 and (3) the in situ simulator (ISS).17 Fischer assay measurements are conducted on 100 g oil shale aliquots sieved to remove particles larger than 2.38 mm. The samples are heated in a steel or aluminum retort at a rate of 12 °C min−1 to 500 °C and held at that temperature for 40 min under a gentle nitrogen stream. The products generated are considered to be comparable to those from surface retorting operations. Hydrous pyrolysis experiments were conducted on oil shale aliquots of 50−200 g, depending on organic richness, in 1 L hastalloy reactors with 400 mL of deionized water. The reactor was purged with helium (final He pressure at room temperature was 25 psia), and samples were heated at a rate of 3 °C min−1 to 360 °C and held for 72 h. The expelled oil and gas products from HP experiments have been shown to be similar to natural petroleum.15 The in situ simulator approach involves loading 100 g of oil shale into a heated reactor connected via flexhose to a helium-purged, evacuated ( T1) represents physically unrealistic values and contributes very little to the models.
Figure 7. Regression coefficient plots from PLSR analysis for the FTIR spectra and trace elements molybdenum and rubidium. 2241
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suggests basin-specific models for trace elements could be developed using relaxometry data. Concentrations of most trace elements showed an inverse correlation with organic content for both infrared spectra and low-field NMR results. Regression coefficients for PLSR models relating relaxometry data with portions of the FTIR spectra provided further confidence in the interpretation of the T1−T2 solid-echo correlation peaks. It is expected that relationships between direct evaluations of hydrogen content (e.g., rock-eval S1 and S2) and relaxometry measurements will be robust and widely applicable to evaluation of source and reservoir rocks.
are inversely related to organic peaks and positively correlated to those related to minerals. While some correlation was observed between the NMR spin-echo results and the trace element concentrations, the quality was lower in most cases (see the Supporting Information). In Figure 5, spin- and solidecho PLSR model results are shown for two elements that were positively correlated with mineral relaxometry peaks, rubidium and molybdenum. Regression coefficients for the solid-echo T1−T2 correlation peak areas for the Mo and Rb PLSR models are plotted in Figure 6. The positively correlated regions are consistent with peaks attributed to mineral water in T1−T2 correlation plots for shales and the regression coefficient peak areas for mineral-related bands in the infrared PLSR model. The trace element concentrations were then correlated to FTIR spectra using PLSR to determine what chemical moiety relationships were observed to determine if the infrared correlation coefficients agreed with the NMR results. The FTIR spectra show even better utility for prediction of trace element concentrations (see the Supporting Information) than the NMR solid-echo data. Excellent correlations were obtained between the spectral data and concentrations for a large number of elements (R2corr, R2cv > 0.9), including many that are not well predicted via NMR. Because FTIR probes a wide range of mineral bonds in addition to hydrogen content associated with organic content and minerals, a better predictive capability is not surprising. To better understand the PLSR results relating infrared and elemental data, the regression coefficients were examined (Figure 7). When the regression coefficients are compared, some of the FTIR results are the same as for the NMR results; an inverse correlation with sample organic content (C−H stretches, 2800−3000 cm−1) is observed for most of the elements along with a positive correlation associated with minerals (Si−O stretch, ∼1000 cm−1). An inverse correlation with the broad carbonate peak (1400−1500 cm−1) is observed for many minerals, although some exceptions such as strontium (not shown) were intensely and positively correlated with carbonate, as would be expected. While these results show excellent predictive capability for trace element concentrations, it is uncertain how transferable the models are between rocks from different formations. Because the trace element concentrations are very dependent on the specific conditions of deposition, it is possible that the results will not be highly transferable between locations for most elements except in cases where the relationships reflect fundamental geochemical processes. It is expected that models calibrated using samples from a particular basin or formation will be useful.
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ASSOCIATED CONTENT
S Supporting Information *
Figures showing spin-echo T1−T2 correlation plots for isolated kerogens and PLSR analyses and a table listing correlation parameters derived from PLSR analysis of trace element relationships to the spectroscopic data sets. This material is available free of charge via the Internet at http://pubs.acs.org.
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AUTHOR INFORMATION
Corresponding Author
*E-mail:
[email protected]. Tel.: (303) 236-1534. Present Address §
Ingrain, 3733 Westheimer Rd., Houston, TX 77027.
Notes
Disclosure: 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.
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ACKNOWLEDGMENTS We thank Michael Lewan for sample procurement (PMZ), assistance with kerogen isolations, and helpful discussion; Alan Burnham for help in procurement of the GGM shale; Endre Anderssen for chemometrics discussion; and Augusta Warden, Max Goldstein, and Jennifer Yee for assistance with running various analyses.
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REFERENCES
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4. CONCLUSION T1−T2 solid-echo correlation data were successfully related to geochemical properties of raw oil shales and spent oil shale samples exposed to a range of pyrolysis conditions using PLSR. The relaxometry peak distributions for isolated kerogen samples were shown to correspond to those attributed to solid organic matter in raw oil shale samples as well as regression coefficients for TOC and S2 determined by PLSR. The solid echo appears to be a better method than the spin echo for measuring T2 when samples contain solid organic matter or exhibit a range of thermal maturities, as spin-echo correlations had less predictive power for geochemical parameters. Both NMR and FTIR spectral results show correlations with trace element concentrations. While this approach to predictive analysis may be formation-limited, it 2242
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