Oil Shale Research - 1 -

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1 - 1 - Organic-Rich Shale Maturity: Velocity and Impedance Study of Bakken Shale Samples Kene Mba 1.0 Executive Summary Bakken Shale core samples and log data were analyzed for variations in velocity, mineralogy and moduli with maturity. Prior to analysis, several measurements were made on the samples including pyrolysis which indirectly measures the maturity of the shale samples. The different property measurement techniques, sample preparation methods, data location, and how the data were analyzed are described here. From the post-measurement analysis, qualitative relationships were made between the Upper and Lower Shales. Quantitative relationships between maturity and kerogen plus clay were determined. Also it was determined that besides organic matter content, and the presence of micro-cracks, clay abundance also controls anisotropy in the shales.

2 Introduction Ten different classes of data have been acquired in the course of this study. This section deals with how these data have been acquired or generated, then measured. The ten data classes are: a. Core b. Well logs c. Bench-top Compressional-Wave (Vp) and Shear-Wave Velocity (Vs) d. Thin section e. Optical Microscopy f. Pyrolysis g. Quantitative Electron Microscope Scanning (QEMSCAN) h. X-Ray Diffraction (XRD) i. Nano-Indentation j. Scanning Acoustic Microscopy (SAM) Except otherwise mentioned in the data descriptions below, all the electronic raw data for this study are located in appropriately named folders in the CD attached to this report. a. Core: Core data were acquired from three different sources: Colorado School of Mines, Green Center core collection USGS Core Research Center, Denver, CO ( North Dakota Industrial Commission (NDIC), Grand Forks, ND ( Core samples from the first two sources served as the database for measurements or processing in c to h above. Pieces of some of the core samples remain in their sample bags in Alderson Hall, AH286. At the same location resides a sample box containing pieces of all thirty samples polished down to 3µm. Table 1 shows the wells and depth from which core was acquired along with data available on each core sample. Samples from the third data source were not included in the study documented here, but are available with the Dr Stephen Sonnenberg (one of the Bakken PIs).

3 - 3 - Table 1: Core data used in study, sources and data available on them. b. Well logs: Electronic well log files from over 80 wells with different data suites are available with this report. Image files for most of these wells, plus hundreds more are available on the NDIC website, which web address is shown in the previous section. Some digital log files are also available in the NDIC website, especially for wells in some newer fields like the Parshall Field. Digital files were also got from a Bakken Characterization Consortium affiliate called TGS. Some digital files were also generated by digitizing across the Bakken Formation interval in the Geology Department s Petra software. Image files for 24 wells, TGS digital files for 44 wells, and Petra-digitized files for 23 wells are included in this report. The log digital and image files are located in Well Logs folder in the attached CD. Altogether, logging data for over 80 wells are included in this database. More TGS digital well data can be got with the help of Dr Stephen Sonnenberg. c. Bench-top Compressional and Shear Velocity: Compressional-wave velocity (Vp) and shear-wave velocity (Vs) were measured on 30 core samples using the Panametric transducers, oscilloscope and pulser set-up in the AH286. A schematic for the sample measurement set-up is shown in Figure 1. Samples were ground flat on the two parallel surfaces on which the transducers were placed, and maltose was used as a coupling agent between transducer and sample faces. For Vs, measurements were made parallel to bedding, Vs0 o and perpendicular to bedding, Vs90 o in order to calculate anisotropy.

4 - 4 - Figure 1: Schematic showing the velocity measurement set-up. d. Thin Section: Thin sections were made on eleven (11) samples (see Table 1) at the Geology and Geological Engineering Thin Section Preparation Lab by John Skok. The thin sections were impregnated with blue dyed epoxy and polished down to a 1µm finish, with no cover slips. e. Optical Microscopy: Petrographic images on the thin section images were captured using a Leica light microscope in the Geology and Geological Engineering Department lab with permission from Dr. Richard Wendlandt. The images were captured at a 500x magnification in polarized and cross-polar light. Rock matrix in most of samples was not resolved. In samples close to formation boundaries, the matrix was better resolved because of the increased grain sizes. f. Pyrolysis: Pyrolysis was done on twenty-two (22) samples (see Table 1). Weatherford s SRA TPH/TOC Version 1.0, a pyrolysis instrument, available at the Geology and Geological Engineering Department (and operated by a graduate student, Hui Jin) was used to measure maturity indices of collected core samples. The instrument determines the following indices: S1 (a measure of hydrocarbon already generated in shale), S2 (a measure of kerogen being converted to hydrocarbon at a certain temperature), S3 (the amount of CO2 produced during pyrolysis of kerogen, TMax (the maximum temperature at S2 pyroysis), TOC (Total Organic Carbon) and HI, (Hydrogen Index). From these parameters, transformation ratio (TR), S2 normalized for oxygen content, and other derivative indices can be calculated.

5 - 5 - Samples used for pyrolysis were pulverized, and passed through a 40 mesh sieve. 100mg of the fine-grained sample were then placed in small tubes, which were sealed with a stopper prior to inserting them into the SRA. Results for the determined indices were then exported into Excel for analysis. g. QEMSCAN: The Quantitative Electron Microscope Scanner focuses electrons generated by a heated tungsten filament on a sample, and then uses the reflected electrons from the samples to generate an image. The reflected electrons include: a) secondary electrons that result from the interaction of the electron beam and the sample surface; b) the backscattered electrons which are high-energy electrons that intensify with increase in atomic number of elements in the rock minerals; and c) characteristic x-rays which are emitted from the inner electron shell of elements. These x-rays used to detect the makeup and relative abundance of elements in the sample. The QEMSCAN image differs from a regular Scanning Electron Microscope, SEM image because it is derived from four Energy Dispersive X-ray Spectroscopy, EDS detectors; and one Electron Back Scatter Electron, BSE detector. This allows for improved topographic imaging of samples. The image that results from a scan is a false-colored image that is based on a Species Identification Protocol, SIP file which has been calibrated using pure mineral standards. The QEMSCAN device used for this work resides at the Advanced Mineralogy Research Center at the Colorado School of Mines. Scans were made on thin sections (polished down to 1µm) at two different resolutions: 30µm and 2µm. The 30µm scan was made for mineral identification, while the 2µm scan was made in an attempt to identify and characterize pore-space and organic matter. The raw image produced was processed to determine mineral associations within the samples, so as to narrow down the rock components to the major minerals. h. XRD: This had not been done as at the time of writing, however samples would be provided to Dr Stephen Sonnenberg to be sent out along with other Bakken samples for XRD measurements. i. Nano-Indentation: The instrument used for nano-indentation is called the MTS Nanoindenter XP, and resides at the Material Science Department of the Colorado School of Mines. It measures the stiffness modulus of a material by forcing the tip of a diamond indenter into the near-surface zone of the material, and dynamically measuring load used and depth displacement. The depth to which the indents can be made is a variable option on the instrument, and can be from the nanometer range up to single-digit micrometer range. Indents were made using an XP indenter mode called the XP CSM Standard Hardness, Modulus and Tip Calibration mode. The CSM stands for Continuous System Measurement which allows for hundreds of measurements to be taken at each indent. Hardness and Modulus are output for each indent made on a sample for each nano-indentation run. Hardness is a measure of the sample s resistance to indentation, while modulus is the sample s Young s Modulus. The tip of the XP indenter is calibrated during each run using a silica standard, whose Young s Modulus is 70GPa +/-2GPa. Measurements for this work were made at a depth displacement of 500nm on flat sample surfaces that were polished down to 6µm.

6 50µm Oil Shale Research The indents were made at an array of 4 indents by 4 indents with a 50µm separation between indents in the X- and Y-directions. Figure 2 displays a schematic of the indentation array used for the samples. 50µm Y Figure 2: Schematic showing the 4 x 4 array used for indent locations. A spacing of 50microns was maintained between each indent location. In a homogenous sample, such as the silica standard, output moduli are very close in value with a small standard deviation, so an average modulus value can be taken for such samples. With heterogeneous samples, such as the organic-rich shales measured, the output modulus varies with the actual component particle that is sampled by an indent. An average modulus output for each sample cannot be used here, as they may be widely varying. This has implications for interpreting the modulus of varying component minerals, and of softer particles (eg. kerogen, clays) in the sample. Fluid-filled pore spaces would not be seen by the XP indent mode used, so indent locations sampling pore spaces are likely to come up erroneous. Further analysis of modulus results would be required to determine indent groupings that could then be averaged. j. SAM: The Scanning Acoustic Microscope, SAM is a device that was originally developed for material science applications, and for imaging cracks and defects in microchips. It uses reflections from ultrasonic frequency waves pulsed into a sample to produce an acoustic image of the micro-structure of that sample. The image produced is a function of the reflection coefficient of the individual particles that make up the sample. The reflection coefficient is in turn a function of density, sound velocity, and sound velocity attenuation properties of these particles. The product image is thus a micro-impedance image whose resolution is in the micrometer range. Velocity, density, reflection coefficient and impedance are related by the following equations: Impedance = Density x Velocity...(1) Z (Mrayl) = Rho (g/cc) x V (km/s)...(2) Where Z = Impedance; Rho = Density; and V = Velocity Z Reflection Coefficien t,r.c. 2 Z 2 X Z 1...(3) Z 1

7 - 7 - Where Z 1 is the couplant (water) impedance Z 2 is the sample s impedance The resolution of an image is dependent on the frequency of the transducer used. The equation below which assumes a sample velocity of 15,000ft/s, and using a 250MHz transducer shows the wavelength to be 18.3µm. So this calculated value is the resolution of the transducer. λ v f 15,000ft/s 250MHz 4572m/s 250MHz 18.3µm...(4) An attempt was made at calibrating the SAM for impedance detection using fourteen (14) standards of known impedance (actual impedance values were measured and showed good match with literature values). This attempt proved futile, as the SAM gave inconsistent gray scale values for the same standards measured at different times. Also, most calibrations showed saturated (white out) values for most of the standards, thus severely narrowing the quantitative detection range for future impedance determination. As at the time of writing, measurements using the 50MHz, 150MHz, and possibly 250MHz transducers are on-going. Figure 3 shows the scan types that can be made using the SAM. For the analyzed samples, C-scans were made to capture lateral variations at different depth into the sample, while B-scans were made to capture layering and anisotropic variations. Figure 3: Output scans from the SAM. C-scan captures lateral variations at depths within sample, while B-scans capture layering and anisotropic variations. (Figure from Prasad, 2001) 1 The cores measured on the SAM were prepared by cutting the samples to have two parallel edges in the direction of bedding, with the exception of thin-section off-cuts from selected samples which were prepared perpendicular to bedding direction. The surface which was selected to be imaged was ground and polished down to a 3µm finish. The SAM used resides at AH286.

8 Depth, ft Oil Shale Research Data Analysis: Results and Preliminary Interpretation Detailed analysis of log data was done, along with some preliminary analysis of bench-top velocity, pyrolysis, nano-indentation and QEMSCAN data. In this section, analysis done on these datasets and preliminary interpretations resulting thereof are discussed. Three datasets not discussed here are: a. Optical Microscopy Images: Poor resolution of matrix precludes meaningful interpretation; b. XRD: Data not available at the time of writing; c. SAM: Data still being acquired at the time of writing. 3.1 Log Data Cross-Plots Cross-plots were made of the maturity dependent log-derived properties to ascertain the relationships between them. Plots based on resistivity, porosity, density, velocity and densityderived TOC using Schmoker 2 equation: TOC = ( /Density) (5) were made for data from the Upper and Lower Shales. Table 2 shows the set of wells from which data subsets were chosen for cross-plots depending on the availability of the investigated properties. The wells cut across mature to immature shales. Figure 4 shows a cross-plot of velocity as a function of depth with resistivity used for color-coding. It can be seen from the figure that there is no relationship between resistivity and velocity, or resistivity and depth Resistivity: Velocity as a function of Depth Velocity, ft/s x 10 4 Figure 4: Cross-plot of velocity and depth with data colored by resistivity. No relationship seen between resistivity and velocity.

9 - 9 - Table 2: Original set of wells used in property cross-plots. Subsets of this were used based on the availability of the investigated property. As maturity increases in organic-rich shale, TOC reduces, and velocity increases because the slower organic matter is converted to hydrocarbons, and then expelled from the shale matrix into surrounding reservoir rocks. This should have an effect on resistivity, as the more mature, higher velocity organic-rich shales should have lower resistivities. This is not the case in the sampled data as seen from Figure 4. This lack of relationship, as well as the obvious lack of a definite relationship between velocity and depth may be attributed to compositional variations in the original deposited shale (Price, 1984) 3. Moreover, a definite conclusion on the observed velocityresistivity relationship would be flawed, and is prevented by the fact that Bakken shales have very high resistivities which would normally wrap around log scales several times, so questioning the accuracy of manually digitized logs. The same goes for any quantitative analysis using the Gamma-Ray (GR) log in the shales, and this introduces an innate flaw in statistical evaluation of Bakken shale logs using GR and Resistivity logs, as shown later in this section. Figures 5 and 6 show plots of velocity as a function of depth with color-coding by density and neutron porosity respectively. It can be seen from both plots that with increasing velocity, density increases and porosity decreases. These relationships may be intuitively expected, as the

10 Depth, ft Depth, ft Oil Shale Research more mature high-velocity organic-rich shales would have reduced organic matter, so a reduction in softer components. Also lower porosity would be expected due to collapse of previously organic matter-filled pore space on compaction from overburden loading. This generalization on why more mature shales have higher densities is challenged later in section 3.5 using nanoindentation and pyrolysis data Density: Velocity as a function of Depth Velocity, ft/s x Figure 5: Cross-plot of velocity and depth with data colored by density. Higher velocity mature shales are denser than lower velocity immature shales. Neutron Porosity: Velocity as a function of Depth Velocity, ft/s x 10 4 Figure 6: Cross-plot of velocity and depth with data colored by neutron porosity. Higher velocity mature shales are less porous than lower velocity immature shales.

11 M-Modulus, MPa Oil Shale Research A key assumption that is not explicitly addressed in the aforementioned plots is that the higher velocity data points are higher maturity, and lower organic matter content. Figure 7 addresses this by showing a plot of log-derived TOC as a function of M-Modulus. M-Modulus = Vp 2 x Density...(6) It shows that the higher velocity, higher density data points also have the lower TOC values. TOC used here was calculated using Equation 5 The data points here are color-coded by neutron porosity. 300 TOC as a function of M-Modulus TOC, wt% Figure 7: Cross-plot of TOC and M-Modulus with data colored by neutron porosity. Higher velocity, higher density mature shales are less porous, and have lower TOC than lower velocity immature shales. 3.2 Statistical Analysis To better understand the scatter in the property cross-plots of section 3.1, and to determine what rock composition anomalies, if any, that control the log-derived measurements, statistical analysis was done using a multivariate method. The method, Principal Component Analysis (PCA) was used to evaluate relationships between properties. PCA reduces the redundancy of multiple measurements which detect the same governing property of a system 4. It does this by using a linear combination of the original data set to generate new data called principal components (PC). Each principal component is orthogonal to each other, thus reducing measurement redundancy. Usually over 80% of the data is explained by the variance in the first few principal components. A cross-plot of these leading components, usually the first two or first three components would show groupings of data points, whose relationship is governed by geographical distribution and or geological variation (lithologic, compositional, compaction, etc) in the case of well log data.

12 Column2 Column3 Oil Shale Research The following logs were used for PCA: Gamma Ray (GR), Resistivity (LLD), Neutron Porosity (NPHI), Density (RHOB), and Sonic (DT). The results of the PCA for the data in the Upper and Lower Bakken shales is documented in the CD file located at: CD Documentation\b. Well Logs\2. DERVIATIVE\Velocity Relationships Study\Statistical Analysis\PCA for 4BakkenWells.xlsx. A review of the principal component cross-plot using all of the logs listed in Table 2 showed no significant data groupings. Sensitivities of the PCA were then done by excluding GR, and then LLD. These still showed no positive results. Finally, the Middle Bakken data was included as a control to ensure that PCA would show the compositional/lithological variations between the Middle Bakken reservoir rocks and the source-rock shales. This yielded the plot in Figure 8 that shows the Middle Bakken data separated from the Upper and Lower Shales. 2 Newdata: Column1 vs Column2 2 Newdata: Column1 vs Column Column Column1 Upper Shale Middle Bakken Lower Shale Figure 8: Left (PC1 vs PC2) and right (PC1 vs PC3). Note that the Middle Bakken reservoir separates from the shales. The shales show no difference between each other, except data points near formation contacts that plot with the Middle Bakken. The Upper and Lower Shales showed no difference in the PCA (Figure 8), thus allowing the inference that when analyzed by well logs, the Upper and Lower Shales are not compositionally or lithologically different. The failure of PCA to find relationships between log-derived measurements in the shales can be attributed to two main factors: a. The GR and Resistivity in the Bakken Shales are such that the high organic matter content cause them to wrap around scaled plots, so making digitizing efforts objective most times, and unreliable for quantitative analysis; b. Density, Neutron Porosity and Sonic are all porosity-dependent measurements, thus preventing a proper investigation of other independent governing properties.

13 3.3 Pyrolysis and Vp-Vs Measurements Oil Shale Research Table 3 shows the maturity indices from pyrolysis of the shales. This data was instrumental to analyzing data generated from other methods. Typically with increasing maturity, TOC, HI and S3 decrease, while TR and TMax increase. This was observed in the samples measured, and maturity shows a general increase with depth. SAMPLE ID Well Name DEPTH, ft Tmax, degc S3 TOC, wt% HI OI TR S1/TOC 1-32 Mertes-2 I-32 Mertes Mertes-3 I-32 Mertes Mertes-1 I-32 Mertes B Fleckten B Pullen B Fleckten E Big Sky E Big Sky E Big Sky R Vaira E Williams B Graham USA R Citgo Chambers R Short Fee E Federal E Federal B Toc Mee USA T418 1 Federal DG T Federal DG E Thompson Unit E Thompson Unit E Thompson Unit Table 3: Maturity indices of 22 samples from pyrolysis arranged by depth. Vp and Vs values measured on bench-top are shown in Table 4. To verify the reliability of the measured data, Vp was plotted against Vs0 o, then compared with Vp-Vs relationships with literature 5 (see Figure 9). The measured data showed a good fit with literature data with a correlation coefficient of The equation of the linear regression line between Vp and Vs is given by this relationship: Vp = Vs (7) Plots of Vp as a function of TOC, HI, TMax, S3 and TR were made to observe relationships between velocity and maturity. Figures 10a and 10b show the plot for TOC. With the exception of three outliers with very low Vp values (see Table 4), the data points plot on a trend which

14 confirms Vernik and Nur (1992) 6 that velocity increases with increasing maturity. The same trend is seen with all the maturity indices above with the highest correlation coefficients of 0.55 and 0.48 for TOC and HI respectively. WELL NAME DEPTH (ft) Vp (m/s) Vp (ft/s) Vs0 o (m/s) Vs0 o (ft/s) Vs90 o (m/s) Vs90 o (ft/s) Vs Anisotropy Vp/Vs0 o I-32 MERTES I-32 MERTES I-32 MERTES FLECKTEN PULLEN FLECKTEN PULLEN BIG SKY BIG SKY BIG SKY VAIRA WILLIAMS GRAHAM USA HAGEN HAGEN CITGO CHAMBERS HAGEN HAGEN HAGEN SHORT FEE SHORT FEE FEDERAL FEDERAL TOC MEE USA FEDERAL DG FEDERAL DG FEDERAL DG THOMPSON UNIT THOMPSON UNIT THOMPSON UNIT Questionable data points Poor fit with maturity indices Table 4: Bench-top Velocity data for 30 core samples. Cells highlighted in red have are low confidence points not included in analysis with maturity data. Cells highlighted in orange show samples whose velocity values appear low and cause a poor fit of velocity data with maturity indices.

15 Compressional Velocity, Vp, ft/s Vp, m/s Oil Shale Research Vs 0 deg y = x R² = Linear (Vs 0 deg) Linear (Castanga Mudrock) Linear (Han '86) Linear (Castagna '93) Vs, m/s Figure 9: Vp-Vs plot from measured data compared with literature values Total Organic Carbon y = x R² = TOC, wt% Figure 10a: Compressional Velocity, Vp as a function of TOC. Outliers with very low velocity values are circled.

16 Compressional Velocity, Vp, ft/s Oil Shale Research Total Organic Carbon y = x R² = TOC, wt% Figure 10b: Compressional Velocity, Vp as a function of TOC. Outliers were taken out, so a better fit was achieved showing Vp increasing with increasing maturity (lower TOC). 3.4 QEMSCAN Eleven (11) samples were run through the QEMSCAN for mineralogy and ten (10) samples for porosity. The mineral assay resulting from the QEMSCAN imaging and analysis is shown by depth in Figure 11 and by formation in Figure 12. Table 5 shows the mineral composition values by percentage volume for each sample, while images are located in the folder CD Documentation\g. QEMSCAN\2. DERIVIATIVE\Mineralogy_Presentable. No particular trend is seen in mineralogy distributions with depth in Figure 11. However, quantitative analysis of Figure 12 in Table 5 shows the dominant minerals in the shales to be quartz (silica), clays (kaolinite, illite, montmorillonite and smectite) and dolomite, with average compositions of 42%, 40% and 7% respectively. The Lower Shale has higher silica content (average 51%) than the Upper Shale (34%), while the reverse is the case for clays with 34% in the Lower Shale and 44% in the Upper Shale. Allowing for the possibility of sample bias in the data, a simplified deduction can be made that the Lower Shale with higher silica content and lower clay content is denser than the Lower Shale. This would cause the Lower Shale to be more brittle, have higher velocities, and hence higher impedance than the Upper Shale. For fracture stimulation purposes, the Lower Shale is more likely to sustain higher fracture lengths, heights and widths than the Upper Shale due to the brittle nature of silica, and the propensity for proppants to be embedded in ductile clays.

17 The limited nature of the sampled data precludes meaningful deductions to be made on mineralogical variation with paleo-geothermal gradients (Price et al, 1984) 3, and or basin location. Though using the averages in Table 6, equivocal deductions can be made pending further investigation with increased data density that in the high paleo-geothermal gradient area, quartz content is lower, with an increase in the presence of carbonate minerals (dolomite and calcite), than in the low paleo-geothermal gradient area. Mineral Assay for Bakken Shales by Depth 1-32 Mertes/7,216ft 1-32 Mertes/7,219ft 1-32 Mertes/7,221ft 1-15 Graham USA/10,368ft 1-24 Citgo Chambers/10,390ft 31-3 Short Fee/10,479ft 11-4 Federal / ft 3-17 Toc Mee USA/10,792ft 1 Federal DG/10,934ft Silica (Quartz + Radiolarian Quartz) Clays K-feldspar Plagioclase Apatite Dolomite Calcite 5-1 Thompson Unit/11,044.8ft 5-1 Thompson Unit/ ft 0% 20% 40% 60% 80% 100% Volume Figure 11: Quantitative mineral composition of Bakken Shale samples by depth. No obvious trends in mineral variation by depth, except for low dolomite content in the shallowest shale samples. QEMSCAN backscatter images at 2µm resolution (CD Documentation\g. QEMSCAN\2. DERIVIATIVE\Porosity_Presentable) were made to address the questions mentioned earlier in this section on the relationships between organic matter content and mineralogy, and implications thereof. In the backscatter images, grayish areas and spots are matrix minerals assemblages, while the black areas and spots represent hydrocarbon- or organic matter-filled pore spaces and cracks. These black areas were quantified using the QEMSCAN image processing software, i intellection, as porosity. From the images, it can be seen that organic matter occurs uniformly dispersed within the matrix (Appendix Figures h and j); occurs as filling or rims, in and around fossil remains (Appendix Figures a, b, d and g); occupies micro-cracks

18 LOWER SHALE UPPER SHALE Oil Shale Research (Appendix Figures a, c, d, f, h, i and j); and occupies spaces within crushed, and porous mineral and fossil fragments (Appendix Figure e). Mineral Assay for Bakken Shales by Formation 1-15 Graham USA/10,368ft 31-3 Short Fee/10,479ft 11-4 Federal / ft 3-17 Toc Mee USA/10,792ft 5-1 Thompson Unit/11,044.8ft 5-1 Thompson Unit/ ft 1-32 Mertes/7,216ft 1-32 Mertes/7,219ft 1-32 Mertes/7,221ft Silica (Quartz + Radiolarian Quartz) Clays K-feldspar Plagioclase Apatite Dolomite Calcite 1-24 Citgo Chambers/10,390ft 1 Federal DG/10,934ft 0% 20% 40% 60% 80% 100% Volume Figure 12: Quantitative mineral composition of Bakken Shale samples by formation. 6 samples were from the Upper Shale, while 5 samples were from the Lower Shale. Increased quartz content and decreased clays can be observed in the Lower Bakken Shales. Mineral Upper Shale Ave, % Lower Shale Ave, % Total Ave, % Quartz Clays K-feldspar Plagioclase Apatite Pyrite Calcite Dolomite Others Biotite/Phlogopite Table 5: Actual mineral content values (% volume) of shale samples by Formation. Note higher quartz content, but lower clay and carbonate content in the Lower Bakken Shales

19 High Geothermal Gradient, % Low Geothermal Gradient, % Total Ave, % Mineral Quartz Clays K-feldspar Plagioclase Apatite Pyrite Calcite Dolomite Others Biotite/Phlogopite Table 6: Actual mineral content values (% volume) of shale samples by Paleo-geothermal Gradient. Note lower quartz content, but higher carbonate content in the high geothermal gradient area. There was no convincing evidence for layered or laminated organic matter occurrence. However, it was observed that in the samples from the less mature, low geothermal gradient area; organic matter occupies larger pores, and fossil or mineral dissolution spaces (compare Figures 13 (mineralogy) and Figure 14 (porosity), see also Appendix Figures a and b). Figure e shows a sample from the well 1-24 Citgo Chambers, which represents an intermediate maturity (T.R.=0.08 and HI=217). From this sample, organic matter presence in partially disintegrated mineral fragments is observed. Samples from mature, high geothermal areas in the basin (Appendix B figures d, g to j) show organic matter occurrence in smaller pore spaces, and squeezed dissolution spaces, evidenced by the flat, lens-like shapes. These flattened shapes are due to the increased mechanical compaction from thicker overburden material with movement towards the basin center. Organic matter occurrence in these flat-shaped bodies causes an increase in the shear wave anisotropy of the mature shale (compare Appendix B figure g (shear wave anisotropy = 1.2) with Appendix B figure f (shear wave anisotropy = 18.6)). With thicker overburden above the shales, pressures are increased, causing even micro-cracks to be less important factors for anisotropy. Vernik and Nur (1992) 6 noted that with increased organic content in a shale, anisotropy increases. However, it is shown here that the manner of occurrence of the organic content, and thickness of overburden also contribute in controlling the amount of anisotropy in the shale. Figure 15 shows the porosity detected by QEMSCAN imaging in each of the samples. QEMSCAN porosity is really a quantitative representation of organic matter encountered in a sample, since organic matter emits the least amount of back-scatter electrons in the shales. In line with this, the QEMSCAN porosity should match up with measured TOC from pyrolysis. The TOC was measured as percentage weight of organic matter to total sample weight, while the QEMSCAN porosity is the percentage volume occupied by organic matter to total volume. This precludes a direct comparison of the values. See Table 7 for the comparison between QEMSCAN porosity and TOC.

20 Figure 13: I-32 Mertes/7219 sample showing mineralogical composition. Note white rimming around distorted fossil forms. Compare with Figure 12 below. Coloring is same as for Figures 9 and 10, showing high silica content. Figure 14: Same area from I-32 Mertes/7219 sample above showing organic matter-filled porosity. White rimming around fossils and wavy partings in Figure 11 above appear are organic matter filled.

21 QEMSCAN Porosity Variation with Depth 1-32 Mertes / 7,216ft: 1-32 Mertes / 7,219ft: 1-32 Mertes / 7,221ft: 1-15 Graham USA / 10,368ft: 1-24 Citgo Chambers / 10,390ft: 31-3 Short Fee / 10,479ft: QEMSCAN Porosity, vol% 3-17 Toc Mee USA / 10,792ft: 1-Federal DG / 10,934ft: 5-1 Thompson Unit / 11,044.8ft: 5-1 Thompson Unit / 11,046.8ft: 0% 5% 10% 15% 20% Porosity, vol% Figure 15: QEMSCAN porosity variation with depth. In backscatter, 2µm images, organic matter-filled porosity is seen as black, as against grey for matrix areas. Sample No. QEMSCAN Porosity, vol% TOC, wt% Wells and Sample Depths Mertes / 7,216ft: Mertes / 7,219ft: Mertes / 7,221ft: Graham USA / 10,368ft: Citgo Chambers / 10,390ft: Short Fee / 10,479ft: Toc Mee USA / 10,792ft: Federal DG / 10,934ft: Thompson Unit / 11,044.8ft: Thompson Unit / 11,046.8ft: Table 7: QEMSCAN porosity and TOC for samples. Sample numbers given for reference in Figure 14.

22 Mineral, %vol TOC minus QEMSCAN Porosity Oil Shale Research From Figure 15 and Table 7, it is seen that QEMSCAN porosity is relatively higher in the shallow samples, 7,216 7,221. This coincides with the higher TOC values for these samples, which are in the immature area of the basin. Another observation that can be made is that the TOC values are higher than the porosity values, with the exception being the No.6 sample which could be considered as an anomaly. This observation is non-intuitive considering that lowdensity organic matter and kerogen would occupy more volume space than the higher-density mineral matrix. A closer observation at the shallow samples mentioned earlier would show that a disproportionate relationship exists between the QEMSCAN porosity and TOC values for the sample No.2, when compared with other shallow samples. This same relationship can be seen for samples 4, 7 and 8. Further investigation into how the mineral composition may be causing this abnormal relationship between QEMSCAN porosity and TOC can be made by plotting variations in individual mineral composition between samples against the properties in question. Figure 16 shows the result of this examination. 90 Quartz Clays TOC-Por Sample Number Mineral Volume and TOC-QEMSCAN Porosity Difference by Sample Figure 16: Comparison of quartz and clay variation for each sample to the relative difference between TOC and QEMSCAN porosity. After all major mineral components were plotted against the relative difference between TOC and QEMSCAN porosity, only silica minerals showed increase and decrease with this relative difference. Dolomite, and to a lesser extent calcite, showed similar trends with this difference. Organic matter would most likely be mistaken for clay minerals in a measurement of this sort, because of their low density; however clay minerals as Figure 16 shows have an opposing trend. The importance of the relationship revealed by this examination is that organic matter would

23 most possibly be associated with silica (possibly radiolarian quartz), dolomite and calcite. The organic matter may be adhered as thin films on the surfaces, or contained within micro-pore spaces of these harder minerals, whose physical properties (density and maybe velocity) may mask the organic matter properties. 3.5 Nano-indentation Nano-indentation was done for the same eleven (11) shale samples used for the QEMSCAN. Results include the Young s Modulus, E or stiffness, and the hardness of the sample in gigapascals, GPa. See Table 8 below for average values for these properties. Averaging is usually done for all indents in each sample at a depth interval where most outputs were considered stable (ie. the output modulus has remained flat through the interval). In the case of this study, the interval between 300nm 400nm was considered most stable. Figure 17 below shows an example of stiffness plot with depth displacement into the sample surface. Though sixteen (16) indents were made for each sample, the nano-indenter returned no values at points were it could not find the surface up to the specified depth of 500nm. Wells and Sample Depth Max E, GPa Min E, GPa Range Ave E, GPa Ave Hardness, GPa TOC, wt% TR Tmax, o C HI 1-32 Mertes/7,216ft Mertes/7,219ft Mertes/7,221ft Graham USA/10,368ft Citgo Chambers/10,390ft Short Fee/10,479ft Federal / ft Toc Mee USA/10,792ft Federal DG/10,934ft Thompson Unit/11,044.8ft Thompson Unit/ ft Table 8: Young s Modulus, E and Hardness values as measured by nano-indentation, with averaging done from the 300nm -400nm interval from sample surface. Maturity indices included for reference. The main issue to investigate during the analysis of nano-indentation data for this study is how kerogen strength changes with maturity. It would be noticed from Figure 17 that a wide range of strengths is measured in the samples. Since the shale samples are non-uniform on a micro or even nano-scale, the indenter measures modulus for different mineral and organic matter components in each sample. Since the maturity indices measured during pyrolysis are just a measure of organic matter, average stiffness values for the entire sample would show a poor correlation with maturity. Plots made between the stiffness results and maturity indices: TOC; transformation ratio, TR; hydrogen index, HI and maximum temperature at S2 peak, TMax; the correlation coefficient value for linear regressions between data points ranged from 0.25 to The poor correlations seen from using all the indentation data emphasized the need to separate out kerogen or organic matter stiffness from the shale minerals prior to seeking relationships with maturity. Table 9 shows literature values for elastic properties of the minerals encountered in the Bakken shale samples 7.

24 Young's Modulus, GPa Oil Shale Research Test Test 2 Test 3 Test 4 Test 5 Test 6 Test 7 Test 8 Test 9 Displacement into surface, nm 1-15 Graham USA/10,368ft Test 10 Test 11 Figure 17: Young s Modulus, E as a function of indenter s displacement into sample surface. Average values for E were taken from nm. Black line represents 33GPa below which kerogen and clay stiffness was assumed. The only mineral not captured in the table is clay, which are soft materials like kerogen. For simplicity, it was considered that clay and kerogen have similar stiffness values in the shales, and that their stiffness was below that of the softest mineral on the table, 33GPa or below (area below black line in Figure 17). So averaging for stiffness was done for all indents which were within this lower strength interval at the 300nm 400nm range. Certain samples whose indents were not stable within this depth range were considered unreliable for analysis, and were not used. Table 10 shows the averaged stiffness values for the softer components, kerogen and clays. The median values gave a slightly better fit than the mean values, and when plotted against the same maturity indices mentioned above give much better correlations with R-squared values ranging from 0.68 to Figure 18 to 21 below show the plots between median values for Young s Modulus of kerogen and clay as a function of maturity indices. It can be seen from the plots stiffness increases as maturity increases.

25 Moduli in Gpa Velocities in m/s Mineral k low k high E low E high µ low µ high Vp Vs ν Quartz (Alpha) Quartz (Beta) K-feldspar Plagioclase feldspar* Dolomite Calcite Apatite Pyrite Micas (Biotite) Clays NA NA NA NA NA NA NA NA NA Kerogen NA NA NA NA NA NA NA NA NA k= Bulk Modulus E= Young's Modulus µ= Shear Modulus ν= Poisson's Ratio NA=Not Available *Plagioclase determined from ranges for Albite, Anorthite, Oligoclase and Labradorite Calculated from relationships among elastic constants (Birch, 1961) Table 9: Literature values for the elastic moduli of certain rock forming minerals seen in the Bakken Shales. k=bulk Modulus, E=Young s Modulus, µ=shear Modulus, Vp=Compressional Wave Velocity, Vs=Shear Wave Velocity, and ν=poisson s Ratio. The box in red shows values of interest.(modified from Gebrande and Alexandrov et al. in Schon, 1996) 7. Some E values and a poisson s ratio value were calculated based on elastic constant equations. Wells and Sample Depth Mean E, GPa Median E, GPa 1-32 Mertes/7,216ft Mertes/7,221ft Graham USA/10,368ft Short Fee/10,479ft Federal / ft Toc Mee USA/10,792ft Federal DG/10,934ft Thompson Unit/ ft Table 10: Average and Median values for Young s Modulus, E for kerogens and clays between interval 300nm 400nm

26 TOC, wt% Transformation Ratio, dec Oil Shale Research TR R² = Kerogen+Clay Median Young's Modulus, GPa Figure 18: Young s Modulus, E for kerogen and clays as a function of transformation ratio. 30 TOC R² = Kerogen+Clay Median Young's Modulus, GPa Figure 19: Young s Modulus, E for kerogen and clays as a function of TOC.

27 Maximum Temperature at S2Peak, degc Hydrogen Index Oil Shale Research HI R² = Kerogen+Clay Median Young's Modulus, GPa Figure 20: Young s Modulus, E for kerogen and clays as a function of Hydrogen Index. 500 TMax R² = Kerogen+Clay Median Young's Modulus, GPa Figure 21: Young s Modulus, E for kerogen and clays as a function of TMax at S2 peak.

28 Transformation Ration shows the best correlation with indentation stiffness with an 84.21% chance of predicting maturity using stiffness. The equation of the linear regression line through the data points may be used for rough estimation of maturity, considering the limited sample database. This equation: TR = E Where TR is the transformation ratio in decimals, and E is the Young s modulus in gigapascals, GPa. For TOC, the next best correlation, the equation of the regression line is: TOC = E Where TOC is the total organic carbon in weight percent, and E is the Young s modulus in gigapascals, GPa. 3.6 Contributions of Mineralogy to Anisotropy: Observations from QEMSCAN and Bench-top Velocity Measurements This section is the subject of an SEG expanded abstract written based on the measured data described above. The abstract is located in CD Documentation\k. Misc\Word Docs\KMba_MPrasad_BakkenMaturity. It describes how the increased presence of clays leads to increased anisotropy as observed from QEMSCAN images and calculated Vs anisotropy.

29 Conclusions The key conclusions that can be drawn from this study are as follows: a. GR and Resistivity logs in the Bakken Shales are so affected by the high organic matter content that they wrap around scaled plots, so making them unreliable for quantitative analysis; b. The Upper and Lower Shales show no difference compositionally or lithologically when log data from these shales are quantitatively analyzed; c. Velocity increases with decrease in organic matter content, and with thermal maturity of shales; d. The Lower Shale has higher silica content, and lower clay content than the Upper Shale; e. In the high paleo-geothermal gradient area, quartz content is lower, while carbonate minerals (dolomite and calcite), than in the low paleo-geothermal gradient area. f. The Young s Modulus of clays and kerogens increase with increasing maturity in the Bakken Shales. g. Increase in clay content with corresponding decrease in silica content in the Bakken Shales leads to an increase in anisotropy. So, in addition to organic matter and micro-cracks, clay presence controls anisotropy in organic-rich shales. These conclusions would be important for consideration in building predictive rock physics models.

30 Acknowledgements Many thanks to Dr Manika Prasad for giving me the opportunity to embark on this project. Thanks for all your support, time, advice and encouraging words. I also acknowledge the support of the Department of Energy (DOE award DE-NT ). Thanks to my erstwhile committee members: Dr Jennifer Miskimins and Dr Stephen Sonnenberg. Thanks also to the members of the Bakken Consortium at the Colorado School of Mines; the Fluids-DHI Consortium of the Colorado School of Mines and the University of Houston; John Skoks of the Thin-Section Preparation Lab in the Geology and Geological Engineering Department; Dr Sarah Appleby and Jane Stammer of the Advanced Mineralogy Research Center, Golden, USA for help with the QEMSCAN; Masood Hashemi of the Materials Engineering Department for help with nanoindentation; and Hui Jin of the Geology and Geological Engineering Department for help with pyrolysis.

31 References 1. Prasad, M., 2001, Mapping impedance microstructures in rocks with acoustic microscopy. The Leading Edge, 20: Schmoker, J.W., and Hester, T.C Organic Carbon in Bakken Formation, United States Portion of Williston Basin. The American Association of Petroleum Geologists Bulletin pp Price, L. C., Ging, T., Daws, T., Love, A., Pawlewicz, M., and Anders, D Organic Metamorphism in the Mississippian-Devonian Bakken Shale North Dakota Portion of the Williston Basin: in J. Woodward, F. F. Meissner and J. C.Clayton, eds. Hydrocarbon source rocks of the greater Rocky Mountain Region, Rocky Mountain Association of Geologists. Denver MATLAB Help Manual 5. Castagna, J. P., Batzle, M. L., and Eastwood, R. L Relationships between compressional-wave and shear wave velocities in clastic silicate rocks. Geophysics, pp Vernik, L. and Nur, A Ultrasonic Velocity and Anisotropy of Hydrocarbon Source Rocks. Geophysics. 57. pp Gebrande and Alexandrov et al. in Schon, Physical Properties of Rocks: Fundamentals and Principles of Petropyhiscs. Elsevier Science, Oxford, U.K. 583p, pp

32 Appendix 1000µm 2µm Figure a: 1-32 Mertes/7,216

33 µm 2µm Figure b: 1-32 Mertes/7,219

34 µm 2µm Figure c: 1-32 Mertes/7,221

35 µm 2µm Figure d: 1-15 Graham USA/10,368

36 µm 2µm Figure e: 1-24 Citgo Chambers/10,390

37 µm 2µm Figure f: 31-3 Short Fee/10,479

38 µm 2µm Figure g: 3-17 Toc Mee USA/10,792

39 - 39-2µm 1000µm Figure h: 1 Federal DG/10,934

40 - 40-2µm 1000µm Figure i: 5-1 Thompson Unit/11,044.8

41 Figure j: 5-1 Thompson Unit/11,046.8