SPWLA-2013-V54n4-A4 - Petrophysical Characterization of the Woodford Shale

368 PETROPHYSICS August 2013

Petrophysical Characterization of the Woodford Shale1

ABSTRACT

PETROPHYSICS, VOL. 54, NO. 4 (AUGUST 2013); PAGE 368382; 21 FIGURES; 5 TABLES

Nabanita Gupta2,3, Chandra S. Rai2, and Carl H. Sondergeld2

Manuscript received by the Editor April 11, 2013; revised manuscript received June 10, 2013.1 Originally presented at the 53rd Annual Logging Symposium, Cartagena, Colombia, June 16-20, Paper LLL2 Mewbourne School of Petroleum and Geological Engineering, University of Oklahoma, SEC-1210, Sarkeys Energy Center, 100 E. Boyd St., Norman, OK 73019-1003, USA; Email: [email protected]; [email protected]; [email protected] Now at Shell Oil Company, 25519 Somerset Meadows Ct., Katy, Tx 77494, USA; Email: [email protected]

Despite the economic success in the Woodford Shale play, variability in petrophysical parameters controlling reservoir quality is poorly understood and hence, exploration activities rely heavily on history matching. This limits the identi cation of new sweetspots and also expansion of exploration activities outside the proven area. Here we present a set of laboratory-measured petrophysical properties collected on 300 samples of the Woodford Shale from six wells. This dataset provided an opportunity to cluster the Woodford Shale in three different petrotypes (good, intermediate and poor). Good correlations between different petrotypes with geological core descriptions, along with the good conformance between different petrotypes with production data ascertain the practical applicability of such petrotyping. It was possible to upscale such petrotypes through calibration of well logs with core measurements. Porosity, bulk density, grain density, mineralogy, acoustic velocities (Vp-fast, Vs-fast and Vs-slow), mercury-injection capillary pressure along with

total organic carbon content (TOC), Rock-Eval pyrolysis, and vitrinite re ectance were measured. Visual inspections were made at the macroscopic-, microscopic- and SEM-scale in order to calibrate petrophysical properties with the actual rock architecture. Mineralogically, the Woodford Shale is a silica-dominated system with very little carbonate present. Crossplots of porosity and TOC clearly separate the lower thermal maturity (oil window) samples from higher thermal maturity (wet gas-condensate window) as porosity is lower at lower thermal maturity. Independent observations made through SEM imaging con rm much lower organic porosity at lower thermal maturity, while organic pores are the dominant pore types in all samples irrespective of thermal maturity. Crack-like pores are only observed in the oil window. Cluster analyses of TOC, porosity, clay and quartz content revealed three clusters of rocks, which can be ranked as good, intermediate and poor in terms of reservoir quality.

INTRODUCTION

The Woodford Shale, which has long been known as the source of most of Oklahomas hydrocarbon reserves, emerged as resource play in its own right following the huge success of the Barnett Shale play in 2005. Geographically, the Woodford Shale play can be grouped into three regions: Woodford, Cana-Woodford (Midwest) and Barnett Woodford (Southwest), with estimated technically recoverable resource as 22.21 Tcf, 5.72 Tcf and 32.15 Tcf, respectively (EIA, 2011). This study focuses on an area in the Midwest where the shale is reported to produce dry gas, condensate as well and oil and has an average thickness of 200 ft. The Woodford Shale is not only characterized by highly heterogeneous nature but also acts as the source, seal, and reservoir, which make it dif cult to identify or rather, de ne the reservoir unit within this rock. Appropriate petrophysical characterization leads to identi cation of different petrotypes

with unique petrophysical properties, which eventually can help to identify sweetspots and to decide appropriate areas for well placement. As production from these shales requires stimulation through hydraulic fracturing, appropriate petrophysical models should help to predict hydrocarbon reserves as well as areas/intervals with high fracability. Centimeter-scale vertical heterogeneity of resource-shales limits the use of eld-scale measurements and misrepresents the petrophysical characterization of such shales to some extent. Petrophysical characterization based on measured petrophysical properties from representative samples of individual lithofacies/petrotypes helps to identify and estimate the range of values for parameters which correlate strongly with reservoir-evaluation attributes such as, gas-in-place and fracability, the two important parameters for assessing the resource potential of shales. Such laboratory-based measurements and analyses require a large dataset, which makes characterization of resource

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Petrophysical Characterization of the Woodford Shale

shales time-intensive but necessary for the proper evaluation of these potential reservoirs (Kale, 2009).

STUDY AREA

The Woodford Shale was deposited in the paleo-Oklahoma Basin which was covered by an epeiric sea during global sea-level transgression (Lambert, 1993; Johnson, 1988) (Fig. 1). Different petrotypes are expected in marine depositional settings (horizontal variability) as well as in different stages of the transgression (vertical variability). Cores collected from six wells located in both hydrocarbon-producing and nonproducing areas (approximately 1,440 mi2) were used in this study (Fig. 2). Thermal maturity of the Woodford Shale in Wells 1, 2, 3, and 6 are within the dry-gas/condensate maturity window, whereas Wells 4 and 5 are within the oil window. Samples were collected from ~200 ft of continuous core acquired from Wells 1 through 3. For Wells 4, 5, and 6 samples were collected from cores taken at discrete depth intervals. After careful visual inspection of the cores, approximately 300 samples were collected at 2 ft intervals in the visually monotonous interval and intermittent samples were collected where there were visually obvious changes in lithology. It is worth mentioning here that sometimes it is possible to miss changes in lithology due to the dark color of the rock. Sharp changes in petrophysical properties along with inspection of microscopic properties were used to track signi cant but nonvisible lithologic changes.

Fig. 1Geology of the study area. (a) Paleogeography of North America at the beginning of late Devonian (Frasnian). The paleolocation of Oklahoma is marked with red outline (modi ed from Comer, 2007). (b) During Late Devonian-Early Mississippian time the Oklahoma Basin covered a vast area of the southern Midcontinent. The Oklahoma Basin was later divided into a number of sub-basins as the result of tectonic activity (modi ed from Johnson, 1988). (c) Isochore map of the Woodford Shale in Oklahoma (modi ed from Comer, 2007).

Fig. 2Locations of the six cored wells used in this study.

LABORATORY MEASUREMENTS

A set of petrophysical properties impacting two critical reservoir-assessment categories, i.e., storage capacity and ow capacity, were measured: crushed rock porosity (I), (bulk density (b), grain density (g) (Karastathis, 2007; Kale, 2009), mineralogy through Fourier Transform Infra-Red transmission spectroscopy (FTIR) (Ballard, 2007; Matteson and Herron, 1993; Sondergeld and Rai, 1993), acoustic velocities (Vp-fast, Vs-fast and Vs-slow) (Birch, 1960; Schreiber et al., 1973; Raina, 2010), total organic carbon (TOC) content, Rock-Eval analyses, and mercury-injection capillary pressure (MICP) (Kale, 2009). In addition, microscopic and submicroscopic inspections of samples were conducted through petrographic analyses of thin sections and scanning electron microscopic (SEM) analyses of focused ion beam (FIB) milled samples. Nuclear magnetic resonance (NMR) measurements were performed on a few samples to gain knowledge of shale wettability. Most of these measurements were made at the Integrated Core Characterization Center (IC3) at the University of Oklahoma. The organic content (TOC) and Rock-Eval measurements were made at a commercial laboratory. Dynamic elastic moduli calculated from ultrasonic acoustic velocities provide indirect estimates for rock-mechanical characteristics. Crushed rock porosity (I) represents the total porosity of shales and includes the free and capillary-bound pore spaces but excludes the spaces occupied by clay-bound water. The FTIR mineralogy system was setup to identify 16 common rock-forming minerals, which were grouped into ve mineral groups (Table 1).


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Tabl e 1Minerals Identi ed Through FTIR

For this study, the ultrasonic measurements were performed on horizontal plugs: One compressional (Vp-fast) and two orthogonally polarized shear-wave velocities (Vs-fast and Vs-slow) were measured (Fig. 3).

F ig. 3Schematic diagram showing the orientation of core plugs (left); Vp-fast and Vs-fast are fast compressional and shear-wave velocities, Vs-slow is the slow shear, where the particle movement is perpendicular to the bedding and the wave is travelling parallel to the bedding (center), and the principal directions and the required elastic constants for anisotropic characterization of the shales (right).

Core plugs were subjected to increasing con ning pressure in eight steps, from 250 to 5,000 psi, and ultrasonic data were collected at each step using an ultrasonic-pulse transmission technique. In addition to the capillary pressure, the MICP measurements also allowed calculation of the pore-throat diameter using the Washburn equation

r = (1)

where, Pcap is the capillary pressure (psi), is interf acial tension, 480 dyne/cm for mercury; k = 0.145; and is the contact angle, 140 for mercury; and r is the pore-throat radius (m). Hence, knowing the pressure at which intrusion takes place allows calculation of the pore-throat radius. For example, at 60,000 psi mercury enters pores with throat diameters of 3 nm.

RESULTS

Tables 2 and 3 summarize the sample distribution for the laboratory-measured petrophysical properties, in the studied wells. Table 2Number of Samples Used in Laboratory Measurements of Petrophysical Properties

Table 3Summary of Laboratory-Measured Petrophysical Properties for the Wells Studied

2kcosPcap


Porosity Porosity (I) ranges between 0 and 10% and shows a normal distribution, especially when wells with high organic maturity are considered. The porosity of samples from wells with low thermal maturity fall into a different distribution. In wells with high thermal maturity (Wells 1, 2, 3, and 6), porosity ranges from 2 to 10% and averages 6%. For wells within the oil window (Wells 4 and 5) porosity ranges from 0 to 5% and averages 2.7%.

Density Bulk density ranges from 2.2 to 2.9 g/cm3, averaging 2.4 g/cm3; grain density ranges from 2.4 to 3 g/cm3, averaging 2.6 g/cm3.

Mineralogy Figures 4 through 6 summarize the mineralogy listed in Table 1. Individual mineral weight percentages for Wells 1 through 6, located in different parts of the basin, show an overall narrow spread while quartz and illite show comparatively wider spread (Fig. 4a). Fig. 4b shows distributions of ve group of minerals mentioned in Table 1. Despite the narrow range in mineralogy, plots of mineral wt% vs. depth for Wells 1, 2 and 3 (Fig. 5) show strong variations. For example, Well 3 has a higher clay concentration compared to Wells 1 and 2, and there is a signi cant decease in clay concentration with shallower depths in Well 2.

Fig. 4Box-and-whisker plots showing (a) distributions of different minerals measured through FTIR, and (b) distributions of the ve mineral groups shown in Table 1.

Fig. 5Variation in mineralogy with depth in Wells 1, 2, and 3.

Ternary plots indicate that the Woodford Shale is a silica-dominated system with


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Fig. 7Crossplot of quartz vs. clays, the two dominant minerals in the Wells 1 through 6, indicates that these two parameters are inversely related.

Ultrasonic Velocity Measurements Native-state acoustic velocities were measured on horizontal core plugs from Wells 1 and 3; 84 velocity measurements were performed. Core plugs required for these measurements could not be recovered from the other studied well. The velocity ranges and averages are listed in Table 4.

Table 4Range and Average of Ultrasonic Velocity Measurements

Ultrasonic velocity measurements show little pressure dependence (Fig. 8). A small increase in velocity at low con ning pressure (Pc) is most likely due to closures of desiccation cracks. Such characters are observed specially in samples with higher clay concentration.

Fig. 8Ultrasonic velocities plotted as a function of con ning pressure. Both (a) compressional-wave, Vp-fast , and (b) shear-wave, Vs-fast, velocities exhibit weak dependence on pressure. Different colors represent samples with different clay concentrations. Note the small increase in velocities (primarily compressional) at low con ning pressure for samples with higher clay concentrations.

It was not possible to obtain three sets of plugs (horizontal, vertical and 45) required to measure the ve independent elastic moduli that are needed to fully de ne the characteristic transverse anisotropy of shales. The ne-scale heterogeneity of the shale (a) makes it dif cult to acquire equivalent core plugs with different orientations, and (b) results in samples that are biased toward the more competent strata.

Total Organic Carbon TOC ranges between 0 and 14 wt%. High TOC values indicate that a signi cant volume fraction of the rock is organic matter since the density of organic matter (1 g/cm3) is much lower than the density of common rock-forming minerals (Sondergeld et al., 2010). This implies that the petrophysical properties of organic matter signi cantly affect the overall rock petrophysical properties. Moreover, SEM imaging has revealed large numbers of pores within the organic matter, which further increases the volume of organic matter for a given wt% TOC (Gupta, 2012).

Rock-Eval Analysis Rock Eval was been performed on 150 samples. In this experiment, organic matter was pyrolized with step increase in temperature. The amplitudes of S1, S2 and S3 peaks recorded in this experiment are representative of liquid hydrocarbons present in the rock, the amount of convertible kerogen, and the amount of inorganic carbon dioxide released, respectively. The temperature for the highest S2 peak represents thermal maturity (Tmax). Average Tmax for the studied Wells 1 through 5 are 467C, 458C, 466C, 439C and 441C, and the equivalent Ro values are 1.25%, 0.99%, 1.23%, 0.74% and 0.78%. Tmax is converted to equivalent vitrinite re ectance value through the following equation

Ro (%) = (0.018 x Tmax) - 7.16 (Espitalie, 1986). (1)

For Wells 1, 2 and 3, S2 peaks are either too low or form a plateau and lack any peak (characteristics of high thermal maturity) (Fig. 9). Consequently limited con dence should be assigned to S2 values and Tmax derived from this S2 peak. Also quantitative use of these parameters should be limited. Measured Ro for Wells 1 through 6 are 1.62%, ~1.6%, 1.67%, 0.54%, 0.54% (Comer, personal communication). Jarvie (1991) mentioned that for high-maturity samples microscopic estimation of Ro is more reliable than Rock-Eval analyses. However, for consistency, such measurements should be made by the same individual to reduce human error.


Fig. 9Pyrograms for samples in (a) Well 4 showing distinct S1 and S2 peaks respectively from left to right, and (b) Well 1, note the low S2 peak.

Both vitrinite re ectance (Ro) and average Tmax values indicate that Wells 1 through 3 and 6 are in condensate/wet-gas window and that Wells 4 and 5 are in the oil window. However, over a 300 ft interval, the average thickness of the Woodford Shale in the study area, thermal maturity may vary. Figure 10 indicates that Wells 4 and 5 have Type II kerogen whereas the high maturity wells falls near the origin and shows a wide range of kerogen type; this plot represents the present day organic matter type. Jarvie et al. (2007) documented that thermal maturity as well as expulsion of hydrocarbon from rocks affect the chemical composition of kerogen and make it dif cult, if not impossible, to recognize original kerogen type in highly thermally mature samples. Since, low maturity samples are plotted as Type II, starting material for the highly thermally mature samples are also interpreted to be Type II. The facts that Type II kerogen results primarily from marine source and the Wells 4 and 5 are located near-shore compared to Wells 1, 2, 3 and 6 which are located deeper marine, further ensure that starting organic matter in the studied wells is Type II. Overall, the wells are grouped into two ranges of maturity: highly mature (Wells 1, 2, 3 and 6) and low mature (Wells 4 and 5). Original kerogen type has been interpreted as Type II.

Mercury-Injection Capillary Pressure (MICP) MICP measurements show absolutely no mercury intrusion below 10,000 psi con ning pressure, indicating pore throats are smaller than 0.01 m. This is equivalent to a matrix permeability of a few hundred nanodarcies. The different shapes of the capillary-pressure curve represent different pore-throat sizes and distributions that have been used for petrotyping.

DISCUSSION

We focus our discussion on distinguishing the essential petrophysical properties and their effect on two critical components of unconventional resource-shales, (1) storage capacity, and (2) deliverability. Similar to other petroleum systems, porosity (I) is directly related to storage capacity. On the other hand, hydrocarbon is generated from organic

matter within the self-sustained resource-shale petroleum system. Hence, the concentration of organic matter (TOC) directly affects the potential volume of hydrocarbon present within the system. However, there is no tool to directly measure either porosity in shale or TOC at the eld-scale and it is economically impossible to collect core or laboratory data from each well. Here we evaluate correlations between these two properties and other petrophysical properties in order to identify petrophysical parameters that will allow us to estimate porosity and TOC. The crossplot of TOC and I (Fig. 11) shows porosity increasing with TOC. The correlation between TOC and porosity improves when wells with similar thermal maturities are considered. Overall, samples with lower thermal maturity (wells within oil window) contain lower I compared to samples with higher thermal maturity (wet-gas/condensate window) as additional pore space is created within the organic matter as more hydrocarbon is expelled with increasing thermal maturity. The good correlation between TOC and porosity is supported by the fact that organic pores are the dominant pore types in this resource shale, as observed through SEM imaging (Gupta, 2012). Previous authors (Curtis et al., 2011; Loucks et al., 2009; Passey et al., 2010) have observed that organic pores are also the most dominant pore type in other resource shales. Although overall porosity increases with thermal maturity, SEM imaging has revealed a more complex aspect of this porosity development (Gupta, 2012).

Fig. 10Crossplot of hydrogen index (HI) and Tmax. Wells 1 through 3 and 6 are at higher maturity levels compared to Wells 4 and 5. HI is calculated using the following S2 x 100/TOC, mg HC/g TOC.



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Fig. 11Crossplot of core porosity (I) vs. TOC shows a good correlation between these parameters. The red circles represent data from the high thermal-maturity wells (Wells 1, 2, 3, and 6). The cyan points are from the low thermal-maturity wells (Wells 4 and 5). The equations for tted regression lines are I = 0.54 x TOC + 3.22 (red); I = 0.83 x TOC - 1.98 (cyan).

Crossplots of porosity and mineralogical composition reveal complex relationships between porosity and the two dominate minerals: quartz and total clays. A crossplot of I vs. clays indicates overall increasing I with increasing clay concentration (Fig. 12). A similar correlation has been observed in the Barnett Shale (Kale, 2009) and in the Thirteen Finger Limestone (Raina, 2010).

Fig. 12Crossplot of core porosity (IHe) vs. total clays. Data are colored with quartz content (wt%). Data bounded by the two ellipses indicate two clusters of rocks. The cluster with higher clay concentration is characterized by low quartz concentration and the other cluster with lower clay concentration is characterized by higher quartz concentration. Points marked by 1 indicate carbonate-rich samples characterized by both low clays and low quartz concentrations.

A closer look of the crossplot of porosity vs. clay indicates two clusters of rocks (1) rocks where the clay concentration is 42%, porosity decreases with increasing clay concentration. The rst cluster is also characterized by overall high quartz

concentration compared to the second cluster. The crossplot of porosity vs. quartz (Fig. 13) does not show any apparent correlation. The correlation between porosity and quartz improves when the data are divided into two groups (1) rocks with a quartz concentration 40%, porosity decreasing with increasing quartz. Overall, the rst group of rocks contains high clay percentages, indicating that most of the samples from this group also belong to cluster 1 on the porosity-clay crossplot (Fig. 12).

Fig. 13Crossplot of core porosity (I) vs. quartz. The crossplot exhibits two clusters of rocks as shown by the two ellipses. IHe increases with increasing quartz for quartz concentration 40%.

As stated previously, the concentration of TOC directly affects the presence of hydrocarbon within self-sustained resource shales. TOC is also correlated with porosity and increases with increasing porosity (Fig. 11). In general, TOC increases with increasing clay content, as observed in other resource shales (Kale, 2009; Raina, 2010). However, the crossplot of TOC vs. clays (Fig. 14a) indicates a more subtle correlation between TOC and clay content for the studied shale. The crossplot indicates three clusters of rocks with unique correlations between these two parameters. Cluster 1 consists of rocks with low clay concentration (0%


content. Cluster 2 includes rocks with 24%


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to collect plugs and ultrasonic measurements from the thin brittle layers, mineralogy data con rms high quartz content of those siliceous layers.

Fig. 16(a) Core sample from Well 3 showing small-scale faulting bounded within the brittle layer. Red dashed lines indicate the boundaries between more brittle (central interval), less brittle and ultimately to the black mudstone, yellow lines indicate vertical-bound fractures within the brittle layer; coin is for scale. (b) Schematic representation of picture (a).

Mercury-Injection Capillary Pressure (MICP) The complex nature of resource-shales limits the quantitative use of capillary-pressure data. However, the shape of the capillary-pressure curves provides an indicator of different petrotypes. Three groups of rocks have been identi ed on the basis of capillary-pressure-curve shape. Typical characteristics of these curves are described below (Fig. 17):

Fig. 17Examples of MICP curve-types A through C in gures (a) through (c), respectively.

Type A. Incremental Hg-intrusion plot increases monotonously and does not reach a plateau even at 60,000 psi pressure (Fig. 17a). This indicates that the pore-throat diameters are either smaller than 3 nm or that pores were initially larger than 3 nm and were compressed in response to increasing con ning pressure during the experiment.

Type B. For this curve type the incremental Hg-intrusion plot reaches a plateau (Fig 17b) indicating that pore throats in this group are larger than 3 nm. This also implies that this group has the highest permeability. In addition, this group is also associated with the highest porosity rangeI = 9.3% for the example shown in Fig. 17b.

Type C. The incremental Hg-intrusion plot has a similar shape as Type A (Figs. 17a, c). However, they show false intrusion, which can be con rmed from the cumulative Hg-intrusion curve (right-hand gure of Fig. 17c). The saturation and desaturation cumulative intrusion curves almost follow each other without much separation between them (lack of hysteresis). Lack of hysteresis between the two curves indicates false intrusion. Signi cant hysteresis is observed in cumulative Hg-intrusion curve Type A. This group also shows very little Hg-intrusion (


in resource shales, and (2) typically there has not been an evaluation of the mechanical properties of rocks that play an important role in hydrocarbon production from resource shales. We have used cluster analyses of the principal petrophysical properties to quantitatively recognize different petrotypes within the Woodford Shale in the study area. Kale (2009) applied a similar cluster-analysis technique to identify different petrotypes within the Barnett Shale. Petrophysical analysis discussed in the previous sections show that only few petrophysical parameters show signi cant dynamic range to be useful for identifying different group of rocks, such as: I, TOC, quartz, illite, smectite-mixed clays, total clays. Mineralogy data from Wells 1 through 6, located in different part of the basin, show that illite is the most dominant clay mineral and hence, total clay instead of illite have been used for cluster analyses. Previous studies (Kale, 2009; Raina, 2010; Sondhi, 2011) on other resource shales also indicate that wide range of porosity, total organic carbon content, and mineralogy are useful in de ning different petrotypes within resource shales. Cluster analysis of different parameters using Gaussian k-means classi cation method (Bradley and Mangasarian, 2000) ultimately helped us to quantitatively de ne these petrotypes. The Gaussian k-means classi cation method analyzes N data points located in I dimensional space and then classi es them into K clusters, so that the variance between any two members from two different groups are more than the variance between any two members of the same group (N, I and K are any integers). Three rock groups, i.e., petrotypes, (K = 3) have been identi ed through this technique using four petrophysical parameters (I = 4): I, TOC, quartz, and clays. Table 3 summarizes the petrophysical properties of the petrotypes identi ed through cluster analysis. Figure 18 shows the petrophysical characteristics of different petrotypes identi ed through cluster analysis. Petrotype 1 shows an intermediate clay content (20%


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a high Poissons ratio, and high Vp/Vs ratio and thus de ned as ductile (Fig. 19). In order to initiate hydraulic fracturing and produce hydrocarbons from these otherwise impermeable resource shales, the ductile hydrocarbon/TOC-enriched intervals require intervening brittle layers. Consequently, the vertical juxtaposition of petrotypes with contrasting mechanical properties (brittle versus ductile) will improve the reservoir quality of resource shales. Among the studied wells, the best producing well, Well 2, is characterized by broadest distribution of petrophysical parameters, thus indicating the presence of different petrotypes within the producing Woodford Shale interval. On the other hand, despite the presence of good reservoir storage capacity (based on the concentration of TOC and porosity) Well 3 is nonproductive. The lack of contrasting petrotypes in the vertical Woodford section combined with high clay content limited the effectiveness of hydraulic fracturing in this well. This is an ideal example of a well with good storage capacity and zero hydrocarbon production due to inability to initiate hydraulic fracturing. Petrotyping was not performed on Well 6, located between Wells 2 and 3 (Fig. 2), due to limited sample availability.

Fig. 19C rossplots of (a) Youngs modulus vs. Poissons ratio, and (b) Youngs modulus vs Vp-fast/Vs-fast ratio. Cir cle size is proportional to TOC concentration.

CALIBRATION OF PETROTYPES WITHMICP DATA

MICP measurements were performed on 110 samples and the MICP characteristics of each petrotype are summarized in Table 5. Petrotype 3 is represented by MICP Type C, Petrotype 2 is represented by MICP Type A, and Rock Type 1 contains almost equal quantity of MICP Type A and MICP Type B. MICP Type C indicates the poorest connectivity between pores. Although SEM imaging has revealed micrometer size pores within Petrotype 3, poor connectivity between those pores (as indicated by MICP Type C) reduces the reservoir quality of this petrotype. MICP Type A (Fig. 17a) indicates pore throats smaller than 3 nm, which resulted from nanometer-size pores present in the studied rocks and as observed through SEM-imaging. MICP Type B (Figure 15b) is indicative of slightly larger pore-throat diameters and is most common within Petrotype 1, in comparison with Petrotypes 2 and 3. Hence, presence of rocks with MICP Type B improves reservoir quality. This integration of petrotype with the MICP curve type improves our understanding about individual petrotypes.

Table 5Distri bution of MICP Curve Types Within Each Petrotype

WELL-LOG ANALYSIS

Generating a large dataset of core-based measurements is expensive and time consuming and not possible in every situation. For practical application, the petrotypes derived from core-based petrotyping must be correlated to well logs to enable prediction of the petrophysical properties in uncored intervals. However, only a limited suite of well logs from one well (Well 1) was available and well-log petrotyping could not be performed. Well logs were used in combination with core data to calculate uid density and TOC and to identify well-log signatures of the petrotypes identi ed from core data. Well-log data were rst shifted to core depth prior to integrating well logs with core data. Depth shifting is critical in these very heterogeneous rocks and it is obvious that the correlation between core and well log, as well as any analyses involving both core and well-log data, will improve


with high-precision depth shifting. In this study, depth shifting was performed using high-resolution gamma-ray correlation.

Total Organic Carbon (TOC) Good correlations between TOC and b and Vp (Figure 13a) indicate that these two parameters can be used to estimate TOC from well logs. TOC is calculated from both compressional velocity-deep resistivity and bulk density-deep resistivity pairs using Passey method (Passey et al., 1990). Ultimately TOC is estimated at well-log scale by taking arithmetic average from these two sources and calibrating it with the core measured TOC. This well-log derived TOC can be used in combination with Well-log measured quartz and clay content in order to identify similar petrotypes at well-log scale as identi ed based on core data.

Well-Log Rock-Type Signatures of Different Petrotypes Plotting TOC against deep resistivity (AT90) Petrotype 1 shows the highest resistivity values compared to Petrotypes 2 and 3 (Fig. 20). Intervals containing Petrotype 3, which characterized by high resistivity, low clay concentration, and high porosity (>6.5%), are the highest quality reservoir intervals. High resistivity values coupled with low clay concentration, high porosity, and high TOC in these intervals indicate that the high resistivity values result from the hydrocarbon enrichment. Most of these intervals are located within the middle Woodford interval, indicating that this is the most productive interval, at least in this well (Fig. 21). Good reservoir intervals alternating with intervals of high Youngs modulus and low Poissons ratio can be used to identify the intervals within the middle Woodford with the highest potential (sweetspots) for placing the horizontal well (Fig. 21). Finally it can be stated that, this analysis highlights the ability to detect reservoir intervals within the Woodford Shale when well logs are used in combination with laboratory-measured core data.

Fig. 20Crossplot of laboratory-measured total organic carbon (TOC) with well-log measured deep resistivity (AT90). Data-point size is proportional to clay concentration. Data points in the yellow ellipse indicate good reservoir rocks characterized by high porosity (>6.5%), data points in the purple ellipse are characterized by very low porosity (~2%).

CONCLUSIONS

Petrophysical properties measured on carefully collected samples indicated that porosity, TOC, quartz and clay concentration are critical parameters for identifying different petrotypes within the Woodford Shale play. Three petrotypes have been identi ed through cluster analyses of these four parameters. A petrotype with intermediate concentrations of clay and quartz is identi ed as the highest potential reservoir rock. A proportional increase in TOC with increasing quartz concentration in this petrotype ensures the brittle nature of TOC-rich intervals. Dynamic elastic moduli calculated from ultrasonic


measurements indicate the ductile nature of the typical TOC-rich interval. Consequently, intervals with the high-quality reservoir potential (intervals with good petrotype), i.e., sweetspots that are ideal intervals for initiating hydraulic fracturing, are those with alternating brittle layers (cherty mudstone facies). This is in contrast to conventional reservoirs where intervals with highest net-to-gross are considered the best intervals for hydrocarbon exploration and production. Geologic analyses indicate that the cherty mudstone facies, which represents storm-related deposits that formed in depressions on the basin oor, makes the best well-placement target. In contrast, basin- oor highs are characterized by rocks with high clay content that were deposited as sediment fell out of suspension, and are least affected by detrital sediments from storm/current ows (good source for detrital sediments). Both thermal maturity and distribution organic carbon control the formation of organic porosity: organic porosity increases with increasing thermal maturity. Heterogeneity in the organic matter causes different distributions of organic carbon, which results in heterogeneous distributions of organic pores at any stage of thermal maturity. Calibrating well logs to core data allows the proper estimation of TOC, total porosity, and uid density from well logs. These parameters, along with mineralogy data, can be used for identi cation of different petrotypes from eld data. The integrated work ow described in this study can also be applied to identify critical petrophysical parameters followed by determination of the sweetspots within any other resource shales.


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Fig. 21Well -log plot of the Petrotype 3 in Well 1. Deep resistivity (AT90) is plotted in Track 1, petrotype is plotted in Track 2. LW, MW, and UW refer to lower, middle and upper member of the Woodford Shale, respectively. (a) Petrotype 1, with I>6.5%, is shown cyan in Track 2. (b) Petrotype 1, with dynamic Youngs modulus >28 GPa and Poisson's ratio


Passey, Q., Creaney, S., Kulla, J., Moretti, F., and Stroud, J., 1990, A Practical Model for Organic Richness from Porosity and Resistivity Logs, AAPG Bulletin, 74(12), 1777-1794.Passey, Q.R., Bohacs, K., Esch, W.L., Klimentidis, R., and Sinha, S., 2010, From Oil-Prone Source Rock to Gas-Producing Shale Reservoir-Geologic and Petrophysical Characterization of Unconventional Shale-Gas Reservoirs, Paper SPE-131350, presented at the International Oil and Gas Conference and Exhibition in China, Beijing, China, 8-10 June.Pittman, E.D., 1992, Relationship of Porosity and Permeability to Various Parameters Derived From Mercury Injection Capillary Pressure Curves For Sandstone, AAPG Bulletin, 76(2), 191-198.Raina, I., 2010, Petrophysical Characterization of Thirteen Finger Limestone, University of Oklahoma, MS Thesis.Rickman, R., Mullen, M.J., Petre, J.E., Grieser, W.V., and Kundert, D., 2008, A Practical Use of Shale Petrophysics for Stimulation Design Optimization: All Shale Plays are Not Clones of the Barnett Shale, Paper SPE-115258, presented at the SPE Annual Technical Conference and Exhibition, Denver, Colorado, USA, 21-24 September.Rushing, J.A., Newsham, K.E.. and Blasingame, T.A., 2008, Rock Typing-Keys to Understanding Productivity in Tight Gas Sands, Paper SPE-114164, presented at the SPE Unconventional Reservoirs Conference, Keystone, Colorado, USA, 10-12 February. Schreiber, E., Anderson, O., and Soga, N., 1973, Elastic Constants and Their Measurements, McGraw-Hill, New York.Sondergeld, C., and Rai, C., 1993, A New Concept in Quantitative Core Characterization, The Leading Edge, 12(7), 774-779.Sondergeld, C.H., Newsham, K.E., Comisky, J.T., Rice, M.C., and Rai, C.S., 2010, Petrophysical Considerations in Evaluating and Producing Shale Gas Resources, Paper SPE-131768, presented at the SPE Unconventional Gas Conference, Pittsburgh, Pennsylvania, USA, 23-25 February. Sondhi, N., 2011, Petrophysical Characterization of Eagle Ford Shale, University of Oklahoma, MS Thesis.Sullivan, K.L., 2006, Organic Facies Variation of the Woodford Shale in Western Oklahoma, University of Oklahoma, MS Thesis.U.S. Energy Information Administration, 2011, Review of Emerging Resources; US Shale Gas and Shale Oil Plays, 82 p. ftp://ftp.eia.doe.gov/natgas/usshaleplays.pdf


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ABOUT THE AUTHORS

Nabanita Gupta received a PhD in Geology from University of Oklahoma (2012), M.S. (2005) from Indiana University, M.Sc. (2003) from Indian Institute of Technology Bombay, India and BSc. (2001) from Jadavpur University, India. She started her professional career

as a Petrophysicist with Shell since 2012. Her research interest is unraveling the mystery of resource shales in an integrated fashion. Along with the Woodford Shale for her PhD research, she has evaluated a number of emerging shale plays. She is a reviewer of the Interpretation by the Society of Exploration Geophysicist. She has worked with few other major and service companies including ExxonMobil (2009) and with Chevron (2008) and Baker Atlas.

Dr. Chandra S. Rai is cur rently Director and Eberly Family Chair Professor at Mewbourne School of Petro leum and Geological Engineering, University of Oklahoma, U.S.A. He has an MS degree from Indian School of Mines and a PhD from the University of Hawaii, both in Geophysics. He worked with Amoco

Production Company for 18 years in various technical and management capacities. He has published more than two dozen technical articles and holds ten US patents. His areas of interest include seismic rock properties, petrophysics, anisotropy, and reservoir characterization.

Carl Sondergeld is currently Professor and the Curtis Mewbourne Chair at the Mewbourne School of Petroleum and Geological Engineering, University of Oklahoma. He earned a Ph.D. in Geophysics from Cornell University and a B.A. and M.A. in Geology from Queens College, CUNY. He spent 19 years at

the Tulsa Research Center of Amoco Production Company where he conducted research in petro- and rock physics He holds 14 US patents. He has been at the University of Oklahoma for 14 years; teaching petrophysics, geological well logging, petrophysics of unconventional resources, and seismic reservoir modeling. He is a two-time winner of the Brandon Grif n award and four time winner of SPE student chapter award of professor of the year. He currently conducts

research on unconventional reservoir rocks, in particular shales, and in the areas of microstructural characterization, anisotropy, NMR, petrophysics, hydraulic fracturing and seismic reservoir modeling. He served as the SEG Distinguished Lecture for the fall 2010. He and Dr. Chandra Rai manage two industrial consortia: Experimental Rock Physics and The Unconventional Shale Consortium.

Gupta et al.

Documents

SPWLA-2013-V54n4-A4 - Petrophysical Characterization of the Woodford Shale