RogerSlatt Shale Gas

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Submitted to AAPG book on gas shales Pore-to-regional-scale, integrated characterization workflow for unconventional gas shales Roger M. Slatt 1 , Paul R. Philp 1 , Neal O'Brien 2 , Younane Abousleiman 1 , Prerna Singh 1,3 , Eric V. Eslinger 4 , Roderick Perez 1 , Romina Portas 1,5 , Elizabeth T. Baruch 5 , Kurt J. Marfurt 1 and Steven Madrid-Arroyo 1 1 Conoco-Phillips School of Geology and Geophyscs, University of Oklahoma 2 Department of Geology, State University of New York, Potsdam 3 Current Address: Chevron-Texaco Inc. 4 Eric Geoscience Inc. and The College of St. Rose, Albany, New York 5 Current address: Conoco-Phillips, Inc.

Transcript of RogerSlatt Shale Gas

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Submitted to AAPG book on gas shales Pore-to-regional-scale, integrated characterization workflow for unconventional gas shales

Roger M. Slatt1, Paul R. Philp1, Neal O'Brien2, Younane Abousleiman1, Prerna Singh1,3, Eric V. Eslinger4, Roderick Perez1, Romina Portas1,5, Elizabeth T. Baruch5, Kurt J. Marfurt1 and Steven Madrid-Arroyo1 1 Conoco-Phillips School of Geology and Geophyscs, University of Oklahoma 2 Department of Geology, State University of New York, Potsdam 3 Current Address: Chevron-Texaco Inc. 4 Eric Geoscience Inc. and The College of St. Rose, Albany, New York 5 Current address: Conoco-Phillips, Inc.

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ABSTRACT Based upon recent studies of Barnett and Woodford gas shales in Texas and Oklahoma, a

systematic characterization workflow has been developed which incorporates litho- and sequence-stratigraphy, geochemistry, petrophysics, geomechanics, well log, and 3D seismic analysis. The workflow encompasses a variety of analytical techniques at a variety of geologic scales. It is designed as an aid to identifying the potentially best reservoir, source, and seal facies for targeted horizontal drilling. Not all of the techniques discussed in this paper have yet been perfected, and cautionary notes are provided where appropriate.

Rock characterization includes: (1) lithofacies identification from core based upon fabric, mineralogic (and chemical if possible) analyses; (3) scanning electron microscopy to identify nano- and micro-fabric, potential gas migration pathways, and porosity types/distribution; (4) determination of lithofacies stacking patterns; (5) geochemical analysis for source rock potential and for paleoenvironmental indicators; (6) geomechanical properties for determining fracture potential of lithofacies.

Well log characterization includes: (1) core-to-log calibration which is particularly critical with these finely laminated rocks; (2) calibration of lithofacies and lithofacies stacking patterns to well log motifs (referred to as 'gamma ray patterns' or GRP's in this paper); (3) identification and regional to local mapping of lithofacies and GRP's from uncored vertical wells; (4) relating lithofacies to petrophysical, geochemical and geomechanical.properties and mapping these properties.

3D seismic characterization includes: (1) structural and stratigraphic mapping using seismic attributes; (2) calibrating seismic characteristics to lithofacies and GRP's for seismic mapping purposes; and (3) determining and mapping petrophysical properties using seismic inversion modeling.

Integrating these techniques into a 3D geocellular model allows for documenting and understanding the fine-scale stratigraphy of shales and provides an aid to improved horizontal well placement. Although the workflow presented in this paper only relates to two productive gas shales, we consider it to be more generically applicable.

INTRODUCTION Recent discoveries of potentially vast global gas resources locked in shales has led to a

need to understand their stratigraphy for (1) regional to local correlations, (2) determining the most favorable internal gas source and migration pathways, and (3) identifying the best strata for horizontal well placement and artificial fracture treatment. Recent shale stratigraphic studies (Bohacs and Schwalbach, 1992; Bohacs, 1998; Macquaker et al., 1998; Schutter, 1998; Almon et al. 2002; Paxton et al, 2006; Loucks and Ruppel, 2007; Singh. 2008 ; Mazzullo et al, 2009) have clearly demonstrated that shales are not usually stratigraphically homogenous, and that their stratigraphic variability can be explained using well established sequence stratigraphic principles. For the past few years, we have been evaluating some U.S. mid-continent gas shales---principally the Barnett and Woodford shales---which has led to development of a systematic, integrated characterization methodology or work flow. The work flow combines a variety of analytical techniques to characterize these strata at a variety of scales (Figure 1). In this paper, we present these techniques, provide some examples to demonstrate our findings, and point out some potential pitfalls in their application. It is not our intent to present a comprehensive analysis of individual shales we have been studying, but rather to provide examples of the techniques, applications and results of our workflow approach for more generic application. This paper is organized approximately according to the headings and subheadings in the work flow (Figure 1). The first part of the workflow---regional tectono-stratigraphic aspects of these gas shales---has been published, so is not repeated here (Montgomery et al., 2005; Pollestro, 2007).

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Figure 1 .Flow chart for integrated characterization of unconventional gas shales.

ROCKS

Identify lithofacies in core and their properties Techniques

Any study of gas shales should begin with the rocks, preferably whole core, but if not available, cuttings, and if possible, outcrops. Core samples from both the Barnett and Woodford shales were studied according to the methods of O'Brien and Slatt (1990)---that is, at the SEM (scanning electron microscope), thin section, and hand specimen (core) scales. Cm-scale core description of sedimentary structures, textures, and stratification styles form the primary basis of core and thin section characterization. Mineralogic analyses were conducted by standard X-ray diffraction (XRD) and Fourier Transform Infrared spectroscopy (FTIR) techniques, supplemented by chemical analyses. These analyses, coupled with Total Organic Carbon content (TOC) by combustion provide the basis for identifying lithofacies based upon compositional and fabric features (Figure 1).

Example: Singh (2008) used the above techniques to identify nine lithofacies (Figure 2 and Figure 3; Table 1) in some Barnett shale core. Similar lithofacies were identified by Hickey and Henk (2007). The dominant minerals comprising the lithofacies are quartz, calcite, dolomite, clay minerals, feldspar, muscovite and phosphatic grains (Figure 2). More recent, unpublished XRD analyses indicate the only clay mineral is mixed layer illite/smectite with 70 to 95% illite layers.

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Figure 2 .Nine lithofacies defined by Singh (2008). Each lithofacies lists the name, the average mineral composition and illustrates the core and thin section characteristics. Numerical coding for figure 4 is: Lithofacies 1 is at the top left (siliceous, non calcareous mudstone) and lithofacies 9 is at upper right (fossiliferous deposits). Also shown is the lithofacies distribution in a cored well.

Figure 3 .Left column shows the distribution of lithofacies in a Barnett core (after Singh, 2008). GRP = gamma ray pattern; red arrows to the left indicate upward increase in carbonate lithofacies; green arrows to the left indicate upward decrease in carbonate lithofacies. The middle curve is the interpreted relative sea level curve of Singh (2008) based upon GRP's; red arrows highlight interpreted deposits of a shallowing sea during deposition; green arrows highlight interpreted deposits of a deepening sea. The right curve is the Residual Hydrocarbon Potential (RHP) curve which shows trends of anoxic-to-oxic (red arrows) and oxic-to-anoxic (green arrows) depositional environments. Note the close correspondence among the three sets of arrows, indicating cyclic variations in water depth and oxicity levels, which we relate to eustatic sea level cyclicity.

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Table 1 Nine lithofacies in Barnett Shale

At one end of the lithofacies spectrum is siliceous mudstone, which is enriched in clay

minerals and TOC (Figure 2; Table 1). Phosphatic deposits are also enriched in TOC. At the other end of the spectrum are carbonate-rich deposits, which include micritic/limy mudstone, fossiliferous deposits, concretions and dolomitic mudstone (Figure 2). Carbonate-rich lithofacies contain sparse TOC (Table 1). Some of the carbonate is detrital and some is authigenic.

Porosity and permeability Techniques

Porosity and permeability measurements are routinely made on core plugs of sandstone and carbonate rocks, so the standard techniques are not discussed here. However, the credibility and reproducibility of porosity and permeability measurements of shales using standard techniques is complicated by small grain size, small pore throats, and small pores (Bowker, 2007). Also, because shales are commonly fissile, it is often difficult to physically acquire an intact core plug for analysis. Because lithofacies may be thinly interbedded , retrieval of a homogeneous sample representative of a given lithofacies is often difficult.

Example: One hundred and eighty-two(182) porosity and permeability core plug measurements of different Barnett lithofacies failed to reveal any significant causal relationship (Figure 4). Even though there is a vague positive relation between porosity and permeability, data points for the nine different lithofacies appear to be randomly dispersed throughout the data cloud. This plot could represent true values from undisturbed and uniform samples, a result of the difficulties in sample collection and measurement described in the pitfalls section of this paper, or a combination.

Figure 4 .Porosity-permeability cross plot from 182 core plug samples from a Barnett Shale core. Different colors are coded to different lithofacies. See Figure 2 for lithofacies 1-9.

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SEM for pore types and networks Techniques

Irrespective of the difficulty in confidently obtaining reliable, reproducible porosity and permeability measurements from shales, pores and their connectivity can be directly observed and to some degree, quantified. We have accomplished this on both Barnett and Woodford shale samples using standard SEM techniques (O'Brien and Slatt, 1990), as well as by higher-resolution field emission scanning electron microscopy (FE-SEM). In addition, the style and degree of alignment of individual grains comprising shale samples were documented. Grain morphologies and elemental compositions (for mineral identification of grains) were also determined from energy dispersive x-ray (EDX) analysis.

Example: Our studies concur with Loucks et al. (2009) conclusion that most pores in the Barnett Shale siliceous mudstone are contained within organic particles rather than within the mudstone matrix. We have also found that fossil fecal pellets, on the order of 100-300um in diameter comprise up to 50% of the grains within some lithofacies (Figure 5A and Figure 5B). The pellets are enriched in micro- to nano-scale pores, which are most visible under the FE-SEM at high magnifications (pores approximately 100nm). Under the SEM/EDX, two types of fecal pellets are recognized: calcium-rich pellets and calcium-phosphatic pellets (Table 2; Figure 5A and Figure 5B, Figure 6 and Figure 7), both of which lack the silicon and aluminum present in the shale matrix (Figure 7C and Figure 7D), and which can occur together in the same pellet-rich laminae (Figure 6 and Figure 7). Some calcium-phosphatic pellets are composed of crystalline phosphatic grains with associated elemental fluorine, confirming the presence of the mineral apatite.

Figure 5 .Scanning Electron Micrographs of Barnett Shale. A. SEM of calcium- pellet morphology. Notice the orientation of particles in matrix surrounding the pellet (arrows). B. SEM of calcium- phosphatic pellet morphology. Notice the sharp contact of pellet edges with the adjacent matrix (arrow). C. Pyrite framboid (red circle) cluster and quartz crystal (blue circle). D. Inside of framboid showing smaller pyrite crystals and internal porosity.

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Figure 6 .EDX dot maps of elemental distributions in Barnett Shale pellet zone. A. Backscatter mode – SEM image of all pellet locations (high concentrations of dots outline the pellets). B. EDX image showing location of calcium (Ca) in pellets (dots). Notice all pellets contain calcium. Dark area is matrix. C. EDX image showing location of phosphorus (P) in some pellets. Notice those pellets also contain calcium (compare to 6B). D. EDX image showing location of silicon (Si) distribution. Silicon is in the matrix and not in the pellets. The gross mineral composition of this sample, determined by X-ray diffraction (XRD) is: Quartz 26%; Feldspar 6%; Calcite 12%; Flourapatite 23%; Kerogen 9%; Pyrite/Marcasite 3%; Illite/muscovite 15%; Misc. 2%.

Figure 7 .Close-up view of two pellets in a shale matrix. A. The upper pellet is a calcite-phosphatic pellet and the lower pellet is a calcite- pellet that has 'exploded' when hit with the electron beam. B. Same view with different lighting. C. EDX distribution of Silicon (Si) highlighting the shale matrix. D. EDX distribution of Aluminum (Al) highlighting the shale matrix. E. EDX distribution of Phosphorous (P) highlighting the upper pellet only. F. EDX distribution of Calcium (Ca) highlighting both upper and lower pellets.

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Table 2 Characteristics of pellets

An interesting feature of the calcium-pellets is that samples which have not been gold-

coated during preparation for analysis change their shape or appear to “explode" under a high voltage (15 KV) electron beam (Figure 7A). Neither coated nor non-coated calcium-phosphatic pellets change their shape when subjected to 15 KV voltage (Figure 7A), thus suggesting the “explosion" is due to the generation of CO2 or other gas (methane?) in the calcite-rich pellets. Although EDX analysis revealed the presence of elemental carbon, whether it is carbon associated with carbonate or organic matter has not been ascertained.

Commonly, pore spaces that do occur within the shale matrix in both the Barnett and Woodford shales appear elongate (Figure 8). An outcrop sample of Woodford Shale exhibits oil droplets which appear to have partially migrated out of the shale matrix and into these elongate pores during a hydrous pyrolysis experiment (Figure 9). It is possible that these elongate pores provide early migration pathways for hydrocarbon molecules, as was determined by O'Brien et al. (1996) from similar experiments performed on the petroliferous Monterey Shale, California. In the case of the Monterey Shale, as well as with the Barnett and Woodford shale samples discussed in this paper, we use the term 'microchannel' for these elongate pores to indicate their potential as hydrocarbon migration pathways. These features superficially resemble parting planes that have unloaded when the rock was brought to the ground surface. However, we have concluded that they are not parting planes parallel to bedding, based upon the following observations: (1) the microchannels do not extend across the entire viewing area of the sample on an SEM stub nor at the core plug scale; (2) they are not perfectly horizontal and parallel to bedding; (3) they form a stairstep pattern. They are interpreted to be primary openings in the original flocculated clay sediment that remained after lithification, thus preventing perfect platy orientation.

Figure 8 .Scanning Electron Micrographs of Woodford shale. A. Microchannel in shale sample. Orange oval highlights bacterium-like structure on a grain surface. B. Four arrows outline a "microchannel". C and D. Potential hydrocarbon migration pathways between grains (red arrows).

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Figure 9 .Scanning Electron Micrographs of Woodford Shale during hydrous pyrolysis experiment of heating to 350oC for 4 days. A. Oil droplet in microfracture. B. Oil 'slick' near microfracture showing the contact between the oil and the matrix. C and D. Oil droplets oozing from rock matrix into open microchannels.

Loucks et al. (2009) have suggested the pores within organic particles are generated during thermal maturation of the organics. Here, we speculate a similar origin for the micro- to nano-scale pores in the fecal pellets. It is conceiveable that pores are generated during maturation of the organic particles within the pellets, and that generated gas first migrates through these pores, then into and through the larger microchannels in the shale matrix. Thus, pellet-rich lithofacies might be preferential zones of gas generation and primary migration.

Geochemistry for source rock potential and paleoenvironmental indicators Techniques

ROCK-EVAL is a classical technique widely used for the characterization of source rock quality. Among the many parameters that can be obtained from ROCK-EVAL analyses, two important parameters in our shale studies are the (1) amount of extractable organic material in the source rock, generally derived from kerogen breakdown (S1 peak on a gas chromatogram), and (2) residual kerogen (S2 peak). The S1+S2 peaks normalized to TOC content of analyzed samples provide the parameter referred to as the residual hydrocarbon potential (RHP) (Fang et al., 1993).

We use RHP as a paleoenvironmental indicator. In order to apply the RHP parameter for this purpose, it is first assumed that all of the samples are at similar levels of maturity, as was determined for shales in our study area (samples from a single well are at similar depths of burial). Therefore any change in S1 and S2 is not due to any maturity changes, but reflects changes in the amount of preserved organic matter (kerogen). Therefore it can be expected that under anoxic conditions in the depositional environment, larger amounts of organic matter (TOC) will be preserved in the sediment and the S2 peak will be greater than under oxic conditions, where less TOC is preserved and the S2 peak will be smaller. Thus, the calculated RHP value [(S1 + S2/TOC)] will be larger for sediment deposited under anoxic conditions than for sediment deposited under oxic conditions.

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Analysis of eukaryotic biomarkers in the extractable material from the shales provides an additional analytical tool for paleoenvironmental interpretation. Biomarkers have been used for many years not only to interpret depositional environments, but also to determine source rocks, maturity, extent of biodegradation and a number of other characteristics.

Examples: Both the RHP and biomarker data were used as indicators of oxic and anoxic environments of deposition. RHP values for a number of samples in a Barnett core revealed cyclicity between oxic and anoxic conditions during sediment deposition (Figure 3): i.e. intervals of low RHP (app. 1.3-1.5) alternating with periods of high RHP (1.6-2.2). Low RHP intervals generally correspond with Singh's (2008) carbonate-rich lithofacies and higher RHP intervals are associated with the more siliceous-organic mudstone lithofacies (Figure 3).

Biomarkers helped to determine the environment of deposition in a Woodford shale core. The upper, quartzose part of this core contains higher concentrations of the eukaryotic biomarkers, specifically C29 steranes, that are associated with oxic conditions (Figure 10). The lower, more clay-rich part of the core contains fewer eukaryotic biomarkers,, indicating more anoxic conditions

Figure 10 .Vertical distribution of geochemical biomarkers in a Woodford Shale core. Higher concentrations of eukaryotic biomarkers-C29 steranes indicate more oxic conditions. Higher concentrations of Chlorobiaceae indicate anoxic conditions (H2S rich conditions), probably indicative of a stratified water column The horizontal dashed line corresponds to the boundary between clay-rich shale below and more quartz-rich shale above.

Geomechanical properties of shales Techniques:

Being able to visualize small-scale properties of shales is not sufficient to mechanically characterize them . Measurements of applied force and displacement are required to quantify shale properties, even when sample sizes are necessarily small owing to difficulties in sampling mentioned above. Two critical properties that affect wellbore stability and hydraulic fracturing are Young's modulus (E) and Poisson's Ratio (v). Experimental rock mechanics techniques that measure these and related properties were used to test small samples of both Woodford and Barnett shale (Abousleiman, et al., 2007 and 2009). Woodford shale samples were preserved at the wellsite in non-reactive decane and mineral oil PG1 in order to prevent possible dessication and rearrangement of grains before analysis. The technique combines a nano-indentor (Fig. 11)---which can measure displacements from 200nm to 10 um, and maximum applied forces <

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1000uN by indenting drill cuttings-size (<1cm) shale samples (Ulm & Abousleiman, 2006; Abousleiman et al., 2009)---, with laboratory-based Ultrasonic Pulse Velocity (UPV) and mineralogic (XRD) measurements. Shown in Figure 11 is a schematic of a nano-indenter, the force-displacement curve from a nano-indentation test, and an Atomic Force Microscope (AFM) image of a nano-indention with dimensions of 4 um × 4 um on the surface of a Woodford sample. From the peak load and the slope of the rebound curve, information about the material hardness (or strength) and elastic modulus (or stiffness) can be obtained from the following equations:

Figure 11 .Schematic of the nano-indentor, a indentation mark on a Woodford Shale sample and a force-displacement curve for the sample (from Abousleiman et al., 2009). See text for explanation.

(1)

(2)

Where H is the hardness of the material, M is the indentation modulus, P is the peak load of the loading curve, h is the indentation depth, and Ac is the area of the contact surface between the indentor cone and the indented material (Oliver & Pharr, 1992). The indentation moduli are directly related to the engineering elastic properties of the material (Oliver & Pharr, 1992; Delafargue & Ulm, 2004). For example, for an isotropic material, the indentation modulus can be expressed in terms of the Young's modulus, E, and Poisson's ratio:

Examples:

Results of a nano-indentor test on a Woodford Shale sample are shown in Figure 11. Indentation depth increased to 1000nm with an increasing indentation load to a peak load of almost 5mN, and then the load was removed. The depth of indentation is a function of the micro- and nano-scale strength properties of the rock, some of which are listed below, with examples.

1. Mineral composition: Elastic and pororelastic moduli and coefficients have been estimated from the measurement of porosity, density and mineral composition. The fracture frequency in a Woodford Shale core appears to be at least generally related to overall mineral composition (Abousleiman et al, 2007 and 2009; Buckner, 2010), with fractures being more abundant in a quartzose upper Woodford interval than in a lower, more clay-rich cored interval (Figure 12). These observations were verified by fracture distributions measured in a borehole image log in the corehole (Portas, 2009; Buckner, pers. comm.. 2010)

2. Lithofacies: Filled fractures sometimes terminate at lithofacies boundaries. An example in Figure 13 shows a fracture within a siliceous shale terminating within a phosphatic bed, then re-emerging back into the siliceous shale which underlies the phosphatic bed. This observation suggests that the more porous phosphatic bed may be able to absorb more stress than the siliceous bed, and thus be less capable of fracturing.

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3. Mineral crystal structure: FE-SEM microscopy revealed aligned, tensional microfractures which resemble mineral fabric (i.e. crystal structure) (Figure 14), suggesting that crystallographic planes of weakness, and thus mineralogy, might help initiate micro-fractures.

4. Microchannels: As noted above, microchannels are common in the shales (Figure 8) They are important not only as potential hydrocarbon migration pathways, but also because they likely will affect geomechanical properties of the shales.

5. Rock Fabric (lineations/laminations/bedding): Sierra et al, (2010) have demonstrated in the Woodford Shale core discussed above that small shale samples are weaker when stress is applied parallel to laminations, then when stress is applied perpendicular to laminations. This affect, which we deem to be very important to geomechanical properties of shales, is most likely a result of the preferred orientation of fabric in most shales.

Figure 12 .The left figure is an ECSTM mineralogy log of the behind-quarry well showing the higher quartz content (yellow) in the upper Woodford and higher clay content (gray) of the middle Woodford Shale. Center figure is an FMITM log of part of the well showing a fracture through thin-bedded strata. Right graph shows the density of fractures per 2.5ft.,as measured from the FMITM log. Lower left figure shows a filled fracture (white vertical plane) in the core.

Figure 13 .Shale core showing a near-vertical filled fracture offset and separated by a phosphatic bed. The phosphatic bed appears more porous than the adjacent shale beds, and thus may be more capable of absorbing fracture stresses.

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Figure 14 .Large figure is FE-SEM Electron Micrograph at 100,000X magnification showing linear tensional partings, possibly aligned with crystal structures. The three smaller figures on the left are---from top to bottom---of a propagated fracture tip at 200,000X, 300,000X and 600,000X magnification.

WELL LOGS

Core-to-log depth correction Techniques:

In order to correlate well log properties to core characteristics, it is essential to first very carefully and accurately determine a core-to-log depth correction. Conventional well logs such as the gamma-ray log do not have the resolving power for detecting thin-bedded lithofacies in uncored wells (Figure 15). As well, the human eye often can miss the fine-scale stratigraphy detected by a borehole image log (Figure 15) (Davis et al., 2006). Therefore, without a precise core-to-log depth correction, even if a gamma scan has been run on core, details of stratification may be overlooked, which adds considerable uncertainty when attempting to relate geological observation and laboratory-derived petrophysical properties with well log-derived properties.

Figure 15 .Depth calibration of calcite concretions in core description (yellow) and static and dynamic FMITM log (white). Notice the stratigraphic detail provided by the FMITM log and the relatively flat gamma ray log. In the core description, concretions are yellow, siliceous mudstone is gray, and siliceous-calcareous mudstone is brown.

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Example: We have found the best way to obtain a reliable core-to-log correction is to calibrate depths using easily-identifiable lithofacies, such as calcareous concretions, which are visible on a borehole image log (Figure 15) and which exhibit relatively high density and velocity (reciprocal of sonic transit time) on logs. In the absence of an image log, radioactive lithofacies, such as phosphatic rocks, can be used if they are thick enough to emit a detectable gamma ray log response. We have also found that the core-to-log depth correction is not a constant length throughout a well, thus corrections must be made at shorter stratigraphic intervals.

Determining lithofacies and their properties in logged, but uncored wells Techniques:

Typical problems associated with calibration of well logs with core characteristics and then prediction of lithofacies in uncored well intervals include: (1) the core-to-log depth correction mentioned above, (2) well log insensitivity to thin beds, (3) well log insensitivity to some textural and bedding features visible in core and sometimes on borehole image logs (Davis et al., 2006), and (4) insufficient core to represent all facies types. However, there is sufficient contrast in mineral composition and TOC content of the lithofacies we studied to affect gamma ray log response, so this log was a primary tool for identification of lithofacies and their stacking patterns in uncored wells.

A second, more automated approach that we tested to identify lithofacies in uncored wells was a probabilistic clustering procedure (PCP) within a proprietary computer program (GAMLS [Geologic Analysis via Maximum Likelihood System]). This approach is described by Eslinger and Everett (2004, 2006 and this volume), so is not repeated here.

For relating geomechanical properties measured on shale samples to well log response, neutron and density porosity (NPHI and DPHI) logs, and more advanced Element Capture Sprectroscopy (ECS), Combination Magnetic Resonance (CMR), Formation Micro Imager (FMI) and Sonic Scanner (MSIP) logs (Herron, 1986; Pemper et al., 2006)---all trademarks of Schlumberger---were calibrated to core, then input to a theoretical GeoGenome model (Abousleiman et al. 2007 and 2009) which estimates shale anisotropic elastic and poroelastic properties from the combined rock-log data set (Ortega et al., 2007; Ortega et al., 2009).

Examples: A comparison between core-described lithofacies and lithofacies predicted using the PCP method shows good predictive capabilities for an interval of thin, compositionally diverse Barnett Shale beds (Figure 16). The method was tested for automated correlations with a set of eleven Barnett Shale wells forming a 42km (~24-mile) long cross section. Four well logs (RHOB, NPHI, GR, and PEF) were used as clustering variables, resulting in the identification and well-to-well correlation of major stratigraphic intervals (Figure 17).

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Figure 16 .Gamma ray (GR) and resistivity logs of a portion of a Barnett Shale core alongside a measured core description and a predicted lithofacies distribution based upon the probabilistic clustering procedure described in the text. Grays are siliceous mudstones, black/tourquise are calcareous mudstones, and purple is phosphatic deposit.

Figure 17 .42km long well log cross section using the PCP method (see text for description) to identify rock groups for correlation purposes. Gray = siliceous, non-calcareous mudstone; Red = phosphatic deposit; green = calcareous mudstone; blue = muddy limestone.

Geomechanical properties calculated from the acoustic log revealed significant variations in

Young's Modulus, Poisson's Ratio and porosity between the less clay rich (20% from ECS log) upper Woodford and the more clay-rich (32%) middle and lower part of the Woodford core (Figure 18). In general, Young's Modulus and Poisson's Ratio are lower for the more clay poor interval, probably due to the higher porosity of that interval. However, at a finer scale (for example, compare at 130ft. and 170ft. on Figure 18) the 130ft. clay rich zone exhibits a relatively low Young's Modulus and high Poisson's Ratio and the 170ft. zone of similar composition exhibits the opposite trend. Thus, the other lithologic factors, mentioned earlier, besides porosity, play a role in geomechanical properties of similar lithologies.

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Figure 18 .Depth plot of gamma ray log, ECSTM mineralogy log (yellow is quartz, gray is clay minerals), porosity (red = density porosity; blue = neutron porosity), and log-derived Young's Modulus and Poisson's Ratio. Contact between the upper and middle/lower Woodford is at 37m (122ft.) (red line). See text for explanation.

Define lithostratigraphy and stacking patterns in uncored wells for (sub)regional mapping

Techniques: Identifying the lithostratigraphy or vertical stacking of a set of shale lithofacies is not as straightforward as it is for sandstones or carbonates owing to shales' finer grain size and degree of stratification. However, once a good match between log and core depths is obtained for a well, it is possible to relate subtle stratigraphic variations in log character to different lithofacies stacking patterns. Once this match is accomplished, lithostratigraphy in uncored wells can be (sub)regionally correlated and mapped.

Example: Singh (2008) identified three distinctive lithofacies stacking patterns in Barnett cores: 1) upward increase in carbonate lithofacies (upward decrease in siliceous lithofacies); 2) upward increase in siliceous lithofacies, (upward decrease in carbonate lithofacies) and 3) no significant vertical change in lithofacies (Figure 19). More TOC is associated with the siliceous lithofacies than with the carbonate rich lithofacies (Table 1), so the corresponding three gamma-ray log patterns, here termed GRP) are: 1) upward decrease, 2) upward increase, and 3) no vertical change in gamma ray log response, respectively (Figure 19). Using these three gamma-ray log motifs, several GRP's were identified and correlated on logs from 602 uncored, vertical wells for subregional mapping in the area (Singh, 2008). For example, Figure 3 shows 13 GRP's in one Barnett core and Figure 20 shows regional maps of two of these (GRP-4 and -5).

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Figure 19 .Characteristics of the three gamma ray stacking patterns (GRP's): Upward-decreasing gamma ray, upward-increasing gamma ray, and upward-constant gamma ray pattern. Each figure illustrates the gamma ray curve, the vertical stacking pattern of lithofacies, and thin section photomicrographs of the lower and upper lithofacies within each GRP.

Figure 20 .Two gross isopach thickness maps of GRP's. Color bars are thicknesses in feet. For GRP-4, red = 90ft. (30m) and tourquise = 10 ft. (3m). . For GRP-5, red = 30ft. (10m) and dark blue = 4 ft. (1.3m) Black dots are cores examined by Singh (2008).

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Interpreting depositional history from GRP stacking patterns and related data Technique:

At this stage of characterization, integration of the above techniques and results provide the means to interpret the origin and depositional history of the shale strata being studied.

Example: Singh (2008) interpreted the (1) upward-increasing carbonate/-decreasing TOC GRP as indicating a progressive shallowing of water over the time interval of GRP deposition, (2) upward-increasing siliceous/-increasing TOC GRP as indicating an upward deepening of water and (3) uniform GRP as indicating aggradation during a time interval of unchanging depositional environment. These three stacking patterns bear resemblance to the retrogradational, progradational, and aggradational parasequence sets of VanWagoner et al. (1990; their Figure 3). This similarity, plus the sub-regional extent of the mapped GRP's (Figure 20) and their correspondence with RHP geochemistry (Figure 3) indicate the depositional history of the shale strata can be explained within the sequence stratigraphic context of cyclical fluctuations in eustatic sea level.

The GRP with an upward-increase in carbonate lithofacies corresponds with oxic (low RHP) conditions, as might be expected in a relatively shallow marine setting. The GRP with the upward-increase in siliceous lithofacies corresponds with anoxic (high RHP) conditions, as would be more likely in a deeper water setting. Although oxic/anoxic near-bottom water conditions are a first-order effect of oceanic circulation and associated oxygenation levels, the cyclicity of the shale strata (Figure 3) point to eustatic causes, as suggested from Loucks and Ruppel's (2007) Mississippian eustatic sea level curve for the Barnett and related strata.

Unfortunately the lack of high-frequency age dates within the Barnett and Woodford shales discussed in this paper precludes determining: (1) the hierarchy of eustatic cyclicity (i.e. 2nd oder, 3rd order, etc.), (2) whether these stacking patterns represent depositional sequences, systems tracts, or parasequences (as defined by VanWagoner et al. (1990) and (3) the influence of periodic tectonic activity on sub-regional depositional patterns (Borges, 2007, Singh, 2008, Abou Elresh and Slatt, in press).

SEISMIC REFLECTION ANALYSIS Techniques

The drilling of horizontal wells in the Barnett and Woodford shales has become the norm (Montgomery, 2005). Although horizontal wells are more appropriate for production, vertical wells provide hard information on the stratigraphy. Horizontal wells limit the ability to apply techniques described above for regional and local mapping of strata that are stratigraphically beneath the horizontal wells. It is possible that some productive zones that are stratigraphically beneath a horizontal well could be missed. To extend stratigraphic analysis to deeper horizons, it is imperative to establish relations between existing vertical wells and seismic reflection data, preferably 3D seismic volumes. Ideally, a structural analysis should be conducted prior to attempting seismic stratigraphic correlations of shales. Examples are provided below.

Example: Local structure Figure 21A displays the major stratigraphic and structural features from a seismic section in part of the Newark East Field. Some major faults appear to extend upward from the basement into the Barnett Shale. The main fault, named the Mineral Wells fault, trends northeast-southwest (Figure 21B), which corresponds to the trend of basement features imaged from a horizontal tilt derivative map, generated from high resolution aeromagnetic data (Figure 21C) (Elebiju et al., 2008).

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Figure 21 .Top figure (A) is a pre-stack, time migrated seismic section showing interpreted major stratigraphic and structural features in part of the Newark East Field. Lower left figure (B) is a horizon slice showing a major northeast-southwest trending fault through the Barnett Shale (after Borges, 2007). Lower right figure (C) is a horizontal derivative of the tilt derivative map (HD_TDR) beneath the Barnett Shale (Elebiju, 2008).

Smaller faults and fault-related karst on the unconformity surface which separates the Barnett Shale from the underlying, water-bearing Ellenburger Group (and Viola) carbonates can affect well performance. Water-encroachment into a wellbore can be a limiting factor in gas production because the water moves upward into the reservoir from these faults and associated fractures. Seismic attribute analysis of 3D seismic volumes in and south of the Newark East field provides a means of imaging the details of unconformity fault- ,fracture- and related karst- patterns and their effect on overlying shales (Baruch et al., this volume). Anomalously thick lower Barnett intervals (potential sweet spots?) deposited over karst sinkholes have also been identified and mapped using seismic attribute analysis (Baruch et al.,this volume).

Example: Stratigraphy and GRP stacking patterns Variations in the compositional, petrophysical, and geomechanical properties of lithofacies implies that some lithofacies would be more favorable horizontal drilling targets than others. Thus, it would be desireable to seismically map individual lithofacies. However, this is not usually feasible because individual lithofacies are often beneath seismic resolution. However, because lithofacies are predictably stratified into the three possible GRP's, mapping the GRP's from seismic does provide a technique for indirectly mapping individual lithofacies. In the study areas, individual GRP's are 10-25m thick (Figure 3), which is at or near the resolution of our seismic volumes (Perez, 2009; Baruch et al., this volume). For example, within 3D seismic volumes, internal seismic reflections are present and mappable between the top of the upper Barnett and base of the lower Barnett (Figure 22) (Borges et al., 2007; Baruch et al.; this volume). Perez (2009) developed acoustic impedance profiles from well and seismic control and correlated GRP horizons, which demonstrated that at least some GRP's could be resolved and mapped serismically (Figure 23).

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Figure 22 .3D seismically-mapped horizons in the southwest part of the Fort Worth Basin (Baruch et al. this volume).

Figure 23 .Seismic reflection and corresponding impedance sections from three Barnett wells showing correlations with GRP's.

Integrating data sets for stratigraphic properties modeling and mapping Techniques:

The principle objective of our stratigraphic characterization workflow is to construct a 3D model of GRP stratification of an area of interest utilizing the techniques described in this paper. Once constructed, various compositional, petrophysical and/or geomechanical properties of lithofacies and/or GRP's can be input into the model for the purpose of selecting most suitable stratigraphic intervals (lithofacies or GRP's) for horizontal drilling.

Example: To demonstrate our objective, a 3D geological/petrophysical model for part of the Newark East Field is shown in Figure 24. The model comprises 14 GRP's that were identified and correlated from lithofacies-calibrated gamma ray, density, and sonic logs from 45 wells in a 3D seismic volume area (Perez, 2009). Horizons used in the model are those that were mapped seismically. The logs were upscaled (blocked) (Figure 25) in order to build the 3D model. Each resulting 3D grid block is 76 x 76m (250 x 250ft.) in the horizontal direction, and the vertical dimension varies according to thickness of each GRP. The resulting model consists of 463,643 cells enclosing an area of approximately 59km2 (19 mi2) (Figure 24). The upscaled GRP's were then interpolated using Sequential Gaussian Simulation (SGS) based upon variograms in order to estimate the petrophysical properties at inter-well locations (Perez, 2009).

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Figure 24 .A. is a 3D GRP model for an area of the Barnett Shale in Newark East Field. B. is a bulk density (RHOB) cross section of the model. C. is a gamma ray (GR) cross section of the model. D. is a sonic transit time (delta-T) cross section of the model.

Figure 25 .Interpreted gamma ray log and blocked/upscaled version based upon GRP distribution and used for seismic inversion.

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Figure 24A shows the resulting 3D distribution of the stacked GPR's, and figures 24 B-D show selected cross-sections of some of the interpolated petrophysical properties. It is particularly important to note that not only do properties vary stratigraphically among GRP's, but they also vary within individual GRP's along their modeled length. This model demonstrates the ability to integrate rock, well log, and seismic data into a coherent, geologically-realistic 3D model that can be used for improved understanding of gas shale stratigraphy, and more importantly, as an aid for stratigraphically-based placement of horizontal wells.

Potential Pitfalls in shale analysis Based upon our studies, the following cautionary notes are provided with regards to

sampling and lithofacies analysis of shale cores.

1. Common procedure calls for sampling core at uniform depth intervals. Shale cores can be stratified at a cm or even mm scale, and sometimes so subtly that fine stratification is missed with the naked eye, and only detectable with high resolution logging tools such as the borehole imager (Figure 15). Thus, it is possible to overlook some lithofacies if sampling is conducted only at pre-set, equal stratigraphic intervals.

2. Both XRD and FTIR techniques determine mineral composition, but in fundamentally different ways. It has become apparent from comparative studies that results using the two analytical methods on the same sample mixes may be inconsistent in terms of the reported absolute weight percentage values for various major minerals (calcite, clays, quartz, etc.). These differences can be attributed to a variety of factors, including sample preparation, machine conditions, and the manner in which quantitative analyses are 'calibrated' using standards. Accordingly, until the 'best' procedures for obtaining accurate and consistent mineral analyses are determined, we treat reported values as semi-quantitative as opposed to quantitative, and we provide mineralogical-based descriptive conclusions more on 'trends' than on absolute percentages.

3. Macro- and micro-fabrics of shales influence rock strength, petrophysical and acoustic properties, and gas migration. As such, fabric analysis at the SEM, thin section, and core-size scales should be included in any shale characterization study (O'Brien and Slatt, 1991). It is insufficient to classify shales only on readily-obtained numerical parameters such as mineral composition and porosity.

4. Sampling and analytical issues related to accurate porosity and permeability determination have been mentioned earlier. At best, most such analyses currently provide 'semi-quantitative' results with possibly significant error bars.

5. Because of the laminated character of gas shales, an accurate core-to-log depth correction is critical before attempting any core-log petrophysical comparisons or analyses.

CONCLUSIONS A workflow has been developed which incorporates a variety of analytical techniques for

characterizing rock, well log, and seismic properties of gas shales at a variety of scales. The objective is to integrate analyzed properties into a geologically-realistic, 3D stratigraphic model to better understand the fine-scale stratigraphy of shales and as an aid to improved horizontal well placement. Although the examples presented in this paper are from the productive Barnett and Woodford shales, the workflow is intended to be for generic use. Some analytical techniques currently are not perfected, so results should be used with caution.

ACKNOWLEDGEMENTS We would like to acknowledge Devon Energy Co. for financial and data support for much of

this research, Ted Champagne of Clarkson University, Potsdam New York and Carol McRobbie of SUNY-Potsdam for technical assistance with SEM and FE-SEM analyses, and Dennis Eberl, U.S. Geological Survey, for XRD and chemical analysis of shales. This research was conducted largely by graduate students in University of Oklahoma's College of Earth and Energy's Institute of Reservoir Characterization and Poromechanics Institute.

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