This paper was prepared for presentation at the Canadian Unconventional Resources Conference held in Calgary, Alberta, Canada, November 2011.

Transcription

1 Sample Size Effects on the Application of Mercury Injection Capillary Pressure for Determining the Storage Capacity of Tight Gas and Oil Shales J.T. Comisky, Apache Corp., M. Santiago, B. McCollom, Poro-Labs Inc., A. Buddhala, University of Oklahoma, K.E. Newsham, Apache Corp. Copyright 2011, Society of Petroleum Engineers This paper was prepared for presentation at the Canadian Unconventional Resources Conference held in Calgary, Alberta, Canada, November This paper was selected for presentation by a CSUG/SPE program committee following review of information contained in an abstract submitted by the author(s). Contents of the paper have not been reviewed by the Society of Petroleum Engineers and are subject to correction by the author(s). The material does not necessarily reflect any position of the Society of Petroleum Engineers, its officers, or members. Electronic reproduction, distribution, or storage of any part of this paper without the written consent of the Society of Petroleum Engineers is prohibited. Permission to reproduce in print is restricted to an abstract of not more than 300 words; illustrations may not be copied. The abstract must contain conspicuous acknowledgment of SPE copyright. Abstract We measured Mercury Injection Capillary Pressure (MICP) profiles on tight shale samples with a variety of sample sizes. The goal was to optimize the rock preparation and data reduction workflow for determining the storage properties of the rock, particularly porosity, from MICP measurements. The rock material was taken from a whole core in the Cretaceous Eagle Ford Formation in the form of a puck or disc. A horizontal 1 inch core plug was cut from this disc and the remaining material was subsequently crushed and sieved through various mesh sizes. MICP profiles up to 60,000 psia were then measured on the 1 inch plug and all of the various crushed and sieved rock particle sizes. In parallel we subsampled the plug material to measure bulk volume, grain volume, and porosity using a crushed rock helium pycnometry method. These additional measurements provided a comparison set of standard petrophysical properties from which we could compare the MICP results. In general our MICP profiles show a very strong dependence on sample size due to two reasons: pore accessibility and conformance. We verify the conformance correction approach of Bailey (2009) which specifically accounts for the pore volume compression of the sample before mercury has been injected into the largest set of interconnected pore throats. This new method is preferred over the traditional method of conformance correction when compared to crushed rock helium porosity since the latter is performed at unstressed conditions. Our results using Bailey s (2009) method reveals that the sample size is optimal for determining porosity in the Eagle Ford, and potentially other tight oil and gas shales. We use mercury injection for determining the various storage properties of tight shale because helium porosimetry is not always possible on fine cuttings samples. There are many instances when limited cuttings may be the only source of rock information available before a whole core is taken. Cuttings profiles also provide a key insight over long formation intervals that may not be available from whole core. Cuttings and core profiles for use in calibrating well logs have proven to be a requirement in these ultra-low perm systems. Introduction The emergence of shale and oil plays in North America has caused the industry to re-examine the methods which we use to quantify the resource and recoverable reserves in place. We recognize that unconventional gas and oil reservoirs are geologically and petrophysically heterogeneous at a variety of scales. This calls for a continuum of measurements to be used that are generally challenged due to the nano-scale pore nature of these rocks. A sampling of recent studies (Sondergeld et al., 2010; Passey et al., 2010; Spears et al., 2011) point out the lack of a standardized protocol such as that established for conventional and tight gas sand (microdarcy) systems in the API-RP40 (API, 1998). There is some common ground in that most laboratories follow a variation of the procedures established by Luffel and Guidry (1992) for determining storage capacity (crushed rock or GRI porosity) and flow capacity (pressure or pulse decay permeability). Other tools such as image analysis via focused ion-beam milling (FIB) and scanning electron microscopy (SEM) (Loucks et al. 2009; Curtis et al., 2010), and direct nuclear magnetic resonance (NMR) detection of fluids in place (Sigal and Odusina, 2010; Ramirez et al., 2011) are currently being researched by both industry and academia alike. We focus on a specific technology known as Mercury Injection Capillary Pressure (MICP) for determining the porosity and density of small and/or irregular samples such as cuttings or crushed whole core material. Several studies (Olson and Grigg,

2 2 2008) and Bailey (2009) point out the usefulness and limitations of using MICP to determine porosity in shale reservoirs. One benefit of using MICP for characterizing reservoirs of all classes is that it continually images the pore throat distribution of a material by incrementally injecting mercury into pore spaces as small as 3.6 nm. Olson and Grigg (2008) show a reasonable correlation between gas-filled (as-received) porosity measured using helium and MICP porosity in high maturity shales. More recently, Bailey (2009) presents an excellent discussion of the details used for applying a modified conformance correction that more accurately replicates the crushed rock porosity measured with helium for a variety of oil and gas bearing shales. We expand upon Bailey s (2009) workflow by examining the effect of sample size on conformance and porosity determination with MICP. This study focuses on core material taken from the Eagle Ford Shale interval in Central Texas (Fig. 1). The field area is located in Burleson County where the Eagle Ford has long been recognized as the source rock associated with oil and gas production in the overlying Austin Chalk interval. The Eagle Ford Formation was deposited during the Cenomanian- Turonian transgression across Texas and it is recognized in the subsurface from Maverick County in the southwest of Texas to Grimes County in Central Texas. Depositional environments as discussed by Liro et al. (1994) include a variety of conditions from anoxic deep marine to lower deltaic. Significant stratigraphic differences are noted across Texas, mostly due to the interplay of 3 rd order eustatic cycles and local changes in topography. Despite its common knowledge as an oil-prone source rock for decades, it was not until 2007 that operators such as Apache, EOG, and Petrohawk were testing the liquid hydrocarbon production potential of the Eagle Ford despite the nanodarcy nature of the rock s flow capacity. More recently, many operators have proven the economicc viability of the Eagle Ford by pushing the envelope in horizontal drilling and hydraulic fracturing. These advances have even prompted several operating companies to place the Eagle Ford in the top 10 oil discoveries in North America. Figure 1 Local basemap showing lateral extent and thermal maturity window for the Eagle Ford shale. Core material and logs are shown in this paper from the field area in the northeastern flank of the play in Burleson County, Texas (U.S. Energy Information Administration, 2010) Background The productive interval of the Eagle Ford Formation exhibits some common petrophysical properties that can be notionally identified on wireline logs. A variety of recent studies (Mullen, 2010; Sondhi, 2011) describe the various petrophysical properties of the Eagle Ford Shale using both cores and logs. An example of the core and wireline log interval of this study is shown in Fig. 2. The productive interval is typically recognized by a slightly higher total Gamma Ray signature, higher resistivity, and convergence of the neutron and density logs compared to the gray shales directly above. Various core measurements were taken as part of both a commercial laboratory and academic study and are described in fuller detail later in this paper. Of particular note in Fig. 2 is the profile showing the Total Organic Carbon (TOC) as measured by a LECO

3 3 carbon analyzer. Measured TOC values range from less than 1 wt% to as high as 6 wt% in the cored interval. Mineralogy as measured by Fourier Transform Infrared Spectroscopy (FTIR) of 160 samples with roughly a 2 ft. vertical spacing (Sondhi, 2011) reveals calcite and clay (both illite and mixed-layer) as the predominate mineralogy with lesser amounts of quartz, feldspar, pyrite, and siderite. The most productive section of the Eagle Ford tends to be lower in total clay and richer in carbonate and organic material (Sondhi, 2011). Analysis of visual kerogen vitrinite reflectance as well as T max and Production Index (PI) values from a Source Rock Analyzer (SRA) place this interval well within the oil window, but not quite in the condensate/wet gas zone. Figure 2 Well log profile for the subject well in Burleson County, Texas. Track 1 shows the core (CGR) and total gamma ray (GR) log. Track 2 shows the array induction resistivity curves. Track 3 shows the density (ZDNC) and neutron logs (CNCF) scaled in limestone units. As-received bulk density core measurements are displayed as blue dots on top of the measured bulk density log (ARCRhob). Track 4 shows photoelectric (PE) and compressional sonic log (DTC). Track 5 shows Total Organic Carbon (TOC) as measured by a LECO carbon analyzer on whole core material. Track 6 shows various crushed rock porosity measurements: as- pycnometer (LPPPOR). received bulk volume gas (ARCBVg), cleaned and dried total porosity (DryCPhi), and low pressure helium The red squares are LPP porosity values measured from the MICP plugs. Track 7 shows extracted saturations: as-received water saturation (ARCSw), as-received oil saturation (ARCSo), and as-received gas saturation (ARCSg). Track 8 shows mineralogy as measured by FTIR for the various mineral groupings The initial measurements of storage capacity were done by a commercial laboratory using a variation of the GRI technique shortly after recovering the core. An oil-basemeasurements were done by taking a puck from the whole core and crushing it to produce accessible pore volumes for synthetic mud was used during the coring process. Shale rock property helium pycnometry. Porosity was measured on fresh core material in the as-received (AR BVG in Fig. 2) state before hydrocarbon extraction and ranges up to 4% of the sample bulk volume. After extraction and humidity oven drying the measured porosity increased to as high as 10% of the sample bulk volume (DRY POR in Fig. 2). We attribute this increase in accessible pore volume after crushing to removal of residual oil and water that were still present during the as-received measurement. Essentially, the accessible pore volume under as-received conditions was made possible by gas exsolutiona and oil shrinkage. Subsequent cleaning and Dean Stark extraction removed the remaining hydrocarbons and provided a larger pore volume for helium to enter for the dry porosity measurements. The extracted saturationss are color-coded by fluid

4 4 type including oil, water, and gas. The extracted water saturations range as high as 80% to as low as 40% of the cleaned and dried pore volume. The core was then stored in sealed plastic bags under room conditions for 2 years at the University of Oklahoma IC 3 laboratory where subsequent studies were performed. The core was resampled and an additional set of as-received bulk volume and grain volume measurements were conducted with a low pressure pycnometer (LPP) to determine porosity and are represented by the solid black circles (LPP Por) (Sondhi, 2011). We observe the LPP helium porosity measurements are close to the clean and dried porosities measured several years earlier by the commercial laboratory, but still higher than the commercial as-received measurements. We interpret this to be a result of desiccation due to storage at typical room conditions and also non-removal of some of the liquid hydrocarbon component. We describe the LPP helium porosity measurement in a later section of this paper. It is worthy to note that MICP has been a viable tool for characterizing reservoir rocks for over 60 years. The petroleum industry was first introduced to the use of MICP by Purcell (1949) and Rose and Bruce (1949) for determining the pore throat distribution and flow capacity of conventional reservoir rocks. Subsequent studies by Swanson (1981), Walls and Amaefule (1985), Katz and Thompson (1986), Pittman (1992), Huet et al. (2005), Dastidar et al. (2007), Comisky et al. (2007), and others show the applicability of MICP for determining the flow capacity of a variety of reservoir rocks, particularly tight gas sands, carbonates, and other challenged reservoir rocks. Simultaneously, other researchers were using MICP to measure how well shale formations could impede the upward flow (i.e. sealing capacity) of hydrocarbons in forming conventional traps. Of particular note is the work of Wardlaw and Taylor (1976) and Sneider et al. (1997) when discussing the effect of sample size (mainly cuttings) conformance on MICP seal capacity measurements. Conformance or closure is a measure of the amount of mercury needed to completely envelope a sample before true intrusion occurs and is discussed in detail by Webb (2001) for application in the materials and powder industry. More recently, Bailey (2009) has emphasized the role of pore compressibility on conformance in shale formations and how it must be considered when compared to independent measurements of porosity such as Gas Research Institute (GRI) method (Luffell and Guidry, 1992; Guidry et al., 1995). Majling et al. (1994) show how MICP could be used to measure the compressibility of aerogels with nanometer-scale pores and Sigal (2009) points out the specific problem of conformance in shale studies, particularly for bulk density determination. The MICP method is outlined here because we think it is a fast, reliable way to estimate porosity, bulk, and grain density from small irregular samples such as cuttings. While small, crushed samples from whole core are used in the GRI method for estimating grain volume and ultimately porosity via helium pycnometry, the method requires a large, in-tact sample to determine bulk volume, usually by simple mercury immersion. This is rarely the case with cuttings since volume and sample size can be quite variable. Determination of lithology from cuttings is relatively straightforward and can be done using the FTIR method outlined here or a host of any other methods (X-Ray Diffraction, Elemental Dispersion Spectroscopy, etc.). Estimates of TOC are routinely done on cuttings and are vitally important in compensating the typical log measurements (density, neutron, and sonic) for too much apparent porosity due to the low density, high hydrogen index, and low bulk modulus of organic matter. Porosity, bulk density, and grain density from cuttings are major variables needed for calibrating rocks to logs; particularly when traditional cores are not available. There may also be circumstances where cores only cover a limited portion of the vertical section of interest. The importance of tying core measurements to logs is discussed by Sondergeld et al. (2010), Quirein et al. (2010) and Ramirez et al. (2011). Experimental Procedures Sample Preparation The basis for our study is to measure and compare MICP, FTIR, and LPP helium porosity, bulk density, and grain density profiles on a variety of sample sizes from the same relative stratigraphic interval. A puck was taken from the 2/3 butt of the core and cut into a 1 inch thick puck (Fig. 3). A 1 inch core plug was then extracted from the middle of the puck and used to measured FTIR mineralogy, LPP porosity, and MICP. The remaining puck was then crushed and homogenized using the equipment in Fig. 4. A mortar and pestle was used to crush the remaining material and a series of US standard mesh sizes (12, 20, 35, and 50) were used to break out several sample size classes of material. Fig. 5 shows an example from a single sample of the variety of particle size classes we considered (plug, +12, , , and ).

5 5 Figure 3 Photo showing an example of the puck material recovered from the 2/3 butt portion of the core and the sampling scheme used to carry out subsequent helium and mercury injection experiments. Low Pressure Pycnometry A low pressure pycnometer (LPP) is used to measure porosity where gas, volatile hydrocarbons and free and bound water are removed as we crush the sample to measure the absolute porosity. A fully automatic gas displacement pycnometer AccuPyc 1330 by Micromeritics is used which includes a sample cell and an expansion chamber. The AccuPyc 1330 pycnometer determines density and volume by measuring the pressure change of wetting gas in a calibrated volume (sample cell and expansion chamber). Helium is used as the purge gas at a pressure of 19.5 psig. Porosities are reproducible to better than ±0.5 p.u. (Karastathis, 2007) Figure 4 Photo showing the mortar and pestle used for crushing the puck in Fig. 3 and US standardd meshes used for sieving material into the various particle size classes. The typical sample size varies from 9 g to 12 g based on the bulk density of the sample. The crushed sample volume should not exceed 10 cm 3 since this is the limitation imposed by the LPP sample holder. The sample is then dried in a vacuum oven at 212 F (100 C) for at least 8 hours (Sondhi, 2009). The samples are cooled in a humidity controlled desiccator for at least 30 minutes before the bulk volume of the sample is evaluated. The bulk volume is calculated from the volume of the mercury that has been displaced by the sample at room temperature. The mass of the sample before crushing is recorded and termed M 1.

6 6 The sample is crushed in a heat-treated steel cylindrical container, and a steel pestle sealed with an O-ring to ensure there is no sample loss during crushing. Stress is induced on the sample by hitting on the pestle with a wooden hammer. Uniform amount of force is applied by hitting for about 100 counts. Once the sample is crushed, it is carefully transferred into the 10 cm 3 sample holder while maintaining minimum weight loss (less than 0.5%). It is again dried at 212 F (100 C) for at least 8 hours in a vacuum oven and cooled in a humidity controlled environment for 30 minutes before its mass is recorded (M 2 ). As all the free, bound and surface water is removed initially through drying the bulk sample, there should be little variation in the sample mass before and after the crushing. Any change in mass is accounted for by material loss of the sample during crushing. This loss (M 1 -M 2, or m) is calculated and if it exceeds 0.5%, the sample is discarded and the procedure is repeated. Once the sample mass loss is within limits, grain volume (V G ) and grain density ( G ) is measured using the low pressure pycnometer, and the corrected grain volume (V Gcorr ) and LPP helium porosity ( He ) are calculated by considering the m term: + ο ఘ ಸ.(1) and = ಳ ಸ.(2) ಳ This method has been researched, developed, and tested over the past few years on a variety of shale plays including the Barnett (Karastathis, 2007; Kale, 2009), The Thirteen Finger (Raina, 2010), and Eagle Ford (Sondhi, 2011). Figure 5 Photo showing a sampling of material from each particle size class along with the plug extracted from the middle of the puck in Fig. 3. Laser Particle Size Analysis We measured the particle size range of the material used for the LPP as a baseline comparison to the MICP crushed and sieved material. We wanted to investigatee if there were any particle size impartialities impressed upon the comparison of LPP and MICP measurements. The uniformity of the crushed sample is measured by analyzing the sample in a single wavelength Laser Diffraction Particle Analyzer, LS by Beckman Coulter, using the Fraunhofer and Mie theories of light scattering, covering a size range from 0.4 µm to 2000 µm (Beckman Coulter, 2009). A differential pressure is created in the sample chamber with vacuum on one end to disperse the sample in air in the form of a vortex. While this is passing through the channel, a laser beam is used to illuminate the particulates, which scatters the incident light on to silicon photo-detectors. The intensity of light on each detector measured as a function of angle is then subjected to mathematical analysis using a complex inversion matrix algorithm (Beckman Coulter, 2009). The result is a particle size distribution displayed as volume % in discrete size classes. Fourier Transform Infrared Spectroscopy (FTIR) and Total Organic Carbon (TOC) Fourier Transform Infrared Spectroscopy provides a detailed qualitative mineralogy of the core. The mineralogical composition is quantified as relative weight percentage based on a model of mineralogical constituents. FTIR produces

7 7 absorption spectra which correspond to the frequencies of vibrations between the bonds of the atoms making up the compound. These spectra are unique for each material resulting in definitive qualitative analysis of different materials. The size of the peaks is a direct indicator of the quantity of the specific compound in the material. Different types of materials (solids, liquids and gases) can be analyzed using the same principle by varying the sample preparation. The current technique is a solid sample FTIR analysis, which uses a powdered rock sample (Sondergeld and Rai, 1993). About 10 g of sample is initially crushed down to homogenize the sample and then a small portion of this is subjected to very fine grinding, down to 8-12 m particle size. Samples thus crushed are dried in a vacuum oven at 212 F (100 C) for at least 12 hours. In addition, any organic matter in the samples (particularly shales) is removed by exposing the samples to low temperature surface oxidation, a procedure called ashing, using a quartz plasma system for 19 hours. This is required to limit the spectral interference of organics with clays, as both have infrared peaks in the same wavelength range. Solid discs of these samples are made by pressurizing g of the sample along with 0.3 g of KBr salt which is also used as a background for the system to cancel the noise. Collected spectra are analyzed using inversion software which provides a quantitative measure of the minerals identified in the library, currently constituting of sixteen minerals. Estimation of the total organic content (TOC) was done using a LECO carbon analyzer at a commercial laboratory on all 4 samples. Up to 1 g of crushed sample is used for the analysis. Inorganic carbon (such as from calcite minerals) is removed by bathing the crushed sample in HCl for several hours. The sample is then combusted and the resulting CO 2 is measured. The calculated TOC is related to the total CO 2 release during oxidation. Both FTIR mineralogy and TOC were measured independently of the helium pycnometer and MICP measurements because we wanted to obtain an additional estimate of grain density. The FTIR data must be renormalized since summing all of the FTIR weight and TOC weight percentages will result in values greater than 100% for each sample. We leave the TOC wt% constant and renormalize the FTIR wt% such that the sum of all values equals 100%. We only renormalize the FTIR values since the TOC is removed during the plasma ashing process. Grain density from the renormalized FTIR wt% and LECO TOC wt% is calculated as follows: ߩ = ቀ ௪ ଵ ఘ ቁ ଵ.(3) where gftir is the grain density calculated from the renormalized FTIR data and LECO TOC measurements, w i is the weight decimal for each component (including TOC), and i is the grain density of each component. Mercury Injection Capillary Pressure MICP injection was carried out on all the sample size ranges and the plug (Fig. 5) for the purpose of investigating the effect of sample size on conformance and apparent porosity. We use an AutoPore IV Mercury Porosimeter from Micromeritics. The AutoPore IV 9520 is a 60,000 psia mercury porosimeter covering the pore diameter range from approximately 360 to.003 µm. This model has four built-in low pressure ports and two high-pressure chambers. The American Society for Testing and Materials has published a MICP testing protocol standard (ASTM-D , 2010) which we follow here: Samples were dried in a convection oven at 212 F (100 C) for approximately 24 hours. Samples were weighed and recorded Analysis conditions were created for 120 pressure points between 1.5psia to 60,000 psia. A blank test was used to obtain values in correcting intrusion data for apparatus compressibility and volume changes due to expansion/contraction because of temperature changes. Samples were loaded into a penetrometer, and then installed into the low pressure port. The first phase of the low pressure analysis is the evacuation from the penetrometer. Samples were evacuated to 50 µmhg for 30 minutes. The penetrometer is then backfilled automatically with mercury. The second phase of the low pressure analysis is the collection of data at pressures up to psia. When the low pressure analysis is complete, we removed the penetrometer from the low pressure port and weigh the total assembly. The penetrometer is then loaded into the high pressure chamber and successive pressure points (up to 60,000 psia) are recorded. Pore volume data are calculated by determining the volume of mercury remaining in the penetrometer stem. As pressure increases, mercury intrudes into the pores of the sample, simultaneously vacating the stem. Intrusion of different size pores occurs at different pressures, following the findings of Washburn (1921). Because mercury has a high surface tension and is

8 8 non-wetting to most materials, its angle of contact and radius of curvature can be used to calculate the pore diameter into which it intrudes at a given pressure. The volume of mercury in the stem of the penetrometer is measured by determining the electrical capacitance. Capacitance is the amount of electrical charge stored per volt of electricity applied. The penetrometer s capacitance varies with the length of penetrometer stem that is filled with mercury. When the penetrometer is initially backfilled with mercury, the mercury extends the entire length of the penetrometer. As increasing pressure causes the mercury to intrude into the sample s pore, the volume of mercury in the penetrometer stem decreases by the amount equal to the volume of the pores filled. This decrease in the length of the penetrometer stem that is filled with mercury causes a reduction in the penetrometer s capacitance. The Autopore software converts measurements of the penetrometer s capacitance into data points showing the volume of mercury intruding into the sample s pores. Data reduction to determine porosity, bulk density, and grain density is presented here. Bulk density is determined once the low pressure cell has been removed and the penetrometer is weighed containing both the sample material and mercury: ൬ ௐ ௐ ௐ ೞ = ௦ ߩ ܥ ൰ ఘ ௩ ൨...(4) ಹ where bmicp is the bulk density determined from MICP in g/cm 3, W s is the weight of the sample in g, Vol p is the volume of the penetrometer in ml or cm 3, W a is the weight of the apparatus including sample and mercury in g, W p is the weight of the empty penetrometer in g, Hg is mercury density in g/cm 3, and C vol is the conformance volume in ml or cm 3. Porosity ( MICP ) is determined once the mercury injection has concluded at a pressure of around 60,000 psia using = ൫ ܥ ௩ ൯ ఘ.(5) ௐ ೞ where PV Hg is the pore volume or the total volume of mercury injected at 60,000 psia in ml or cm 3. volume (C vol ) is a critical term for calculating the sample bulk density (Eq. 4) and porosity (Eq. 5). The conformance Additionally, the bulk volume of the sample (BV Hg ) in ml or cm 3 is needed to determine the skeletal or grain density ( gmicp ) in g/cm 3 :.(6) ߩ ܤ = ௦ = ߩ ௐ ೞ ( ಹ ಹ ).(7) Modified Conformance Volume Calculation As discussed by Wardlaw and Taylor (1976), Sneider et al. (1997), Shafer and Neasham (2000), and Webb (2001), conformance has been recognized as a source of error when calculating petrophysical properties from MICP for both conventional and unconventional rocks. We follow the conformance correction of Bailey (2009) during the data reduction process for determining sample bulk density (Eq. 4), porosity (Eq. 5), and grain density (Eq. 7). To the authors knowledge, this is the first published material specifically addressing the conformance problem in unconventional rocks where MICP must be done on crushed samples to ensure pore access. The rationale behind this correction for tight rocks is that crushed samples have an increased conformance due to mercury entering in between the particles during the low pressure cycle. During the pressure increase cycle in the high pressure cell, additional mercury volume is introduced into the penetrometer and an apparent intrusion is typically observed. Bailey (2009) attributes this additional apparent intrusion to the compressibility of the crush particles since entry pressures for tight shales are typically greater than 1,000 psia. Since crushed rock helium pycnometry is always done at near atmospheric conditions, it is vitally important to pick the correct value for conformance if one wants to calculate a comparable porosity from MICP. Bailey (2009) uses a pore volume compressibility calculated directly from the MICP experiment by considering: = ܥ ଵ ಹ ಹ ಹ.(8)

9 9 Figure 6 Series Series of plots documenting the conformance and pre pre-intrusion intrusion volume workflow. a) Mercury injection pressure (PcHg) as a function of the compressibility (CpMICP) calculated usi using Eq. 8 (solid black dots). The red line is the modeled compressibility curve (CpModel) using Eq. 9.. The light brown diamonds represent the absolute error between the CpModel and CpMICP and is used as a visual aid in picking the conformance pressure (vertical purple line) and intrusion pressure (vertical brown line). b) Cumulative volume of mercury intruded as a function of mercury injection pressure. The dark circles are the raw data, purple dots are conformance corrected, and red dots are intrusion correcte corrected. d. c) Pore throat radius calculated from the Washburn (1921) ( equation vs. the incremental intrusion volume of mercury. d) Mercury saturation (S Hg) as a function of mercury injection pressure for raw data, conformance-corrected data, and intrusion-corrected corrected data. where CpMICP is the pore volume compressibility (psia-1) and PcHg is the mercury pressure in psia. Once calculated at each pressure step, the compressibility behavior can be modeled as power function and will appear as the linear portion on a loglog plot of mercury pressure (PcHg) vs. CpMICP MICP: ܥ ܥ...(9) where CpModel is the model-based pore compressibility behavior (psia-1), Cpo is the intercept, and m is the slope. An example calculation of conformance is shown in Figs. 6a through 6d. The strategy is to model the linear portion (on a log-log plot) of the CpMICP curve via Eq. 9 and to look for any deviation (Fig. 6a) between the CpMICP and CpModel curves. Any deviation from the CpMICP model odel on the llow pressure portion of the curve is due to true conformance (i.e. mercury enveloping the particles) while deviation at the high end is due to actual intrusion of mercury into the pore throats of the sample. The Cp Error noted in Fig. 6aa is simply the absolute value of the difference between the log10(cpmicp) and log10(cpmodel) and is used as ann aid in picking the cconformance pressure and intrusion pressure. Once conformance pressure is determined, the associated volume at that specific pres pressure (Fig. 6b) can be used in Eq. 4 and Eq. 5 to determine the

10 10 conformance-corrected bulk density and porosity. Another way to visualize true conformance is by plotting the pore throat radius distribution and looking for the peak at high apparent aperture (Fig. 6c). Additionally, one can examine the highh pressure portion of the MICP curve (Fig. 6a) and recognize true intrusion by locating the deviation of the calculated compressibility (Eq.8) compared to the modeled compressibility (Eq. 9). The volume of mercury injected into the penetrometer is called the pre-intrusion Volume (Fig. 6b) and can be substituted into Eq. 4 and Eq. 5 for C vol to determine the intrusion-corrected bulk density and porosity. A comparison of the raw, conformance- for comparison to the corrected, and intrusion-corrected MICP curves are presented in Fig. 6d. Results and Discussion The LPP helium measurements of porosity, bulk density, and grain density were used as our baseline same properties determined from MICP (Table 1). The bulk density as determined by mercury immersion and weighing of the pre-crushed sample ranges from 2.43 g/cm 3 to 2.86 g/ cm 3. Grain density as determined by LPP pycnometry is quite variable and ranges from 2.64 g/cm 3 to g/cm 3 and porosity ranges from 2.02% to 7.98%. We attribute this range in rock properties to the varying FTIR mineralogy and TOC as shown in Table 2. We note that the lowest porosity (2.02%) is associated with Sample 1 which also has the lowest organic content, highest calcite content and highest measured grain density. Conversely, Sample 3 has the highest porosity (7.98%), relatively lower calcite content (30%) and corresponding highest measured TOC (3.33 wt%). TABLE 1 LOW PRESSURE PYCNOMETRY (LPP) HELIUM MEASUREMENTS Sample Bulk Density Grain Density LPP Crushed Porosity g/cm 3 g/cm 3 % We used Eq. 3 to calculate a grain density from the renormalized FTIR weight percentages and LECO TOC results in Table 3. A direct comparison of the LPP helium grain density and calculated grain density from FTIR and TOC is shown in Fig. 7. We note that in all but one of the samples is the LPP measured grain density greater than the grain density calculated using the FTIR and TOC data in Table 3. Other workers (Quirein et al, 2010) show that uncertainty between the measured and calculated grain densities can be quite high using a similar methodology. Figure 7 Crossplot plot showing the relationship between the crushed rock LPP grain density measured using helium (Table 1) and the calculated grain density (Eq. 3) using the corrected-ftir wt% and TOC in Table 3.

11 11 Component TABLE 2 RAW FTIR AND TOC MEASUREMENTS Grain Density (g/cm 3 ) Raw Data Sample 1 Sample 2 Sample 3 Sample 4 Average Quartz (wt%) Calcite (wt%) Dolomite (wt%) Siderite wt(%) Aragonite (wt%) Illite (wt%) Smectite (wt%) Kaolinite (wt%) Chlorite (wt%) Mixed Clay (wt%) Orthoclase (wt%) Oligoclase (wt%) Albite (wt%) Anhydrite (wt%) Apatite (wt%) Pyrite (wt%) TOC (wt%) Total Component TABLE 3 RENORMALIZED FTIR AND RAW TOC MEASUREMENTS Grain Density (g/cm 3 ) Renormalized Data Sample 1 Sample 2 Sample 3 Sample 4 Average Quartz (wt%) Calcite (wt%) Dolomite (wt%) Siderite wt(%) Aragonite (wt%) Illite (wt%) Smectite (wt%) Kaolinite (wt%) Chlorite (wt%) Mixed Clay (wt%) Orthoclase (wt%) Oligoclase (wt%) Albite (wt%) Anhydrite (wt%) Apatite (wt%) Pyrite (wt%) TOC (wt%) Total FTIR + TOC Grain Density (g/cm 3 ) LPP Grain Density (g/cm 3 )

12 12 A recurring theme in shale core analysis has been the effect of sa sample mple size on porosity and/or permeability measurements. measurements To address this we measured the particle size distribution of 3 of our LPP crushed rock samples. Fig. 8 shows 3 Laser Profile Size Analysis (LPSA) results for the LPP material after crushing and hel helium ium pycnometry had been completed. Overlain are the mesh sizes typically used in industry, and also are included are the mesh sizes (12, 20, 35, and 50) used in the present study. The cumulative distribution of particle sizes from 3 of samples show a wid widee range of variability, with 30% 30 to 70% of the sample weight being finer than 50 mesh mesh.. This upper mesh threshold represents the finest particles used for the MICP measurement. Virtually none of the LPP material was coarser than the 12 mesh size fraction. The range of particle sizes in Fig. 8 shows that we are not biased to one sample size class with the LPP helium measurements. The bias of particle size on MICP measurements; however, is investigated in the upcoming set of observations. Figure 8 Cumulative ative particle size distribution as measured by LPSA on the LPP helium porosity crushed rock material for 3 of the 4 samples. The US Standard mesh size ranges are plotted as vertical lines to show the relationship between mesh size and particle par size in microns. We note a dramatic difference between the measured, raw raw-uncorrected uncorrected MICP profiles as a function of particle size for any given sample. Fig. 9a is a typical example of the raw, uncorrected MICP curves from Sample #3. The pore throat radius distribution in Fig. 9b shows that for each sample size class we are observing a variable amount of large apparent pore throats as a percentage of the incremental intrusion. This portion of the pore throat distribution is associated with the true tr conformance volume and is detected using the methodology outlined in Fig Figs. 6a through 6d.. The finer pore throats ranging from 0.01 to microns are sensed on the high pressure portion of the MICP curve and all size classes exhibit the same peak at about microns. crons. Once corrected for conformance, the MICP curves tend to collapse more readily, but still exhibit some differences (Fig. 9c). ). When corrected for compressibility determined using Fig. 6b all of the curves tend to collapse to the same intrusion profile ile with some small differences noted (Fig. 9d). Despite our attempt to consistently model conformance, w we show in Fig.10a that sample size class lass has a strong dependence on MICP porosity. The raw, uncorrected MICP porosity estimates in Fig. 10a range from 0.65% to 24.21%. Even when similarly corrected for conformance, we still observe a strong dependence of size class on the corrected MICP porosity (Fig. 10b), although the range is tighter (0.51% to 9.91%) 9.91%). In all cases, we observe for any given sample ple that the MICP porosity is smallest for the core plug and largest for finest particle size range ((-30+50). This was expected for the plug plu material since it had remained uncrushed and was subject to incomplete intrusion due to pore access limitations (Bustin et al., 2008). 2008) We expected a certain amount of convergence ergence between the MICP porosity and LPP helium porosity once the sample had been sufficiently crushed to a particle size class with the proper surface area to volume ratio for full intrusion. intrusion A comparison of the LPP helium elium porosity and MICP porosity for each particle size class and sample is shown in Fig We use the crossplot

13 13 in Fig. 12 to qualitatively determine that the best sample size class falls in the range. When only the plug, +12, and particle le size classes are used for MICP we observe a consistent underestimation of porosity compared to the LPP helium measurements (Figs. 11 and 12).. We think this is due to pore access problems and the non non-intrusion intrusion of mercury into the entire sample, even whenn crushed. Conversely, the sample size class consistently overestimates porosity using MICP, even when corrected for conformance. This may be due to excessive microfracturing of the particles where artificial pore space is created due to the crushing ing process. Additional measurements and observations are needed to confirm this. Figure 9 This This series of plots shows a set MICP measurements from a single sample, but using different sample size classes including the plug and the following size ize ranges ((+12, , , and ) ). a) Raw MICP data plotted as mercury saturation (SHg) vs. mercury intrusion pressure (PcHg). b) Pore size distribution of uncorrected MICP measurements. c) ConformanceConformance corrected SHg vs. PcHg. d) Intrusion-corrected corrected SHg vs. PcHg. Additionally, we estimate bulk density and grain density from MICP (Eqs. 4 and 7) and compare them to our results from the LPP measurements (Table 1) and FTIR+TOC +TOC calculations (Eq. 3). The bulk density estimation from MICP shows a weaker reliance on sample size than what was observed for the porosity measurement (Fig. 13). ). Overall there is a reasonable agreement between the bulk density measured during the LPP process (orange circles in Fig ) and MICP estimates. Grain density also shows a weak dependence on sample size, although it s estimation from both MICP and LPP is lower than what is estimated from the FTIR+TOC results (Table 3 and Fig. 14). ). It is worth noting that MICP grain density has h no relation to the conformance volume (Eq.. 7) and will have the same values for raw and corrected measurements.

14 14 Figure 10 Bar graphs of porosity calculated from MICP as a function of particle size class per sample a) with no conformance correction and b) with ith conformance correction LPP and GRI porosities measured on crushed material are limited when considering stress stress-related related issues because they are done at near atmospheric conditions. Bailey (2009) points out the dilemma of co comparing mparing unstressed shale porosity por measurements to subsurface logs although this subject has been thoroughly covered for conventional reservoirs and even tight gas sands (Teeuw, 1971; Anderson, 1988; Nieto et al., 1994). Consequently, one of the ramifications that come from recognizing ng compressibility on the MICP intrusion curve is that we can compute a hydrostatically-stressed hydrostatically porosity at the intrusion pressure. re. This is done by using the pre-intrusion ntrusion volume determined in Fig. 6b as the conformance volume (Cvol) in Eq. 4 and Eq. 5. These intrusion-corrected corrected porosity values are substantially lower (Fig. 15) than the LPP measurements and may not be correct specifically for the Eagle Ford since they are made at various hydrostatic confining pressures. Bailey (2009) provides a workflow w for determining the proper correction for any given subsurface stress.

15 15 One of the advantages of the Autopore IV equipment used for measuring the MICP profiles in this study is that it can be programmed to begin intrusion at higher pressures than what has been shown here thus far.. By beginning the t process at a higher pressure close close to the one needed to fully envelop envelope each of the sample particles one one can minimize the conformance phenomena while not sacrificing any petrophysical information about the sam sample. ple. The conformance pressures for this study are variable but generally do not fall below 10 psi psia,, regardless of particle size class or sample porosity (Fig. ( 16a). Intrusion pressure on the other hand (Fig. 16b)) is intrinsically related to the rock s pe petrophysical trophysical properties (Katz and Thompson, 1986; Pittman, 1992) and should be interpreted differently. In general, we see that the low porosity sample (Sample 1 in Fig. 16b) has the highest intrusion pressure, regardless of particle size class. Conversel Conversely, y, the highest porosity sample (Sample 3 in Fig. 16b) consistently exhibits the lowest intrusion pressure. Figure 11 Bar Bar graphs showing conformance conformance-corrected MICP porosity as a function of particle size class for Samples 1 through 4. The LPP helium porosity (LPP Por) measured with helium for each sample is displayed as an orange circle with the value with the value annotated. Additional MICP profiles (Figs. 17aa through 17d 17d) were started at an injection pressure of 10 psia compared to the 1.5 psia shown in Figs. 9a through 9d.. It is worth noting that the MICP profile iin Figs. 9a through 9d and Figs. Fig 17a through 17d are from the same portion of crushed material (Sample #3). When injection begins at 10 psia and the same workflow for picking conformance is followed (Figs. 6aa through 6d 6d), there is very little difference between the raw (Fig. 17b) and conformancecorrected (Fig. 17c) curves.. The large conformance volumes evident in Fig. 6b representing the large apparent pores are not present for the same sample mple in Fig. 17b when injection begins at 10 psi psia.. Finally, when corrected for pre-intrusion volume, the curves are essentially identical (Fig. 6d and Fig. 17d). We also note that MICP P porosity calculated using Eq. Eq 4 and Eq. 5 (Fig. 18) are similar and show that the particle size range is still optimal for comparison to LPP helium porosity. Similar results were derived for MICP-derived derived bulk density and grain density density.

16 16 Figure 12 Crossplot comparison of MICP porosity rosity compared to the crushed rock LPP porosity for each sample size. A one-to-one one solid line and +/- 1 p.u. dashed lines are used to define measurement accuracy. Figure 13 Bar Bar graphs showing the comparison of the measured bulk density from the LPP measurements (orange circles) and bulk density from conformance-corrected corrected MICP measurements for each sample size class (vertical bars).

17 17 Figure 14 Bar Bar graphs showing the comparison of the measured grain density from the LPP measurements (orange circles), grain grai density calculated from FTIR mineralogy and LECO TOC (blue circles) circles), and grain density from MICP measurements for each sample size class (vertical bars). Figure 15 Bar graph comparison of porosity calculated using the pre-intrusion ntrusion volume as the conformance conformanc volume for all sample sizes compared to LPP helium porosity (orange circles).

18 18 Figure 16 Bar graphs showing the magnitude of the a) conformance pressure and b) the intrusion pressure for each sample size class

19 19 Figure 17 This series of plots shows a set MICP measurements from a single sample, but using different sample size distributions including the plug and the following size ranges anges ((+12, , , and ) ). All sample sizes were run starting at an intrusion pressure of 10 psia except ept for the plug. a) Raw MICP data plotted as mercury saturation as a percentage of pore volume (S Hg) vs. mercury intrusion pressure (PcHg). b) Pore size distribution of uncorrected MICP measurements. c) Conformance-corrected Conformance SHg vs. PcHg. d) Intrusion-corrected SHg vs. PcHg.

20 20 Figure 18 Crossplot comparison of conformance conformance-corrected corrected MICP porosity compared to the crushed rock LPP porosity for each sample size class. All samples were run starting at an intrusion pressure of 10 psia with exce exception ption to the plug. A one-to-one one solid line and +/- 1 p.u. dashed lines are used to define measurement accuracy. Conclusions Porosity, mineralogy, and TOC are int intrinsically tied within the Eagle Ford Formation. ormation. Zones with high calcite content tend to bee lower in porosity and total clay, but higher in TOC. Sample size ize class plays a large role in the raw MICP profile and the associated apparent porosity. porosity Conformance has been re-examined examined for ultra ultra-low low permeability rocks to account for sample pore volume compressibility. By including this compression as porosity, best agreement is found between MICP and LPP helium porosity using the size range range. Bulk density from MICP agrees well with those measured in the LPP process via simple mercury immersion. Grain density is still problematic due to the poor match between FTIR+TOC calculated grain densities, LPP helium measured grain densities, and MICP grain densities. The conformance pressure as we have defined it here has as a very limited range (10 to 30 psia) psi and is almost independent of sample size class. For this reason we have performed experiments with intrusion beginning at a 10 psia injection pressure to reduce the conformance and see no difference in the calculated porosity, grain density, and bulk density from MICP. The intrusion-corrected corrected porosity picked using a very high conformance pressure is more indicative of a hydrostatically stressed porosity and cannot be compared directly to crush crushed ed rock LPP since the latter is done at near atmospheric conditions. ditions. Stressed porosity measurements are currently not commercially available for tight shales and the MICP method presented here may provide some future direction. Nomenclature BVHg = bulk volume from MICP, L3, cm3 [ml] PVHg = pore volume from MICP, L3, cm3 [ml] CpMICP = pore volume compressibility from MICP, Lt 2/m, psi-1 CpModel = pore volume compressibility model, Lt 2/m, psi-1 Cpo = pore volume compressibility model intercept term, Lt 2/m, psi-1 CVol = conformance volume, L3, cm3 [ml] m = power law exponent of pore volume compressibility model, (Lt 2/m)2, psi-2