CSUG/SPE 149432

Sample Size Effects on the Application of Mercury Injection CapillaryPressure for Determining the Storage Capacity of Tight Gas and Oil ShalesJ.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, 15–17 November 2011.

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 notbeen 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 toreproduce 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.

AbstractWe 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 EagleFord Formation in the form of a puck or disc. A horizontal 1 inch core plug was cut from this disc and the remainingmaterial was subsequently crushed and sieved through various mesh sizes. MICP profiles up to 60,000 psia were thenmeasured on the 1 inch plug and all of the various crushed and sieved rock particle sizes. In parallel we subsampled the plugmaterial to measure bulk volume, grain volume, and porosity using a crushed rock helium pycnometry method. Theseadditional measurements provided a comparison set of standard petrophysical properties from which we could compare theMICP results.

In general our MICP profiles show a very strong dependence on sample size due to two reasons: pore accessibility andconformance. We verify the conformance correction approach of Bailey (2009) which specifically accounts for the porevolume compression of the sample before mercury has been injected into the largest set of interconnected pore throats. Thisnew method is preferred over the traditional method of conformance correction when compared to crushed rock heliumporosity since the latter is performed at unstressed conditions. Our results using Bailey’s (2009) method reveals that the -20+35 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 notalways possible on fine cuttings samples. There are many instances when limited cuttings may be the only source of rockinformation available before a whole core is taken. Cuttings profiles also provide a key insight over long formation intervalsthat may not be available from whole core. Cuttings and core profiles for use in calibrating well logs have proven to be arequirement in these ultra-low perm systems.

IntroductionThe emergence of shale and oil plays in North America has caused the industry to re-examine the methods which we use toquantify the resource and recoverable reserves in place. We recognize that unconventional gas and oil reservoirs aregeologically and petrophysically heterogeneous at a variety of scales. This calls for a continuum of measurements to be usedthat are generally challenged due to the nano-scale pore nature of these rocks. A sampling of recent studies (Sondergeld etal., 2010; Passey et al., 2010; Spears et al., 2011) point out the lack of a standardized protocol such as that established forconventional and tight gas sand (microDarcy) systems in the API-RP40 (API, 1998). There is some common ground in thatmost laboratories follow a variation of the procedures established by Luffel and Guidry (1992) for determining storagecapacity (crushed rock or GRI porosity) and flow capacity (pressure or pulse decay permeability). Other tools such as imageanalysis 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 anddensity of small and/or irregular samples such as cuttings or crushed whole core material. Several studies (Olson and Grigg,

2

2008) and Bailey (2009) point out the usefulness and limitations of using MICP to determine porosityOne benefit of using MICP for characterizing reservoirs of all classesof a material by incrementally injecting mercury into pore spaces as small as 3.6 nmreasonable correlation between gas-filled (asshales. More recently, Bailey (2009) presents an excellent discussion of the details used for applying a modifiedconformance correction that more accurately replicates the crushed rock porosity measured with helium for a variety of oiland gas bearing shales. We expand upon Bailey’s (2009) workflow by examining the effect of sample size on conformanceand porosity determination with MICP.

This study focuses on core material taken from thelocated in Burleson County where the Eagle Fordproduction in the overlying Austin Chalk interval.Turonian transgression across Texas and it is recognized in the subsurface fromto Grimes County in Central Texas. Depositional environmenconditions from anoxic deep marine to lower deltaic. Significant stratigraphic differences are noted across Texas, mostly duto the interplay of 3rd order eustatic cycles and local chansource rock for decades, it was not until 2007hydrocarbon production potential of the Eagle Fordmany operators have proven the economic viability of thehydraulic fracturing. These advances have evenoil discoveries in North America.

Figure 1—Local basemap showing lateral extent and thermal maturity window for theshown in this paper from the field area in the nAdministration, 2010)

BackgroundThe productive interval of the Eagle Ford Fidentified on wireline logs. A variety of rproperties of the Eagle Ford Shale using both cores and logsis shown in Fig. 2. The productive interval is typicallyresistivity, and convergence of the neutron and density logs compared to the gray shales directly above. Various coremeasurements were taken as part of both ain this paper. Of particular note in Fig. 2 is the profile showing the Total Organic Carbon (TOC) as measured by a LECO

) point out the usefulness and limitations of using MICP to determine porosityOne benefit of using MICP for characterizing reservoirs of all classes is that it continually images the pore throat distribution

by incrementally injecting mercury into pore spaces as small as 3.6 nm. Olson and Grigg (2filled (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 modifiedance 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

material taken from the Eagle Ford Shale interval in Central Texas (Fig. 1Eagle Ford has long been recognized as the source rock associated with oil a

Austin Chalk interval. The Eagle Ford Formation was deposited during the Cenomanianand it is recognized in the subsurface from Maverick County in the southwest

Depositional environments as discussed by Liro et al. (1994conditions from anoxic deep marine to lower deltaic. Significant stratigraphic differences are noted across Texas, mostly du

ic cycles and local changes in topography. Despite its common knowledge as an oil2007 that operators such as Apache, EOG, and Petrohawk were testing

Eagle Ford despite the nanoDarcy nature of the rock’s flow capacity.operators have proven the economic viability of the Eagle Ford by pushing the envelope in

cturing. These advances have even prompted several operating companies to place the

Local basemap showing lateral extent and thermal maturity window for the Eagle Ford shale. Core material and logs arefrom the field area in the northeastern flank of the play in Burleson County, Texas

Eagle Ford Formation exhibits some common petrophysical properties that can beidentified on wireline logs. A variety of recent studies (Mullen, 2010; Sondhi, 2011) describe the various petrophysic

hale using both cores and logs. An example of the core and wireline log intervalroductive 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 coreas part of both a commercial laboratory and academic study and are described in fuller detail later

2 is the profile showing the Total Organic Carbon (TOC) as measured by a LECO

CSUG/SPE 149432

) point out the usefulness and limitations of using MICP to determine porosity in shale reservoirs.the pore throat distribution

. Olson and Grigg (2008) show areceived) 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 modifiedance 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

Fig. 1). The field area ishas long been recognized as the source rock associated with oil and gas

deposited during the Cenomanian-Maverick County in the southwest of Texas

1994) include a variety ofconditions from anoxic deep marine to lower deltaic. Significant stratigraphic differences are noted across Texas, mostly due

ges in topography. Despite its common knowledge as an oil-pronethat operators such as Apache, EOG, and Petrohawk were testing the liquid

flow capacity. More recently,in horizontal drilling and

companies to place the Eagle Ford in the top 10

shale. Core material and logs arein Burleson County, Texas (U.S. Energy Information

ormation exhibits some common petrophysical properties that can be notionallySondhi, 2011) describe the various petrophysical

core and wireline log interval of this studyrecognized 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 coreand are described in fuller detail later

2 is the profile showing the Total Organic Carbon (TOC) as measured by a LECO

CSUG/SPE 149432

carbon analyzer. Measured TOC values range frommeasured by Fourier Transform Infrared Spectroscopy (FTIR) of2011) reveals calcite and clay (both illite and mixedfeldspar, pyrite, and siderite. The most productive section of tcarbonate and organic material (Sondhi, 2Production Index (PI) values from a Source Rock Analyzer (SRA)quite in the condensate/wet gas zone.

Figure 2—Well log profile for the subject well in Burleson County, Texas. Tracklog. Track 2 shows the array induction resistivity curves. Track 3 shows the density (ZDNC) and neutron logs (CNCF) scaled inlimestone 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) asmeasured by a LECO carbon analyzer on whole core material. Track 6 shows various crushed rock porosity measurements: asreceived bulk volume gas (ARCBVg), cleaned and dried total porosity (DryCPhi), and low pressure helium pycnometer (LPPPOR).The red squares are LPP porosity values measured from the MICP plugs. Track 7 shows extracted saturations: assaturation (ARCSw), as-received oil saturation (ARCSo), and asmeasured 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 techniqueshortly after recovering the core. An oil-measurements were done by taking a puck from the whole core and crushing it to produhelium pycnometry. Porosity was measured on fresh core material in thehydrocarbon extraction and ranges up to 4% of the sample bulk volume. After extraction and humidity omeasured porosity increased to as high as 10% of the samplin accessible pore volume after crushing to removal of residual oil and water that were stillmeasurement. Essentially, the accessible pore volume under asand oil shrinkage. Subsequent cleaning and Deanlarger pore volume for helium to enter for the dry porosity measurements. The

range from less than 1 wt% to as high as 6 wt% in the cored interval. Mineralogy asmeasured by Fourier Transform Infrared Spectroscopy (FTIR) of 160 samples with roughly a 2 ft. vertical spacing

both illite and mixed-layer) as the predominate mineralogy with lesser amounts of quartz,The most productive section of the Eagle Ford tends to be lower in total clay and richer in

(Sondhi, 2011). Analysis of visual kerogen vitrinite reflectance as well as Tm a Source Rock Analyzer (SRA) place this interval well within the oil window, but not

profile for the subject well in Burleson County, Texas. Track 1 shows the core (CGR) and total gammthe array induction resistivity curves. Track 3 shows the density (ZDNC) and neutron logs (CNCF) scaled inreceived 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) ascarbon analyzer on whole core material. Track 6 shows various crushed rock porosity measurements: as

received bulk volume gas (ARCBVg), cleaned and dried total porosity (DryCPhi), and low pressure helium pycnometer (LPPPOR).ity values measured from the MICP plugs. Track 7 shows extracted saturations: as

received oil saturation (ARCSo), and as-received gas saturation (ARCSg). Track 8 shows mineralogy asineral groupings

The initial measurements of storage capacity were done by a commercial laboratory using a variation of the GRI technique-based synthetic mud was used during the coring process. Shale rock proper

measurements were done by taking a puck from the whole core and crushing it to produce accessible pore volumes forelium pycnometry. Porosity was measured on fresh core material in the as-received (AR BVG in Fig.

to 4% of the sample bulk volume. After extraction and humidity oy increased to as high as 10% of the sample bulk volume (DRY POR in Fig. 2). We attribute this increase

to removal of residual oil and water that were still present during the asccessible pore volume under as-received conditions was made possible by

. Subsequent cleaning and Dean Stark extraction removed the remaining hydrocarbons and provided alarger pore volume for helium to enter for the dry porosity measurements. The extracted saturations are color

3

in the cored interval. Mineralogy as160 samples with roughly a 2 ft. vertical spacing (Sondhi,

layer) as the predominate mineralogy with lesser amounts of quartz,tends to be lower in total clay and richer in

. Analysis of visual kerogen vitrinite reflectance as well as Tmax andplace this interval well within the oil window, but not

the core (CGR) and total gamma ray (GR)the array induction resistivity curves. Track 3 shows the density (ZDNC) and neutron logs (CNCF) scaled inreceived 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) ascarbon analyzer on whole core material. Track 6 shows various crushed rock porosity measurements: as-

received bulk volume gas (ARCBVg), cleaned and dried total porosity (DryCPhi), and low pressure helium pycnometer (LPPPOR).ity values measured from the MICP plugs. Track 7 shows extracted saturations: as-received water

received gas saturation (ARCSg). Track 8 shows mineralogy as

The initial measurements of storage capacity were done by a commercial laboratory using a variation of the GRI techniquemud was used during the coring process. Shale rock property

ce accessible pore volumes for(AR BVG in Fig. 2) state before

to 4% of the sample bulk volume. After extraction and humidity oven drying the2). We attribute this increase

present during the as-receivedconditions was made possible by gas exsolutiona

removed the remaining hydrocarbons and provided aextracted saturations are color-coded by fluid

4 CSUG/SPE 149432

type including oil, water, and gas. The extracted water saturations range as high as 80% to as low as 40% of the cleaned anddried pore volume.

The core was then stored in sealed plastic bags under room conditions for 2 years at the University of Oklahoma IC3

laboratory where subsequent studies were performed. The core was resampled and an additional set of as-received bulkvolume and grain volume measurements were conducted with a low pressure pycnometer (LPP) to determine porosity andare represented by the solid black circles (LPP Por) (Sondhi, 2011). We observe the LPP helium porosity measurements areclose to the clean and dried porosities measured several years earlier by the commercial laboratory, but still higher than thecommercial as-received measurements. We interpret this to be a result of desiccation due to storage at typical roomconditions and also non-removal of some of the liquid hydrocarbon component. We describe the LPP helium porositymeasurement 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 petroleumindustry was first introduced to the use of MICP by Purcell (1949) and Rose and Bruce (1949) for determining the pore throatdistribution 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), andothers show the applicability of MICP for determining the flow capacity of a variety of reservoir rocks, particularly tight gassands, carbonates, and other challenged reservoir rocks. Simultaneously, other researchers were using MICP to measure howwell 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 samplesize (mainly cuttings) conformance on MICP seal capacity measurements. Conformance or closure is a measure of theamount 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 porecompressibility on conformance in shale formations and how it must be considered when compared to independentmeasurements 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 poresand 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 densityfrom small irregular samples such as cuttings. While small, crushed samples from whole core are used in the GRI method forestimating grain volume and ultimately porosity via helium pycnometry, the method requires a large, in-tact sample todetermine bulk volume, usually by simple mercury immersion. This is rarely the case with cuttings since volume and samplesize can be quite variable. Determination of lithology from cuttings is relatively straightforward and can be done using theFTIR 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 bulkmodulus of organic matter. Porosity, bulk density, and grain density from cuttings are major variables needed for calibratingrocks to logs; particularly when traditional cores are not available. There may also be circumstances where cores only covera limited portion of the vertical section of interest. The importance of tying core measurements to logs is discussed bySondergeld et al. (2010), Quirein et al. (2010) and Ramirez et al. (2011).

Experimental Procedures

Sample PreparationThe 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 thecore 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 tomeasured FTIR mineralogy, LPP porosity, and MICP. The remaining puck was then crushed and homogenized using theequipment 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 singlesample of the variety of particle size classes we considered (plug, +12, -12+20, -20+35, and -35+50).

CSUG/SPE 149432

Figure 3—Photo showing an example of the puck material recovered from the 2/3 butt portion of the core and the sampling schemeused to carry out subsequent helium and mercury injection experiments.

Low Pressure PycnometryA 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 autAccuPyc 1330 by Micromeritics is used which includes a sample cell and an expansion chamber.pycnometer determines density and volume by measuring the pressure change of wetting gas in a calibrated volumecell and expansion chamber). Helium is used as the purge gas at a pressure of 19.5 psig. Porosities are reproducible to bettethan ±0.5 p.u. (Karastathis, 2007)

Figure 4—Photo showing the mortar and pestle used for crushing the puck in Fig. 3material into the various particle size classes.

The typical sample size varies from 9 g to 12should not exceed 10 cm3 since this is the limitavacuum oven at 212°F (100°C) for at least 8 hoursdesiccator for at least 30 minutes before the bulk volume of the sample ivolume of the mercury that has been displaced by the samplerecorded and termed M1.

Photo showing an example of the puck material recovered from the 2/3 butt portion of the core and the sampling schemed mercury injection experiments.

A low pressure pycnometer (LPP) is used to measure porosity where gas, volatile hydrocarbons and free and bound waterare removed as we crush the sample to measure the absolute porosity. A fully automatic gas displacement pycnometer

by Micromeritics is used which includes a sample cell and an expansion chamber.pycnometer determines density and volume by measuring the pressure change of wetting gas in a calibrated volumecell and expansion chamber). Helium is used as the purge gas at a pressure of 19.5 psig. Porosities are reproducible to bette

Photo showing the mortar and pestle used for crushing the puck in Fig. 3 and US standard meshes used for sieving

g to 12 g based on the bulk density of the sample. Thee limitation imposed by the LPP sample holder. The sample is the

for at least 8 hours (Sondhi, 2009). The samples are cooled in a humidity controlleddesiccator for at least 30 minutes before the bulk volume of the sample is evaluated. The bulk volume is calculated from the

of the mercury that has been displaced by the sample at room temperature. The mass of the sample

5

Photo showing an example of the puck material recovered from the 2/3 butt portion of the core and the sampling scheme

A low pressure pycnometer (LPP) is used to measure porosity where gas, volatile hydrocarbons and free and bound wateromatic gas displacement pycnometer

by Micromeritics is used which includes a sample cell and an expansion chamber. The AccuPyc 1330pycnometer determines density and volume by measuring the pressure change of wetting gas in a calibrated volume (samplecell and expansion chamber). Helium is used as the purge gas at a pressure of 19.5 psig. Porosities are reproducible to better

and US standard meshes used for sieving

he crushed sample volumesample is then dried in a

(Sondhi, 2009). The samples are cooled in a humidity controlleds evaluated. The bulk volume is calculated from the

of the sample before crushing is

6

The sample is crushed in a heat-treated steel cylindricis no sample loss during crushing. Stress is induced on the sample by hitting on the pestle with a wooden hammer. Uniformamount of force is applied by hitting for about 100 counts. Onccm3 sample holder while maintaining minimum weight loss (less than 0.5%). It is again dried athours in a vacuum oven and cooled in a humidity controlled environmall the free, bound and surface water is removed initially through drying the bulk sample, there should bethe sample mass before and after the crushingcrushing. This loss (M1-M2, or m) is calculated and if it exceeds 0.5%, the sample is discarded and the procedure isOnce the sample mass loss is within limits, grpycnometer, and the corrected grain volume (term:

ܸீ ൌ ܸீ +ο

ఘಸ……….(1)

and

߮ு =ಳିಸ

ಳ……….(2)

This method has been researched, developed, and testedBarnett (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 thpuck in Fig. 3.

Laser Particle Size AnalysisWe 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 investigate if thereLPP and MICP measurements. The uniformity of the crushed sample is measured by analyzing the sample in a singlewavelength Laser Diffraction Particle Analyzer, LS 13light scattering, covering a size range from 0.4 µm to 2000 µm

A differential pressure is created in the sample chamber with vacuum on one end to disperse the sample in air in the foof a vortex. While this is passing through the channel, a laser beam is used to illuminate the particulates, which scatters tincident light on to silicon photo-detectors. The intensity of light on each detector measured as a function of angle is thensubjected to mathematical analysis using a complex inversion matrix algorithmparticle size distribution displayed as volume % in discrete size classes

Fourier Transform Infrared SpectroscopyFourier 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

steel cylindrical container, and a steel pestle sealed with an Ois no sample loss during crushing. Stress is induced on the sample by hitting on the pestle with a wooden hammer. Uniformamount of force is applied by hitting for about 100 counts. Once the sample is crushed, it is carefully transferred in

minimum weight loss (less than 0.5%). It is again dried at 212°F (100°C)hours in a vacuum oven and cooled in a humidity controlled environment for 30 minutes before its mass

bound and surface water is removed initially through drying the bulk sample, there should bebefore and after the crushing. Any change in mass is accounted for by material loss

) is calculated and if it exceeds 0.5%, the sample is discarded and the procedure isloss is within limits, grain volume (VG) and grain density (G) is measured using

ume (VGcorr) and LPP helium porosity (He) are calculated

has been researched, developed, and tested over the past few years on a variety of shale plays including theBarnett (Karastathis, 2007; Kale, 2009), The Thirteen Finger (Raina, 2010), and Eagle Ford (Sondhi, 2011).

Photo showing a sampling of material from each particle size class along with the plug extracted from the middle of the

We measured the particle size range of the material used for the LPP as a baseline comparison to the MICP crushed andsieved material. We wanted to investigate if there were any particle size impartialities impressed upon the comparison of

The uniformity of the crushed sample is measured by analyzing the sample in a singleaction Particle Analyzer, LS 13-320 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 foof a vortex. While this is passing through the channel, a laser beam is used to illuminate the particulates, which scatters t

detectors. The intensity of light on each detector measured as a function of angle is thensubjected to mathematical analysis using a complex inversion matrix algorithm (Beckman Coulter, 2009particle 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

CSUG/SPE 149432

al container, and a steel pestle sealed with an O-ring to ensure thereis no sample loss during crushing. Stress is induced on the sample by hitting on the pestle with a wooden hammer. Uniform

it is carefully transferred into the 10212°F (100°C) for at least 8

mass is recorded (M2). Asbound and surface water is removed initially through drying the bulk sample, there should be little variation in

loss of the sample during) is calculated and if it exceeds 0.5%, the sample is discarded and the procedure is repeated.

measured using the low pressurecalculated by considering the m

a variety of shale plays including theBarnett (Karastathis, 2007; Kale, 2009), The Thirteen Finger (Raina, 2010), and Eagle Ford (Sondhi, 2011).

e plug extracted from the middle of the

We measured the particle size range of the material used for the LPP as a baseline comparison to the MICP crushed andpartialities impressed upon the comparison of

The uniformity of the crushed sample is measured by analyzing the sample in a singlelter, using the Fraunhofer and Mie theories of

A differential pressure is created in the sample chamber with vacuum on one end to disperse the sample in air in the formof a vortex. While this is passing through the channel, a laser beam is used to illuminate the particulates, which scatters the

detectors. The intensity of light on each detector measured as a function of angle is thenBeckman Coulter, 2009). The result is a

Fourier Transform Infrared Spectroscopy provides a detailed qualitative mineralogy of the core. The mineralogicalconstituents. FTIR produces

CSUG/SPE 149432 7

absorption spectra which correspond to the frequencies of vibrations between the bonds of the atoms making up thecompound. These spectra are unique for each material resulting in definitive qualitative analysis of different materials. Thesize 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 samplepreparation. 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 subjectedto very fine grinding, down to 8-12 m particle size. Samples thus crushed are dried in a vacuum oven at 212°F (100°C) forat least 12 hours. In addition, any organic matter in the samples (particularly shales) is removed by exposing the samples tolow temperature surface oxidation, a procedure called ashing, using a quartz plasma system for 19 hours. This is required tolimit 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 0.0005 g of the sample along with 0.3 g of KBr salt which is alsoused as a background for the system to cancel the noise. Collected spectra are analyzed using inversion software whichprovides 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 all4 samples. Up to 1 g of crushed sample is used for the analysis. Inorganic carbon (such as from calcite minerals) is removedby bathing the crushed sample in HCl for several hours. The sample is then combusted and the resulting CO2 is measured.The calculated TOC is related to the total CO2 release during oxidation.

Both FTIR mineralogy and TOC were measured independently of the helium pycnometer and MICP measurementsbecause we wanted to obtain an additional estimate of grain density. The FTIR data must be renormalized since summing allof the FTIR weight and TOC weight percentages will result in values greater than 100% for each sample. We leave the TOCwt% constant and renormalize the FTIR wt% such that the sum of all values equals 100%. We only renormalize the FTIRvalues since the TOC is removed during the plasma ashing process. Grain density from the renormalized FTIR wt% andLECO TOC wt% is calculated as follows:

ி்ூோߩ = ቀ∑௪

ఘ

ୀଵ ቁ

ିଵ

……….(3)

where gFTIR is the grain density calculated from the renormalized FTIR data and LECO TOC measurements, wi is the weightdecimal for each component (including TOC), and i is the grain density of each component.

Mercury Injection Capillary PressureMICP 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 fromMicromeritics. The AutoPore IV 9520 is a 60,000 psia mercury porosimeter covering the pore diameter range fromapproximately 360 to .003 µm. This model has four built-in low pressure ports and two high-pressure chambers. TheAmerican Society for Testing and Materials has published a MICP testing protocol standard (ASTM-D4404-10, 2010) whichwe 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 25.92 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 pressureincreases, mercury intrudes into the pores of the sample, simultaneously vacating the stem. Intrusion of different size poresoccurs at different pressures, following the findings of Washburn (1921). Because mercury has a high surface tension and is

8 CSUG/SPE 149432

non-wetting to most materials, its angle of contact and radius of curvature can be used to calculate the pore diameter intowhich 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 varieswith 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. Asincreasing pressure causes the mercury to intrude into the sample’s pore, the volume of mercury in the penetrometer stemdecreases by the amount equal to the volume of the pores filled. This decrease in the length of the penetrometer stem that isfilled with mercury causes a reduction in the penetrometer’s capacitance. The Autopore software converts measurements ofthe 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 oncethe low pressure cell has been removed and the penetrometer is weighed containing both the sample material and mercury:

ெߩ ூ = ܹ௦ ܸ ݈ − ൬ௐ ೌିௐ ିௐ ೞ

ఘಹ൰− ௩൨ൗܥ ..........(4)

wherebMICP is the bulk density determined from MICP in g/cm3, Ws is the weight of the sample in g, Volp is the volume of

the penetrometer in mL or cm3, Wa is the weight of the apparatus including sample and mercury in g, Wp is the weight of theempty penetrometer in g, Hg is mercury density in g/cm

3, and Cvol is the conformance volume in mL or cm3.

Porosity (MICP) is determined once the mercury injection has concluded at a pressure of around 60,000 psia using

߮ெ ூ = ൫ܲ ுܸ − ௩൯ܥఘ್ಾ ು

ௐ ೞ……….(5)

where PVHg is the pore volume or the total volume of mercury injected at 60,000 psia in mL or cm3. The conformance

volume (Cvol) is a critical term for calculating the sample bulk density (Eq. 4) and porosity (Eq. 5).

Additionally, the bulk volume of the sample (BVHg) in mL or cm3 is needed to determine the skeletal or grain density

(gMICP) in g/cm3:

ܤ ுܸ = ܹ௦ ெߩ ூ⁄ ……….(6)

ெߩ ூ =ௐ ೞ

(ಹିಹ)……….(7)

Modified Conformance Volume CalculationAs 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 bothconventional and unconventional rocks. We follow the conformance correction of Bailey (2009) during the data reductionprocess 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 MICPmust be done on crushed samples to ensure pore access. The rationale behind this correction for tight rocks is that crushedsamples 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 penetrometerand an apparent intrusion is typically observed. Bailey (2009) attributes this additional apparent intrusion to thecompressibility of the crush particles since entry pressures for tight shales are typically greater than 1,000 psia. Sincecrushed rock helium pycnometry is always done at near atmospheric conditions, it is vitally important to pick the correctvalue 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)

CSUG/SPE 149432

Figure 6—Series of plots documenting the conformance and prea function of the compressibility (CpMICP) calculated usi(CpModel) using Eq. 9. The light brown diamondsaid in picking the conformance pressure (verticalmercury intruded as a function of mercury injection pressure. The dark circles are the raw data, purple dots are conformancecorrected, and red dots are intrusion correcteincremental intrusion volume of mercury. d) Mercury saturation (Sconformance-corrected data, and intrusion-corrected data.

where CpMICP is the pore volume compressibility (psiapressure step, the compressibility behavior can be modeled aslog plot of mercury pressure (PcHg) vs. CpMICP

ெܥ ௗൌ ܥ ܲு

..........(9)

where CpModel is the model-based pore compressibility

An example calculation of conformance is shown ina log-log plot) of the CpMICP curve via Eq. 9Any deviation from the CpMICP model on the lenveloping the particles) while deviation at the high end is due to actual intrusion of mercury into the pore throats of thesample. The “Cp Error” noted in Fig. 6a islog10(CpModel) and is used as an aid in picking the cdetermined, the associated volume at that specific pres

Series of plots documenting the conformance and pre-intrusion volume workflow. a) Mercury injection pressure (Pc) calculated using Eq. 8 (solid black dots). The red line is the modeled compressibility curve

. The light brown diamonds represent the absolute error between the CpModel and CpMICPaid in picking the conformance pressure (vertical purple line) and intrusion pressure (vertical brown line). b) Cumulative volume ofmercury intruded as a function of mercury injection pressure. The dark circles are the raw data, purple dots are conformancecorrected, and red dots are intrusion corrected. c) Pore throat radius calculated from the Washburn (incremental intrusion volume of mercury. d) Mercury saturation (SHg) as a function of mercury injection pressure for raw data,

corrected data.

is the pore volume compressibility (psia-1) and PcHg is the mercury pressure in psia.pressure step, the compressibility behavior can be modeled as power function and will appear as the

MICP:

compressibility behavior (psia-1), Cpo is the intercept, and m is the slope.

conformance is shown in Figs. 6a through 6d. The strategy is to model the linear portioncurve via Eq. 9 and to look for any deviation (Fig. 6a) between the C

odel on the low pressure portion of the curve is due to true conformancewhile deviation at the high end is due to actual intrusion of mercury into the pore throats of the

a is simply the absolute value of the difference between then aid in picking the conformance pressure and intrusion pressure. Once conformance pressure is

determined, the associated volume at that specific pressure (Fig. 6b) can be used in Eq. 4 and

9

intrusion volume workflow. a) Mercury injection pressure (PcHg) as(solid black dots). The red line is the modeled compressibility curve

pMICP and is used as a visualpurple 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 conformanced. c) Pore throat radius calculated from the Washburn (1921) equation vs. the

) as a function of mercury injection pressure for raw data,

is the mercury pressure in psia. Once calculated at eachpower function and will appear as the linear portion on a log-

is the slope.

The strategy is to model the linear portion (onCpMICP and CpModel curves.

curve is due to true conformance (i.e. mercurywhile deviation at the high end is due to actual intrusion of mercury into the pore throats of the

simply the absolute value of the difference between the log10(CpMICP) andOnce conformance pressure is

and Eq. 5 to determine the

10

conformance-corrected bulk density and porosity.radius distribution and looking for the peak at

Additionally, one can examine the high pressure portion of the MICP curvelocating the deviation of the calculated compressibility (Eq.8) compared to the modeledof mercury injected into the penetrometer is called theEq. 5 for Cvol to determine the intrusion-ccorrected, and intrusion-corrected MICP curves are presented in Fig. 6d.

Results and DiscussionThe LPP helium measurements of porosity, bulk density, and grain density were used as our baseline for comparison to thesame properties determined from MICP (Table 1the pre-crushed sample ranges from 2.43 g/cmvariable and ranges from 2.64 g/cm3 to 2.92 g/cmproperties to the varying FTIR mineralogy and TOC as shown inassociated with Sample 1 which also has the lowest organic content, highest calcite condensity. Conversely, Sample 3 has the highest porosity (7.98%), relatively lower calcite content (30%) and correspondinghighest measured TOC (3.33 wt%).

TABLE 1—LOW PRESSURE PYCNOMETRY (LPP) HELIUM MEASUREMENTS

Sample Bulk Density

1

2

3

4

We used Eq. 3 to calculate a grain density from the renormalized FTIR weight percentages and LECO TOC3. A direct comparison of the LPP helium grain density and calculated grain density from FTIR and TOC is shown inWe note that in all but one of the samples is the LPP measured grain density greater than the grain density calculathe FTIR and TOC data in Table 3. Other workers (Quirein et al, 2010) show that uncertainty between the measured andcalculated grain densities can be quite high using a similar methodology.

Figure 7—Crossplot plot showing the relationshipthe calculated grain density (Eq. 3) using the corrected

corrected bulk density and porosity. Another way to visualize true conformance is by plotting the pore throatradius distribution and looking for the peak at high apparent aperture (Fig. 6c).

Additionally, one can examine the high pressure portion of the MICP curve (Fig. 6a) and recognize true intrusionlocating the deviation of the calculated compressibility (Eq.8) compared to the modeled compressibilityof mercury injected into the penetrometer is called the pre-intrusion Volume (Fig. 6b) and can be substituted in

corrected bulk density and porosity. A comparison of the raw, conformancecorrected MICP curves are presented in Fig. 6d.

measurements of porosity, bulk density, and grain density were used as our baseline for comparison to theTable 1). The bulk density as determined by mercury immersion and weighing of

crushed sample ranges from 2.43 g/cm3 to 2.86 g/ cm3. Grain density as determined by LPP pycnometry is quiteto 2.92 g/cm3 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%) isassociated with Sample 1 which also has the lowest organic content, highest calcite content and highest measured graindensity. Conversely, Sample 3 has the highest porosity (7.98%), relatively lower calcite content (30%) and corresponding

LOW PRESSURE PYCNOMETRY (LPP) HELIUM MEASUREMENTS

Bulk Density Grain Density LPP Crushed Porosity

g/cm3 g/cm3 %

2.860 2.919 2.02

2.473 2.616 5.45

2.430 2.640 7.98

2.566 2.779 7.67

We used Eq. 3 to calculate a grain density from the renormalized FTIR weight percentages and LECO TOCA direct comparison of the LPP helium grain density and calculated grain density from FTIR and TOC is shown in

We note that in all but one of the samples is the LPP measured grain density greater than the grain density calcula. Other workers (Quirein et al, 2010) show that uncertainty between the measured and

calculated grain densities can be quite high using a similar methodology.

plot showing the relationship between the crushed rock LPP grain density measured using helium (Table 1) andthe calculated grain density (Eq. 3) using the corrected-FTIR wt% and TOC in Table 3.

CSUG/SPE 149432

Another way to visualize true conformance is by plotting the pore throat

and recognize true intrusion bycompressibility (Eq. 9). The volume

can be substituted into Eq. 4 andA comparison of the raw, conformance-

measurements of porosity, bulk density, and grain density were used as our baseline for comparison to thehe bulk density as determined by mercury immersion and weighing of

. Grain density as determined by LPP pycnometry is quiterom 2.02% to 7.98%. We attribute this range in rock

. We note that the lowest porosity (2.02%) istent and highest measured grain

density. Conversely, Sample 3 has the highest porosity (7.98%), relatively lower calcite content (30%) and corresponding

LOW PRESSURE PYCNOMETRY (LPP) HELIUM MEASUREMENTS

We used Eq. 3 to calculate a grain density from the renormalized FTIR weight percentages and LECO TOC results in TableA 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. Other workers (Quirein et al, 2010) show that uncertainty between the measured and

between the crushed rock LPP grain density measured using helium (Table 1) and

CSUG/SPE 149432 11

TABLE 2—RAW FTIR AND TOC MEASUREMENTS

Raw Data

ComponentGrain Density

(g/cm3)Sample 1 Sample 2 Sample 3 Sample 4 Average

Quartz (wt%) 2.65 0.00 0.00 0.88 5.51 1.60

Calcite (wt%) 2.71 70.53 52.70 31.20 30.39 46.20

Dolomite (wt%) 2.84 0.00 0.00 0.00 0.05 0.01

Siderite wt(%) 3.87 2.62 8.10 10.03 9.17 7.48

Aragonite (wt%) 2.93 0.00 0.00 0.00 0.00 0.00

Illite (wt%) 2.75 8.67 19.63 29.10 14.71 18.03

Smectite (wt%) 2.01 0.00 0.96 3.40 0.07 1.11

Kaolinite (wt%) 2.6 0.37 6.40 8.24 5.31 5.08

Chlorite (wt%) 3.36 0.00 0.94 5.28 5.87 3.02

Mixed Clay (wt%) 2.7 0.00 3.55 3.84 14.78 5.54

Orthoclase (wt%) 2.55 0.00 0.00 0.00 0.00 0.00

Oligoclase (wt%) 2.65 0.00 2.88 0.85 4.76 2.12

Albite (wt%) 2.62 3.07 2.09 3.41 0.49 2.27

Anhydrite (wt%) 2.97 0.00 0.00 0.00 0.00 0.00

Apatite (wt%) 3.19 2.03 2.75 2.25 1.89 2.23

Pyrite (wt%) 5.01 12.72 0.00 1.51 7.01 5.31

TOC (wt%) 1.25 0.53 2.67 3.33 2.28 2.20

Total 100.53 102.67 103.33 102.29 102.20

TABLE 3—RENORMALIZED FTIR AND RAW TOC MEASUREMENTS

Renormalized Data

ComponentGrain Density

(g/cm3)Sample 1 Sample 2 Sample 3 Sample 4 Average

Quartz (wt%) 2.65 0.00 0.00 0.85 5.38 2.08

Calcite (wt%) 2.71 70.15 51.29 30.17 29.69 37.08

Dolomite (wt%) 2.84 0.00 0.00 0.00 0.04 0.01

Siderite wt(%) 3.87 2.60 7.88 9.69 8.96 8.85

Aragonite (wt%) 2.93 0.00 0.00 0.00 0.00 0.00

Illite (wt%) 2.75 8.62 19.11 28.14 14.38 20.56

Smectite (wt%) 2.01 0.00 0.94 3.29 0.07 1.43

Kaolinite (wt%) 2.6 0.37 6.23 7.97 5.19 6.47

Chlorite (wt%) 3.36 0.00 0.91 5.11 5.73 3.92

Mixed Clay (wt%) 2.7 0.00 3.45 3.71 14.44 7.21

Orthoclase (wt%) 2.55 0.00 0.00 0.00 0.00 0.00

Oligoclase (wt%) 2.65 0.00 2.80 0.82 4.65 2.76

Albite (wt%) 2.62 3.06 2.04 3.30 0.48 1.94

Anhydrite (wt%) 2.97 0.00 0.00 0.00 0.00 0.00

Apatite (wt%) 3.19 2.01 2.68 2.18 1.85 2.24

Pyrite (wt%) 5.01 12.65 0.00 1.46 6.85 2.77

TOC (wt%) 1.25 0.53 2.67 3.33 2.28 2.68

Total 100.00 100.00 100.00 100.00 100.00

FTIR + TOC GrainDensity (g/cm3)

2.891 2.693 2.704 2.829 2.779

LPP Grain Density(g/cm3)

2.919 2.616 2.640 2.779 2.739

12

A recurring theme in shale core analysis has been the effect of saTo address this we measured the particle size distribution of 3 of our LPP crushed rock samples.Size Analysis (LPSA) results for the LPP material after crushing and helthe mesh sizes typically used in industry, and also are included are the mesh sizes (12, 20, 35, and 50) used in the presentstudy. The cumulative distribution of particle sizes from 3 of samples show a widthe sample weight being finer than 50 meshmeasurement. Virtually none of the LPP material was coarser than the 12 mesh size fraction.Fig. 8 shows that we are not biased to one sample size class withMICP measurements; however, is investigated in the upcoming set of observations.

Figure 8—Cumulative particle size distribution as measured by LPSA on the LPP helium porosity crushed rock material for 3 of the4 samples. The US Standard mesh size ranges are plotted as vertical lines to show the relationship between mesh size and parsize in microns.

We note a dramatic difference between the measured, rawgiven sample. Fig. 9a is a typical example of the raw, uncorrected MICP curves from Sample #3. The pore throat radiusdistribution in Fig. 9b shows that for each sample size class we are observing a variable amount of large apparent porethroats as a percentage of the incremental intrusion. This portion of the pore throat distribution is associated with the trconformance volume and is detected using the methodology outlined in Figfrom 0.01 to 0.001 microns are sensed on the high pressure portion of the MICP curve and all size classes exhibit the samepeak at about 0.003 microns. Once corrected for conformance, the MICP curves tend to collapse more readily, but stillexhibit some differences (Fig. 9c). When correctedcollapse to the same intrusion profile with some small differences noted

Despite our attempt to consistently model conformance, won MICP porosity. The raw, uncorrected MICP porosity estimates in Fig.similarly corrected for conformance, we still observe a strong dependence10b), although the range is tighter (0.51% to 9.91%)smallest for the core plug and largest for finest particle size range (had remained uncrushed and was subject to incomplete intrusion due to pore access limitationsexpected a certain amount of convergence between the MICP porosity and LPP heliumsufficiently crushed to a particle size classthe LPP helium porosity and MICP porosity for each

A recurring theme in shale core analysis has been the effect of sample size on porosity and/or permeability measurementsTo address this we measured the particle size distribution of 3 of our LPP crushed rock samples. Fig. 8Size Analysis (LPSA) results for the LPP material after crushing and helium pycnometry had been completed. Overlain arethe mesh sizes typically used in industry, and also are included are the mesh sizes (12, 20, 35, and 50) used in the presentstudy. The cumulative distribution of particle sizes from 3 of samples show a wide range of variability, with 30

50 mesh. This upper mesh threshold represents the finest particles used for themeasurement. Virtually none of the LPP material was coarser than the 12 mesh size fraction. The range of particle sizes in

to one sample size class with the LPP helium measurements. The bias of particle size onMICP measurements; however, is investigated in the upcoming set of observations.

ative particle size distribution as measured by LPSA on the LPP helium porosity crushed rock material for 3 of the4 samples. The US Standard mesh size ranges are plotted as vertical lines to show the relationship between mesh size and par

We note a dramatic difference between the measured, raw-uncorrected MICP profiles as a function of particle size for anyis a typical example of the raw, uncorrected MICP curves from Sample #3. The pore throat radiusshows 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 trvolume and is detected using the methodology outlined in Figs. 6a through 6d. The finer pore throats ranging

from 0.01 to 0.001 microns are sensed on the high pressure portion of the MICP curve and all size classes exhibit the samecrons. Once corrected for conformance, the MICP curves tend to collapse more readily, but still

). When corrected for compressibility —determined using Fig. 6b—ile with some small differences noted (Fig. 9d).

Despite our attempt to consistently model conformance, we show in Fig.10a that sample size class has a strong dependenceMICP 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 porositythe range is tighter (0.51% to 9.91%). In all cases, we observe for any given sample that the MICP porosity is

smallest for the core plug and largest for finest particle size range (-30+50). This was expected for the pluand was subject to incomplete intrusion due to pore access limitations (Bustin et al., 2008)

ergence between the MICP porosity and LPP helium porosity once the sample hadwith the proper surface area to volume ratio for full intrusion

elium porosity and MICP porosity for each particle size class and sample is shown in Fig. 11

CSUG/SPE 149432

mple size on porosity and/or permeability measurements.Fig. 8 shows 3 Laser Profile

ium pycnometry had been completed. Overlain arethe mesh sizes typically used in industry, and also are included are the mesh sizes (12, 20, 35, and 50) used in the present

e range of variability, with 30% to 70% of. This upper mesh threshold represents the finest particles used for the MICP

The range of particle sizes inThe bias of particle size on

ative particle size distribution as measured by LPSA on the LPP helium porosity crushed rock material for 3 of the4 samples. The US Standard mesh size ranges are plotted as vertical lines to show the relationship between mesh size and particle

uncorrected MICP profiles as a function of particle size for anyis a typical example of the raw, uncorrected MICP curves from Sample #3. The pore throat radiusshows 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. The finer pore throats ranging

from 0.01 to 0.001 microns are sensed on the high pressure portion of the MICP curve and all size classes exhibit the samecrons. Once corrected for conformance, the MICP curves tend to collapse more readily, but still

—all of the curves tend to

lass has a strong dependencefrom 0.65% to 24.21%. Even when

size class on the corrected MICP porosity (Fig.ple that the MICP porosity is

This was expected for the plug material since it(Bustin et al., 2008). We

porosity once the sample had beenproper surface area to volume ratio for full intrusion. A comparison of

Fig. 11. We use the crossplot

CSUG/SPE 149432

in Fig. 12 to qualitatively determine that the best sample size class falls in the12+20 particle size classes are used for MICP we observe a consistent underestimation of porosity compared to the LPPhelium measurements (Figs. 11 and 12). We think this is due to pore access problems and the nonthe entire sample, even when crushed. Conversely, theMICP, even when corrected for conformance. This may be due to excessive microfracturing of the particles wherepore space is created due to the crushing process. Additional measurements and observations are needed to confirm this.

Figure 9—This series of plots shows a set MICP measurements from a single sample, but using different sample sizeincluding the plug and the following size ranges ((SHg) vs. mercury intrusion pressure (PcHg). b) Pore size distribution of uncorrected MICP measurements. c) Conformancecorrected SHg vs. PcHg. d) Intrusion-corrected S

Additionally, we estimate bulk density and grain density from MICPLPP measurements (Table 1) and FTIR+TOC calculationsreliance on sample size than what was observed for the porosity measurementagreement between the bulk density measured during the LPP process (orange circles in Fig. 13density also shows a weak dependence on sample size, although it’s estimation from both MICP and LPP isis estimated from the FTIR+TOC results (Table 3 andthe conformance volume (Eq. 7) and will have the same values for raw and corrected measurements.

to qualitatively determine that the best sample size class falls in the -20+35 range. When only the plug, +12, andle size classes are used for MICP we observe a consistent underestimation of porosity compared to the LPP

. We think this is due to pore access problems and the non-intrusion of mercury inton crushed. Conversely, the -35+50 sample size class consistently overestimates porosity using

MICP, even when corrected for conformance. This may be due to excessive microfracturing of the particles whereing process. Additional measurements and observations are needed to confirm this.

This series of plots shows a set MICP measurements from a single sample, but using different sample sizeize ranges (+12, -12+20, -20+25, and -35+50). a) Raw MICP data plotted as mercury saturation

). b) Pore size distribution of uncorrected MICP measurements. c) Conformancecorrected SHg vs. PcHg.

bulk density and grain density from MICP (Eqs. 4 and 7) and compare them to our results from the+TOC calculations (Eq. 3). The bulk density estimation from MICP shows a weaker

than what was observed for the porosity measurement (Fig. 13). Overall there isthe bulk density measured during the LPP process (orange circles in Fig. 13) and MICP

density also shows a weak dependence on sample size, although it’s estimation from both MICP and LPP isis estimated from the FTIR+TOC results (Table 3 and Fig. 14). It is worth noting that MICP grain density h

. 7) and will have the same values for raw and corrected measurements.

13

When only the plug, +12, and -le size classes are used for MICP we observe a consistent underestimation of porosity compared to the LPP

intrusion of mercury into35+50 sample size class consistently overestimates porosity using

MICP, even when corrected for conformance. This may be due to excessive microfracturing of the particles where artificialing process. Additional measurements and observations are needed to confirm this.

This series of plots shows a set MICP measurements from a single sample, but using different sample size classes35+50). a) Raw MICP data plotted as mercury saturation

). b) Pore size distribution of uncorrected MICP measurements. c) Conformance-

and compare them to our results from thedensity estimation from MICP shows a weaker

). Overall there is a reasonableand 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). It is worth noting that MICP grain density has no relation to

14

Figure 10—Bar graphs of porosity calculated from MICP as a function of particle sizecorrection and b) with conformance correction

LPP and GRI porosities measured on crushed material are limited when considering stressdone at near atmospheric conditions. Bailey (2009) points out the dilemma of comeasurements to subsurface logs although this subject has been thoroughly covered for conventionaltight gas sands (Teeuw, 1971; Anderson, 1988; Nieto et al.,recognizing compressibility on the MICP intrusion curve is that we can compute aintrusion pressure. This is done by using theEq. 4 and Eq. 5. These intrusion-correctedmay not be correct specifically for the Eagle Ford(2009) provides a workflow for determining the proper

of porosity calculated from MICP as a function of particle size class per sample a) with no conformanceith conformance correction

porosities measured on crushed material are limited when considering stress-related issues because they areBailey (2009) points out the dilemma of comparing unstressed shale por

to subsurface logs although this subject has been thoroughly covered for conventionalgas sands (Teeuw, 1971; Anderson, 1988; Nieto et al., 1994). Consequently, one of the ramifications

ng compressibility on the MICP intrusion curve is that we can compute a hydrostaticallyre. This is done by using the pre-intrusion volume determined in Fig. 6b as the conformance volume

corrected porosity values are substantially lower (Fig. 15) than the LPP measurementsEagle Ford since they are made at various hydrostatic confining pressures. Bailey

w for determining the proper correction for any given subsurface stress.

CSUG/SPE 149432

per sample a) with no conformance

related issues because they aremparing unstressed shale porosity

to subsurface logs although this subject has been thoroughly covered for conventional reservoirs and evenramifications that come from

hydrostatically-stressed porosity at thentrusion volume determined in Fig. 6b as the conformance volume (Cvol) in

than the LPP measurements andare made at various hydrostatic confining pressures. Bailey

CSUG/SPE 149432

One of the advantages of the Autopore IV equipment used for measuring the MICP profiles in this study is that it can beprogrammed to begin intrusion at higher pressures thanhigher pressure—close to the one needed to fully envelopphenomena while not sacrificing any petrophysical information about the samare variable but generally do not fall below 10 psipressure on the other hand (Fig. 16b) is intrinsically related to the rock’s pe1986; Pittman, 1992) and should be interpreted16b) has the highest intrusion pressure, regardless of particle size class. Converselin Fig. 16b) consistently exhibits the lowest intrusion pressure.

Figure 11—Bar graphs showing conformanceThe LPP helium porosity (LPP Por) measured with helium for each sample is displayed as an orange circle with the value with thevalue annotated.

Additional MICP profiles (Figs. 17a through 17dshown in Figs. 9a through 9d. It is worth noting that the MICP profile ifrom the same portion of crushed material (Sampleconformance is followed (Figs. 6a through 6dcorrected (Fig. 17c) curves. The large conformance volumes evident in Fig. 6bpresent for the same sample in Fig. 17b when injection begins at 10 psithe curves are essentially identical (Fig. 6d and Fig. 17d).(Fig. 18) are similar and show that the -20+35 particle size range is still optimal for comparison to LPP helium porosity.Similar results were derived for MICP-derived bulk density and grain density

One of the advantages of the Autopore IV equipment used for measuring the MICP profiles in this study is that it can beprogrammed to begin intrusion at higher pressures than what has been shown here thus far. By beginning t

close to the one needed to fully envelope each of the sample particles—one can minimizephenomena while not sacrificing any petrophysical information about the sample. The conformance pressures for this studyare variable but generally do not fall below 10 psia, regardless of particle size class or sample porosity (

) is intrinsically related to the rock’s petrophysical properties (Katz and Thompson,interpreted differently. In general, we see that the low porosity sample (Sample 1

16b) has the highest intrusion pressure, regardless of particle size class. Conversely, the highest porosityin Fig. 16b) consistently exhibits the lowest intrusion pressure.

Bar graphs showing conformance-corrected MICP porosity as a function of particle size classPor) measured with helium for each sample is displayed as an orange circle with the value with the

a through 17d) were started at an injection pressure of 10 psia. It is worth noting that the MICP profile in Figs. 9a through 9d and Fig

from the same portion of crushed material (Sample #3). When injection begins at 10 psia and the same workflow for pickinga through 6d), there is very little difference between the raw (Fig

. The large conformance volumes evident in Fig. 6b representing the large apparent poresmple in Fig. 17b when injection begins at 10 psia. Finally, when corrected for

identical (Fig. 6d and Fig. 17d). We also note that MICP porosity calculated using Eq20+35 particle size range is still optimal for comparison to LPP helium porosity.

derived bulk density and grain density.

15

One of the advantages of the Autopore IV equipment used for measuring the MICP profiles in this study is that it can be. By beginning the process at a

one can minimize the conformanceple. The conformance pressures for this study

, regardless of particle size class or sample porosity (Fig. 16a). Intrusiontrophysical properties (Katz and Thompson,

differently. In general, we see that the low porosity sample (Sample 1 in Fig.y, the highest porosity sample (Sample 3

class for Samples 1 through 4.Por) measured with helium for each sample is displayed as an orange circle with the value with the

compared to the 1.5 psiaFigs. 17a through 17d are

and the same workflow for picking. 17b) and conformance-

representing the large apparent pores are not. Finally, when corrected for pre-intrusion volume,

P porosity calculated using Eq. 4 and Eq. 520+35 particle size range is still optimal for comparison to LPP helium porosity.

16

Figure 12—Crossplot comparison of MICP porosity compared to the crushed rock LPP porosity for each sample size. A onesolid line and +/- 1 p.u. dashed lines are used to define measurement accuracy.

Figure 13—Bar graphs showing the comparison of the measured bulk density from the LPPdensity from conformance-corrected MICP measurements for each sample size

rosity compared to the crushed rock LPP porosity for each sample size. A onedashed lines are used to define measurement accuracy.

Bar graphs showing the comparison of the measured bulk density from the LPP measurements (orange circles) and bulkcorrected MICP measurements for each sample size class (vertical bars).

CSUG/SPE 149432

rosity compared to the crushed rock LPP porosity for each sample size. A one-to-one

measurements (orange circles) and bulk

CSUG/SPE 149432

Figure 14—Bar graphs showing the comparison of the measured grain density from the LPP measurements (orange circles), graidensity calculated from FTIR mineralogy and LECO TOC (blue circles)size class (vertical bars).

Figure 15—Bar graph comparison of porosity calculated using thesizes compared to LPP helium porosity (orange circles).

Bar graphs showing the comparison of the measured grain density from the LPP measurements (orange circles), graidensity calculated from FTIR mineralogy and LECO TOC (blue circles), and grain density from MICP measurements for each sample

porosity calculated using the pre-intrusion volume as the conformancporosity (orange circles).

17

Bar graphs showing the comparison of the measured grain density from the LPP measurements (orange circles), grainand grain density from MICP measurements for each sample

ntrusion volume as the conformance volume for all sample

18

Figure 16—Bar graphs showing the magnitude of the a) conformance pressure and b) the intrusion pressure for each sample sizeclass

showing the magnitude of the a) conformance pressure and b) the intrusion pressure for each sample size

CSUG/SPE 149432

showing the magnitude of the a) conformance pressure and b) the intrusion pressure for each sample size

CSUG/SPE 149432

Figure 17—This series of plots shows a set MICP measurements from a single sample, but using different sample size distributionsincluding the plug and the following size ranges (pressure of 10 psia except for the plug. a) Raw MICP data plotted as mercury saturation as a percentage of pore volume (Smercury intrusion pressure (PcHg). b) Pore size distribution of uncorrected MICP measurements. c) ConformancePcHg. d) Intrusion-corrected SHg vs. PcHg.

set MICP measurements from a single sample, but using different sample size distributionsanges (+12, -12+20, -20+35, and -35+50). All sample sizes were run starting at an intrusion

ept for the plug. a) Raw MICP data plotted as mercury saturation as a percentage of pore volume (S). b) Pore size distribution of uncorrected MICP measurements. c) Conformance

19

set MICP measurements from a single sample, but using different sample size distributions35+50). All sample sizes were run starting at an intrusion

ept for the plug. a) Raw MICP data plotted as mercury saturation as a percentage of pore volume (SHg) vs.). b) Pore size distribution of uncorrected MICP measurements. c) Conformance-corrected SHg vs.

20

Figure 18—Crossplot comparison of conformancesample size class. All samples were run starting at an intrusion pressure of 10 psia with exceline and +/- 1 p.u. dashed lines are used to define measurement accuracy.

Conclusions

Porosity, mineralogy, and TOC are intcontent tend to be lower in porosity and total clay, but higher in TOC.

Sample size class plays a large role in the raw MICP profile and the

Conformance has been re-examined for ultracompressibility. By including this compression as porosity,porosity using the -20+35 size rangevia simple mercury immersion.calculated grain densities, LPP helium measured grain densities, and MICP grain densities.

The conformance pressure as we have defined it hereindependent of sample size class. For this reason we have performedpsia injection pressure to reduce the conformancebulk density from MICP.

The intrusion-corrected porosity picked using a very high conformance pressure is more indicative of ahydrostatically stressed porosity and cannot be compared directly to crushatmospheric conditions. Stressed porosity measurements are currently notand the MICP method presented here

NomenclatureBVHg = bulk volume from MICP, L

3, cmPVHg = pore volume from MICP, L

3, cmCpMICP = pore volume compressibility from MICP, LtCpModel = pore volume compressibility model, Lt

Cpo = pore volume compressibility model intercept term, LtCVol = conformance volume, L

3, cm3 [mL]m = power law exponent of pore volume compressibility model, (Lt

comparison of conformance-corrected MICP porosity compared to the crushed rock LPP porosity for eachsample size class. All samples were run starting at an intrusion pressure of 10 psia with exception to the plug. A one

1 p.u. dashed lines are used to define measurement accuracy.

Porosity, mineralogy, and TOC are intrinsically tied within the Eagle Ford Formation. Zones with high calcitee lower in porosity and total clay, but higher in TOC.

ize class plays a large role in the raw MICP profile and the associated apparent porosity

examined for ultra-low permeability rocks to account for sample pore volumecompressibility. By including this compression as porosity, best agreement is found between MICP

20+35 size range. Bulk density from MICP agrees well with those measured in the LPP processvia simple mercury immersion. Grain density is still problematic due to the poor match between FTIR+TOCcalculated grain densities, LPP helium measured grain densities, and MICP grain densities.

The conformance pressure as we have defined it here has a very limited range (10 to 30 psiof sample size class. For this reason we have performed experiments with intrusion beginning at

injection pressure to reduce the conformance and see no difference in the calculated porosity, grain density, and

corrected porosity picked using a very high conformance pressure is more indicative of ahydrostatically stressed porosity and cannot be compared directly to crushed rock LPP since the latter is

ditions. Stressed porosity measurements are currently not commercially availablepresented here may provide some future direction.

, cm3 [mL], cm3 [mL]

pore volume compressibility from MICP, Lt 2/m, psi-1

pore volume compressibility model, Lt 2/m, psi-1

pore volume compressibility model intercept term, Lt 2/m, psi-1

[mL]power law exponent of pore volume compressibility model, (Lt 2/m)2, psi-2

CSUG/SPE 149432

corrected MICP porosity compared to the crushed rock LPP porosity for eachption to the plug. A one-to-one solid

ormation. Zones with high calcite

apparent porosity.

low permeability rocks to account for sample pore volumebetween MICP and LPP helium

density from MICP agrees well with those measured in the LPP processmatch between FTIR+TOC

as a very limited range (10 to 30 psia) and is almostwith intrusion beginning at a 10

and see no difference in the calculated porosity, grain density, and

corrected porosity picked using a very high conformance pressure is more indicative of aed rock LPP since the latter is done at near

available for tight shales

CSUG/SPE 149432 21

M1 = mass of LPP sample before crushing, m, gM2 = mass of LPP sample after crushing, m, gVB = bulk volume of LPP sample before crushing, L

3, cm3 [mL]VG = grain volume from helium LPP, L

3, cm3 [mL]VGcorr = corrected grain volume from helium LPP, L

3, cm3 [mL]wi = weight decimal of each mineral/solid component, m/m, dec

Wa = weight of MICP apparatus, m, gWp = weight of empty MICP penetrometer, m, gWs = weight of dry MICP sample, m, g

m = mass difference of LPP sample before and after crushing, m, g He = porosity determined from LPP, L

3/L3, % MICP = porosity determined from MICP, L

3/L3, % bMICP = bulk density from MICP, m/L

3, g/cm3

g = grain density from helium LPP, m/L3, g/cm3

gFTIR = grain density from FTIR and TOC, m/L3, g/cm3

Hg = mercury density, m/L3, g/cm3

i = grain density for each mineral/solid component, m/L3, g/cm3

gMICP = grain density from helium LPP, m/L3, g/cm3

AcknowledgementsThe authors would like to thank Apache Corporation for donating core material and granting permission to publish the EagleFord data presented in this study. We would like to thanks Dr. Carl Sondergeld and Dr. Chandra Rai at the University ofOklahoma Mewbourne School of Petroleum and Geological Engineering for their continued experimental and researchleadership through the IC3 Laboratory as well as the support of Mr. Gary Stowe. The students at the IC3 Laboratory whohave helped develop and implement many of the experimental procedures presented here include Namrita Sondhi, IshanRaina, Sagar Kale, and Argyrios Karastathis. Finally, we would like to thank Thaimar Ramirez for thoughtful editorialfeedback.

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