Characterization of multi-fractured horizontal shale wells using drill cuttings: 1. Fluid-in-place estimation

Journal of Natural Gas Science and Engineering xxx (2016) 1e12

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Characterization of multi-fractured horizontal shale wells using drill cuttings: 1. Fluid-in-place estimation C.R. Clarkson a, *, B. Haghshenas b a b

Department of Geoscience, University of Calgary, 2500 University Drive NW Calgary, Alberta T2N 1N4, Canada Department of Chemical and Petroleum Engineering, University of Calgary, 2500 University Drive NW Calgary, Alberta T2N 1N4, Canada

a r t i c l e i n f o

a b s t r a c t

Article history: Received 3 January 2016 Received in revised form 22 February 2016 Accepted 23 February 2016 Available online xxx

The most common method to produce shale gas and liquid-rich (gas condensate and oil) shale reservoirs is through long horizontal wells completed in multiple hydraulic fracturing stages. Usually the only reservoir samples gathered from these wells are drill cuttings obtained at multiple intervals along the well. While drill cuttings collection is inexpensive, and can allow for high density sampling in heterogeneous reservoirs, quantitative analysis of cuttings for reservoir properties remains a challenge. An important property to assess from drill cuttings is fluid storage capacity. In shales, fluid storage may occur via multiple mechanisms within the complex pore structure, including adsorption in microporosity (pore width < 2 nm), multi-mechanism storage (free gas and adsorption) in mesopores (pore width between 2 and 50 nm), and free gas storage in macroporosity (pore width greater than 50 nm). Rigorous models that account for these complex storage mechanisms over the wide range of pore sizes typically observed in shales are therefore required. A further complication, particularly important for liquid-rich shales, is the alteration of fluid properties, which can affect both fluid storage and producibility, due to pore confinement. Rigorous models are also required to account for these effects so that resource inplace and recovery can be properly predicted. In this work, the use of high-precision, low-pressure adsorption data, combined with the simplified local density model (SLD) is explored for the purpose of 1) estimating high-pressure hydrocarbon storage and 2) calculating fluid property alteration using artificial drill cuttings. Use of low-pressure adsorption equipment, commonly used for estimating internal surface area and pore size distributions, is necessary to allow quantitative analysis of small sample sizes typical of cuttings. Artificial cuttings, created by crushing larger core samples, are used to enable comparison with more conventional methods applicable to large sample sizes. It is demonstrated herein for the first time that the SLD model, after calibration to low-pressure, nonhydrocarbon gas adsorption data (CO2 and N2), may be used to predict high-pressure hydrocarbon gas adsorption. Further, it is demonstrated that the SLD model may be used to predict phase behavior changes of liquid-rich gases in confined pores. The end result is that fluid storage properties from small masses of cuttings may be determined rapidly and accurately enabling the quantification of fluid-inplace along the length of a horizontal well. © 2016 Elsevier B.V. All rights reserved.

Keywords: Shale gas Liquid-rich shale Drill cuttings Low-pressure adsorption Simplified local density model

1. Introduction Shale reservoirs are highly heterogeneous, from the nano-scale to field-scale, presenting a tremendous challenge for the characterization of key reservoir properties affecting fluid flow and storage (Clarkson et al., 2016). While long horizontal wells completed

* Corresponding author. E-mail address: [email protected] (C.R. Clarkson).

in multiple hydraulic fracturing stages (multi-fractured horizontal wells or MFHWs) have enabled commercial production from these complex reservoirs, optimal development of these resources relies on the identification of changes in reservoir quality both laterally and vertically in the target interval. This is a challenge for MFHWs because the only reservoir samples that are typically available are small masses of drill cuttings, collected periodically through the vertical, bend and lateral sections of the horizontal well. While cuttings collection is inexpensive, and samples can be collected

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Please cite this article in press as: Clarkson, C.R., Haghshenas, B., Characterization of multi-fractured horizontal shale wells using drill cuttings: 1. Fluid-in-place estimation, Journal of Natural Gas Science and Engineering (2016), http://dx.doi.org/10.1016/j.jngse.2016.02.056

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Fig. 1. Variability in rock composition and mechanical properties as determined from cuttings collected along two horizontal wells drilled in Horn River Basin shales.1 YM ¼ Young's Modulus; PR ¼ Poisson's Ratio. From Weedmark (2014).

frequently, allowing in turn for rock quality variations to be assessed, quantitative determination of key shale reservoir properties from such small sample sizes requires further development. Recent studies have demonstrated that rock composition and mechanical properties may be quantified from drill cuttings. Weedmark (2014) and Spencer and Weedmark (2015) developed indirect, semi-empirical methods to extract rock mechanical properties from mineralogical data, obtained in turn from x-ray fluorescence (XRF) analysis, and rock fabric. Such studies have proven instructive for illustrating the variability in rock quality along horizontal wells. In Fig. 1, rock compositional and mechanical properties obtained from cuttings are plotted for two horizontal laterals drilled to the same target reservoir interval e the variability between the wells as well as within a given lateral is evident. While rock composition is an important control on rock mechanical properties, it is also an important control on pore structure and fluid storage. Pores in shales are affiliated with both organic and inorganic matter components, and range in size from nanometers to millimeters (Ross and Bustin, 2008, 2009; Loucks et al., 2009, 2012; Nelson, 2009; Tian et al., 2013; Chalmers et al., 2012a,b; Milliken et al., 2013; Chen and Xiao, 2014). Fig. 2 provides a series of SEM images that illustrate the various pore associations that can occur in shale, including inter- and intra-particle pores in the inorganic fraction, and intra-particle pores in organic matter (Loucks et al., 2012). It is therefore expected, given the compositional variability often encountered along the length of a horizontal well, that pore structure and pore association would similarly be variable, complicating assessments of fluid-in-place. Fluid storage mechanisms in shales are variable, depending not only on pore structure, but temperature and pressure conditions as well as the fluid type and composition. It is generally acknowledged that, for gases, several storage mechanisms exist, including gas

1 Cuttings depth location from the horizontal well is based on lag time, the equation for which is: Lag Time ¼ Depth/Annular Velocity. For example, if the annular velocity from the horizontal well is calculated to be 90 m/min, then at a depth of 3000 m, the cuttings would take 33.33 min to get to surface. It is important to note that hole conditions can affect the annular velocity.

adsorption on the internal surface area of organic matter and clays, free gas storage in inter- and intra-granular porosity in the inorganic matter fraction (Fig. 2), free gas storage in intra-particle porosity of organic matter (Fig. 2), and solution gas storage in entrained fluids and bitumen (see Clarkson et al., 2016 for recent discussion). However, a complication arises in that gas storage in shales is usually not directly measured from cuttings because the entrained gases, light-end hydrocarbons and other volatiles are lost during retrieval from the subsurface. The challenge is therefore to estimate, indirectly, the original gas content using a combination of laboratory-based measurements and modeling. Traditional approaches for indirect determination of gas content of shales from core samples include quantification of gas-saturated porosity, so that free gas storage may be quantified, and independent assessments of adsorbed gas storage. These measurements are commonly performed for larger scale core samples, but not for cuttings due to sample size, expense, amongst other factors. However, even when performed on core samples, as noted by Ambrose et al. (2012), such assessments generally do not consider the volume occupied by the adsorbed gas fraction in the pore space, therefore resulting in an over-estimate of free gas storage. This is illustrated using simplified local density model (SLD) predictions (see Section 3 for description of this model) of gas density gradients in a slit-shaped pore with a width of 2 nm and 10 nm (Fig. 3). In the 2 nm pore, the adsorbed phase occupies a larger portion of the pore space than in the 10 nm pore, but in both cases it is clear that assuming that a bulk phase (compressed or free gas storage) exists across the entire pore cross-section would be in error. It is further evident that the density of the gas changes continuously from pore wall to pore center and that distinction between adsorbed and bulk phases is not straight-forward in certain pore size ranges. Rigorous methods for estimating gas storage as a function of pore size (that take into account these complexities) are therefore necessary for accurate gas content assessments if direct measures of gas content are not available, as is the case with cuttings. Finally, while it is clear that for gases pore confinement effects must be taken into account to assess gas storage accurately, the fluid storage assessments for liquid-rich gases are even more

Please cite this article in press as: Clarkson, C.R., Haghshenas, B., Characterization of multi-fractured horizontal shale wells using drill cuttings: 1. Fluid-in-place estimation, Journal of Natural Gas Science and Engineering (2016), http://dx.doi.org/10.1016/j.jngse.2016.02.056

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Fig. 2. Examples of pore types that can exist in shales, illustrated using SEM images. The pore classification used at the top of the figure is from Loucks et al. (2012). Modified from Clarkson et al. (2016). The reader is referred to the works of Loucks et al. (2012) and Fishman et al. (2012) for a detailed description of pore types and each image (images A-F, H-M from Loucks et al., 2012; image G from Fishman et al., 2012).

complex. Recent studies (e.g. Devegowda et al., 2012) have demonstrated that the phase envelope of a liquid-rich gas such as a gas condensate may actually be shifted in the presence of pore confinement. This has led to the concept of “dew point suppression” (Singh and Singh, 2011; Devegowda et al., 2012; Ma and Jamili, 2014; Didar and Akkutlu, 2015; Pitakbunkate et al., 2015) meaning that condensate may drop out from gases at lower pressures (and temperatures) than that for the bulk-phase. The problem is that conventional PVT testing of fluids does not consider pore confinement, and these effects typically have to be modeled. In summary, there are several challenges for indirect fluid-inplace assessment using drill cuttings obtained from horizontal wells including, but not limited to: 1) small sample sizes; 2) rock compositional and pore structure variability; 3) multiple storage mechanisms depending on pore size, fluid type and composition, and P-T conditions; 4) lack of high-pressure hydrocarbon adsorption data; 5) fluid phase property alteration due to pore confinement. In the current work, a novel approach and workflow is developed to address these challenges, which combines careful laboratory measurements and sophisticated modeling. For challenges 1) and 2), the low-pressure adsorption (LPA) technique is used to characterize pore size distributions and surface areas from small masses of “artificial” drill cuttings. The LPA method is used

because it is relatively rapid, non-destructive, can be performed on multiple samples at a time, and very importantly, has the accuracy and precision to be performed on small (1e2 g) sample sizes typical of drill cuttings. “Artificial” cuttings, derived by crushing/sieving core samples and sub-sampling, were used in order to perfect the procedures and enable comparison between fluid content estimates obtained for larger sample sizes. For challenges 3) - 5), the simplified local density model (SLD) (Rangarajan et al., 1995), whose parameters were calibrated to low-pressure adsorption data collected using N2 and CO2, is used to predict high-pressure hydrocarbon gas storage. The SLD model rigorously takes into account the multiple storage mechanisms that may occur as a function of pore size in shale. Further, for liquid-rich shale cases, fluid phase envelope shifts can also be modeled for accurate assessment of fluid phase changes as a function of pressure. In the following, the above approach/workflow is demonstrated using samples obtained from the Duvernay shale, a prospective liquid-rich shale play in Western Canada. The experimental techniques are first described, followed by details of the SLD model. Application of the SLD model for 1) predicting high-pressure hydrocarbon gas storage and 2) fluid phase envelope alteration is then demonstrated. While the current study (Part 1) focuses on fluid-in-place

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Fig. 3. Use of the SLD model to estimate gas density profiles (red solid line) in organic matrix-pores in (a) a 2 nm diameter pore and (b) a 10 nm diameter pore. L is the width of the pore, while z is the distance from one of the pore walls. The approximate adsorbed layer thickness in both cases is highlighted in purple shading. The bulk phase density near the center of the pore is shown with a dashed red line. Modified from Clarkson et al., 2016. (For interpretation of the references to color in this figure legend, the reader is referred to the web version of this article.)

estimation, Part 2 (accompanying paper, Haghshenas et al., 2016) of this study uses two of these samples for estimating gas-transport properties (permeability/diffusivity) from low-pressure rate-ofadsorption data. 2. Experimental methods The four samples analyzed in this study are from the Duvernay shale in Western Canada. These samples were sub-sampled from core plugs, retrieved from a vertical well, and were previously analyzed by Ghanizadeh et al. (2015) for geochemical and petrophysical properties (Table 1 provides some results); the core plugs were first analyzed then crushed down to a 20/35 mesh size from which a 1e2 g split was obtained for analysis in this study. The crushed samples represent “artificial” cuttings to test the methods developed in this study prior to application to actual drill cuttings extracted from horizontal wells. By sub-sampling rock previously tested, the properties derived from core plugs and crushed samples can be compared. Not shown in the table are two additional larger samples for which high-pressure adsorption measurements were performed - these samples were from the same depth range, and with similar TOC contents, as the samples analyzed in this study (Table 1). Because actual high-pressure adsorption analysis could not be performed on the artificial cuttings samples due to small sample masses, the high-pressure adsorption data from these larger samples allowed for calibration of the SLD model. The 1e2 g artificial cuttings samples were evacuated and dried overnight at 60  C until a pressure of <0.13 Pa (0.001 mmHg) was reached prior to gathering the low-pressure adsorption data. CO2 adsorption isotherm data were then collected at 273 K (ice/water bath), and N2 adsorption/desorption isotherms at 77 K (liquid nitrogen at atmospheric pressure), using a Micromeritics 3Flex™ automated volumetric low-pressure adsorption device. Adsorption measurements using these two gases at different temperatures can be used to estimate pore volume in the micropore to macropore range2 (Gregg and Sing, 1982; Clarkson and Bustin, 1999; Chalmers and Bustin, 2007; Ross and Bustin, 2009; Bustin et al., 2008). Surface area was extracted from the low-pressure adsorption data using traditionally-applied methods such as Langmuir,

2 The IUPAC classification of pore sizes (IUPAC, 1994) is used herein: micropores (pore width < 2 nm), mesopores (pore width between 2 and 50 nm) and macropores (pore width > 50 nm).

DubinineAstakhov (DeA), DubinineRadushkevich (DeR), and BrunauereEmmetteTeller (BET) - these methods are comprehensively discussed by Gregg and Sing (1982). Pore size distributions were obtained from Density-Functional-Theory (DFT) (Do and Do, 2003; Adesida et al., 2011) for both CO2 and N2 adsorption, and from the BarretteJoynereHalenda (BJH) model (Barrett et al., 1951) for N2. Finally, the isotherm data were also interpreted using the SLD model, allowing comparison of the results for surface area and average pore size with the previously listed models.

3. SLD model Clarkson and Haghshenas (2013) recently reviewed adsorption models that are commonly applied to coal and shale reservoirs. The SLD model has gained popularity for modeling high-pressure shale gas adsorption as demonstrated by Chareonsuppanimit et al. (2012). Rangarajan et al. (1995) originally articulated the physical premises and assumptions of SLD theory as used in this work. The basic assumption of the model is that the chemical potential of the fluid at any point near the adsorbent surface is equal to the bulkphase chemical potential [i.e. mðzÞ ¼ mb ]. The chemical potential at any point above the surface is then defined as the sum of the fluidefluid and fluid-solid interactions and the equilibrium chemical potential is calculated as follows:

mðzÞ ¼ mff ðzÞ þ mfs ðzÞ ¼ mbulk

(1)

Where mbulk is chemical potential of the bulk-phase, mff is chemical potential of the fluidefluid and mfs is chemical potential of the fluid-solid. Therefore, at equilibrium, there will be no chemical potential gradient from the surface of the solid to the bulk fluid outside (Chen et al., 1997). The pore geometry most widely assumed with SLD for carbon adsorbents is a two-surface slit, with a specified distance (width) L, between which the fluid molecules reside (Fig. 3). L is defined as the distance between the two orthogonal planes that are tangential to the surfaces of the first graphite planes on opposing sides of the slit. In the present work we have adapted this geometry and are therefore assuming that nanopores in shale have this shape. A molecule residing within a slit has fluidesolid interactions with both surfaces at distances z and L e z. The equilibrium criterion for chemical potentials is summarized as (Fitzgerald et al., 2006):

Please cite this article in press as: Clarkson, C.R., Haghshenas, B., Characterization of multi-fractured horizontal shale wells using drill cuttings: 1. Fluid-in-place estimation, Journal of Natural Gas Science and Engineering (2016), http://dx.doi.org/10.1016/j.jngse.2016.02.056

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Table 1 Geochemical and other petrophysical data obtained for Duvernay shale core plugs used to derive artificial cuttings (crushed-rock) samples. Sample no.

Depth [m]

TOC [wt%]

Fraction

2

3244.21

7

Bulk fraction Clay fraction Bulk fraction Clay fraction Bulk fraction Clay fraction Bulk fraction Clay fraction

4

3249.36

5

3251.49

8

a

3257.06

3.5

3.6

4.5

Weight [%]

Clay content [%]

Quartz þ feldspars content [%]

Carbonate content [%]

Pyrite [%]

98.43

15

54

25

6

1.57

47

38

25

8

97.41

27

51

17

5

2.59

45

37

17

0

93.93

28

47

16

7

6.07

60

33

17

0

98.74

22

41

33

4

1.26

47

32

33

6

Cleaned/drieda Grain density [g/cm3]

Total porosity [vol%]

2.61

5.9

2.61

5.7

2.58

5.4

Dean-Stark extraction was performed using Toluene-Methanol (10 days) to remove residual fluid, and the samples were dried in a vacuum oven at 110  C (10 days).

mff ðzÞ þ mfs ðzÞ þ mfs ðL  zÞ ¼ mbulk

(2)

This equation indicates that the chemical potential of the adsorbed fluid reflects the proximity of the fluid to the molecular wall of the adsorbent. Thus, the SLD model considers inhomogeneity of the adsorbed phase in describing the molecular interactions of the adsorbed fluid with the adsorbent (Chen et al., 1997). The fluid-solid chemical potential is given as:

h i mfs ðzÞ ¼ NA Jfs ðzÞ

(3)

Where NA is Avogadro's number, and Jfs is the fluid-solid potential function, typically described by an integrated potential function such as the 10e4 Lennard-Jones model:

Jfs z

¼

4pratoms εfs s2fs

4 s4fs 1X  5z010 2 i¼1 ðz0 þ ði  1Þsss Þ4

s10 fs

! (4)

with:

sss þ sff 2

εfs ¼

pffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffi εss  εff

!

f0

(6)

In above equations T is temperature, fbulk refers to bulk fugacity, fff (z) is fluid fugacity at position z and f0 refers to the same arbitrary reference state for both equations. Substituting for mff, mfs and mbulk in Eq. (2) results in:

  Jfs ðzÞ þ Jfs ðL  zÞ fff ðzÞ ¼ fbulk exp  kB T

(7)

which gives the local adsorbed-phase fugacity at each position z. In the above equation, kB is the Boltzmann constant (1.3806488  1023 m2 kg s2 K1). In this study, the PR-EOS was used to calculate the bulk density as follows:



RTð1 þ ð1 

aðTÞrbulk pffiffiffiffiffi pffiffiffiffiffi  2Þbrbulk Þð1 þ ð1 þ 2Þbrbulk

(8)

where:

aðTÞ ¼ 0:45724

Where sss is the carbon interplanar distance, sff is molecular diameter of the adsorbate, εff is the fluidefluid interaction energy parameter, εss is the solidesolid interaction energy parameter, εfs is the fluidesolid interaction energy parameter, and ratoms ¼ 0.382 atoms/Å2. The chemical potential of the bulk fluid (unlike fluid-solid chemical potential), cannot be calculated directly but can be expressed in terms of a more practical parameter, fugacity:

mbulk ¼ m0 ðTÞ þ RT ln

fff ðZÞ

p 1 ¼ RTrbulk 1  brbulk

sss z0 ¼ z þ 2 sfs ¼

mff ðZÞ ¼ m0 ðTÞ þ RT ln

  fbulk f0

(5)

Similarly, the chemical potential of fluidefluid interactions can be given as:

b ¼ 0:077796

R2 Tc2 aðTr ; uÞ Pc

RTc Pc

i2 h  1=2 aðTr ; uÞ ¼ 1 þ k 1  Tr

k ¼ 0:37464 þ 1:5422u  0:26992u2 The bulk fugacity equation, also calculated with PR-EOS, is as follows:

Please cite this article in press as: Clarkson, C.R., Haghshenas, B., Characterization of multi-fractured horizontal shale wells using drill cuttings: 1. Fluid-in-place estimation, Journal of Natural Gas Science and Engineering (2016), http://dx.doi.org/10.1016/j.jngse.2016.02.056

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ln fbulk ¼

brbulk arbulk 1  brbulk    ln½ 1  brbulk RTð1 þ 2brbulk  b2 r2 Þ RTrbulk bulk pffiffiffiffiffi 1 þ ð1 þ 2Þbrbulk a pffiffiffiffiffi   pffiffiffiffiffiffiffiffiffiffiffi ln½ 1 þ ð1  2Þbrbulk 2 2bRT (9)

where rbulk is density of the bulk-phase, and a and b are PR-EOS constants. Now, substituting the fff(z), the PR-EOS is again employed to calculate the local density of the adsorbed phase, r(z):

ln fff ðzÞ ¼

brðzÞ aðT;zÞ rðzÞ 1  brðzÞ    ln½ 1  brðzÞ RTð1 þ 2brðzÞ  b2 r2ðzÞ Þ RTrðzÞ pffiffiffiffiffi 1 þ ð1 þ 2ÞbrðzÞ aðzÞ pffiffiffiffiffi  pffiffiffiffiffiffiffiffiffiffiffi ln½  1 þ ð1  2ÞbrðzÞ 2 2bRT (10)

Using this equation, the density profile can be calculated across the pore width (see Fig. 3). Gibbs excess adsorption can then be obtained through the following integration:

nGibbs ¼

ASlit 2

Lsff =2

Z

h

i rðzÞ  rbulk dz

(11)

sff =2

In this work, Simpson's numerical integration (Atkinson, 1989) procedure was used with 100 subdivisions across the slit. For the slit geometry considered herein, the lower limit of integration, sff/2, is the location of the center of an adsorbed molecule touching the left plane surface, and the upper limit, L- sff/2, is the location of the center of an adsorbed molecule touching the right plane surface. The three physical parameters pore width (L), surface area (ASlit) and fluidesolid interaction energy parameter (εfs/kB), are obtainable through regression of experimental data using the SLD model. In this way SLD can be used as a reverse solution for characterizing pore properties. Traditionally adsorption models predict absolute adsorption, not excess adsorption, as given with Eq. (11). The absolute amount adsorbed can be estimated from following equation:

nabs ¼ Va ra

(12)

or more practically from:

 nabs ¼ nGibbs

ra ra  rbulk

 (13)

In the above equation, average adsorbed density, ra , can be calculated as:

Z ra ¼

Lsff =2 h

sff =2

i rðzÞ dz

L  sff

(14)

The absolute amount adsorbed can be significantly different from the experimentally inferred Gibbs excess adsorption, especially past the Gibbs excess maximum. As noted in the literature, for some supercritical fluids (e.g., CO2) adsorbed at high pressure, the absolute amount adsorbed approaches a saturation value but the excess amount adsorbed reaches a maximum plateau and then begins to decrease with increasing pressure. This has created some uncertainty as to the utility of the excess function formalism of adsorption thermodynamics for high-pressure adsorption (Myers

and Monson, 2002). The SLD model appears to be viable for correlating isotherms past the Gibbs excess maximum and in estimating absolute adsorption from the Gibbs excess adsorption (Fitzgerald et al., 2006). 4. Workflow for prediction of high-pressure hydrocarbon adsorption using SLD model The SLD model has advantages over traditional adsorption models in that it directly uses pore structure information (surface area, ASlit, and pore width, L) in order to predict adsorption. Therefore, if the model can be calibrated to low-pressure adsorption data through adjustment of ASlit and L, then high-pressure adsorption can be predicted, which is more meaningful for shale gas content determination. However, the low-pressure adsorption data collected with the 3Flex device is for N2 and CO2, meaning that, in order to predict high-pressure hydrocarbon adsorption, the εfs/kB for the hydrocarbon fluid must also be determined. The following workflow was developed for the Duvernay shale artificial cuttings samples to enable prediction of high-pressure hydrocarbon adsorption from the SLD model using low-pressure, non-hydrocarbon adsorption: 1. Measure low-pressure adsorption using CO2 and N2 (see “Experimental methods” section) 2. Interpret adsorption data using traditionally-applied models for surface area and pore size distributions (Langmuir, D-R, D-A, BET, DFT and BJH) 3. Fit SLD model to CO2 and N2 adsorption data by adjusting ASlit and L 4. Compare SLD-derived pore structure and surface area information with traditionally-applied models to ensure consistency 5. Predict high-pressure CO2 and N2 adsorption using SLD model 6. Adjust εfs/kB in SLD model to match actual measured highpressure hydrocarbon adsorption on larger samples with similar depth, TOC content and thermal maturity 7. Use SLD model to predict high-pressure hydrocarbon adsorption using εfs/kB obtained from step 6, and ASlit and L obtained from step 3 Step 6 is necessary because SLD model calibration to lowpressure adsorption data was achieved using εfs/kB for N2 and CO2 gas systems and is not applicable to hydrocarbon gases. With ASlit and L obtained from low-pressure adsorption data modeling, the εfs/kB for the hydrocarbon gas of interest (methane in this study) must be adjusted to match actual high-pressure adsorption data obtained on larger samples. As mentioned in the “Experimental methods” section, measured high-pressure adsorption isotherms were available for two larger Duvernay shale samples extracted from the same core interval (and with similar TOC content) as the artificial cuttings samples analyzed in this study. Although step 5 is not necessary for prediction of high-pressure hydrocarbon adsorption, prediction of high-pressure N2 and CO2 may be useful for making enhanced recovery predictions, as further discussed in the “Discussion” section. In the following, the workflow described above is applied to the artificial Duvernay cuttings samples. 5. Results 5.1. Low-pressure N2 and CO2 adsorption In this section, steps 1e4 of the workflow provided in the previous section are illustrated. Measured low-pressure isotherms for N2 and CO2 are shown in

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Fig. 4. N2 isotherms (Fig. 4a) are Type IV according to the IUPAC classification (Sing et al., 1985), and have an adsorption/desorption hysteresis loop indicating the presence of mesoporosity, which has been observed in other shale samples (Kuila and Prasad, 2011). CO2 adsorption isotherms (Fig. 4b) are Type I, indicative of microporous solids. Adsorption between samples is highly variable - the sample with the highest TOC content (#2) also has the largest amount of adsorption, and conversely, the lowest TOC sample (#4) has the lowest adsorption. If TOC consists primarily of kerogen, then the amount of microporosity, and hence adsorption, is expected to increase with TOC (Ross and Bustin, 2009), which is consistent with the current dataset. However, some control of inorganic matter type (e.g. clay content) is also expected. Importantly, although these samples were collected only a few meters apart in a vertical wellbore, they exhibit significant compositional and adsorbed gas content variability, which may also be expected in a lateral wellbore. The next steps in the workflow involve fitting the different models to the low-pressure adsorption data to provide surface area and pore size distribution estimates. BET, Langmuir, BJH and DFT models were used to interpret the N2 data, while BET, Langmuir, DR, D-A, and DFT models were used for CO2 analysis. The SLD model was also applied to both gas adsorption datasets - the fit of this model to the adsorption branch of N2 and CO2 isotherm data is shown in Fig. 5, and model fitting parameters are provided in Table 2. Fig. 6 compares surface area calculations for all models. As was expected from the isotherm data (Fig. 4), the sample #2 has the highest surface area, whereas sample #4 has the lowest. However, surface area estimates obtained from N2 and CO2 isotherms are not the same, with surface areas from N2 generally being much larger. For example, sample #2 surface area obtained from N2 adsorption is in the range of 35e52 (m2/g) as compared to 9e24 m2/g obtained from CO2 adsorption; sample #4 surface area ranges from 11 to 12 m2/g obtained from N2 adsorption and 5e10 m2/g from CO2 adsorption. Generally, surface areas estimated from all models are in good agreement (Fig. 6), except the D-A model for CO2. This provides important validation for the SLD model, which has not historically been applied to low-pressure adsorption data to our knowledge. Pore size distributions (PSDs) for the artificial cuttings samples obtained from analyzing N2 data with the BJH and DFT models, and from CO2 data with the DFT model are shown in Fig. 7. Although some of the PSDs are missing due to lack of overlap between CO2 and N2 data, all samples appear to exhibit a multi-modal pore structure with peaks in the 0.4e0.6 nm range (micropores), 1e2 nm range (micropores inferred, data missing), and 2e4 nm range (mesopores). Sample #2 has the largest mesopore and micropore volume, while sample #4 has the smallest. Again, comparing the results of the SLD model with the other models, the average pore size obtained from the SLD model match to the CO2 isotherms is 1.1 nm (micropores) and 2.9 nm (mesopores) from the match of N2 isotherms (Table 2). These results are within acceptable range of those obtained from the DFT and BJH models. It is worth noting the SLD model normally uses one gasspecific parameter (fluid-solid interaction parameter) plus two adsorbent-specific parameters (surface area and pore width) that are independent of the adsorbing gas species. However, with the large difference observed between CO2 and N2 estimations of pore size and surface area, it appears that the parameters derived for the adsorbent are not independent of the adsorbing gas species. This is likely due to the difference in pore accessibility of the two gases at the different temperatures. Therefore, in this study, the model was fitted specifically to each gas dataset to give the best estimates of the parameters of interest. It is clear that the main parameter that

7

controls the adsorption capacity is surface area. This conclusion is easily explainable with the SLD governing equation (Eq. (11)). The fluidesolid interaction energy parameter (εfs/kB) extracted using the SLD model for N2 and CO2, respectively, is 21 (K) and 50 (K). Importantly, from the gas storage estimation perspective, the broad pore size range observed (Fig. 7) means that the storage mechanisms would be expected to differ substantially in the pore structure of these shales. Referring to the SLD simulations in Fig. 3, pores in the <2 nm range would be expected to have limited to no free gas storage and strong gas density gradients from pore wall to center, while pores in the 10 nm range or greater have a distinct bulk phase at the center of the pore. A model that accurately accounts for these storage mechanisms is therefore required to quantify gas storage in the full pore-size spectrum - the SLD model appears to be suitable for this purpose. 5.2. High-pressure N2 and CO2 adsorption In this section, step 5 of the workflow is illustrated. Using pore structure information obtained from the match of the SLD model to the low-pressure adsorption data, the SLD model can now be used to predict high-pressure adsorption (excess and absolute) for these same gases (Fig. 8) on the artificial cuttings samples. The temperature and pressure ranges were selected to be consistent with those expected for the Duvernay shale. At low pressure (sub-atmospheric), the difference between absolute and excess adsorption is negligible, and therefore absolute adsorption is not plotted in Fig. 4. However, for higher pressures the difference is more substantial as shown in Fig. 8. 5.3. High-pressure CH4 adsorption In this section, use of the SLD model for predicting highpressure and high-temperature hydrocarbon adsorption from parameters derived from low-pressure adsorption measured on artificial cuttings is demonstrated (steps 6 and 7 of workflow). As noted previously, this is necessary because there often is not enough sample mass from cuttings to perform the high-pressure adsorption measurements directly. CH4 adsorption is predicted in this case because it is the major component of natural gas and gas condensates in shales. Unfortunately, no CH4 adsorption isotherms could be measured on the 4 artificial cuttings samples, however these measurements were made on larger samples in the same depth interval. Two complications arise for predicting CH4 adsorption with the SLD model for the artificial cuttings: 1) ASlit and L values obtained from low-pressure adsorption are different for N2 and CO2, and one set of parameters must be selected to make the prediction and 2) the fluid-solid interaction parameter for CH4 is not known a-priori. To resolve complication 2), the fluid-solid interaction parameter (εfs/kB) was adjusted to match the high-pressure CH4 isotherms collected for the larger samples in the interval (Fig. 9) in order to “calibrate” this value for the artificial cuttings samples. To address complication 1), the ASlit/L combination resulting in a fluid-solid interaction value most consistent with literature values (e.g. Chareonsuppanimit et al., 2012) was chosen. The resulting fluidesolid interaction energy parameter for CH4 is 31 K. The high-pressure CH4 adsorption predictions for the four Duvernay shale artificial cuttings samples, using low-pressure N2 adsorption-derived ASlit/L and the adjusted fluid-solid interaction parameter, are given in Fig. 10. As expected from the shale adsorption literature (Hartman et al., 2011), the high pressure adsorption capacity of CH4 lies between N2 and CO2 curves (compare Fig. 10 with Fig. 8).

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Fig. 4. (a) Low-pressure N2 adsorption/desorption isotherms and (b) low-pressure CO2 adsorption isotherms collected for 4 Duvernay shale artificial cuttings samples.

6. Discussion In this work, a methodology for estimating high-pressure hydrocarbon storage in shales from cuttings is proposed. This methodology combines low-pressure adsorption measurements performed on small sample sizes (1e2 g), as commonly obtained from drilling, with rigorous modeling (simplified local density model) to estimate hydrocarbon storage. The method has been applied to “artificial” cuttings created by crushing core plug samples retrieved from a vertical well. An alternative for indirectly estimating adsorbed gas content from cuttings is to derive correlations between rock compositional data (such as TOC) and adsorbed gas content measured on selected larger samples (often obtained from vertical wells). With a correlation in place, rock compositional measurements performed on cuttings can then be used to estimate gas content. The problems with this approach are several fold: 1) a large number of samples spanning a wide compositional range is necessary to generate the correlations 2) there are often a number of factors besides TOC

affecting adsorption (not just TOC, but also clay content, thermal maturity of organic matter etc.) 3) free gas and adsorbed gas storage need to be estimated to obtain the total gas-in-place. For 1) a statistically significant number of samples need to be analyzed to obtain such correlations, which can be time-consuming and expensive. For 2), multi-variate analysis needs to be considered because TOC is not the only control on gas adsorption in shale. For 3), the conventional procedure for gas-in-place assessment is to measure pore volume independently from adsorption and sum the free-and adsorbed gas contents. As noted previously, there are potential errors in this procedure, if the adsorbed-phase volume is not accounted for in the analysis. The SLD model provides a method for doing this. Lastly, as with adsorbed gas content, correlations between porosity and rock composition would be required to estimate free gas storage from cuttings compositional data, and these correlations may be even more uncertain than those for adsorbed gas content. In the view of the authors, it is better to estimate gas storage more directly from cuttings using the procedure outlined herein.

Fig. 5. SLD model match to low-pressure (a) N2 isotherms and (b) CO2 isotherms. The SLD model was fitted to the adsorption branch of both isotherm datasets. The relative pressure range of around 0.05e0.2 was selected for nitrogen because this is the pressure range used for BET model analysis. Modified from Clarkson et al., 2016.

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Table 2 SLD model fitting parameters. Adsorbed Gas

N2

CO2

Parameter

Pore width (L) Surface area (ASlit) fluidesolid interaction energy parameter (εfs/kB) Pore width (L) Surface area (ASlit) fluidesolid interaction energy parameter (εfs/kB)

Unit

nm m2/g K nm m2/g K

Sample no. 2

4

5

8

2.9 40 21 1.1 11 50

2.9 12 21 1.1 6 50

2.9 20.5 21 1.1 7.5 50

2.9 24 21 1.1 7 50

Fig. 6. Specific surface areas calculated from all models from (a) low-pressure N2 adsorption data and (b) low-pressure CO2 adsorption data.

Fig. 7. Pore size distributions obtained from N2 adsorption data (using BJH and DFT models) and from CO2 adsorption data using the DFT model. Modified from Clarkson et al., 2016.

This study has focused on dry gas (methane) storage estimation. However, as previously noted, liquid-rich shale reservoirs are now being exploited and there is much interest in estimating not only the gas content, but the possible liquids dropout in the reservoir. However, as also noted, pore confinement effects can have a significant effect on phase properties that are not captured with conventional lab testing. The SLD model can be used to determine the impact of pore confinement on fluid phase properties as illustrated in Fig. 11. Here, a gas condensate fluid system was analyzed and SLD model used to predict the phase envelope for a gas

condensate system as a function of pore size, from 300 nm to 2 nm. The phase envelope shifts from higher saturation pressures/temperatures to lower values and correspondingly, predicts a later onset for condensate dropout in shale reservoirs than for bulk systems. This is consistent with the reported “dewpoint suppression” behavior that others have reported for liquid-rich shale systems (Singh and Singh, 2011; Devegowda et al., 2012; Ma and Jamili, 2014; Didar and Akkutlu, 2015; Pitakbunkate et al., 2015), and in part explains the appearance of leaner fluid production than expected for these systems. It therefore may be possible to not only estimate gas content from cuttings using the approach described herein, but also predict fluid phase behavior and liquid dropout using the SLD model - this could prove extremely valuable if phase behavior may be expected to change along the length of a horizontal well. However, the explorations provided in Fig. 11 need to be validated further with carefully-designed experiments to verify their accuracy. Although not addressed in this study, important information for exploring enhanced gas/gas condensate recovery can also be obtained from the workflow provided herein. The SLD model can be used to predict not only high-pressure hydrocarbon adsorption, but also high-pressure adsorption of any gas component for which lowpressure adsorption measurements are performed (e.g. N2, CO2 etc.). Mixed gas adsorption modeling may then be performed using the individual gas components, to explore the possibilities of enhanced gas/gas condensate recovery (see Clarkson and Haghshenas, 2013). Finally, the workflow developed herein for gas content estimation has only been tested on artificial cuttings. In future work, an actual cuttings data set retrieved from a horizontal lateral drilled in a tight gas/shale reservoir will be analyzed to demonstrate practical application of the methodology.

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Fig. 8. High-pressure excess and absolute adsorption isotherms predicted from the SLD model for (a) N2 and (b) CO2 for 4 Duvernay shale artificial cuttings samples.

7. Conclusions Long horizontal wells drilled to produce shale reservoirs may encounter a wide variety of rock types and reservoir quality. The current practice for stimulating these wells is to apply fracture stages/perforation clusters evenly along the length of the well regardless of reservoir quality. While this practice has resulted in commercial production from shale gas and liquid-rich shale resources, it is not efficient, often resulting in low-producing or even non-productive stages. It is therefore desirable to develop methods to evaluate rock quality along the length of the well with the intent of screening intervals for stimulation, optimizing stimulation design, and providing valuable input data for forecasting the wells. Cuttings retrieved from drilling the well are typically the only reservoir samples collected, and thus a premium has been placed on deriving as much information from them as possible. In this Part 1 of a two-part series on cuttings analysis, the topic of gas content estimation from cuttings has been addressed. A

Fig. 9. Fit of the SLD model to high-pressure and high-temperature (383.15 K) CH4 isotherms measured on 2 Duvernay samples taken from the same interval as the artificial cuttings samples. Solid lines are SLD model fit to the experimental data. Modified from Clarkson et al., 2016.

novel approach/workflow has been introduced wherein lowpressure adsorption (LPA) analysis equipment, capable of analyzing small sample sizes typical of cuttings, is used to estimate pore structural parameters (surface area and pore size) necessary for predicting gas storage. Because analysis can be performed on multiple samples at a time, and is relatively quick to perform, LPA has the necessary throughput to process the multitude of cuttings analysis required to evaluate reservoir property heterogeneity along a horizontal well. The simplified local density (SLD) model is used to obtain pore structural parameters from low-pressure adsorption data and predict high-pressure gas adsorption of hydrocarbon gases. The SLD model was selected because it is capable of rigorously modeling the mixture of gas storage processes that may be occurring in the various pore sizes of shale. The SLD model was also demonstrated to be capable of predicting changes in phase behavior of liquid-rich fluids (e.g. gas condensate) caused by pore confinement. The introduced workflow has been demonstrated using “artificial” cuttings samples derived from crushing core plugs obtained in

Fig. 10. Predicted high-pressure, high-temperature methane isotherms for 4 Duvernay artificial cuttings samples. Modified from Clarkson et al., 2016.

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rbulk r(z) ra

11

Bulk gas molar density, gmol/cm3 Local density of the adsorbed phase, gmol/cm3 Average adsorbed phase density, gmol/cm3

References

Fig. 11. Use of the SLD model to predict the phase envelope for a gas condensate system as a function of pore size, from 300 nm to 2 nm. Modified from Clarkson et al., 2016.

turn from cores cut from a vertical well drilled through the Duvernay shale of Western Canada. Acknowledgments Clarkson would like to thank Alberta Innovates Technology Futures, Encana and Shell for supporting his Chair in Unconventional Gas and Light Oil Research, Department of Geoscience, University of Calgary. All authors would like to thank the sponsors of Tight Oil Consortium (www.tightoilconsortium.com) for their ongoing support of our research. Finally, partial support for this research has been provided through an NSERC Discovery Grant to Clarkson. Nomenclature

Field Variables a Fluidefluid attraction parameter b Fluidefluid repulsive parameter ASlit Surface area of the slit pore, m2/g f bulk Bulk gas fugacity, Pa fff (z) Fluid fugacity at position z, Pa KB Boltzmann constant, J/K NA Avogadro's number nabs Absolute adsorption, gmol/g nGibbs Gibbs adsorption, gmol/g p Pressure, Pa R Universal gas constant, 8.314 Pa m3/(gmol.K) T Temperature, K Va Adsorbed phase volume, m3 z Distance from the surface of slit wall, m Greek variables mbulk Chemical potential of the bulk-phase mff Chemical potential of the fluidefluid mfs Chemical potential of the fluid-solid Jfs Fluid-solid potential function sss Carbon interplanar distance, nm sff Molecular diameter of the adsorbate, nm εff Fluidefluid interaction energy parameter εss Solidesolid interaction energy parameter εfs Fluidesolid interaction energy parameter

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