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A Comparison of Measurement Techniques for Porosity and Pore Size

Distribution in Shales (Mudrocks): A Case Study of Haynesville, Niobrara,

Monterey and Eastern European Silurian F...

Chapter  in  AAPG Memoir · January 2015

DOI: 10.1306/13592019M1123695

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1

A Comparison of Measurement Techniques for Porosity and Pore Size Distribution in 1

Mudrocks: A Case Study of Haynesville, Niobrara, Monterey and Eastern European 2

Silurian Formations 3

Milad Saidian*1, Utpalendu Kuila1a, Manika Prasad1, Leo Alcantar-Lopez2, Saul Rivera1b, 4

Lemuel J. Godinez1c, 1Colorado School of Mines, 2Chesapeak Energy, aNow at Cairn India, b 5

Now at Chesapeake Energy, c Now at Oasis Petroleum 6

1. Abstract 7

Porosity and pore size distribution (PSD) are required to calculate reservoir quality and volume. 8

Numerous inconsistencies have been reported in measurements of these properties in mudrocks. 9

We investigate these inconsistencies by evaluating the effects of fine grains, small pores, high 10

clay content, swelling clay minerals and pores hosted in organic content. Using mudrocks from 11

the Haynesville, Eastern European Silurian, Niobrara and Monterey formations, we measured 12

porosity and pore or throat size distribution using subcritical nitrogen (N2) gas adsorption at 77.3 13

K, mercury intrusion (MI), water immersion (WI), and helium porosimetry based on Gas 14

Research Institute standard methodology (GRI). We used Scanning Electron Microscope (SEM) 15

images to understand the pore structure at a microscopic scale. We find that differences in the 16

porosity and PSD measurement techniques can be explained with thermal maturity, texture and 17

mineralogy, specifically clay content and type and total organic matter (TOC) variations. We 18

separated the samples from each formation into groups based on their clay and TOC contents and 19

further investigated the effects of geochemical and mineralogical variations on porosity and 20

PSD. We find that porosity and PSD measurement techniques can provide complementary 21

information within each group provided the comparison is made between methods appropriate 22

2

for that group. Our intent is to provide a better understanding of the inconsistencies in porosity 23

measurements when different techniques are used. 24

2. Introduction 25

Thanks to new technologies such as hydraulic fracturing and horizontal drilling in the last 26

decade, unconventional reservoirs gained oil and gas industry’s attention as valuable resources 27

for energy production. Passey et al. (2010) define “unconventional reservoirs” as a wide range of 28

hydrocarbon-bearing rocks that are not economically producible without stimulation techniques. 29

Although the term unconventional reservoir lacks adequate lithologic definition, in this paper, we 30

refer to tight oil, or gas-producing reservoirs which may or may not be organic rich and are often 31

called “shales”. In today's terminology, shale reservoirs are either siliciclastic or carbonate 32

mudrocks. They need not necessarily contain clay minerals. Although alternate terms have been 33

used for shale reservoirs such as unconventionals, self-resourcing rocks, organic-rich rocks and 34

mudstones, the term shale has endured. In this work we use the term mudrock to refer to these 35

reservoirs. 36

Mudrocks are fine-grained rocks containing silt-size particles with 50 % of the grain diameters 37

less than 62.5 µm (Folk, 1974, Friedman 2003, and Javadpour, 2005). Like other sedimentary 38

rocks, they are composed of a wide range of minerals such as clay, quartz, feldspar, carbonates, 39

and heavy minerals such as pyrite (Passey et al., 2010). Besides mineral components, mudrocks 40

may contain organic matter as a significant component of the rock (Bohacs et al., 2013). 41

Porosity and pore size distribution are used for reservoir evaluation (Ambrose et al., 2010), 42

permeability prediction (Nelson, 2009), and elastic property calculations (Kuila and Prasad, 43

2011). A major challenge in estimating transport and storage capacity of mudrocks is the poor 44

3

understanding of their pore properties including size, distribution and pore hosting components 45

(Nelson, 2009, Chalmers et al., 2012, Loucks et al., 2012, Kuila et al., 2014a, Kuila et al., 46

2014b). Lack of accessibility of the displacement fluid to the pore system is the main deterrent to 47

reliable and accurate laboratory measurements. Limited accessibility can be due to extremely 48

low permeability, complicated mineral surface-fluid interactions, or insufficient equilibration 49

time. 50

It is important to not only quantify pore space with visual techniques such as Scanning Electron 51

Microscopy (SEM) (for example, Ambrose et al., 2010, Curtis et al., 2010, Lemmens et al., 52

2010, Bernard et al., 2012, Curtis et al., 2012, Alcantar-Lopez and Chipera, 2013, Zargari et al., 53

2013) or CT-Scanning (Coshell et al., 1994, Wildenschild and Sheppard, 2012, Milliken et al., 54

2013) but also with non-visual techniques. Some examples of non-visual techniques are nitrogen 55

gas adsorption (N2) (Echeverria et al., 1999, Chalmers et al., 2012, Kuila et al., 2012), mercury 56

intrusion (MI) (Howard, 1991), water immersion porosimetry (WI) (Howard, 1991, Kuila et al., 57

2014a) and nuclear magnetic resonance (NMR) (Sondergeld et al., 2010a, Jiang et al., 2013, 58

Rylander et al., 2013, Rivera et al., 2014, Saidian et al., 2015,). 59

Porosity and pore size distribution are commonly measured with techniques such as mercury 60

intrusion and helium expansion. These approaches yield consistent values for conventional rocks 61

(Hossain et al., 2011). For mudrocks, however, the methods to measure porosity and PSD need 62

careful selection. Large variations in their pore sizes and shapes can result in up to 50% 63

inconsistency in porosity values (Howard, 1991; Katsube and Scromeda, 1991; Katsube et al., 64

1992; Dorsch and Katsube, 1996, Sondergeld et al., 2010b). Discrepancies in results arise 65

because the techniques are based on specific physical phenomena and use different displacement 66

fluids. Since accessibility to the pore space depends on the fluid, each technique yields different 67

4

results. These differences can be exploited considering the fact that each technique measures a 68

different portion of the pore space. A combination of methods can help fully characterize 69

complex pore spaces. Furthermore, within each technique, repeatability can be compromised due 70

to different pretreatment methods such as grinding and sieving, laboratory conditions such as 71

relative humidity and temperature, and millimeter scale heterogeneity which hinders the ability 72

to produce sample aliquots for multiple measurements (Passey et al., 2010; Kuila, 2013). 73

Differences in porosity values measured by different laboratories or under different pretreatment 74

conditions are well documented (Passey et al, 2010, Sondergeld et al., 2010b, Comisky et al., 75

2011). A methodical comparison of porosity values in mud rocks determined by various 76

techniques that examines the compositional, textural and geochemical reasons for data variations 77

is lacking. 78

The main objective of our work is to understand various pore sensing techniques on the basis of 79

textural, mineralogical and geochemical differences. We present porosity obtained from helium 80

expansion, mercury intrusion, water immersion and nitrogen adsorption. We also present pore-81

size distribution data obtained from mercury intrusion and nitrogen adsorption. We investigate 82

the controlling factors on the results of each experimental method and evaluate data variations 83

together with textural, mineralogical and geochemical differences. Further, we compare porosity 84

values measured with various techniques with the pore size distributions measured with three 85

different techniques. Finally, we provide recommendations for a new approach for pore size 86

distribution comparison in mudrocks. 87

In this study we use the pore size classification suggested by Rouquerol et al. (1994). In this 88

classification micro, meso and macro pores have <2 nm, 2-50 nm and >50 nm pore width. All 89

5

the pore or throat size distribution spectra are plotted using the diameter or width of the pores 90

and these terminologies are used interchangeably. 91

3. Materials 92

Below we briefly describe the dominant mineralogy, thermal maturity, organic matter type, and 93

organic matter content for the samples used for this study. The details of each sample set have 94

been published by Rivera, 2014 and Godinez; 2014 (Monterey), Kuila, 2013 (Haynesville, 95

Niobrara, and Silurian). 96

3.1 Haynesville Formation 97

34 samples were taken from Upper Jurassic Haynesville Formation (Kuila, 2013). The samples 98

were clay-rich (up to 73 wt% mainly illite with <5% to 9% expandable smectite layers) with 99

moderate amounts of quartzo-feldspathic (average of 26 wt%) and a variable amount of calcite 100

(between 1 to 35 wt%). A moderate amount of dolomite (up to 44 wt%) was observed in 2 101

samples (Figure1a). In the Haynesville sample set the TOC varied between 0.5-6.3 wt%; the 102

kerogen was in the gas window based on average Tmax of 424 °C and HI which varied from 19 to 103

57 with an average of 36. RockEval S2 data between 350-400 °C showed a moderate amount of 104

bitumen. Figure 2a and b show the SEM images for two Haynesville samples. Figure 2a shows 105

silt-sized quartz, calcite and plagioclace particles surrounded by a mixture of illite and clay-sized 106

quartz. Organic matter is dispersed between clay-sized particles. Figure 2b represents a dolomite 107

rich sample with intergranular spaces filled with a mixture of quartz and clay minerals as well as 108

organic matter. 109

3.2 Silurian Formation 110

6

The fourth sample set consisted of 22 samples taken from the Silurian play in Eastern Europe 111

(Kuila, 2013). The samples contained up to 52 wt% quartz and up to 57 wt% clay which is 112

mostly illite (Figure 1b). The kerogen was thermally mature in the gas window and TOC ranged 113

between 1-6 wt %. No pyrogram peaks were observed in the Rock Eval results up to 550 °C 114

which confirmed the absence of any pyrolyzable kerogen or bitumen in these samples. The HI 115

varied from 0 to 9 with an average of 3. Figure 3a and b show SEM images of two Silurian 116

samples. Silt-size particles such as quartz, pyrite, and dolomite are surrounded by a matrix of 117

illite and chlorite and organic matter is dispersed between clay particles. 118

3.3 Niobrara Formation 119

22 marl and chalk samples came from a well in the Berthoud Field, Larimer County, CO, USA 120

(Kuila, 2013), specifically from the Fort Hays limestone and the overlying Smoky Hill members 121

of the Niobrara formation. They were calcite-rich rocks with moderate amounts of clay (up to 35 122

wt% with an average of 16.5 wt%), quartz (average of 11 wt%) and pyrite (Figure 1c). Type II 123

kerogen was thermally mature and was in the oil window based on average Tmax of 436 °C and 124

HI which varied from 119 to 386 with an average of 306. TOC varied from 0.1 to 5.3 wt% and 125

RockEval programs between 450-500 °C showed an abundance of bitumen in these samples. 126

SEM images for two samples are shown in Figure 4a and 4b. Figure 4a shows relatively more 127

homogeneous intercrystalline pore distribution which is filled with organic matter. Figure 4b 128

shows larger intercrystalline pores filled with organic matter. Note that the larger intercrystalline 129

pores are within the peloid structures. 130

3.4 Monterey Formation 131

7

A combination of 12 sidewall and conventional core samples were taken from an oil producing 132

well drilled at the western flank of the southern San Joaquin Basin in California. The samples 133

were predominantly quartz phase porcellanites containing moderate amounts of clay (up to 24 134

wt% with an average of 8 wt%) and pyrite (up to 7 wt% with an average of 3 wt%). A smaller 135

sample set (3 samples) were calcite-rich (Figure 1d). TOC varied from 0.85-4.95 wt% and 136

RockEval S2 data showed an abundance of bitumen (Rivera, 2014). Kerogen was Type II with 137

thermal maturity in the oil window based on average Tmax of 438 °C and the hydrogen index (HI) 138

varying from 184 to 473 with an average of 374. SEM images of Monterey samples (Figure 5) 139

show moderate carbonate and dominant quartz grains with the presence of organic matter filling 140

up the intergranular and intercrystalline pores. 141

4. Methods 142

Several methods have been used to measure porosity and pore or throat size distribution. In this 143

section, we describe the pretreatments, displacement fluids, and methods as well as the 144

associated challenges for each measurement. In the results and discussion part we will 145

investigate how these challenges and limitations affect the porosity measurement for different 146

sample sets. 147

4.1 Helium Expansion using the Gas Research Institute (GRI) Method and Helium Injection 148

Under Confining Stress 149

The porosity was measured by a commercial laboratory using the Gas Research Institute (GRI) 150

helium porosimetry technique. Due to sample limitations only Silurian and the Haynesville 151

samples were used for these measurements. The porosity was also measured using helium 152

injection under confining stress for the Monterey samples. In the GRI technique, bulk rock 153

volume is measured by mercury immersion using Archimedes’ principle and crushed rock grain 154

8

volume is measured by Boyle’s Law (Luffel and Guidry, 1992; Luffel et al., 1992; GRI-155

95/0496). We measured the porosity of cleaned and dried cylindrical Monterey samples with a 156

CMS300TM helium injection porosimeter. 157

Helium expansion, using either crushed or intact samples, measures pores that are connected and 158

accessible by helium gas. In nanodarcy permeability rocks, temperature fluctuations can 159

compromise pressure equilibration and decrease the measurement accuracy. On the other hand, 160

using crushed samples allows the gas to access isolated pores that might be inaccessible 161

otherwise. Sondergeld et al. (2010b) and Passey et al. (2010) reported very high disparities in the 162

porosity and permeability values measured by different laboratories using same samples. Kuila et 163

al. (2014a) provided a thorough discussion of the GRI technique and investigated the effect of 164

pretreatment and laboratory conditions on the final grain density and porosity results. For 165

example, the Dean-Stark extraction pretreatment with a hot solvent can create porosity in 166

thermally mature samples, such as the Niobrara and Monterey, by dissolving bitumen and lead to 167

porosity overestimation. Luffel and Guidry (1992) suggested 30 minutes as the equilibration time 168

for shales as opposed to 1 minute for conventional rocks. 169

4.2 Water Immersion (WI) 170

The original protocol for WI porosity measurements was recommended by the American 171

Petroleum Institute (API RP40). In this study, we used the adapted protocol for mudrocks 172

developed by Kuila et al. (2014a). In this adapted protocol, approximately 5 grams of rock chips 173

are first dried and weighed in air. They are then saturated and weighed again in water. This 174

protocol of using intact samples and measuring grain density at low humidity increases the 175

repeatability of the experiment (Kuila et al., 2013). 176

9

Immersion porosity measurements assess the pores available to the saturating fluid (water or oil). 177

Thus, the fluid type, the pore surface wettability, and the saturation method affect the porosity 178

values. Also presence of expandable clays such as smectite would affect the accuracy of this 179

technique. 180

4.3 Mercury Intrusion (MI) 181

In MI porosimetry, small intact rock chips are heated up to 200 °C for 12 hours and degassed for 182

30 minutes at 50 µmHg to remove water and volatile hydrocarbons. Mercury is then injected in 183

the sample at uniform pressure steps from 0.14 to 420 MPa. Pressure is considered equilibrated 184

when the injection rate falls below 0.001 µl/g/s. The Washburn model (Washburn, 1921) is used 185

to convert the pressure data to pore throat size distribution. A conformance correction, for 186

example, the Bailey method from Comisky et al. (2011) must be applied to high pressure 187

measurements to account for mineral compressibility. The porosity is calculated by measuring 188

the bulk volume of the sample submerged in the mercury and the pore volume measured by the 189

volume of intruded mercury. 190

We used MI to measure porosity and pore throat size distributions for all sample sets. The MI 191

technique measures pore and bulk volume as well as pore-throat size distribution. Since MI can 192

only measure the volume of pores with throat-diameter larger than 3.6 nm (at 420 MPa), a 193

significant portion of the pore space in fine-grained rocks is neglected. 194

4.4 Nitrogen Adsorption (N2) 195

This technique has traditionally been used to measure total specific surface areas (TSSA) in 196

conventional reservoir rocks. Due to practical limitations N2 technique can only assess the 197

volume of pores with a diameter of less than 200 nm (Gregg and Sing, 1983). 1-3 grams of 198

10

samples are crushed, sieved through a 40 mesh (420 µm) sieve, and then degassed under vacuum 199

at 200 °C until the outgassing rate is less than 0.005 Torr/min over a 15 minute interval. Nitrogen 200

is injected into the analysis chamber and adsorption of nitrogen to the degassed sample starts. 201

The adsorption takes place under constant temperature of liquid nitrogen. Pressure and adsorbed 202

quantity of nitrogen are recorded as isotherms and used to calculate pore size distribution. We 203

used the Barrett, Joyner and Halenda (BJH) inversion (Barrett et al., 1951) as recommended by 204

Kuila (2013) for mudrocks. This inversion method calculates the PSD assuming non-connecting 205

cylindrical pores. The total pore volume is calculated by measuring the amount of nitrogen 206

adsorbed in the sample. The Harkins and Jura (1944) thickness curve is utilized for both BJH 207

inversion and micropore (pores smaller than 1.7 nm) volume calculation with t-plot analysis. The 208

total pore volume is calculated by combining the micropore volume and the total volume 209

measured from 1.7 nm to 200 nm. We used this technique to measure pore size distribution and 210

pore volume in all samples. 211

5. Results 212

We analyzed samples with representation from major lithology types (Figure 1): predominantly 213

carbonate (Niobrara); predominantly quartz (Monterey); predominantly clay (Haynesville); and a 214

mineral mixture (Silurian). Each sample set has varying amounts of clay and TOC contents. This 215

richness of data allowed us to analyze and explain porosity mismatch between the methods for 216

mineralogy, pore size distribution, and measurement condition effects. 217

5.1 Haynesville Formation 218

Porosity values are measured by N2, WI, GRI and MI techniques (Figure 6). The following 219

observations can be made from this figure: 220

11

• MI underestimates the porosity when compared with N2 and WI (Figure 6a and 6b). 221

• WI and N2 porosities are comparable within 2 p.u. difference (Figure 6c). 222

• GRI and N2 are comparable within 2 p.u. difference (Figure 6d). 223

• Except some low content samples that show higher GRI porosity, the WI and GRI 224

porosity show similar porosities within 2 p.u. difference (Figure 6e). Note that in Figure 6 225

N2 and WI and GRI experiments are not performed for all samples. 226

• The presence of clays does not affect the porosity measurements and porosity value 227

increases with clay content. 228

The PSD was measured using the N2 technique (Figure 7). As shown in this figure the PSD 229

spectra varies significantly for different samples. Figure 7a and 7b show the PSD colorcoded by 230

clay and TOC content, respectively. Samples with high clay (low TOC) show large amplitudes at 231

the small mesopore range (< 10nm). Samples with low clay (high TOC) show large amplitudes at 232

the big mesopores (10-50 nm) and macropore range (>50 nm). MI throat size distribution was 233

also measured for a subset of Haynesville samples (Figure 8). The MI spectra suggests that there 234

are pores with throats smaller then 3.6 nm which are not assessed by this technique. 235

5.2 Silurian Formation 236

We measured the porosity of the Silurian samples using WI, MI and N2 techniques (Figure 9). 237

The following observations can be made: 238

• MI, N2 and WI measurements show an increase in porosity with increase in TOC (Figure 239

9a to 9c). 240

• MI highly underestimates the porosity compared to WI and N2 techniques (Figure 9a and 241

9b). 242

12

• WI and N2 porosities are comparable withing 2 p.u. difference (Figure 9c). 243

• GRI generally underestimates the porosity compared to WI and N2 in these samples 244

(Figure 9d and 9e). 245

We measured PSD using N2 (Figure 10) and MI (Figure 11) techniques for all Silurian samples. 246

The N2 PSD spectra are colorcoded by both clay (Figure 10a) and TOC (Figure 10b) contents. 247

We make the following observatins: 248

• Significant variation in amplitude in the small mesopore range (<10 nm) is observed. 249

• The big mesopore (10-50 nm) and macropore range (>50 nm) show similar spectra with 250

small variations in amplitude. 251

• TOC and clay content do not show a clear effect on the PSD spectra. 252

• The MI PSD results (Figure 11) suggest that there are pores that are not accessible using 253

the MI technique. 254

• Clay content does not show a effect on the MI spectra amplitude (Figure 11a) whereas 255

TOC and spectra amplitude show a direct correlation (Figure 11b). 256

5.3 Niobrara Formation 257

Figure 12 shows the comparison between porosity values measured using the N2, MI and WI 258

techniques. Two distinct groups of data are observed in Figure 12a. Group 1 (data circled by a 259

blue dashed line in Figure 12a) shows higher WI porosity compared to N2 porosity, whereas 260

Group 2 (data circled by a red solid line in Figure 12a) shows more comparable WI and N2 261

porosities (within 2 p.u. difference). We make the following observations for samples with a 262

wide distribution of pore sizes and high clay content such as the Niobrara samples: 263

13

• Comparing N2 and WI samples show that N2 underestimates the porosity for some 264

samples and WI overestimates the porosity for some samples (Figure 12a). 265

• WI and MI porosities are comparable within 2 p.u. for low clay content (<10 wt%) 266

samples. WI overestimates the porosity for high clay samples (>10 wt%) (Figure 12b). 267

• N2 shows higher porosity for high clay content samples and MI shows higher porosity for 268

low clay samples (Figure 12c). 269

We measured PSD for Niobrara samples using the N2 (Figure 13) and MI (Figure 14) 270

techniques: 271

• The shapes of the PSD spectra (Figure 13) for low clay content samples show increasing 272

PSD amplitude which suggests the presence of pores larger than 200 nm. The high clay 273

content samples show a dominant pore size of 80-100 nm. 274

• Similar to N2-PSD data, the MI PSD data (Figure 14) show two different sets of spectra. 275

One set shows a PSD spectrum with a dominant pore size 80-100 nm (low clay content) 276

and the other set shows an abundance of small pores smaller than 20 nm (high clay 277

content) (Figure 14). 278

• The shape of MI spectra for high clay samples indicates the presence of pores that are not 279

accessible by mercury. 280

5.4 Monterey Formation 281

Porosity values were measured for Monterey samples using WI, MI, HE and N2 techniques 282

(Figure 15). We make the following observations: 283

• WI, MI and HE porosities show comparable values within 2 p.u. difference (Figure 15a 284

to 15c). 285

14

• HE and WI show the best correlation (Figure 15b). 286

• N2 significantly underestimates the porosity for high porosity (>5 p.u.) samples. 287

• N2 and HE show comparable values (within 2 p.u.) for low porosity samples (<5 p.u.). 288

• Clay content does not affect the porosity measurements. 289

Pore size distributions were measured for Monetery samples using N2 (Figure 16) and MI 290

(Figure 17) techniques, colorcoded by clay content: 291

• The N2 pore size amplitude (Figure 16) for pores larger than 10 nm varies significantly 292

and shows no correlation with the clay content. 293

• The amplitide for small mesopores (<10 nm) is small except for one high clay content 294

sample (Figure 16). 295

• The MI throat size distributions (Figure 17) also show a significant variation in both 296

amplitude and throat size. 297

• For these spectra clay content does not show any correlation with the shape and 298

amplitude either. 299

6. Discussion 300

The main driver for this comparative study was to analyze the differences in each method and to 301

exploit these differences to learn more about the samples honoring the mineralogical and 302

geochemical properties of each sample set. Samples were chosen from different formations. 303

Haynesville and Silurian formations are highly mature (gas window) with a very low hydrogen 304

index (average of 36 and 3, respectively). The Niobrara and Monterey samples are less mature 305

(oil window) with a very high hydrogen index (average of 306 and 374). We discussed the 306

results considering the effect of thermal maturity, TOC and clay content on the porosity, pore 307

size distribution and pore types. We worked with the strengths and drawbacks of each method as 308

15

listed in Table 2 for porosity and pore size distribution measurement. Here, we discuss our 309

results and evaluate the benefits and applicability of each technique for organic-rich mudrocks. 310

As mentioned in Table 2 MI technique measures the porosity for pores with a throat diameter 311

smaller than 3.6 nm. Underestimation of MI porosity values for Haynesville (Figure 6a and 6b) 312

and Silurian (Figure 9a and b) is because of the high thermal maturity (low HI) and consequently 313

the abundance of organic matter (OM)-hosted pores in these samples. OM-hosted pores are in 314

the mesopore (2-50 nm) and small macropore size (50-200 nm) range and not accessible by MI 315

technique. MI porosity values for low clay content (< 10 wt%) Niobrara (Figure 12b) and 316

Monterey samples (Figure 15a and c) are comparable with other techniques. The porosity in 317

these samples are mainly intercrystalline and accessible by mercury. 318

WI porosity measures the pores that are filled with distilled water. The presence of expandable 319

clays in the samples causes overestimation of porosity. Mineralogy of Niobrara samples showed 320

presence of expandable clays (smectite) in these samples. The clay content in other sample sets 321

mainly consisted of Kaolinite and illite which are less expandable. The effect of expandble clays 322

in Niobrara high clay (> 10 wt%) samples can be seen when WI is compared against N2 (Figure 323

12a) and MI (Figure 12b). In more thermally mature samples such as Haynesville (Figure 6c) 324

and Silurian (Figure 9c) and low clay content samples such as Monterey (Figure 15a and 15b) 325

the clay swelling is not significant and WI porosity is comparable with other techniques within 2 326

p.u. difference. As mentioned in Table 2 water might not be able to fill the hydro-phobic organic 327

hosted pores. We can not directly observe this phenomena in the porosity data presented in this 328

study but there are some indications of the effect of hydrophobic pores on WI porosity 329

measurement. There is higher scatter in the WI-N2 porosity comparison for Haynesville (Figure 330

6c) and Silurian (Figure 9c) samples compared to the HE-WI porosity comparison for Monterey 331

16

samples (Figure 15b). Monterey samples are in the oil window and intercrystalline pores are the 332

dominant pore types, but Haynesville and Silurian are in the gas window and OM-hosted pores 333

form the dominant pore type which is hydrophobic. 334

Since the N2 technique can not assess pores with a diameter larger than 200 nm, the comparable 335

N2 porosity for Haynesville (Figure 6c and 6d) and Silurian (Figure 9c) shows the abundance of 336

pores within the mentioned range in these samples. High Clay (> 10 wt%) Niobrara samples also 337

show the presence of OM-hosted pores. The N2 technique is able to measure the porosity in 338

High Clay (> 10 wt%) Niobrara samples while the MI technique underestimates and WI 339

overestimates the porosity. OM-hosted pores are not abundant in the low porosity (< 5 p.u.) 340

Monterey samples. The similarity of N2 porosity with other techniques (Figure 15a to 15c), for 341

these samples is due to the presence of intercrystalline pores with a diameter smaller than 200 342

nm. 343

GRI porosity was measured for Haynesville samples and a limited number of Silurian samples. 344

The higher scatter in low clay (<35 wt%) Haynesville samples (Figure 6e) is due to hot solvent 345

extraction prior to GRI measurements. Removal of soluable organic matter increases the porosity 346

measured by the GRI technique. This effect is ruled out for Silurian samples since no soluble 347

bitumen was observed in these samples. The underestimation of GRI for Silurian samples might 348

be due to inacscessibility of gas to the micropores. The HE technique successfully measures 349

porosity for the Monterey samples since no OM-hosted pores are present in these samples. 350

Comparison of porosity values measured by different techniques show that when the N2 and MI 351

techniques measure comparable porosity with other techniques, the pore size distribution 352

assessed by these techniques can be used to study the pore structure. For example the N2 pore 353

17

size distribution can be used to study the pore structure for highly mature gas window samples 354

(Haynesville and Silurian), Clay rich oil window samples (Niobrara with clay content more than 355

10 wt%), and oil window tight samples ( Monterey samples with porosity lower than 5 p.u.). MI 356

pore throat size distribution spectra can not be used in gas window samples (Haynesville and 357

Silurian), but it is an appropriate technique for studying the pore structure in oil window low clay 358

(<10 wt%) Niobrara and high porosity Monterey (>5 p.u.) samples. 359

The pore size distributions in Figure 7 are divided into 5 groups based on the PSD spectra shapes 360

(Figure 18). The correlation between the TOC and clay content for the Haynesville samples 361

(Figure 19) shows an inverse correlation between TOC and clay content. Clay content cut offs 362

for different groups are defined as <35 wt%, 35-50 wt%, 50-60 wt% and >60 wt%. Group 5 363

samples are dolomite rich samples with >30 wt% dolomite content. Group 1 samples (Figure 364

18a) show dominant pore size of 20 nm. These samples have high TOC and consequently an 365

abundance of OM-hosted pores (Figure 20). Group 2 to 5 (Figure 18b to d) show an increase in 366

the small mesopore size range (<10 nm) and decrease in the big mesopore (10-50 nm) and 367

macropore size (>50 nm) range. Group 2 samples have slightly lower TOC and higher clay 368

content compared to Group 1. They show an abundance of OM-hosted pores and the presence of 369

intercrystalline pores in clay minerals (Figure 21). SEM images for Group 3 samples (Figure 22) 370

confirm the N2 PSD results (Figure 18c) which demonstrate both intercystalline (< 10 nm) and 371

OM-hosted pores (> 10 nm). Group 4 samples are clay rich (60-80 wt%) and show an abundance 372

of small mesopores (<10 nm) measured by N2 PSD (Figure 18d). SEM images (Figure 23) show 373

that intercrystalline pores in clay minerals are the dominant pores in these samples. Group 5 374

samples are dolomite rich (> 30 wt%) with a very low TOC content (< 1.5 wt%). The spaces 375

18

between dolomite grains are filled with a mixture of clay and quartz particles which host the 376

intercrystalline pores in these samples (Figure 24). 377

N2 PSD for Silurian samples (Figure 10) show relatively similar amplitudes for large mesopores 378

(10-50 nm) and macropores (>50 nm). The PSD amplitude for pores with a diameter smaller than 379

10 nm varies significantly. The TOC-Clay content correlation for the Silurian samples (Figure 380

25) shows two different trends; low clay-low TOC samples (Group 1) and high clay-high TOC 381

samples (Group 2). In Group 1 the amplitude for pores with a diameter less than 10 nm increases 382

as the clay content increases (Figure 26a). However in Group 2 the peak amplitude for pores 383

smaller than 10 nm increases as the TOC increases (Figure 26b). The SEM images for a sample 384

from Group 1 (Figure 27) show the presence of both OM-hosted pores and intercrystalline pores. 385

However, Group 2 sample SEM images show an abundance of OM-hosted pores. The 386

intercrystalline pores are filled with organic matter which hosts the majority of the pores (Figure 387

28). 388

Although both Haynesville and Silurian are in the gas window and show relatively similar TOC-389

Clay content correlations, there are key differences between the pore structure of these samples: 390

• The clay content varies between 30 to 60 wt% for the Silurian samples and 20-80 wt% for 391

the Haynesville samples. 392

• The dominant pore type for the Silurian samples is OM-hosted and the pore structure is 393

dictated mainly by TOC, whereas for the Haynesville samples both clay content and TOC 394

control the pore structure. 395

• Silurian samples belong to a geologically older formation than the Upper Jurrasic 396

Haynesville samples which come from an over-pressured formation. 397

19

N2 PSD for the Niobrara samples are divided into two groups based on the PSD spectra shapes 398

(Figure 29). The PSD of the Group 1 samples (Figure 29a) shows the presence of pores larger 399

than 200 nm that were not assessed by the N2 technique. The PSD of the majority of Group 2 400

samples (Figure 29b) show that the dominant pore size is close to 100 nm. These groups 401

correspond to the groups shown in Figure 12a. Group 1 samples belong to Chalky and Group 2 402

samples belong to the Marly formations. TOC-Clay correlation for these samples (Figure 30) 403

demonstrates that the majority of Group 1 samples show TOC less than 2 wt% and Group 2 404

samples show TOC higher than 2 wt%. In Group 1 samples the pores are mainly intercrystalline 405

and reside in the clay minerals that fill the space between calcite grains (Figure 31). Figure 31a 406

shows how quartz, calcite and clay particles fill the space between calcite grains and form the 407

intercrystalline pore structure. No organic matter or organic-hosted pores are present in this 408

sample. Figure 31b shows a connected network of intercrystalline pores which are accessible to 409

displacement fluids for porosity measurements. The pores in the Group 2 samples (Figure 32) 410

reside in the organic matter and show smaller pores compared to Group 1. A significant amount 411

of TOC is visible in this sample which fills the intercrystalline pores (Figure 32a). Figure 32b 412

shows the organic-hosted porosity. As shown by the annotations in this figure the pores are 413

smaller than 100 nm. Small pore size and possibly lack of connectivity limit the accessibility of 414

the displacement fluids to these pores for porosity measurements. 415

The clay content range (20-40 wt%) and TOC-Clay content correlation for the Group 1 Niobrara 416

samples (Figure 25) are similar to the Group 2 Haynesville samples (Figure 19). The N2 PSD 417

measured for these samples are also similar (Figure 18b and Figure 29b). Since both groups of 418

samples have high TOC and an abundance of OM-hosted pores, this similarity confirms that 100 419

nm is the signature pore diameter for these pore types. 420

20

Based on the porosity comparisons presented in Figure 15 the Monterey samples can be divided 421

into two groups; Group 1 with low porosity samples (< 5 p.u.) and Group 2 with high porosity 422

samples (>5 p.u.). N2 PSD data for both groups are shown in Figure 33a and b, respectively. 423

Both groups show very low amplitudes at small mesopores range (<10 nm), except one high clay 424

sample in Group 1. N2 PSD of Group 1 samples represents the whole pore structure whereas the 425

Group 2 PSD spectra only represents pores smaller than 200 nm. Since the Monetery samples 426

have very low clay content there is no correlation between the PSD spectra and clay content for 427

either groups. The PSD spectra are mainly a function of the intercrystalline pores in the quartz 428

phase. Group 1 samples have intercrystalline pores in the clay phase and interacrystalline pores 429

within the quartz and calcite grains (Figure 34). The intercrystalline space is filled with organic 430

matter which holds very low OM-hosted porosity. Group 2 samples show larger intercrystalline 431

pores compared to Group 1 samples. The pores are developed in the space between quartz grains 432

(Figure 35). 433

Although the N2 technique does not assess the whole pore space for either Group 1 Niobrara 434

(Figure 29a) or Group 2 Monterey (Figure 33b) samples, the N2 PSD spectra shapes are 435

different. Since MI PSD is the appropriate technique for pore structure characterization for these 436

samples we will discuss the main reason for this difference in the following section. 437

Throat size distribution for Group 2 Haynesville samples (Figure 8) indicates that there are pores 438

with throat diameter smaller than 3.6 nm that are not assessed by the MI technique. Based on N2 439

PSD results, Group 2 samples have the highest macropore volume amongst all groups in this 440

sample set, so the MI technique would not be able to assess a major part of the pore space for the 441

other groups either. 442

21

Similar to Haynesville samples, Silurian MI throat size distribution also showed that there are 443

pores with throat diameter smaller than 3.6 nm that are not reached by the mercury. The MI 444

spectra for Group 1 samples (Figure 36a) do not demonstrate the same clay content dependency 445

as N2 spectra for these samples (Figure 26a). On the other hand MI spectra amplitude of Group 2 446

samples (Figure 36b) increases with increasing TOC, similar to N2 PSD (Figure 26b). 447

Throat size distributions assessed by the MI technique for Groups 1 and 2 Niobrara samples are 448

shown in Figure 37a and 37b, respectively. For the samples in Group 1 (Figure 37a) mercury 449

successfully accessed the pores which were not accessible by N2. On the other hand, the pore 450

throat size spectra for Group 2 show that there are pores associated with throats smaller than 3.6 451

nm. 452

MI PSD spectra show more heterogeneity in the Monterey samples (Figure 38) compared to the 453

Niobrara samples (Figure 37). The dominant throat size distributions for Monterey samples vary 454

significantly from less than 20 nm for Group 1 samples (Figure 38a) to more than 200 nm for 455

Group 2 samples (Figure 38b). Besides heterogeneity, Group 2 samples show a wider range of 456

throat size distribution for individual samples, whereas in Group 1, Niobrara samples MI PSD 457

shows a more uniform PSD with variable amplitudes (Figure 37a). 458

7. Conclusions 459

The porosity and pore size distribution were measured for four sample sets from different 460

formations ranging from high thermal maturity (gas window Haynesville and Silurian) to low 461

maturity (oil window Niobrara and Monterey). The clay content and TOC vary significantly 462

within each sample set. Based on the results presented in this study the following conclusions 463

can be drawn: 464

22

- Using samples from different formations and maturities, we have shown that without a 465

priori knowledge regarding thermal maturity, TOC, clay type and content porosity and pore 466

size distributions in mudrocks cannot be assessed or studied using a single method. Our 467

assessment of pore size sensitivity for each technique emphasizes the significance of 468

combining various techniques to describe the full pore topology in organic-rich mudrocks. 469

- MI technique underestimates the porosity for thermally mature samples (Haynesville) with 470

an abundance of OM-hosted pores. It also underestimates the porosity in less mature 471

Niobrara samples when TOC is high and tight Monterey samples (<5 p.u.). 472

- WI provides reliable porosity when samples are thermally mature or when no expandable 473

clays are present. 474

- N2 technique is appropriate for porosity and pore size distribution assessment for thermally 475

mature samples (Haynesville and Silurian) or samples with an abundance of OM-hosted 476

pores (Niobrara). It also successfully assesses the low porosity Monterey samples. 477

- The reliability of the measured porosities depends on the accessibility of the displacement 478

fluid to the pores. Thus, pore accessibility or wettability of the solid to the probe fluid can 479

be investigated by comparing the pore size distributions measured by different techniques. 480

A combination of methods is required to reliably measure porosity and pore size 481

distribution in mudrocks. 482

8. Acknowledgements 483

We would like to thank Douglas McCarty, Arkadiusz Derkowski, Benjamin Harrell and Cesar 484

Mapeli for their help with the experiments and fruitful discussions. We thank Chesapeake 485

Energy Reservoir technology Center and Don Harville for their collaboration. We also thank 486

OCLASSH and Center for Rock Abuse groups for support and funding of the research. 487

23

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Biographies 623

Milad Saidian is currently a petroleum engineering PhD candidate at Colorado School of 624

Mines. He holds BS degree from Petroleum University of Technology and MS degree from 625

Sharif University of technology. His PhD research interest is fluid properties and pore space 626

characterization using Nuclear Magnetic Resonance for conventional and unconventional 627

reservoir rocks focusing on porosity and pore size distribution measurement methods. His 628

industrial work experience includes internships at BP America and Maersk Oil Houston Inc. as 629

petrophysicist, NIOC as reservoir engineer and Maroon Oil and Gas Production Company as 630

production field engineer. 631

Utpalendu Kuila is Senior Petrophysicist with Rajasthan Exploration Team at Cairn India Ltd. 632

He has a B.Sc. and M.Sc. in geology and a Ph.D. in Petroleum Engineering. He has a worked as 633

a visiting scholar in CSIRO Petroleum, Perth Australia and as an Associate Researcher in 634

Institute of Geological Sciences, Polish Academy of Science. 635

636

30

Manika Prasad is Associate Professor at Colorado School of Mines. She has a BS, an MS 637

(Diplom-Geologie), and a PhD (magna cum laude) in geophysics. Manika worked at University 638

of Hawaii's Mineral Physics Laboratory and at Stanford Rock Physics Laboratory, and now co-639

directs the Center for Rock Abuse at CSM. Her students have won student paper awards. She 640

was named Outstanding Mentor to Native American students at Stanford. Manika's quest is to 641

understanding how ant-sized phenomena control elephant-sized features. 642

Leo Alcantar-Lopez holds B.S. degree in Biology from National University of Mexico at 643

1985. He worked at Chesapeake Energy, Oklahoma City, from March 2007 to present as Sr. 644

Staff Scientist with main focus on SEM analysis and research on shale organic matter structural 645

development. Experience includes 25 years in instrumental analysis seven of which have been 646

on SEM. Attended Lehigh Microscopy School in 2013. 647

Saul Rivera is currently a petrophysicist at Chesapeake Energy working on unconventional and 648

conventional plays. His topics of interest include porosity, permeability, and mechanical 649

properties. He holds a BSc and MSc in Petroleum Engineering from Colorado School of Mines. 650

Saul is an active member of SPWLA, SPE, and AAPG. 651

Lemuel J. Godinez Graduated from Colorado School of Mines in 2011 and 2014 with a BS and 652

MSc in Petroleum Engineering. His MSc research focused on the relationship between 653

mineralogy and fluid saturation distribution in the Miocene Monterey formation. He has been an 654

intern for Venoco Inc., as a Petrophysical Engineer, Operations Engineer and a Field Engineer. 655

He is currently a Reservoir Engineer for an E&P operator based in Houston. 656

657

658

1

Figure 1: Mineralogy of (a) Haynesville, (b) Silurian, (c) Niobrara (Chalk, Marl and Fort Hays),

and (d) Monterey samples measured by QXRD and colorcoded by TOC. See sample discriptions

for more information. The TOC values for Monterey samples used in this study are not available.

Figure 2: SEM images for two Haynesville samples. (a) Silt-sized quartz, calcite and plagioclace

particles surrounded by a mixture of illite and clay-sized quartz. Organic matter is dispersed

between clay-sized particles. (b) Dolomite rich sample with intergranular spaces filled with a

mixture of quartz and clay minerals as well as organic matter. The Haynesville samples are clay-

rich (illite and smectite) with moderate amounts of quartz and calcite. Moderate amount of

dolomite is observed in some samples. The TOC varies between 0.5-6.3 wt%; the kerogen is in

the gas window based on average Tmax of 424 °C and HI varying from 19 to 57 with an average

of 36. RockEval S2 data between 350-400 °C show a moderate amount of bitumen. Qz= Quartz,

Cal=Calcite, Dol=Dolomite, Py=Pyrite, Pl=Plagioclase, Ilt=Illite, Chl=Chlorite, OM=Organic

Matter.

Figure 3: SEM images for two Silurian samples. (a) and (b) Silt-size particles such as quartz,

pyrite, and dolomite are surrounded by a matrix of illite and chlorite. Organic matter is dispersed

between clay particles.The Silurian samples are quartz- and clay-rich (mostly illite and smectite).

The kerogen is thermally mature in the gas window and TOC ranges between 1-6 wt %. No

pyrogram peaks were observed in the Rock Eval results up to 550 °C which confirms the

absence of any pyrolyzable kerogen or bitumen in these samples. The HI varies from 0 to 9 with

an average of 3. Qz= Quartz, Cal=Calcite, Dol=Dolomite, Py=Pyrite, Pl=Plagioclase, Ilt=Illite,

Chl=Chlorite, OM=Organic Matter, TiO2=Titanium Dioxide.

2

Figure 4: SEM images for two Niobrara samples. (a) shows relatively more homogeneous

intercrystalline pore distribution which is filled with organic matter. (b) shows larger

intercrystalline pores filled with organic matter. Note that the larger intercrystalline pores are

within the peloid structures. Niobrara samples are calcite-rich rocks with moderate amounts of

clay, quartz and pyrite. Type II kerogen is thermally mature and is in oil window based on

average Tmax of 436 °C and HI varying from 119 to 386 with an average of 306. TOC varies

from 0.1 to 5.3 wt% and RockEval programs between 450-500 °C show abundance of bitumen in

these samples. Qz= Quartz, Cal=Calcite, Dol=Dolomite, Py=Pyrite, Hem= Hematite,

Pl=Plagioclase, Ilt=Illite, OM=Organic Matter, TiO2=Titanium Dioxide, Ms=Mascovite.

Figure 5: SEM images for two Monterey samples. (a) and (b) moderate carbonate and dominant

quartz grains with presence of organic matter filling up the intergranular and intercrystalline

pores. The Monetery samples are predominantly porcellanites containing moderate amounts of

clay and pyrite. A smaller sample set is calcite-rich. TOC varies from 0.85-4.95 wt% and

RockEval S2 data show an abundance of bitumen (Rivera, 2014). Kerogen is Type II with

thermal maturity in the oil window based on average Tmax of 438 °C and hydrogen index (HI)

varying from 184 to 473 with an average of 374. Qz= Quartz, Cal=Calcite, Dol=Dolomite,

Py=Pyrite, Hem= Hematite, Pl=Plagioclase, OM=Organic Matter.

Figure 6: Porosity comparison for Haynesville samples. The porosities are measured by (a) WI

and MI, (b) N2 and MI, (c) WI and N2, (d) N2 and GRI and (e) WI and GRI. Data points are

color-coded by clay content. Since all the measurements are not performed for all samples, circle

and square symbols are used to differentiate different samples with similar measurements. (a)

and (b) MI underestimates the porosity when compared with WI and N2. (c) WI and N2 show

comparable porosity values within 2 p.u.. (d) and (d) GRI and N2 show similar porosity values

3

within 2 p.u. and (e) Except some low content samples that show higher GRI porosity, the WI

and GRI porosity show similar porosities within 2 p.u. difference

Figure 7: N2 PSD spectra of Haynesville samples colorcoded by (a) clay content and (b) TOC

content. Samples with high clay and low TOC show high amplitudes at small mesopore range (<

10nm) and samples with low clay and high TOC show high amplitudes at big meso pores (10-50

nm) and macropores range (>50 nm).

Figure 8: MI throat size distributions for Group 2 which has the highest macropore volume

compared to other gropus. The MI porosity was highly underestimated compared to other

techniques. This implies that the throat size distributions do not assess the whole pore space.

Figure 9: Porosity comparison for Silurian samples. The porosities are measured by (a) WI and

MI, (b) N2 and MI, (c) WI and N2, (d) WI and GRI and (e) N2 and GRI, all colorcoded by TOC

content. (a) to (c) MI, N2 and WI measurements show an increase in porosity with increase in

TOC. (a) and (b) MI highly underestimates the porosity compared to WI and N2 techniques (c)

WI and N2 porosities are comparable withing 2 p.u. difference. (d) and (e) GRI generally

underestimates the porosity compared to WI and N2 in these samples.

Figure 10: The pore size distribution measured by N2 technique for Silurian samples colorcoded

by (a) clay content and (b) TOC content. (a) and (b) Significant variation in amplitude in small

mesopore range (<10 nm) is observed. The big mesopore (10-50 nm) and macropore range (>50

nm) show similar spectra with small variations in amplitude. TOC and clay content do not show

a clear effect on the PSD spectra.

4

Figure 11: The throat size distribution measured by MI technique for Silurian samples

colorcoded by (a) clay content and (b) TOC content. The MI PSD results suggest that there are

pores that are not accessible by MI technique. (a) Clay content does not show a effect on the MI

spectra amplitude whereas (b) TOC and spectra amplitude show a direct correlation.

Figure 12: Porosity comparison for Niobrara samples. The porosities are measured by (a) WI and

N2, (b) WI and MI, and (c) MI and N2, all colorcoded by total clay content. (a) Comparing N2

and WI samples show that N2 underestimates the porosity for some samples and WI

overestimates the porosity for some samples. (b) WI and MI porosities are comparable within 2

p.u. for low clay content (<10 wt%) samples and WI overestimates the porosity for high clay

samples (>10 wt%). (c) N2 shows higher porosity for high clay content samples and MI shows

higher porosity for low clay samples.

Figure 13: PSD for Niobrara samples using N2 technique, colorcoded by clay content Some of

the spectra (low clay content) show an increasing PSD amplitude which suggest the presence of

pores larger than 200 nm. The other group of spectra (high clay content) shows a dominant pore

size of 80-100 nm.

Figure 14: The PSD measured by MI technique, colorcoded by clay content. The MI PSD data

show two different sets of spectra. One set shows a PSD spectrum with a dominant pore size 80-

100 nm (low clay content) and the other set shows abundance of small pores smaller than 20 nm

(high clay content). The shape of MI spectra for high clay samples indicates the presence of

pores that are not accessible by mercury.

Figure 15: Porosity comparison for Monterey samples. The porosities are measured by (a) MI

and WI, (b) HE and WI, (c) HE and MI, (d) N2 and WI, (e) N2 and MI, and (f) N2 and HE, all

5

colorcoded by total clay content. (a) to (c) WI, MI and HE porosities show comparable values

within 2 p.u. difference. (b) HE and WI show the best correlation. (d) to (f) N2 significantly

underestimates the porosity for high porosity (>5 p.u.) samples. N2 shows comparable values

(within 2 p.u.) for low porosity samples (<5 p.u.). Clay content does not affect the porosity

measurements.

Figure 16: Pore size distribution spectra measured for Monetery samples using N2 technique,

colorcoded by clay content. The N2 pore size amplitude for pores larger than 10 nm varies

significantly and shows no correlation with the clay content. The amplitide for small mesopores

(<10 nm) is small except for one high clay content sample.

Figure 17: Throat size distribution spectra measured for Monetery samples using MI technique

colorcoded by clay content. The MI throat size distributions also show a significant variation in

both amplitude and throat size. For these spectra clay content does not show any correlation with

the spectra and amplituse either.

Figure 18: N2 PSD spectra of Haynesville samples for (a) Group 1, (b) Group 2, (c) Group 3, (d)

Group 4 and (e) Group 5. The samples are grouped based on the PSD spectra shape. The specttra

can be divided in two parts; pores smaller than 10 nm and larger than 10 nm. Feom Group 1 to 4

the amplitude of small pores increases and the amplitude of large pores decreases. Group 5

samples are dolomite rich and behave differently compared to the rest of groups.

Figure 19: Correlation between TOC and clay content for Haynesville samples. The samples can

be divided in five groups based on the TOC and clay content. Clay content cut offs for different

groups are defined as <35 wt%, 35-50 wt%, 50-60 wt% and >60 wt%. Group 5 samples are

dolomite rich samples with more than 30 wt% dolomite content.

6

Figure 20: SEM images of Haynesville samples in Group 1. These samples are TOC rich (> 6

wt%) and show dominant pore size of 20 nm (Figure 18a). As a result of thermal maturation lots

of pores are developed within the organic matter. Qz=Quartz, Cal=Calcite, Ilt=Illite,

OM=Organic Matter.

Figure 21: SEM images of Haynesville samples in Group 2. These samples have slightly lower

TOC and higher clay content compared to Group 1. Although OM-hosted pores are the dominant

pore types, the images demonstrate presence of intercrystalline pores in clay minerals.

Qz=Quartz, Py=Pyrite, Ilt=Illite, Chl=Chlorite, OM=Organic Matter.

Figure 22: SEM images of Haynesville samples in Group 3. These samples have a combination

of organic- and mineral- hosted pores. The N2 PSD results (Figure 18c) also indicates the

presence both intercystalline (< 10 nm) and OM-hosted pores (> 10 nm). As a result of thermal

maturation lots of pores are developed within the organic matter. Qz=Quartz, Ilt=Illite,

OM=Organic Matter.

Figure 23: SEM images of Haynesville samples in Group 4. These samples are clay rich (60-80

wt%) and show abundance of small mesopores (<10 nm) measured by N2 PSD (Figure 18d).

The SEM images show that intercrystalline pores in clay minerals are the dominant pores in

these samples. Qz=Quartz, Ilt=Illite.

Figure 24: SEM images of Haynesville samples in Group 5. These samples are dolomite rich (>

30 wt%) which have very low TOC content (< 1.5 wt%). The space between dolomite grains are

filled with a mixture of clay and quartz particles which host the intercrystalline pores in these

samples. Although the organic content is minimal in this Group, organic-hosted pores are

developed where OM is present. Qz=Quartz, Cal=Calcite, Dol=Dolomite, Py=Pyrite, Ilt=Illite.

7

Figure 25: The relationship between clay and TOC content for Silurian samples. The samples

can be divided in two groups. Group 1 which has clay and TOC content lower than 48 and 1.8

wt%, respectively and Group 2 which has TOC and Clay content higher than the mentioned

values. In Group 1 the TOC is relatvely constant wheras in Group 2 the TOC and clay content

have inverse correlation.

Figure 26: The pore size distribution measured by N2 technique for (a) Group 1 and (b) Group 2

a. Increasing in clay content for samples in Group 1 (a) results in increase in pore size

distribution amplitude at 3 nm pore size range. The samples of Group 2 (b) do not follow the

same trend. The highest amplitudes belong to the samples with the highest TOC content and

samples with either high clay or TOC content show higher amplitude at 1.7-5 nm pore size

range.

Figure 27: SEM images of Silurian samples in Group 1. Both OM-hosted and intercrystalline

pores are present in these samples. In this group the amplitude for pores with diameter less than

10 nm increases as the clay content increases (Figure 26a). Qz=Quartz, Ilt=Illite, OM=Organic

Matter.

Figure 28: SEM images of Silurian samples in Group 1. The intercrystalline pores are filled with

organic matter which holds the majority of the pores. The organic hosted pores are in the

mesopore range. The size of the pores and possibly lack of connectivity limits the capability of

the porosity and pore size distribution measurements to assess the pores. Qz=Quartz, Ilt=Illite,

OM=Organic Matter.

Figure 29: Examples of PSD measured by the N2 technique for samples from (a) Groups 1 and

(b) Group 2. The PSD of the Group 1 samples (a) shows presence of pores larger than 200 nm

8

that were not assessed by the N2 technique. The PSD of the majority of Group 2 samples (b)

shows that the dominant pore size lies around 100 nm.

Figure 30: The Niobrara samples are divided in two groups based on their TOC contents. Group

1 has low TOC content (below 2 wt%) and group 2 has high TOC content (above 2 wt%). Clay

and TOC content control the porosity and pore size distribution in Niobrara samples.

Figure 31: SEM images of Silurian samples in Group 1. Quartz, calcite and clay particles fill the

space between calcite grains and form the intercrystalline pore structure. No organic matter and

organic-hosted pores are present in this sample. The intercrystalline pores form a connected

network which is accessible to displacement fluids for porosity measurements. Qz=Quartz,

Cal=Calcite, Ilt=Illite.

Figure 32: SEM images of Niobrara samples in Group 2. Significant amount of TOC is visible in

this sample which fills the intercrystalline pores. The OM-hosted pores are developed in the

organic matter. As it is shown by the annotations in the pores are smaller than 100 nm and

possibly not connected. Both small pore size and lack of connectivity limit the accessibility of

the displacement fluids to these pores for porosity measurements. Cal=Calcite, Ilt=Illite,

Py=Pyrite, OM=Organic Matter.

9

Figure 33: Pore size distribution spectra measured for Monetery samples using N2 technique for

(a) Group 1 and (b) Group 2. Since samples in Group 1 (a) show more comparable N2 porosty

values when compared with HE and MI techniques we can consider the spectra in (a) a suitable

representation of pore size distribution for these samples. N2 measures a highly underestimated

porosity for Group 2 samples, so N2 spectra (b) does not represent the whole pore space.

Figure 34: SEM images of Monterey samples in Group 1. These samples have intercrystalline

pores in clay phase and interacrystalline pores within the quartz and calcite grains which are

exposed due to polishing. The intercrystalline space is filled with organic matter which holds

very low OM-hosted porosity. Qz= Quartz, Cal=Calcite, Dol=Dolomite, Ilt=Illite, Py=Pyrite.

Figure 35: SEM images of Monterey samples in Group 2. These samples show larger

intercrystalline pores compared to Group 1 samples. The pores are developed in the space

between quartz grains. Qz= Quartz, Ilt=Illite, OM=Organic Matter.

Figure 36: The throat size distribution measured by MI technique for (a) Group 1 colorcoded by

caly and (b) Group 2 colorcoded by TOC. (a) The MI spectra for Group 1 samples do not

demonstrate the same clay content dependency as N2 spectra for these samples (Figure 26a). (b)

On the other hand MI spectra amplitude of Group 2 samples increases with increasing TOC,

similar to N2 PSD (Figure 26b).

Figure 37: Throat size distributions assessed by MI technique for samples in (a) Group 1 and (b)

Group 2, colorcoded by clay content. (a) For Group 1 samples mercury successfully assessed the

pores which were not accessible by N2 (Figure 29a). (b) On the other hand for samples in Group

2 mercury did not reach the small pores hosted by the organic matter, these pores were assessed

by N2 (Figure 29b).

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Figure 38: Throat size distribution spectra measured for Monetery samples using MI technique

for (a) Group 1 and (b) Group 2. The dominant throat size distributions for Monterey samples

vary significantly from less than 20 nm for Group 1 (a) and more than 200 nm for Group 2

samples (b).

1

(a) (b)

(c) (d)

Figure 1

(a) (b)

Figure 2

2

(a) (b)

Figure 3

(a) (b)

Figure 4

3

(a) (b)

Figure 5

(a) (b) (c)

(d) (e)

Figure 6

4

(a) (b)

Figure 7

Figure 8

5

(a) (b) (c)

(d) (e)

Figure 9

(a) (b) Figure 10

6

(a) (b) Figure 11

(a) (b) (c)

Figure 12

7

Figure 13

Figure 14

(a) (b) (c)

8

(d) (e) (f)

Figure 15

Figure 16

Figure 17

9

(a) (b) (c)

(d) (e)

Figure 18

Figure 19

10

(a) (b)

Figure 20

(a) (b)

Figure 21

(a) (b)

Figure 22

11

(a) (b)

Figure 23

(a) (b)

Figure 24

12

Figure 25

(a) (b) Figure 26

13

(a) (b)

Figure 27

(a) (b)

Figure 28

14

(a) (b)

Figure 29

Figure 30

15

(a) (b)

Figure 31

(a) (b)

Figure 32

16

(a) (b) Figure 33

(a) (b)

Figure 34

Figure 35

17

(a) (b)

Figure 36

(a) (b)

Figure 37

18

(a) (b)

Figure 38

Table 1. Summary of measurement techniques and number of samples measured. Method Haynesville Monterey Niobrara Silurian

GRI 23 NA NA 9 Helium Injection (HE) NA 11 NA NA Water Immersion (WI) 34 12 18 21 Mercury Intrusion (MI) 13 12 10 22 Nitrogen Adsorption (N2) 22 12 22 19

Table 2. Summary of the disadvantages of each measurement technique and the resulting effect on porosity measurements. Methods Disadvantage Effect on Porosity Measurement

GRI - Hot solvent extraction removes soluble bitumen

- Creates artificial porosity and causes overestimation

- Causes higher grain volume and porosity underestimation

Water Immersion

- Clay minerals can swell with water

- Water might not fill organic-hosted pores and these pores might be hydrophobic

- Porosity overestimation in smectite rich samples

- Partial saturation of organic-hosted pores and porosity underestimation

Mercury Intrusion

- Does not measure pores associated with throat smaller than 3.6 nm

- Clay tactoid pores are missed

- Misses pores with throat smaller than 3.6 nm and causes porosity underestimation

- Causes porosity underestimation in clay-rich samples

Nitrogen Adsorption

- Does not measure pore bodies larger than 200 nm

- Gas might get sorbed in certain minerals or organic matter

- Misses pore bodies larger than 200 nm and causes porosity underestimation

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