Sagar Kale Shalegasthesis

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UNIVERSITY OF OKLAHOMA

GRADUATE COLLEGE

PETROPHYSICAL CHARACTERIZATION OF BARNETT SHALE PLAY

A THESIS

SUBMITTED TO THE GRADUATE FACULTY

in partial fulfillment of the requirements for the

Degree of

MASTER OF SCIENCE

BY

SAGAR KALE

Norman, Oklahoma

2009


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PETROPHYSICAL CHARACTERIZATION OF BARNETT SHALE PLAY

A THESIS APPROVED FOR THE

MEWBOURNE SCHOOL OF PETROLEUM AND GEOLOGICAL ENGINEERING

BY

Dr. Chandra Rai, Chair

Dr. Carl Sondergeld

Dr. Richard Sigal


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Copyright by SAGAR KALE 2009

All Rights Reserved.


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To my parents and sister for their motivation & constant support and to my wife for

her encouragement and patience


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ACKNOWLEDGEMENT

I would like to begin by expressing sincere gratitude to the members of my committee,

Dr. Chandra Rai, Dr. Carl Sondergeld and Dr. Richard Sigal, for their constructive

criticism and invaluable advice. I thank them for regularly taking time off their busy

schedule for evaluating my work at every stage. A successful completion of this work

wouldnt have been possible without their help. I also thank Dr. Deepak Devegowda

for his help with the cluster analysis exercise.

I would like to express sincere thanks to Mr. Gary Stowe and Mr. Bruce Spears for

teaching me how to operate various equipments at Integrated Core Characterization

Center. I also thank Mr. Moin Khan for helping me make the petrophysical

measurements on Barnett shale samples. My thanks to fellow graduate students,

undergraduate students at IC3

lab as well as the faculty and the staff at Mewbourne

School of Petroleum and Geological Engineering for their help all through my

Masters.

Last but not the least, I would like to express my heartfelt gratitude to my parents and

my sister who believed in me and encouraged me to take up higher studies. I can not

thank them enough for their selfless love and affection. My accomplishments so far

are as much theirs as they are mine. I take this opportunity to say a big Thank you to

my wife, Dhanashree, who has been a constant source of inspiration throughout my

work.

Sagar Kale


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TABLE OF CONTENTS

LIST OF FIGURES ..viii

LIST OF TABLES .....xii

ABSTRACT ...xiii

1. INTRODUCTION 1

1.1Natural gas industry in USA 1

1.2Introduction to shales and their importance as a resource ...2

1.3 Challenges in petrophysical characterization of shale gas play ...5

1.4 Purpose and scope of the study 6

1.5 Geology of Barnett shaleFort Worth basin ..7

2. LITERATURE REVIEW .12

2.1 Definition of mudrocks and shales .12

2.2 Mineralogy and clay structure ....13

2.3 Kerogen and its types .16

2.4 Total organic carbon and thermal maturity 18

2.5 LECO Method for estimating TOC .. .21

2.6 Rock-Eval pyrolysis/oxidation technique for estimating TOC and thermal

maturity 22

2.7 Vitrinite reflectance measurement .26


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2.8 Geochemical data reported in Barnett shale ...27

2.9 Rock typing techniques ..28

2.9.1 Rock Quality Index (RQI) and Flow Zone Indicator (FZI)

technique for rock typing .28

2.9.2 Winlands R35 approach ..30

2.9.3 Pitmans modification of Winlands approach...30

2.9.4 Prediction of permeability from Hg injection data .31

2.10 Rock type through Principal Component and Cluster Analysis ..35

2.10.1 Principal Component analysis ...35

2.10.2 Cluster analysis .36

3. EXPERIMENTAL PROCEDURE ...37

3.1 Sampling procedure . ..37

3.2 Helium porosity, bulk and grain volume measurement .38

3.3 FTIR mineralogy ....41

3.4 Mercury injection capillary pressure measurement ...44

3.5 Total organic carbon (TOC) measurement 51

4. OBSERVATION AND RESULTS ...53

4.1 Helium porosity .53


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4.2 FTIR mineralogy 54

4.3 Total organic carbon (TOC) and thermal maturity 60

4.4 Mercury injection capillary pressure measurement ...62

4.5 Classification of lithofacies 66

4.6 Rock typing 70

4.7 Rock typing through Principal Component & Cluster analysis .78

4.8 SEM study of rock types 88

4.9 Correlation of petrofacies with production data .90

4.10 Summary of observations and results ..94

4.11 Field Applications 97

5. CONCLUSIONS AND RECOMMENDATIONS ...98

5.1 Conclusions 98

5.2 Recommendations ..99

REFERENCES .......101

APPENDIXA ..106

APPENDIXB ..112


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LIST OF FIGURES

1.1 Production from six major shale plays from United States over last ten years (NCI,

2008) .. 3

1.2 Number of producing wells each year in Barnett shale and timeline of major

modifications in drilling and completion techniques (Martineau, 2007) ...4

1.3 Map of Barnett shale play showing geographic, tectonic features and variation in

thickness of Barnett shale (Pollastro, 2003) ...8

1.4 Location map of study wells (Modified after Singh, 2008) ...10

1.5 Generalized stratigraphic column and stratigraphic subdivision of Barnett shale

(Modified after Pollastro, 2003 and Montgomery et al., 2005)11

2.1 Sheet structure of illite (Grim, 1968) .14

2.2 Sheet structure of smectite (Grim, 1968) ...15

2.3 Sheet structure of kaolinite (Grim, 1968) ..16

2.4 Model of organic carbon distribution (Jarvie, 1991) .19

2.5 Conversion of convertible organic matter into EOM and secondary cracking of oil

into gas with increasing thermal maturation (Modified after Jarvie, 2004) .21

2.6 Van Krevelen diagram (Emeis and Kvenvolden, 1986) ........................................24

2.7 Three Stages in Rock-Eval Pyrolysis (Modified after Jarvie, 2004) ..................25

2.8 Change in color and vitrinite reflectivity with increasing thermal maturation in

Barnett shale samples (Modified after Jarvie, 2004) ...26


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3.1 Barnett shale core images ..38

3.2 Histogram showing particle size distribution 40

3.3 Plasma asher setup for removing organic matter from the sample before FTIR

mineralogy measurement .44

3.4 Cumulative Hg intrusion plot showing real and false intrusion .47

3.5 Penetrometer and its components ......49

3.6 AutoPore IV machine used for running mercury injection measurement ..50

4.1 Histogram showing porosity variation of the dataset .....53

4.2 Waterfall chart showing mineralogy variation in all four wells 54

4.3 Contribution of each carbonate to overall carbonate content averaged over entire

dataset ...56

4.4 Contribution of each clay mineral to overall clay content averaged over entire

dataset ...57

4.5 Average mineral composition and standard deviation of the entire dataset ...57

4.6 Porosity variation with calcite content ...58

4.7 TOC variation with calcite content 59

4.8 Histogram showing calcite variation of the dataset ...60

4.9 Histogram showing TOC variation of the dataset ..61

4.10 Incremental and cumulative mercury intrusionplot for type A samples ..63


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4.11 Incremental and cumulative mercury intrusion plot for type B samples ...63

4.12 Incremental and cumulative mercury intrusion plot for type C samples ...64

4.13 Lithofacies occurrence for the dataset ..67

4.14 Average porosity, TOC and calcite content of each lithofacies ...68

4.15 Average porosity, TOC and calcite content of three petrofacies .74

4.16 Porosity - TOC plot for all the samples from well C and well D ...75

4.17 Relationship between Hg rock types and petrofacies ..77

4.18 Relationship between principal components and percentage variance they explain

(11 variable case)..80

4.19 Pairwise scatterplot showing relationship of variables with each other and with

the three principal components (11 variable case)81

4.20 Relationship between principal components and percentage variance they explain

(5 variable case)83

4.21 Pairwise scatterplot showing relationship of variables with each other and with

the three principal components (5 variable case)......84

4.22 Average porosity, TOC and calcite content of three clusters ...87

4.23 Secondary electron image of petrofacies 1 sample using environmental scanning

electron microscope .88




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LIST OF TABLES

Table 4.1 Thermal maturity data for all four wells ..61

Table 4.2 Porosity and TOC data for three Hg rock types ...69

Table 4.3 Mineralogy data for three Hg rock types .69

Table 4.4 Porosity and TOC data for three petrofacies 73

Table 4.5 Mineralogy data for three petrofacies ..73

Table 4.6 One to one correspondence betweenpetrofacies and Hg rock types ...78

Table 4.7 Porosity and TOC data for three clusters .85

Table 4.8 Mineralogy data for three clusters ...85

Table 4.9 Relationship between clusters, petrofacies and Hg rock types 87

Table 4.10 Comparison of production data with petrofacies 1 thickness 91


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ABSTRACT

With the contribution of shale gas towards the overall natural gas production in North

America increasing steadily, petrophysical characterization of these unconventional

plays has become extremely important for identifying sweet spots in the reservoir.

However; conventional techniques of rock typing, based on porosity permeability

cross-plots, do not work in shales due to a lack of dynamic range for these parameters

and the difficulties involved in a direct permeability measurement. In this exercise,

rock typing has been attempted by integrating geological core description with the

petrophysical measurements.

Petrophysical measurements are made on shale plugs recovered from four wells from

different parts of the Newark East field. Measurements of porosity, mineralogy and

total organic carbon (TOC) are made on approximately 800 plugs. Mercury injection

capillary pressure measurements are done on approximately 130 plugs to obtain a

dataset of petrophysical parameters for rock typing.

Based on core description, Singh (2008) identified 10 distinct lithofacies in the Barnett

shale that represented unique geological settings at the time of deposition. However,

she did not rank the lithofacies in terms of their importance towards gas production. I

have tried to bridge the gap between the geological core description and the well

productivity by supplementing the geological core description with the petrophysical

description of the core.

From the petrophysical measurements on the samples from all ten lithofacies, it is

observed that some of these lithofacies have similar petrophysical properties. That


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presents an opportunity of combining them into fewer groups so that each group has

unique petrophysical properties. These groups are termed as petrofacies. Even

though the petrofacies have a narrow dynamic range for some of the petrophysical

parameters, they are distinct in terms of their calcite content. Porosity and TOC also

show a strong correlation with calcite as both of them decrease with increasing calcite

content.

Based on porosity, TOC and calcite content, I have reduced the 10 lithofacies into 3

petrofacies Samples from petrofacies 1 have 0-10% wt. calcite content with a porosity

range of 6.0-6.3% (average 6.1%) and a TOC of 4.7-5.0%. Petrofacies 2, with 10-25%

wt. calcite, has a porosity range of 5.8-6.3% (average 6.0%) with a TOC 3.4-3.8%.

Petrofacies-3, with over 25% wt. calcite, has a porosity range of 3.4-4.0% (average

3.7%) and a TOC from 1.6-1.9%.

After ranking the petrofacies on the basis of their petrophysical properties, it is

observed that petrofacies 1 represents calcite lean reservoir rock (


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long and nearly continuous intervals of petrofacies 1, over the perforated zone,

produces better than the well where petrofacies 1 is discontinuous and separated by

thick intervals of petrofacies 2 and 3 over the perforated zone. This proves that

petrofacies 1 not only has the best petrophysical properties; it is a better gas producer

as well.

Petrofacies 1 represents the best reservoir rock in the field.


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Chapter1

INTRODUCTION

1.1 Natural gas industry in USA:

The importance of natural gas in the energy industry has increased steadily. Relative

abundance of natural gas and its clean burning characteristics make it a very attractive

energy resource. Natural gas finds extensive use in residential and commercial

applications, for power generation and as an alternative transportation fuel.

However for a very long time natural gas production was not actively pursued mainly

due to lack of infrastructure for transporting it to its end users. Natural gas associated

with oil production was considered nuisance and used to be flared. However with the

growing natural gas pipeline network, advent of specialized ships for transporting

natural gas as LNG, depleting oil reserves and stringent anti flaring regulations have

made natural gas production attractive. In United States, where extensive natural gas

pipeline network is already in place, we now see many independent operators dealing

solely with natural gas.

USA is one of the largest producers of natural gas with annual natural gas production

of 20.6 TCF in year 2008. EIA forecasts production of 25.4 TCF by 2030 (Annual

Energy Outlook, 2009). US dry natural gas reserves are estimated as 1680 TCF as of

year-end 2007 with over 40% contribution coming from unconventional resources

such as tight gas sands, shale gas and coal bed methane. Shale gas alone contributes

274 TCF, over 16% of total reserves. However some estimate that this value can be as

high as 842 TCF, over 37% of total reserves. (NCI, 2008)


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1.2 Introduction to shales and their importance as a resource:

Shales are defined as laminated sediments containing very fine grade (less than 4

micron in size) particles predominantly made of clays. Shales are the most common

sedimentary rocks in the earths curst and they are characterized by low porosities and

nano-darcy permeabilities. The first commercial gas well drilled in USA was actually

producing from shales. It was drilled in 1821 in Fredonia, New York. However, for a

very long time after that, sandstones and carbonates overshadowed shales as reservoir

rocks. Shales, due to their ultra-low permeability, were only considered as geological

seals; preventing hydrocarbon escape from conventional reservoirs. They were drilled

through to reach conventional sandstone or carbonate reservoirs. Since shales were not

treated as reservoir rocks, they were never studied in detail.

However shales can serve as a source and a reservoir rock if they are rich in organic

matter. The organic matter, under anaerobic conditions, transforms into high

molecular weight polymeric compounds called kerogen due to bacterial action. At

high pressures and temperatures, kerogen transforms into liquid hydrocarbons and

further into methane depending on prevailing temperature and pressure (Tiab and

Donaldson, 2004). This gas is then either adsorbed on the surface of the organic matter

or clay, escapes through the network of natural fractures or remains within the pores of

the shale (Frantz, 2005). Shales can hold enormous amount of free and adsorbed gas,

most of which can be commercially recovered through modern drilling and completion

techniques. Fig.1.1 shows natural gas production from six major shale gas plays in

mainland USA. It can be seen that natural gas production from Barnett shale far

exceeds that from other shale plays. Barnett has grown from 94 MMSCF/day to 3


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BSCF/day over last 10 years, an increase of over 3000% (NCI, 2008). This increase in

production for Barnett shale also coincides with the modification in drilling and

completion techniques as shown in Fig.1.2.

Fig.1.1: Daily gas production from six major shale gas plays in USA over last ten years (NCI, 2008);

contribution of Barnett shale to overall shale gas production in United States has steadily increased over last tenyears

Over 6200 wells, both horizontal and vertical, are completed in Barnett; producing 2

BSCF/day natural gas by September 2006. After 1997, the wells were completed with

water fracturing and sand as proppant. The horizontal wells that are 1000 to 3500 ft

long are completed with multistage hydraulic fracturing (Martineau, 2007).


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Fig.1.2: Number of producing wells in Barnett with time along with start periods of major completion and

drilling events (Martineau, 2007);operators started out drilling vertical wells and fracturing them with foam orcross-linked gel in Barnett shale, by 1997 water fracturing replaced gel fracturing, by 1999 operators started

refracturing the wells that were originally gel fractured with water. It resulted into almost 60% increase inrecoverable reserves. By 2003, horizontal drilling became popular as it reduced the risk of fracturing waterbearing Ellenberger formation. Most of the drilling in Barnett since 2003 has been horizontal.

Although Barnett shale is also produced from Delaware basin in West Texas, it is the

Fort Worth basin in North-East Texas that contributes the most to overall gas

production from Barnett shale play. Data presented in Fig.1.1 and Fig.1.2 represents

Barnett shale from Fort Worth basin only.

Since shales are now considered as reservoir rocks with potential for significant gas

production; the interest in their petrophysical characterization, necessary for better

prediction of reserves and identification of gas rich zones, has grown tremendously.


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1.3 Challenges in petrophysical characterization of shale gas play:

Petrophysical characterization of the reservoir aims to classify the reservoir rocks into

groups with unique petrophysical parameters. Such groups are known as rock types.

Since each rock type has a distinct range of petrophysical properties, it can be used for

distinguishing good reservoir rock from bad.

In conventional sandstones, rock types are identified from porosity-permeability cross-

plots. RQI (Rock Quality Index), Winlands R35 method use these cross-plots for rock

typing. (Various methods for rock typing, reported so far, are summarized in Chapter-

2 on literature review.) However porosity and permeability values from conventional

reservoirs typically have a very wide dynamic range which makes identifying rock

types from such cross-plots very easy. Rock types so identified usually differ by an

order of magnitude in permeability and have broad range for porosity.

Rock typing from porosity-permeability cross-plots is not feasible in shale gas

reservoirs for two main reasons. First: unlike sandstones, the dynamic range for

porosity in shales is very narrow; second: shales are characterized by nano-darcy

matrix permeabilities which are few orders of magnitude lower than even tight gas

sands.

For reservoir characterization, we need a significantly large dataset of petrophysical

parameters. Generation of such a large dataset, through core plug measurement, is

expensive and time consuming. We typically overcome this problem by correlating the

rock types with the log measurements from the same interval and then use these

correlations along with logs for predicting petrophysical properties in the uncored


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intervals. However in shales, rock types may have overlapping ranges for certain

petrophysical parameters; making it harder to establish such correlations. Since shales

are heterogeneous at all scales, correlations established in one part of the field often do

not hold up in the other parts.

Since the dynamic range of petrophysical properties in shales is rather narrow, it raises

a question of how important petrophysical characterization really is in these

unconventional plays. However when we look at the production profiles and well

completions in Newark East field, we observe that some part of the field produce

much better than others. This could mean that the reservoir rock is different in

different parts of the field and subtle differences in their petrophysical properties may

be responsible for this big variation in production. These subtle differences in

petrophysical properties, if captured through rock typing, can help us distinguish

between the good and the bad reservoir rock. Petrophysical characterization of shale

gas reservoir through rock typing if successfully done can provide that vital piece of

information for optimizing gas production from these unconventional resources.

1.4 Purpose and scope of the study:

This study attempts a petrophysical characterization of a Barnett shale play through

rock typing so that the sweet spots in the reservoir can be identified and targeted

through suitable completion techniques for optimal gas production. Rock typing is

based on geological core description (Singh, 2008) and the petrophysical

measurements made on approximately 800 core plugs, from four wells located in

different parts of Newark East Field at Integrated Core Characterization Center at


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University of Oklahoma. The measurements made were helium porosity, FTIR

mineralogy, total organic carbon (TOC) and Hg injection capillary pressure. Rock

types so identified are observed to differ in terms of their petrophysical properties with

a little overlap. Comparison of these petrophysical properties among different rock

types helps us identify the best reservoir rock type in the field.

In order to verify that the reservoir rock type identified as a best reservoir rock is

indeed the best, we compared the production data for two wells with the thickness of

each rock type over the perforated interval of the well. The results of this comparison

are discussed under the Observations and Results (Chapter 4).

In a completely independent exercise, we attempted rock typing through principal

component and cluster analysis using a program called Efacies developed by Dr. A.

Datta-Gupta from Texas A&M University. The details of this method are reported

under Literature Review (Chapter 2). The results of this exercise are included in

Observations and Results (Chapter 4).

1.5 Geology of Barnett shaleFort Worth basin:

Fort Worth basin is a north-south elongated basin in North-East Texas. It roughly

covers 15,000 square miles (Singh, 2008). As shown in Fig.1.3, Fort Worth basin is

enclosed by Red River arch from North, Munester arch from North-East, Ouachita

thrust belt from East, Llano uplift from South and Bend arch from West. The

generalized stratigraphic column is shown in Fig.1.5. Barnett shale from Fort Worth

basin is of Mississipian age, marine shelf deposit (Shelley et al., 2008). It is black,

organic rich, calcareous shale with nanodarcy range matrix permeability (Shelly et al.


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2008). It is also a source rock for many conventional clastic and carbonate reservoirs

such as Pennsylvanian Bend, Strawn and Canyon groups of Fort Worth basin

(Pollastro et al., 2007). Barnett shale is therefore a fully self sustained petroleum

system that serves as a source, a seal and a reservoir rock (Jarvie, 2005).

As shown in Fig.1.3A) and Fig.1.3 B), thickness of Barnett shale in Fort Worth basin

decreases from NE-SW transect (A-A).

Fig.1.3: A) Map of US Geological Survey Province 50, Fort Worth Basin, TX, showing major tectonic

features, geographic extent of Barnett shale, lines of well log cross section A-A and B-B and contours

showing thickness of Barnett shale in the Fort Worth basin, B) Generalized NE-SW well log cross section

showing thickness of Barnett Shale, C) Generalized SE-NW well log cross section showing thickness of

Barnett Shale (Pollastro, 2003)

Barnett is almost 1000 feet thick close to Munester Arch towards North East but it

gradually decreases to 100 feet towards the South-West end of the basin. Along the

NW-SE transect (B-B), as shown in Fig. 1.3 A) and Fig. 1.3 C); Barnett is much


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thinner with maximum thickness of around 300 feet and minimum thickness of around

10 feet over Chappel limestone shelf.

Our focus in this study is on Newark East field, a mature gas field located in the

North-East region of the basin, closer to Munester Arch where the thickness of Barnett

shale is probably the highest. The core area (Highlighted with green in Fig.1.4) of this

filed is located in Denton, Wise and Tarrant county; however the field also extends

into Parker county. The 4 cored well studied and discussed in this thesis are marked on

Fig.1.4. Two of these four wells (Well A and Well B) are in the core producing

area, towards the right of Viola Pinchout, where Barnett is sandwiched between two

carbonate formations with Marble Falls on the top and Viola at the bottom. Here

Barnett is also subdivided into two producing intervals, upper and lower by another

carbonate rich interval called Forestburg. Forestburg thickness varies from 20 feet to

150 feet (Shelley et al., 2008). Forestburg thinning is observed from NE to SW before

it completely disappears in the extended part of the field as shown in Fig.1.5.

In the extended part of the field, towards the left of Viola Pinchout, the formation

overlying Barnett is still Marble Falls but the underlying formation is Ellenberger

limestone.


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Fig.1.4: Map showing the core area of Newark East Field, Viola Pinchout and approximate locations ofcored wells studied in this thesis (Modified after Singh, 2008); area shaded in green represents original corearea of production in Barnett shale play. Two of the cored wells (Well A and Well B) included in this study

are from the core area. They are vertical wells. Well C and well D are from the extended area and they are

horizontal wells.

Well A Well BWell C

Well D

Well A Well BWell C

Well D


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Fig.1.5: (A) Generalized stratigraphic column (Modified after Pollastro, 2003). (B) Stratigraphic subdivisionof Barnett shale and details of formations overlying and underlying Barnett shale at different locations along

NE-SW transect (Modified after Montgomery et al., 2005)


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Chapter2

LITERATURE REVIEW

With the introduction of hydraulic fracturing and horizontal drilling, the number of

completions and overall gas production from Barnett shale has increased exponentially

over last ten years. Since 2001, Barnett shale has been the largest contributor to

overall shale gas production in United States (see Fig. 1.1).

In spite of the growing interest in shales, our understanding of these unconventional

gas plays is still limited. The aim of this chapter is to review the state of knowledge on

shale gas plays in general and Barnett shale in particular.

The chapter describes the structure, the mineral composition and the geochemistry of

shales. It details the techniques for TOC and thermal maturity measurements and also

summarizes the geochemical measurements reported on Barnett shale. The chapter

covers various rock typing techniques reported on conventional reservoir rocks and

explains why they do not work in shales. It concludes by providing a brief summary

on principal component and cluster analysis techniques used for identifying rock

types.

2.1 Definition of mudrocks and shales:

Mudrocks are siliclastic sedimentary rocks, made up of silt sized particles (0.0625 to

0.0039 mm) and clay sized particles that are even finer (< 0.0039 mm). They are the

most common class of sedimentary rocks constituting 50-80% of earths sedimentary

rock shell (Prothero and Schwab, 2004). Shales are a class of mudrocks that exhibit


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laminations, fissility along the bedding planes and are predominantly made up of clay

sized particles. Shales are believed to be deposited under low energy environment

where weak currents carry flake shaped mica and clay minerals without too much

abrasion. Shales therefore exhibit a preferred fabric caused by the preferential

alignment of flaky mica and clay minerals into thin, parallel sheets. The thickness of

these parallel sheets can range anywhere between 0.5 mm to 10 mm (Prothero and

Schwab, 2004). This sheet like fabric/bedding plane is responsible for the fissility in

shales. They are observed to split or peal off along the bedding plane very easily.

2.2 Mineralogy and clay structure:

Clays are the dominant minerals in shales with illite, kaolinite, smectite, chlorite being

the most common clays. Clays have two basic building blocks which can be used to

construct most of the clay minerals. The first is a tetrahedral silicate sheets with

oxygen ions at the corners. The second is an octahedral arrangement where hydroxyl

ions occupy the corners and surround calcium, magnesium or aluminum ions. Most of

the clays are composed of sandwiches of these building blocks repeated over and over.

Most of the clays exhibit 2:1 or three sheet sandwich structures, in which an

aluminum-hydroxyl octahedral sheet is sandwiched between two oxygen-silicon

tetrahedral sheets. The cations present in these sandwiches are responsible for the

variation in clays. Illite is the most abundant clay mineral. It is potassium rich 2:1 clay

that has an octahedral sheet (where K+ has replaced Al+3 as cation) sandwiched

between two tetrahedral sheets (where silicon is sometimes substituted by aluminum)

(see Fig. 2.1). The presence of potassium ion provides the structure with strong ionic

bonding, preventing water from percolating through and clay from swelling (Prothero


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and Schwab, 2004). Smectites on the other hand have small amount of Mg+2

ion

substituting Al+3 ion in the octahedral structure resulting into slightly negatively

charged octahedral layers (negative charge is smaller than the charge in illite). The

positive ions such as Na+, K+ and Ca+2 from interlayers balance this charge (see Fig.

2.2). Since the net charge in smectites is lower than illite, water is readily absorbed in

the interlayers which makes the smectite structure expandable (Prothero and Schwab,

2004). They are known to double in volume when they absorb water. Chlorites too

have 2:1 sheet structure with the octahedral interlayer having Mg+2

and Fe+2

replacing

Al+3 at the lattice center.

Fig.2.1: Sheet structure of illite (Grim, 1968); potassium rich 2:1 clay with an octahedral sheet sandwichedbetween two tetrahedral sheets


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Fig.2.2: Sheet structure of smectite (Grim, 1968);2:1 sheet structure with small amount of Mg+2 substituting

Al+3 in the octahedral sheet

Kaolinite on the other hand has a much simpler structure with one tetrahedral sheet

and one octahedral sheet in each repeating layer (see Fig. 2.3). These layers are tightly

bonded together by hydrogen as cation. Unlike most other clays, kaolinites lack space

for water, hydroxyl or larger cations between the layers (Prothero and Schwab, 2004).


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Fig.2.3: Sheet structure of kaolinite (Grim, 1968); simple sheet structure with one tetrahedral sheet and one

octahedral sheet in repeating layers

Natural clays are often observed as regular or random mixtures of multiple clay types.

They are termed as mixed clays with the mixture of illite-montmorillonite being the

most common. In addition to clays, other minerals such as quartz, feldspar, carbonates

(calcite, dolomite, siderite and aragonite), pyrites, apatites and anhydrite are also

found in shales. FTIR mineralogy technique (Explained in Chapter 3), used for

mineralogy measurement of Barnett shale samples at Integrated Core Characterization

Center, can determine the presence of these minerals along with their quantities in

weight percentages.

2.3 Kerogen and its types:

Shale plays rich in organic matter are the only once with commercial importance. It is

the organic matter in shale that makes it a source rock. Organic matter in shale is in a

form of kerogen. Kerogen is a product of anaerobic decomposition of organic matter

from dead plants and animals. At deeper burial depths, high temperature and high

pressure convert these decomposition products into high molecular weight polymeric

compounds of carbon, hydrogen and oxygen. They are called kerogen.


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As the sediments get buried deeper, they encounter increased pressures and

temperatures that induces further reactions; transforming the kerogen through liquid

bitumen into liquid petroleum product (Tiab and Donaldson, 2004). If oil produced

this way stays trapped in a source rock, the overburden and the high temperature at

deeper depths results in cracking of oil into gas.

The type of hydrocarbons produced depends on the kerogen type. Type I kerogen is

deposited in a relatively quite and calm lacustrine environment (deposition in lakes)

(Tissot, 1984). It is derived primarily from algal matter and it has undergone

considerable modification due to microorganisms residing in the sediment. In terms of

elemental composition, type I kerogens are known to have high hydrogen to carbon

ratio (>1.3) and low oxygen to carbon ratio (


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2.4 Total organic carbon and thermal maturity:

The extent of organic matter inside a sample is characterized by a term called Total

Organic Carbon or TOC. TOC values are reported in terms of weight percentage of

organic carbon; meaning 1% wt. TOC represents 1 gm of organic carbon in 100 gm of

sediment sample (Jarvie, 1991). TOC is composed of three components. It has 1.

Extractable organic matter (EOM) 2. Convertible carbon and 3. Residual carbon

fraction or dead carbon. Extractable organic matter represents organic carbon present

as oil and gas that is already been produced but not yet expelled. Convertible carbon

represents that fraction of TOC which has the potential to transform into oil and gas.

Under right pressure and temperature conditions, this part of the TOC would transform

itself into bitumen then into oil and finally into gas. Residual carbon fraction

represents that part of the organic matter which has no potential to generate oil and gas

because of its low hydrogen content per unit of organic carbon. As convertible carbon

transforms itself into oil and gas, it leaves behind a dead residue; increasing the

residual carbon fraction of TOC. Fig.2.4 shows the organic carbon distribution in

sediment sample. The picture shows all three constituents of TOC.


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Fig.2.4: Model of organic carbon distribution (Jarvie 1991; adapted and modified from Espitalie, 1982);

TOC is made up of oil and gas already produced and kerogen which accounts for organic matter capable ofproducing oil and gas and dead carbon.

From Fig.2.4, it can be seen that for a commercial gas accumulation to take place,

having only high TOC is insufficient. High TOC also needs to be complemented by

high thermal maturation to ensure that convertible carbon has seen high temperatures

and pressures necessary for its transformation into commercially valuable oil and gas

products. At sufficiently high temperatures, the oil produced is known to undergo

secondary cracking and transform into gas. Secondary cracking depends on the

temperatures reached and the composition of oil. In Barnett shale formation, it is

known to take place at temperatures beyond 150oC (302

oF) (Jarvie, 2004).

During thermal maturation of organic carbon, TOC of the sample stays unchanged;

however the contribution of each component to TOC undergoes a change. As shown


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in Fig. 2.5, when an immature shale sample, capable of producing oil and gas (i.e.

having convertible carbon) undergoes thermal maturation, convertible carbon

transforms into oil, gas and leaves behind carbon residue or dead carbon. This results

into increase of EOM as well as residual carbon and decrease of convertible carbon.

The oil and gas produced often undergo periodic expulsion as the pressure builds up

during the process of thermal maturation and sources hydrocarbons to conventional

reservoir rocks with good storage and flow characteristics.

However in Barnett shale and other shale plays of commercial importance, the gas

produced is known to stay behind in the source rock instead of being expelled. Jarvie

et al. (2007) propose two main mechanisms of gas storage in a source rock: 1. Gas

stays behind in a source rock in a sorbed state (absorbed and adsorbed) to or within the

organic matrix; with organic richness, kerogen type and maturity impacting the

sorptive capacity. 2. As a free gas in pore spaces and fractures; created either by

organic matter decomposition or other diagenetic or tectonic processes (Jarvie et al.,

2007). It has also been reported that, in Barnett shale, reservoir rocks with high TOC

and high thermal maturity produce better (Jarvie, 2004); confirming that organic rich

portion of Barnett shale retains significant amount of produced gas. Therefore TOC

content and thermal maturity can be used as an indicator of a gas bearing zone. It

justifies using TOC as one of the petrophysical parameters for identifying the best

petrofacies in Barnett shale play.


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Fig.2.5: Conversion of convertible organic matter into oil and gas with increasing thermal maturation

(Modified after Jarvie, 2004); Thermal maturation stages of organic matter: Stage 1: A sample with highamount of convertible organic matter yet to undergo thermal maturation, Stage 2: Conversion of this organic

matter into oil and gas upon thermal maturation and increase in residual carbon, Stage 3: Continuedconversion of organic matter into oil and gas upon further maturation as well as secondary cracking of oil intogas

TOC measurements provide information about its organic richness and thermal

maturity measurement provides information about the level of maturation of the

organic matter into commercially valuable oil and gas.

The measurement techniques for estimating the TOC content of the organic matter and

the extent of its maturation are summarized below.

2.5 LECO method for estimating TOC:

LECO TOC method gives a measure of the total organic carbon present in a sample.

This technique requires about 1 gm of crushed sample (Jarvie, 1991). In this method,

inorganic carbon in the form of carbonates is separated from the organic matter by

soaking the sample in HCl for 12 to 16 hours with intermittent stirring. Carbonates

Convertible CarbonDead

Carbon

Total Organic Carbon (TOC)

EOM Carbon

Convertible

Carbon

Dead

Carbon


EOM Carbon

Convertible

Carbon

Dead Carbon


EOM Carbon

Gas Oil

OilGas

Gas Oil

IncreaseinThermalMaturity

&

SecondaryCrackingof

Oil Convertible Carbon

Dead

Carbon


EOM Carbon

Convertible

Carbon

Dead

Carbon


EOM Carbon

Convertible

Carbon

Dead Carbon


EOM Carbon

Gas Oil

OilGas

Gas Oil

IncreaseinThermalMaturity

&

SecondaryCrackingof

Oil


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present in the sample are converted into CO2 and bubble out of HCl solution. Once the

conversion of carbonates into carbon dioxide is complete, no more effervescence from

the solution is observed. The remaining sample is then freed of HCl by rinsing it with

water. Filter paper and filtering flask is used during rinsing to avoid losing any

sample. Rest of the sample is then oxidized to convert organic carbon into carbon

dioxide. CO2 vapors are routed through an infrared detector which determines the

amount of CO2 produced and then back calculates the organic carbon present in the

sample using the material balance.

2.6 Rock-Eval pyrolysis/oxidation technique for estimating TOC and thermal

maturity:

In addition to the total organic content of the sample, this technique can determine the

kerogen type and the thermal maturity of the sample.

Rock-Eval method combines pyrolysis with oxidation cycle for complete

geochemical analysis of the sample. The sample undergoes programmed temperature

heating in a pyrolysis oven. The temperature of the oven is increased up to 300oC and

held constant to allow release of free hydrocarbons present in the sample. Free

ionization detector (FID) connected with the pyrolysis oven records the first peak

corresponding to the release of free hydrocarbons. This peak is labeled as S1.

Temperature is then gradually ramped up to 600oC at a rate of 25

oC per minute. This

results in cracking of organic matter or convertible carbon resulting into yet another

peak. The second peak recorded by FID is labeled as S2 and it corresponds to the

convertible carbon present in the sample. S1 and S2 peaks when normalized by


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multiplying by 0.083 to provide estimates of EOM and convertible carbon respectively

in terms of gm of carbon per 100 gm of sample (0.083 is derived from average wt.

percent of carbon in hydrocarbons and by conversion of mg of hydrocarbon per gm of

rock or parts per thousand to parts per hundred (%)) (Jarvie, 1991). S 2 peak is also

used to determine hydrogen index (S2100/TOC) of the sample. Hydrogen index (HI),

measured in terms of mg of hydrocarbon per gm of TOC, is an indicator of the

hydrogen content per unit of organic carbon and along with oxygen index (OI)

determines the type of kerogen present. Oxygen index (OI) of the sample is

determined by allowing the sample to cool down to room temperature after pyrolysis

is complete. During cool down, the trapped CO2 is released resulting in a third peak

S3. S3 is used to determine OI (S3100/TOC) of the sample. OI is measured in terms of

mg of CO2 per gm of TOC. Knowing HI and OI, the kerogen type can be easily

determined from Van Krevelen plot as shown in Fig. 2.6.

However as kerogen undergoes thermal maturation, convertible organic carbon

transforms itself into oil and gas and hydrogen deficient residual carbon resulting into

reduction of hydrogen index. OI also decreases upon thermal maturation. Therefore,

in order to determine the kerogen type, HI and OI measurement needs to be made on a

sample with low thermal maturity (~0.6% Ro).


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Fig. 2.6: Van Krevelen diagram (Emeis and Kvenvolden, 1986). Hydrogen and oxygen index cross-plot for

estimating kerogen type of the source rock; Type I kerogens have HI greater than 600 and OI less than 50when they are thermally immature, Type II kerogens tend have HI in between 200 and 600 when thermallyimmature, Type III kerogens are characterized by OI that can be as high as 160 and HI under 200 when

thermally immature. As samples undergo thermal maturation, both HI and OI of the sample decrease.

The temperature corresponding with S2 peak is called as Tmax and it is a measure of

thermal maturity of the sample. Any sample with Tmax less than 430oC is considered

thermally immature. Samples with Tmax between 430-450oC are considered thermally

mature and in oil or wet gas window and the samples with Tmax over 450oC are

considered to be in gas window (Emeis and Kvenvolden, 1986).

The TOC value used for normalizing S2 and S3 peaks can be determined independently

or can be obtained from Rock-Eval measurement itself. In a direct measurement of

TOC using Rock-Eval technique, EOM and convertible carbon present in the sample

is determined through S1 and S2 peaks observed during pyrolysis. However for

completing the TOC estimation, the measure of residual carbon is also needed and it is


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obtained by oxidizing the sample following pyrolysis. The sample is heated in an

oxidation oven so that residual carbon converts to CO2 and then by measuring the

amount of CO2 generated during oxidation, the residual organic carbon can be back

calculated. The residual carbon is then added to the extractable carbon and the

convertible carbon to obtain a full measure of TOC. Fig.2.7 shows the process of

Rock-Eval measurement.

Fig.2.7: Rock-Eval Pyrogram showing three stages during pyrolysis (Modified after Jarvie, 2004); S1 peakrepresents extractable organic matter in the sample in the form of oil and gas that is already been generated, S2peak represents convertible carbon which is capable of transforming into oil and gas upon thermal maturation.The temperature corresponding to this peak represents Tmax which gives the thermal maturity of the sample, S3

peak corresponds with the release of trapped CO2 during cool down following pyrolysis and is used to estimate

OI.

Time

Yield

TimeTime

Yield


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2.7 Vitrinite reflectance measurement:

Vitrinite reflectance (%Ro) is an optical technique used to gauge the thermal

maturation of the source rock. It provides an indication of the maximum

paleotemperatures seen by the source rock (Jarvie et al., 2007). This is an optical

method in which microscopic examination of kerogen or the entire rock sample is

carried out and the reflectivity of the particle is recorded via a photomultiplier tube.

Since formation of vitrinite indicates thermal maturity of the rock sample, samples

with higher vitrinite reflectance are more thermally mature. Fig.2.8 shows change in

color and vitrinite reflectivity with organic maturation. It also shows typical vitrinite

reflectance values observed in Barnett shale samples in different stages of thermal

maturation.

Fig. 2.8: Change in color of samples having different thermal maturity (%Ro) observed in Barnett shale

samples (Modified after Jarvie, 2004); samples with over 1.0% Ro are considered thermally mature and arelikely to have produced oil and gas in a commercially significant quantity


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2.8 Geochemical data reported on Barnett shale:

Jarvie (2004), Jarvie et al. (2007), Pollastro et al. (2007), Zhao et al. (2007) report that

Barnett shale is organic rich, kerogen type II, oil prone marine shale with an initial

average TOC content of 5.5-6.4%, hydrogen index of 340-430 mg HC per gm of TOC

with a vitrinite reflectance of 0.44%Ro. Convertible carbon from Barnett shale is

known to generate about 30% gas in oil window (0.6-0.99%Ro). The oil produced,

upon further maturation, undergoes secondary cracking to produce more gas. Present

day Barnett shale is known to be in a wet gas window between 1.1-1.4 %Ro in most

parts.


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2.9 Rock typing techniques:

Gunter et al. (1997) defines rock types as units of rocks deposited under similar

geological condition, undergone similar digenetic processes resulting in unique

porosity-permeability relationships, capillary pressure profile and water saturations for

a given height above free water. Thus rock typing is a technique of classifying

reservoir rocks with similar petrophysical properties into groups so that each group is

characterized by a set of unique petrophysical properties. Rock samples classified

under one rock type tend to have similar storage and flow characteristics, similar

response on a capillary pressure curve and similar relative permeability curves.

Petrophysical properties of rock types identified from core measurement are correlated

with the log response in a cored interval and these correlations along with the log

measurements are then used to predict the petrophysical properties in the uncored

interval of the reservoir.

Some of the commonly used techniques for rock typing in conventional sandstone and

carbonate reservoirs are described below:

2.9.1 Rock Quality Index (RQI) and Flow Zone Indicator (FZI) technique for

rock typing:

Amaefule et al. (1993) introduced a concept of Rock Quality Index (RQI) by

modifying general form of Kozeny-Carman equation. RQI correlated porosity and

permeability as follows:


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e

kSqrtRQI

0314.0 .....(2.1)

Where, k = absolute permeability of the system in md and e is effective porosity. RQI

is expressed in m.

RQI calculated above is then plotted against on a log-log plot.

Where, is normalized porosity index defined as a ratio of pore volume to grain

volume and calculated as follows:

ee

z

1

..(2.2)

A plot of RQI vs. yields a straight line with unit slope and its intercept when =1

is termed as Flow Zone Indicator (FZI). FZI can also be calculated as follows:

z

RQIFZI

..(2.3)

Rock samples with similar FZI values are observed to fall on one line on a log-log plot

of RQI and . They constitute one rock type. Samples from different rock types have

different FZI values and they are observed to fall on other parallel straight lines. FZI

includes geological features such as texture and mineralogy of distinct pore geometries

since parameters such as surface area, shape factor and tortuosity of flow path go into

its calculation. A clear distinction between clean sand and shale can be obtained

through FZI index calculation as clay rich, poorly sorted, fine grained sedimentary

rocks have a lower FZI value whereas less shaly, coarse grained and well sorted sands

have high FZI value.


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2.9.2 Winlands R35 approach:

Winlands R35 approach is based on mercury injection, air permeability and porosity

measurements on a core sample. Based on the measurements made on a set of 82

sandstone and carbonate samples, Winland developed following correlation:

log864.0log588.0732.0log 35 kR .....(2.4)

Where,

R35 represents pore throat radius (m) corresponding to 35% mercury saturation

during capillary pressure measurement, k is air permeability in md and is porosity

in percent. 35% mercury saturation was chosen because the best correlation, between

the pore throat radius estimated using the equation and measured through the mercury

injection experiment, was observed at this saturation.

Samples with high porosity and permeability have larger R35 values and samples with

lower porosity and permeability have smaller R35 values. Knowing the porosity and

the permeability, one can calculate a corresponding R35 value for every sample and

then plot them on a Winland Plot (A semi-log permeability vs. porosity plot with

standard R35 pore throat lines). Cluster of samples with similar R35 values are grouped

into individual rock types.

2.9.3 Pitmans modification of Winlands approach:

Winland in his approach did not offer justification for using a correlation between

porosity, permeability and pore throat radius at 35% mercury saturation. Pittman

(1992) suggested a modification to Winlands approach by introducing a concept of


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dominant pore throat radius. He proposed a concept of grouping the rocks based on the

dominant pore throat radius in the samples.

The dominant pore throat radius was estimated by plotting mercury saturation divided

by capillary pressure on y-axis and mercury saturation on x-axis. The apex or

inflection point on this graph represented the dominant pore throat radius as a function

of saturation. Pittman also extended the correlation between porosity, permeability and

pore throat radius to saturations other than 35%. Hence after determining the

saturation at which maximum samples exhibit inflection, an appropriate equation can

be chosen to correlate porosity, permeability with pore throat radius. Samples with

similar pore throat radii are grouped together into one rock type.

2.9.4 Prediction of permeability from Hg injection data:

If the direct measurement of permeability is not available, permeability can be

predicted from Hg injection data. Swanson (1981) and Thomeer (1960, 1983)

proposed a relationship to estimate permeability from Hg injection data.

Thomeer (1960, 1983) observed a correlation between air permeability (k), Hg

displacement pressure (Pd), total connected pore space (Sb) and the shape of the

capillary pressure curve expressed in terms of pore geometric factor (Fg). He proposed

a following equation to correlate them:

23334.1

8068.3

d

b

gP

SFk ....(2.5)

The correlation was developed based on the Hg injection measurements on sandstones

and carbonate samples from different formations. All three parameters required for


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estimating permeability were obtained by plotting measured values of capillary

pressure (Pc) and percentage pore volume occupied by mercury (Sb) on a log log

scale. The plot is hyperbolic in shape for conventional reservoir rocks.

Swanson (1981) developed his correlation based on the same plot. However, his

correlation uses capillary pressure (Pc) and percentage pore volume (Sb) corresponding

to the inflection point of the hyperbolic plot. He proposed following correlation to

estimate air permeability.

691.1

399

c

b

P

Sk ....(2.6)

These equations predict permeability well as long as the air permeability of the sample

is 10 millidarcy and higher. These equations do not take into account Klinkenberg gas

slippage, hence they do not predict accurately at lower permeability values.

Kamath (1992) has also reported a similar observation. He observes that the accuracy

of permeability estimation declines with the permeability level of the sample. He

reports that for a 95% confidence interval, the permeability estimation ability is within

a factor of 3.3 for high permeability (>10 millidarcy) samples. For samples with

moderate permeability (between 1 to 10 millidarcy), the estimation ability is within a

factor of 5.4 and in the low permeability samples (


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permeability which is well outside the range of permeability within which these

correlations work. During Hg injection measurements on shale samples, the entire

connected pore space is often not filled up as the pore throats controlling access to

large pore volumes are smaller than what Hg can pass through at 60,000 psi. It means

that the capillary pressure against Hg saturation plot which is used for obtaining the

parameters to be used in Swansons and Thomeers correlation is not always available

in shales. Therefore, permeability prediction using Swansons and Thomeers

approach is not feasible in shale samples.

We observe that all the conventional approaches of rock typing correlate porosity with

permeability measured over the cored interval. Direct measurements of both porosity

and permeability are made on core samples constituting the sample set. If a direct

measurement of permeability through pressure or pulse decay technique is not

available, it can be estimated from Hg injection measurement. The sample set in most

of these correlations is limited to sandstones and carbonates and does not include

shales. The core samples in these sets have a wide dynamic range for porosity and

have air permeabilities in a millidarcy range at least.

However, with nanodarcy matrix permeability, the conventional techniques of

permeability measurement do not work in shales. Since permeability prediction from

Hg injection also does not work, coming up with permeability dataset required for

porosity-permeability correlation is almost impossible. In conventional reservoirs,

there is also a considerable variation in porosity; whereas in shales, porosity has a very

narrow dynamic range. Since both porosity and permeability lack the dynamic range

necessary for rock typing, identifying rock types through conventional techniques in


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shale gas reservoirs is not possible. Hence instead of using porosity-permeability

cross-plots, we have attempted rock typing using geological core description and

petrophysical measurements other than permeability in Barnett shale play.


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2.10 Rock typing through Principal Component and Cluster analysis:

In an entirely independent exercise, rock typing was attempted on the same dataset

through multi-variable analysis based on Principal Component and Cluster Analysis

techniques. Rock typing was carried out using efacies program developed by Dr. A.

Datta-Gupta from Texas A&M University. Lee et al. (2002) have described this

technique in detail. A summary on techniques behind the working of this program is

provided below:

2.10.1 Principal Component analysis (PCA):

Principal Component analysis (PCA) technique is used for summarizing the data

effectively without losing too much information. PCA technique reduces the

dimensionality of the problem by introducing principal components. Principal

components are identified within the defined variable space. They provide an alternate

coordinate system in multi-dimensional space for displaying data without too much

lose of information. Principal components are constructed through linear combination

of variables. The eigenvectors and covariance matrix provide the coefficients for

principal component transformations.

The total variance of the dataset is the sum of individual variances associated with

each principal component. Hence addition of every principal component increases the

percentage of variance explained. The maximum number of principal components

equals the number of variables and all the principal components together explain

100% variance. Principal components correlate well with the variables in the problem;

e.g. principal component 1 (PC1) may correlate well with porosity and PC2 may show


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a good correlation with clay percentage. The first few principal components often

explain most of the variance in the dataset and are usually adequate to reveal the

structure of the dataset without too much loss of information. By selecting only the

first few principal components for data analysis, one can reduce the dimensionality of

the problem (Lee et al., 2002). For example, in a seven variable problem, if the first

four principal components explain over 90% variance of the dataset then by selecting

the first four principal components for cluster analysis, the dimensionality of the

problem can be reduced from seven to four. Every principal component is then a

coordinate of the data point in a four dimensional space.

2.10.2 Cluster analysis:

Cluster analysis aims to classify data points into groups that are internally

homogeneous and externally isolated (Lee et al., 2002). The number of clusters to be

identified is specified by the user.

In a rock typing exercise, where input is in the form of petrophysical properties

measured at every depth, a cluster represents a collection of samples with similar

petrophysical properties which are considerably different from the petrophysical

properties of the samples from another cluster.


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Chapter3

EXPERIMENTAL PROCEDURE

This section describes the laboratory procedures followed for making petrophysical

measurements in detail.

3.1 Sampling procedure:

Cores recovered from four wells located in different parts of Newark East field were

used in this study. The core recovered is continuous and has a very few missing

intervals. Porosity and mineralogy measurements are made on samples recovered at

every two feet interval. Two foot interval for sampling is selected to ensure fair

representations of all possible mineral compositions and minimize sampling bias

towards certain lithofacies. Due to the time and expense involved in mercury injection

measurements, samples for these measurements are recovered at every ten feet. TOC

measurements are performed on the samples from selected depths. Dataset of

petrophysical measurements used in rock typing has 796 porosity and mineralogy

measurements, 436 TOC measurements and 130 Hg injection results.

Fig. 3.1 shows core images taken at various depths in the interval of study. The extent

of heterogeneity of Barnett shale play is clearly visible in these pictures. We

frequently observe carbonate rich zones (predominantly calcite) and fine interbedding

of calcite and clay in addition to clay rich zones throughout the cored interval. In order

to ensure that the samples, used in the measurements made at one depth, are as


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homogeneous as possible; we recover a large single piece of a rock at every depth and

break it down into smaller pieces.

Fig. 3.1: Barnett shale core images. (Top left:) 7491.5 ft, well A, sharp boundary between clay rich and calcite

rich zones; (Top right:) 7544.6 ft, well A, clay rich black shale; (Bottom left:) 7595.3 ft, well A, fracturedrock, predominantly calcite; (Bottom right:) 7606.6 ft, well A, inter-bedding of thin streaks of calcite with clay

3.2 Helium porosity, bulk and grain volume measurement:

Porosity measurement is used to measure the void space in the sample. Void volume

of a sample is calculated by subtracting the grain volume of the sample from its bulk

volume. Mathematically, porosity is expressed as follows:

100100

B

GB

B

P

V

VV

V

V (3.1)

Where is the pore volume of the rock sample, is the bulk volume of the sample,

is the grain volume of the sample and is the porosity of the sample. Bulk

7491.5 7544.6

7595.3 7606.6

7491.5 7544.6

7595.3 7606.6


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volume of the sample is obtained by using mercury immersion technique where the

sample is immersed in a mercury bath and the Hg displacement is recorded as it equals

the bulk volume of the sample from Archimedes Principle.

In order to estimate pore volume of the sample; gas, free water and volatile

hydrocarbon filled pore space must be accounted for. Karastathis (2007) developed a

technique for estimating water and volatile hydrocarbon free effective porosity,

exclusively for shales, at IC3

lab. The brief summary of this methodology is as

follows:

About 10~14 gm of a shale sample is recovered and heated in a oven at 100oC for 12

hours to remove free water present in the sample. After the first heating cycle, the

sample is allowed to cool in a desiccator for 30 minutes before recording its bulk

volume ( ) using a technique already described above. The sample is then crushed

using a crucible and pestle. Based on the particle size distribution measurements, the

mean particle size of the crushed sample averaged over a total of 41 samples from all

four wells is 392 +/- 192 m. Histogram showing variation of mean particle size is

shown in Fig. 3.2.


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Fig.3.2: Histogram showing particle size distribution for a set of 41 crushed samples from all four wells;

mean particle size shows a wide variation from 100 to 800 m. Average mean particle size for the dataset is 392

+/- 192 m.

Sample is handled with extreme care during the process of crushing so that the weight

loss is minimized (


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and without the sample is used to determine the grain volume ( ) and grain density

( ) of the sample in the cell. Grain volume of the sample is corrected for grains lost

during the process of crushing using following formula:

G

GG

mVV

~..(3.2)

Where, is corrected grain volume and is weight loss during crushing.

Water and volatile hydrocarbon free effective porosity of the sample is then estimated

as follows:

100

~

B

GB

V

VV .....(3.3)

3.3 FTIR mineralogy:

Mineral composition of the samples is obtained by using Fourier Transform Infrared

Spectroscopy (FTIR). Sixteen minerals, typically found in sedimentary rocks, can be

detected and quantified using this technique. These minerals are quartz, calcite,

dolomite, siderite, aragonite, illite, kaolinite, smectite, chlorite, mixed clay, oligoclase

feldspar, orthoclase feldspar, albite, pyrite, apatite and anhydrite. Accuracy of

measurement of FTIR mineralogy technique is comparable with other quantitative

methods of mineralogy measurement such as X-ray diffraction and thin section

analysis. The major advantage of FTIR technique is that it is much faster than other

techniques with the results available in a matter of minutes after initial sample


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preparation is complete. This enables the use of FTIR technique at the wellsite for

acquiring mineralogy at multiple data points over a large cored interval.

The principle behind FTIR mineralogy technique is as follows: Most of the minerals

constituting sedimentary rocks have covalent bonds between the atoms. These

covalent bonds have resonating frequencies in mid-infrared region (4000 cm-1

to 400

cm-1). The resonant frequencies depend on the bond type and the atoms bonded

together. Naturally, these frequencies are different for different minerals. When a

sample containing these minerals is exposed to radiation from mid-infrared region,

minerals constituting the sample absorb energy at characteristic resonant frequencies.

Part of the energy that is absorbed by the bond is termed as its absorbance and the rest

is termed as transmittance. In FTIR technique, the transmittance at every frequency is

measured then converted into absorbance at that frequency using equation 3.4.

100log 10T

A (3.4)

Where, is the absorbance and is percent transmittance (Griffiths and De Haseth,

1986). Absorbance is then plotted against frequency to generate a FTIR spectrum for

the sample. FTIR spectrum so obtained is then inverted using software developed in

IC3 lab to identify and quantify the minerals present in the sample. The minerals are

quantified in terms of their weight percentages.


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The FTIR measurement technique is discussed in detail by Sondergeld and Rai (1993)

and Ballard (2007). The procedure for measuring mineralogy using FTIR technique is

summarized below:

We start with very small amount of crushed sample (already available from helium

porosity measurement) and grind it further to a point where we are unable to feel any

particles and the sample is as fine as talc powder. After grinding the sample, moisture

and organic carbon present in the sample is removed. This is necessary because both

moisture and organics tend to exhibit very strong peaks in mid-infrared region;

masking the absorption peaks of other minerals. Moisture is removed by heating the

powdered sample at 100oC overnight. Organic matter is then removed by oxidizing the

sample in a low temperature plasma asher. A sample is combusted in presence of

oxygen at a low temperature for 16 hours. Carbon dioxide formed during the oxidation

of organic matter is continuously removed using a vacuum pump. The set up of

plasma asher is shown in Fig. 3.3.


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Fig.3.3: Plasma asher setup. Samples are introduced in a plasma asher using a porcelain boat. The machine ishooked up to oxygen cylinder that introduces oxygen into the oxidation chamber. Here the samples are oxidizedat a low temperature for a period of sixteen hours. CO2 produced during oxidation of the sample is continuously

removed by using a vacuum pump. The change in the color of the sample from black to gray indicates that theoxidation process is complete.

After removing the organic matter, a small amount (0.0005 gm) of powdered sample is

mixed with 0.3 gm of potassium bromide (KBr) and pressed under 10 ton pressure in a

die to form a semi-transparent disc. The disc is placed in a sample holder and run in

Fourier Transform Infrared Spectrometer (FTIR) to acquire the spectrum. Mineralogy

of a sample is then obtained through an inversion of this spectrum.

3.4 Mercury injection capillary pressure measurement:

Mercury injection capillary pressure measurement estimates pore throat distribution in

a sample and also measures the connected pore space. In conventional reservoir rock,

this measurement can be used to measure effective porosity and estimate permeability

of the sample.

Oxidation Chamber

Oxidation Chamber


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The experiment is conducted on 8-10 gm chip by introducing mercury under pressure.

The pressure is increased from the entry pressure (set at 5 psi) to 60,000 psi at

predefined pressure steps. The pressure table is set in such a manner that the pressure

points are evenly spaced on a logarithmic scale. There are a total of 197 pressure

points. At each pressure point, the pressure is held constant to allow mercury to

intrude into the pore space. The system waits till a pressure equilibrium (stable over 60

second period) is reached to ensure that the mercury intrusion at that pressure step is

indeed complete. The intrusion volume at each pressure step is recorded. After

reaching 60,000 psi pressure, pressure is reduced stepwise down to 19 psi and

extrusion volume at each pressure step is recorded. The number of steps during

extrusion are fewer than intrusion as it takes longer for pressure to equilibrate during

extrusion.

Since pore throats control access to the pore space, mercury first fills up the pore

space it can access through the largest pore throats. Mercury then passes through

smaller and smaller pore throats as pressure increases and eventually fills up the entire

connected pore space. At 60,000 psi pressure, mercury can enter pore throats as small

as 3 nanometers in diameter.

In shales, pore throats that control access to most of the pore space are very small.

Most of the intrusion in shale samples is typically seen at pressures above 10,000 psi.

This corresponds to a pore throat diameter of 18 nanometers and matrix permeabilities

of few tens of nano-darcies (Sigal, 2007).


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Mercury injection capillary pressure measurement is based on Washburns equation:

R

CosP RockHgc1

.(3.5)

Where, = Capillary pressure, = Interfacial tension of mercury, =

Contact angle between Hg and rock sample, = Pore threat radius

Since the interfacial tension of mercury (480 dyne/cm) and contact angle (140o)

between mercury and rock sample is already known, knowing the pressure at which

the mercury passes through the pore throats; the pore throat radius can be estimated.

In addition to obtaining pore throat distribution, the data obtained during intrusion and

extrusion cycle are also used for plotting incremental and cumulative Hg intrusion

curves. An incremental intrusion curve is obtained by plotting incremental Hg

intrusion as a function of pressure. Cumulative intrusion for a pressure point is

estimated by summing incremental intrusion at all pressure points less than and equal

to that pressure point. These data when plotted against pressure gives a cumulative

mercury intrusion curve. Incremental intrusion curves are typically made by including

data obtained during intrusion cycle only. Cumulative intrusion curves are made by

including the data points from intrusion as well as from extrusion cycle. The shapes of

the incremental and cumulative mercury intrusion curves are often unique for different

rock types; hence these curves can be very useful during rock typing. A cumulative Hg

intrusion plot is used to verify if the observed intrusion into the sample is real.

Whenever the mercury intrusion into the sample is real, hysteresis between saturating


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and desaturating curves is observed (see Fig. 3.4, left). This is because not all the

mercury that enters the pore space comes out when the pressure is released. Lack of

hysteresis on a cumulative intrusion plot is an indication that the intrusion is not real

(see Fig. 3.4, right).

Fig.3.4: Cumulative Hg intrusion plots showing real and false intrusion; (left): There is a considerable

hysteresis between the saturating and the desaturating curve. Some of the Hg that went into the pore space

during the intrusion cycle remained inside even after the pressure was released. It means that the Hg intrusion

in a sample is real, (right): Lack of hysteresis between the saturating and the desaturating curve indicates false

intrusion.

Before running a Hg injection experiment, a sample is first dried to remove any free

water present in the pore space. The heating is carried out 12 hours at 100oC

temperature. After cooling to room temperature in a desiccator over 30 minutes, the

sample is placed in a penetrometer. Penetrometer is a glass apparatus used in this

experiment. It is sealed using a cap, plastic o-ring and apiezon grease. Sealed

penetrometer is introduced in a low pressure section of AutoPore IV machine. After

evacuating the low pressure chamber down to 200 mm of Hg column, pressure is held

at that level to remove any remaining moisture and air from the sample and the

penetrometer system. Mercury is introduced into the penetrometer at 5 psi through its

stem. Hg fills up the stem and the remaining volume in the penetrometer bulb as it

Normalized Cumulative Hg Intrusion Curve -

Real Intrusion

0

0.1

0.2

0.3

0.4

0.5

0.6

0.7

0.8

0.9

1

1 10 100 1000 10000 100000

Pressure, psi

NormalizedCumulativeHg

Intrusion

Normalized Cumulative Hg Injection Curve - False

Intrusion

0.00

0.02

0.04

0.06

0.08

0.10

0.12

0.14

0.16

0.18

1 10 100 1000 10000 100000

Pressure, psi


Intrusion

Saturating Curve

Desaturating Curve

Desaturating Curve

Saturating Curve

Normalized Cumulative Hg Intrusion Curve -

Real Intrusion

0

0.1

0.2

0.3

0.4

0.5

0.6

0.7

0.8

0.9

1

1 10 100 1000 10000 100000

Pressure, psi


Intrusion

Normalized Cumulative Hg Injection Curve - False

Intrusion

0.00

0.02

0.04

0.06

0.08

0.10

0.12

0.14

0.16

0.18

1 10 100 1000 10000 100000

Pressure, psi


Intrusion

Saturating Curve

Desaturating Curve

Desaturating Curve

Saturating Curve


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completely surrounds the sample. Mercury is pressurized from outside using nitrogen

in a low pressure section of AutoPore IV machine. As Hg is pressurized from

outside, it applies equivalent hydrostatic pressure on the sample and begins intruding

into the pore space. Pressure is raised stepwise up to 25 psi in a low pressure section

and then up to 60,000 psi after transferring the penetrometer into high pressure

section. Mineral oil is used in a high pressure section for applying pressure.

Penetrometer stem is made up of glass and has metallic coating from outside. Since

mercury and metal coating are conductors and glass is an insulator, a penetrometer

filled with mercury acts like a capacitor whose capacitance changes with the receding

level of mercury in the penetrometer stem. Thus the amount of Hg intrusion is

recorded by measuring the change in capacitance of the penetrometer stem at every

pressure step.

Penetrometers come in different stem volumes. Depending on the pore space inside

the sample, a penetrometer with an appropriate stem volume can be selected. Since

shale samples have a very low connected porosity, penetrometers with the least stem

volume (0.366 cc) are selected. These penetrometers are called type-I penetrometers,

with the minimum and maximum measurable volume of 0.0784 cc and 0.3136 cc,

respectively.

Fig. 3.5 shows penetrometer assembly in detail and Fig. 3.6 shows AutoPore IV

machine used for running Hg injection measurements.


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Fig.3.5: Penetrometer along with its components; after placing the sample in a penetrometer bulb,

penetrometer is tightly sealed using a metallic cap, plastic o-ring and apiezon grease. Penetrometer is thenintroduced into the low pressure section of AutoPore IV machine for running the experiment.

Cap Components

Stem

Bulb

Cap Components

Stem

Bulb


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found for shale samples, hence the mercury injection curves for the shale samples are

not corrected for the blank effect.

Conformance error occurs when mercury entering the penetrometer does not fill up the

empty space inside the penetrometer completely. This is typically observed in samples

with an irregular shape and/or a rough surface. Since mercury does not conform to the

sample at the filling pressure, it starts to fill up the remaining void space around the

sample as the pressure is raised during the experiment. This results into reduction of

Hg filled volume of a penetrometer stem and gets recorded as mercury intrusion into

the sample. Depending on the aperture size of these surface irregularities, the

conformance error may or may not be significant. If the surface irregularities have

large apertures that Hg can fill up at lower pressures then conformance error is not

likely to be significant. This is because the conformance error will not influence the

shape of the capillary pressure curve as most of the Hg intrusion in shales occurs only

after 10,000 psi pressure. However, conformance error may be significant incase the

aperture size of the surface irregularities is small enough that Hg conforms to the

sample beyond 10,000 psi pressure. Sigal (2009) has discussed both these errors in

detail.

3.5 Total organic carbon (TOC) measurement:

Total organic carbon (TOC) measures the organic matter present in shale. Shales

samples with higher TOC content are richer in terms of organics and possess higher

hydrocarbon generation potential. Along with the TOC content, the thermal maturity


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of shale samples is equally important. It indicates the extent of transformation of the

organic matter into oil and gas. TOC and thermal maturity measurements on Barnett

shale samples are carried out by a commercial lab.


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Chapter4

OBSERVATIONS AND RESULTS

Helium porosity and FTIR mineralogy measurements were carried out on 796 core

plugs recovered from four wells from different parts of Newark East field. The results

obtained from these measurements are summarized and discussed in this chapter.

4.1 Helium porosity:

Water free effective porosity, measured using a technique developed by Karastathis

(2007), yielded average helium porosity of 5.7% for 796 samples. As shown in Fig.

4.1, helium porosity of these samples exhibits a very wide range (2-10 %). The

accuracy of these measurements is +/-0.5 % and the standard deviation is 2.1 %.

Fig.4.1: Porosity histogram for 796 samples from 4 different wells;porosity measurement on the entire sample

set shows a wide variation (210%). Average porosity for the dataset is 5.7 +/- 2.1%.

Porosity Histogram - All Four Wells

0

20

40

60

80

100

120

140

160

1 2 3 4 5 6 7 8 10 More

Porosity (%)

No.ofSamples

Total no. of samples = 796

Average Porosity = 5.7+/- 2.1%


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4.2 FTIR mineralogy:

FTIR mineralogy technique was used to determine the mineral composition of the

sample set. Sixteen minerals typically found in the sedimentary rocks were identified

as well as quantified using this technique. Mineralogy of the samples exhibited

considerable variation with depth in all the four wells (Refer Fig. 4.2).

Fig.4.2: Bar charts showing mineral variation for all four wells; mineral composition with depth for bothupper (UB) and lower Barnett (LB) is shown for wells A, B and C is shown. Forestburg interval that

separates UB and LB has disappeared in well D; hence Barnett in this well is continuous without any

distinction as UB and LB. 1. UB seems to be richer in carbonate and leaner in clays where as it is other way


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around in LB in wells A, B and C 2. Apart from carbonate rich intervals, quartz content stays fairlyuniform throughout in wells A, B and C whereas in well D it is observed to decrease with depth. 3. Clay

content increases with depth in well D.

We know that calcite rich Forestburg interval separates Barnett shale into two parts.

The upper part (also known as Upper Barnett) seems richer in carbonates than the

lower part (also known as Lower Barnett) in well A, well B and well C.

Carbonate content in well A is fairly uniform throughout lower Barnett with

occasional carbonate rich streak but in lower Barnett of well B carbonate content is

observed to be highly variable with both thick and thin carbonate intervals occurring

intermittently. Well C exhibits a stark contrast in the carbonate content of the upper

and the lower Barnett with the carbonate content of upper Barnett being significantly

higher than that of the lower Barnett. Wells A, B and C have higher clay content

in the lower Barnett t

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Sagar Kale Shalegasthesis