CALIBRATING NMR RESPONSE TOCAPILLARY PRES SURE CURVESIN FINE GRAINED LITHOLOGIES:

PRETTY HILL FORMATION,OTWAY BASIN

Thesis submitted for the degree ofMaster of Science

(Petroleum Geology &, Geophysics)

ALI AL.GHAMDI

Australian School of Petroleum,The University of Adelaide

August 2006

Statement of Authenticity

I declare that this thesis does not incorporate without acknowledgement

aîy material previously submitted for a degree. or diploma in any

university; and that to the best of my knowledge it does not contain any

materials previously published or written by another person except where

due reference is made in the text.

I give consent to this copy of my thesis, when deposited in the University

Llbrary, being made available for loan and photocopying, subject to the

provisions of the Copyright Act 1968.

Ali Al-Ghamdi

Augus 2006

I

ABSTRACT

Nuclear magnetic resonance (NMR) tools are commonly used in formation

evaluation. NMR T2 distribution data have been used by previous authors

to build down-hole pseudo capillary pressure curves in reservoir quality

rocks. The objective of this study is to generate NMR-derived down-hole

pseudo capillary pressure curves in very fine grained lithologies in order to

determine whether it is possible to estimate capillary displacement

pressures and thereby sealing .capacity.

NMR T2 relaxation time distributions of flood plain facies at Redman- 1

well in Otway Basin, SE of South Australia, were converted to pseudo

capillary pressure curves. The generated curves were compared to mercury

injection capillary pressure (MICP) curves. The petrophysical properties

and mineralogy of 11 flood plain samples were atalyzed using the

following measurement techniques: MICP, Scanning Electron Microscopy

(SEM), core porosity and permeability, X-Ray Diffraction (XRD), optical

microscopy of thin section and X-Ray Fluorescence XRF.

Displacement pressures were calculated from pseudo capillary pressure

curves and compared with actual MICP curves at different saturations

percentages of non-wetting phase. The best percentage in displacement

pressure estimation is the 20%o saturation with correlation coefficient of

0.59. Statistically, the correlation coefficient of the 2Qo/o saturation is too

low for meaningful calibration.

The reason for the lack of robust calibration is related to the actual

properties of the rock: the Redman-l flood plain samples have high iron

contents (Fe2O3 content ranges between 5.21-7 .16 wt%) with

correspondingly increased magnetic susceptibility and elevated internal

field gradients. The iron is mainly associated with chlorite and biotite in

the samples studied. The NMR T2 response is affected significantly by the

internal magnetic field gradient which depends on the magnitude of the

l1

magnetic susceptibility. Surface relaxivity changes and high pore to throat

size ratio also contribute to the difference between the two measurements.

Study conclusions are that T2 response in high iron content rock is affected

by many factors such as magnetic susceptibility, surface relaxivity and

aspect ratio. Therefore, using the NMR response to estimate capillary

displacement pressures in iron-rich, fine grained rocks is not

recommended. However, further studies in rocks of low magnetic

susceptibility might yield more significant correlations.

111

TABLE OF CONTENTS

Statement of Authenticity

Abstract

Table of Contents

Acknowledgements

CHAPTER ONE: INTRODUCTION

1.1 Background

L2 Project Aims

1.3 Structure of the Thesis

CHAPTER TWO:

2.1

2.2

2.3

2.4

2.5

2.6

I

1l

1V

vll

5

6

7

9

11

13

l3

13

15

l6

l8

18

2t

22

25

iv

1

3

4

NMR, CAPILLARITY ANDGEOLOGICAL REVIEW

NMR Principles

2.1.1 Physics of Nuclear Magnetic Resonance

2.1.2 NMR T2 Distribution

2.1 .3 Relaxation Mechanisms

2.1.3.1 Grain Surface Relaxation

2.I.3.2 Bulk Fluid Relaxation

2.1.3.3 Molecular Diffusion Relaxation

2.1 .4 Multi-Exponential Decay

NMR Application in Formation Evaluation

NMR Pore Size Distribution

Capillary Pressure (Pc)

2.4.1 Mercury Porosimetry andPore Size Distribution

2.4.2 Seal Capacity Determination

NMR and Displacement Pressure Estimation

Geological Overview of Penola Trough

CHAPTER THREE: PETROPHYSICAL ANDMINERALOGICAL MEASUREMENTS

3. 1 Introduction

3.2 Sample Selection and Preparation

3.3 Pore Measurement Techniques

3.3.1 Mercury Injection Capillary Pressure (MICP)

3.3.2 Scanning Electronic Microscopy (SEM)

3.3.3 Porosity and Permeability Measurements

3.3.4 Porecast Analysis

3.4 Mineralogical Determination Techniques

3.4.1 X-Ray Diffraction Analysis (XRD)

3.4.2 X-Ray Fluorescence (XRF)

3.4.3 Thin Section Petrology

3.4.4 Point Counting

CHAPTERFOUR: PETROLOGY

4.I Core Description

4.2 Detrital Mineralogy

4.2.1 Quartz

4.2.2 Feldspars

4.2.3 Mica

4.2.4 Clays

4.2.5 Lithic Grains

4.2.6 Accessory Minerals

4.3 Authigenic Minerals

4.3.1 Chlorite

4.3.2 Laumontite

4.3.3 Diagenetic Accessory Minerals

29

29

31

31

35

36

38

39

39

40

4T

42

43

44

41

47

52

54

55

55

58

58

58

59

V

CHAPTER FIVE:

5.1

5.2

5.3

5.4

5.5

5.6

5.7

PETROPHYSICAL PROPERTIESDETERMINATION

Introduction

Porosity and Permeability Estimation(NMR & Lab)

Capillary Pressure and Pore ThroatSize Determination

T2 Distribution Processing

T2 Pseudo Capillary Pressure Curve

5.5 . 1 Corrected Cumulati ve T2 Distribution

5.5.2 Creating Pseudo Capillary Pressure Curves

Pseudo Pc and MICP Curves Calibration

Estimation of NMR Displacement Pressure

6l

6l

65

68

70

70

74

76

77

CHAPTER SIX: DISCUSSION

6.1 Introduction

6.2 Discussion

APPENDIX - B

APPENDIX - C

APPENDIX - D

Mercury Inj ection CapillaryPressure Data Results

NMR Data Results

XRD Analysis Results

XRF Analysis, Point Counting &Porosity and Permeability Results

88

88

CHAPTER SEVEN: CONCLUSIONS & RECOMMENDATIONS

7 .l Conclusions 98

7 .2 Recommendations 100

REFERENCES 102

APPENDIX - Ar07

130

142

150

VI

Acknowledgments

I would like to acknowledge;

o Saudi Aramco for sponsoring my M.Sc. studies.

o Origin Energy and PIRSA for access to log and core data.

. My supervisors: Professor John Kaldi (ASP), Dr. Peter Boult

(PIRSA) and Dr. Peter Tingate (ASP).

o Staff and student at Australian School of Petroleum for their

assistance, in particular, Dr. Ric Daniel.

Finally, I would like to thank my wife and family for their constant support

during my studies.

vll

CHAPTER 1

INTRODUCTION

1.1 Background

Nuclear magnetic resonance (NMR) is an important tool in formation

evaluation for measuring lithology-independent porosity and estimating

permeability. It is also a hydrocarbon identification tool. The NMR T2

distribution is approximately equivalent to a pore size distribution which is

acquired every =7 5 .2cm (6in). The T2 distribution curve has been

converted to a continuous down-hole pseudo capillary pressure curve to

delineate reservoir rocks in many previous studies. The down-hole pseudo

capillary pressure curve can be used to estimate height above free water

level, water saturation and displacement pressure. This is very important

information for reservoir development. Also, the pattern of T2 used for

facies distribution.

Mercury injection capillary pressure (MICP) measurement is used in seal

evaluation to identify the pressure required to displace the wetting phase,

and to determine pore throat size. Sealing capacity can be calculated from

the displacement pressure in seal and reservoirs samples and also the fluid

properties. MICP measurements are commonly obtained on scattered core

and cutting samples which might not truly represent the different vertical

facies. In addition, clay rich samples af e subj ect to fracturing and

alteration from their in situ conditions when brought to surface and stored,

which makes MICP analysis difficult.

Reservoir pseudo capillary pressure (Pc) curves have been derived from

NMR response successfully in many previous studies (e.g. Marschall et al,

1995; Lowden et al, 1998; Boult et al, 1999; Glorioso et al, 2003). NMR

pseudo Pc curves were derived by calibrating the NMR T2 distribution to

laboratory capillary pressure data in reservoir intervals. In Redman- 1,

cumulativ e T2 relaxation curves have been calibrated to capillary pressure

I

curves from core analysis of the Pretty Hill Sandstone reservoir (Boult et

â1., 1999). A good displacement pressure (Pd) estimation from the T2

distribution was reached by applying the appropriate effective surface

relaxivity (pe) factor (Figure 2.9). This technique enables direct water

saturation calculation from the NMR log given height above free water

level (FWL) determination.

The work by Boult et al (1999) did not include the fine grained (seal)

lithology intervals. This study investigates the possibility of estimating

displacement pressure (Pd) via pseudo capillary pressure curves derived

from seal lithologies. If displacement pressure (Pd) can be estimated from

T2 distribution in seal lithologies, the need for acquiring MICP

measurements will be minimized. Moreover, seal capacity measurement

will be available for the entire seal interval with a vertical resolution of a

few inches.

If this methodology could be applied successfully in fine grained rocks, a

continuous pseudo capillary pressure curve at every =l5.2cm (6in) could be

obtained over seal intervals. Also, measurements could be made at in situ

conditions and the extra cost for laboratory measurement could be avoided

when data from the NMR tool are available.

2

I .2 Proj ect Aims

The main aim of this project was to use the Redman-1 core to undertake a

detailed assessment of whether NMR T2 data could be used for estimating

displacement pressures from down-hole pseudo capillary pressure curves in

siliciclastic seal lithologies. This has been achieved by calibrating the

NMR T2 distribution with actual mercury injection capillary pressure

(MICP) curves in porous reservoir intervals in previous studies (e.9., Boult

et al., 1999; Glorioso et al.,2003). A similar methodology was applied to

intervals which have small pores to investigate how the NMR T2

distribution behaves between very small pores and to determine if it can be

used to determine pore throal size accurately.

The calibration aimed to give a better understanding of the behavior of T2

distribution in very fine lithologies, and how it can be applied to estimate

capillary pressure. Moreover, if the calibration was adequate in estimating

the displacement pressure, it could then be applied to evaluating seal

capacities in such rocks as the Laira Formation, whose lowermost section

comprises a floodplain shale facies.

The main study tasks were to;

generate pseudo capillary pressure from T2 distribution in very fine

grained lithologies;

calibrate NMR T2 pseudo capillary pressure curves with actual

laboratory MICP curves;

examine the accuracy of the estimated capillary pressure from T2

pseudo capillary pressure in a micro-pore system.

o

O

o

J

o

a

understand the contribution of clay bound water to NMR response rn

fine grained lithologies and how far the calibration could account for

the difference between the two measurements.

identify the effect of mineralogy and clay types on T2 behavior tn

flood plain facies.

1.3 Structure of the Thesis

The thesis starts with an introduction to the NMR tool and the theory of

pore size measurements. Then, a description is given of the different

laboratory measurements used to identify the petrophysical and

mineralogical characteristics of the Redman- 1 samples. Chapter Four

presents the petrology of the Redman-1 core using different techniques

such as core descriptions, thin sections, XRD and XRF analyses.

Petrophysical properties and the methodology of obtaining pseudo capillary

pressure curves for the fine grained samples is described in Chapter Five.

The calibration between the two measurements, NMR T2 and MICP curves,

is presented with an interpretation of statistical confidence. The different

factors which might affect the NMR response in such a lithology are

discussed in the following chapter. Finally, conclusions from this study and

recommendations for further investigations using NMR are presented.

4

CHAPTER TWONMR, CAPILLARITY AND

GEOLOGICAL REVIEW

2.1 NMR Principles

The principle of the nuclear magnetic resonance (NMR) tool is based on

measurements of the relaxation of pore fluids to provide formation

evaluation and petrophysical data including porosity, irreducible water

saturation, permeability and pore-size distributions (Coates et al., 1999).

NMR measurements record the behavior of hydrogen protons in the pore

space of the formation. Thus, fluids containing hydrogen (water, oil and

gas) are visible to the tool; the latest generation of NMR tools can even

detect the hydrogen protons on clay surfaces. NMR porosity measurement

does not include the hydrogen which is part of the matrix composition of

the rock; therefore it provides a lithology and clay-independent total

porosity value.

Nuclear magnetic resonance (NMR) signals from hydrogen protons were

discovered in 1946 by Purcell and Bloch (Kenyon et aI, 1995) and in the

1950s were recognized by the oil and gas industry as a potential tool in

formation evaluation. In the late 1 95 0s, NMR measurements became

developed further to provide information on formation pore fluids and pore

structure (Allen et aL, 2000). The first NMR log was run in 1960,

measuring the signal from protons precessing (spinning) in the Earth's

magnetic field (Brown and Gamson, 1960).

More advances in NMR measurements and interpretation occurred in the

1960s and late 1970s, including:

the development of a relationship between relaxation time and

permeability of sandstones developed by Seevers (I966),

o

5

the concept of a free fluid index and new methods to measure

permeability using NMR introduced by Timur (1968), and

the relationship between pore size, fluid and matrix was introduced

in 1969 by Loren and Robinson (Kenyon et al, 1995).

Now, NMR can give information, not only on the saturation of fluids in the

rocks, but also on the pore size distributions of different rock types (Coates

et al., 1999).

2.1.1 Physics of Nuclear Magnetic Resonance

NMR measurement uses a static magnetic field to polarize the hydrogen

protons that are randomly oriented in the formation fluids. The constant

magnetic field (Bo) of the tool aligns or polarizes the spin axes of the

protons in the longitudinal direction which is the direction of Bo (Fig 2.1

A). Then an oscillating magnetic field,B,, is applied to tip the proton spin

axes to 90' of the transverse plane, perpendicular to longitudinal direction.

When the oscillating field is subsequently removed, the protons begin

dephasing and tipping back, or relaxing, toward their equilibrium position,

parallel to Bodirection (Fig 2.1 B), aligned to the initial static magnetic

field (Kenyon et al, 1995). Dephasing time for protons to return to the

initial or longitudinal direction called longitudinal relaxation (T1).

Thus, the net magnetization decreases as the dephasing progresses. The

tool measures radiofrequency quantity, which protons radiate during

relaxation process, in the transverse direction as a decayingT2 signal. This

signal is called a spin echo. 180'pulse sequences are used repeatedly to

generate a series of so-called spin-echo trains. The entire pulse sequence,

which is composed of 90'pulse and followed by long series of l80"pulses

(Figure 2.1 C-F), is called a CPMG sequence (after its inventors Carr,

Purcell, Meiboom, and Gill). CPMG sequences increase strength of the

a

a

6

radiofrequency decay in order to minimize noise contribution to the signal.

The decay in amplitude of the spin-echo train is recorded versus time and

forms the raw NMR data (Figure 2.2).

The number of hydrogen nuclei associated with the fluids in the pores

within the sensitive volume is proportional to the initial amplitude of the

spin-echo train. Thus, this initial amplitude is calibrated to give porosity.

The accuracy of the raw reported porosity depends on a sufficiently long

polarization time (TW) to achieve complete polarization and a sufficiently

short inter-echo spacing (TE) to record the decays from extremely small

pores (clay pores). TE is the time between the individual echoes in an echo

train whereas TW is the time between the end of the measurement of one

echo train and the beginning of the next echo train. Both the inter-echo

spacing and polarization time can be adjusted to vary the resolution content

of the acquired data.

There are some properties of the pore fluids that may affect the echo trains.

Those properties are the hydrogen index (HI), which is the density of

hydrogen atoms in the fluid; the longitudinal relaxation time (Tr), which is

an indication of the longitudinal relaxation speed of aligned proton in the

fluids; transverse relaxation time (Tr), which is an indication of the

transversely oriented relaxation speed of aligned proton in the fluids; and

diffusivity (D) which is a measure of the extent to which molecules move

at random in the fluid (Kenyon et al, 1995).

2.1.2 NMR T2 Distribution

The amplitude of the spin-echo train decay can be calculated accurately by

the sum of decaying exponentials, each of them with a different decay

constant. The set of all the decay constants forms the decay spectrum or

transverse-relaxation-time (Tz) distribution. The spin-echo train decay data

7

can be converted to a Tz distribution using the mathematical process of

inversion.

Proton Spins Aligning withMagnetic Field

Magnetization (Polarization ). i.:. : :t ,.

A

90 Degree Rotation in PulsedRF Field

c

Bo (Constant)

Procession in ConstantMagnetic Field

Bo (Constant)

Reverse Procession in ConstantMagnetic Field

. - ...i'

FBo(Constant)

Bo (Constant)

B,-r (Constant)D

q?RF

180 Degree Flip in Pulsed RF Field

Bo (Constant)

RF

Figure 2.1 Demonstration of transverse plane and how spins aremanipulated to acquire relaxation time. A and B are the process toobtain the longitudinal relaxation (T1), where C to F show the processin CPMG process to manipulate hydrogen nuclei in order to get T2and minimize the Bo effect.

8

4()

35

3()

25Å€i zod)rlElsô-ëro

5

o

E

Time (ms)

Figure 2.2 Typical decay of a spin echo train illustrated as

porosity unit (p.u) in Y axis versus time (m.s) in X axis (modifiedfrom Coates et al, 1999).

Although calibration adjustments are necessary, the porosity can be related

to the area under the Tz distribution curve. In water-saturated rocks, it can

be proven mathematically that the decay curve associated with a single

pore will be a single exponent with a decay constant proportional to pore

size; that is, small pores have small Tz values and large pores have large Tz

values (Brownstein and Tarr, 1979; Kenyon, 1992).

Figure 2.3 shows the Tz distribution that was derived from the spin-echo

train in Figure 2.2. Rock samples will have a distribution of pore sizes at

any depth in the well-bore; hence, the multi-exponential decay represents

the distribution of pore sizes at that depth, with each Tz value

corresponding to a different pore size (Figure 2.4).

2.I.3 Relaxation Mechanisms

One of the most important requirements for the proper application of NMR

in formation evaluation is to understand the nature of NMR relaxation of

fluids in rock pores. The values of T1 and T2 are affected by the physical

and chemical properties of the fluids, the uniformity of the static magnetic

9

amplitude e ðlibrûted to porositY

inform¿tion for texture antl lluid typesDec8y ratæ

Initial!+i!

I

-1

J¡

tI

5() 1

!a¡

2.Oo

1.AO

1,60

1.40

't.20

1,(X)

o-ao

o.60

o.40

o.20

o.ooo.l 1 'ao loo l,ooo lo,ooo

Tz Relaxation Tiure (ms)

Figure 2.3 Typical NMR T2 distribution for reservoir rock(coates et al, 1999).

field, the variations in pore sizes, the differences between the magnetic

susceptibility of pore fluids and rock grains, the fluid viscosity and

diffusion, and the presence of paramagnetic impurity ions on the pore walls

(Kleinberg and Horsfield, 1990).

For fluids in rock pores three independent relaxation mechanisms are

involved (Kleinberg et al., 1994):

o Grain surface relaxation, which affect both Tl and T2

relaxation

. . Bulk relaxation, which affect both T1 and T2 relaxation

o Relaxation by molecular diffusion in magnetic field gradients,

which only affects T2 relaxation.

Relaxation processes act in parallel. Thus their rates are additive

(Kleinberg et aI.,1994).

-3>\

eôÊ{EIËÐ

10

1

T2 ),(Eq.2.1)

B

In this equation (llT2) S is the surface contribution, (llT2) B is the bulk

contribution and (llT2) D is the diffusion in field gradient contribution.

Similarly for T1 with excluding the diffusion effect,

(8q.2.2)

The surface relaxation mechanism will usually be dominant for the wetting

phase, and the bulk relaxation mechanism will dominate for the non-

wetting phase.

2.1.3.1 Grain Surface Relaxation

Molecules in fluids are in constant motion (Brownian motion) and diffuse

about a pore space, hitting the grain surface several times during one NMR

measurement. When this happens two interactions may occur. First,

hydrogen.protons can transfer nuclear spin energy to the grain surface

allowing realignment with the static magnetic field, Bo. This contributes to

longitudinal relaxation, Tr. Second, protons may be irreversibly dephased,

contributing to transverse relaxation, Tz (Kleinberg and Horsfield, 1990). It

has been shown that in most rocks, grain-surface relaxation is the most

important influence on Tr and Tz. The ability of grain surfaces to relax

protons is called surface relaxivity, p (Kleinberg and Horsfield, 1990).

Different surfaces are not equally effective in relaxing hydrogen protons.

Sandstones are about three times more efficient in relaxing pore water than

carbonates. Also rocks with a high content of iron or other magnetic

minerals have larger than usual values of surface relaxivity and, therefore,

shorter T2 relaxation times. Pore size also plays an important role in

11

surface relaxation. The speed of relaxation depends on how frequently

protons can collide with the surface and this depends on the surface-to-

volume ratio (S/V). Proton collisions are less frequent in large pores as

they have a smaller S/V ratio (Figure 2.4). Therefore, relaxation times are

relatively longer. Similarly, small pores have larger S/V and shorter

relaxation times (Sen et al., 1990).

For a single pore, the nuclear spin magnetization decays exponentially, so

the signal amplitude as a function of time in a T2 experiment decays with a

characteristic time constant. Therefore (Kenyon et al., L995),

Smallpore Lorge pore

Thns, mßêc Thæ, msôe

Figure 2.4 Grain surface relaxation. Small pores have faster decay, shortT2, and large pores have slower decay, longT2, (Kenyon et al, 1995).

1

T 2 Pore

Similarly,

stÈ

{åÊ

I

-=Tl @q.2.a)

t2

Where,

p2: T2 surface relaxibity (Tz relaxing strength of the grain surfaces)

pr : Tr surface relaxibity (Tz relaxing strength of the grain surfaces)

(S/V)po.. : Ratio of pore surface to fluid volume

2.1.3.2 Bulk Fluid Relaxation

Even if grain surfaces and internal field gradients are absent, relaxation

occurs in the bulk fluid. Bulk relaxation is caused by the collision between

fluid protons and controlled by physical properties, viscosity and chemical

composition of the fluids. It can often be neglected in I00% water

saturated rock, but it is important when water is in very large pores, such

as in vuggy carbonates, and, therefore hydrogen protons rarely contact a

surface. Bulk relaxation is also important when hydrocarbons are present.

The non-wetting phase does not contact the pore surface, and so it cannot

be relaxed by the surface relaxation mechanism. Also, increasing fluid

viscosity shortens bulk relaxation times (Sen et al., 1990; Morris et al.,

ree4).

A bulk relaxation correction must be made when the mud filtrate contains

ions of chromium, manganese, iron, nickel or other paramagnetic ions. A

sample of mud filtrate can be measured at the well site to calculate the

correction.

2.1.3.3 Molecular Diffusion Relaxation

'When there are gradients in the static magnetic field, molecular motion

increases proton dephasing and hence increases the Tz relaxation rate (llTz). The movements of molecules into a region in which the strength of the

magnetic field is different can cause this dephasing. Therefore, hydrogen

13

nuclei relax faster in particular regions due to the inhomogeneties in the

static field gradients. Small diffusion effects can be eliminated when the

echo spacing (TE) is decreased. This internal field gradient can increase

when there is a magnetic susceptibility between the fluids and grain

surfaces and it can be sometime uncontrollable. Diffusion has no influence

on Tr relaxation rate (1/ Tr). In the absence of Bo gradients, molecular

diffusion does not enhance NMR relaxation (Bendel, 1990).

2.t .4 Multi-Exponential Decay

Since the pore space in most reservoirs contains more than one fluid, the

spin echo-train does not decay with a single Tz value but instead with a

distribution of Tz values that are described by Kenyon (1989) in the

following equation:

7..BQ) > (Eq.2.s)

Where,

B(t) : Measured Magnetization at time t

Bi(O) : Initial magnetization from the itr' component of relaxation

Tzi: Decay constant of the itn component of transverse relaxation

The summation is for the entire sample, all pores and all different types of

fluid.

If a single pore contains 100% water, then that pore will have a single Tz

distribution that will depend on its size, and its spin-echo train will exhibit

a single exponential decay. In contrast, if there are multiple pores with

100% water saturation, then there will be multiple Tz values that

correspond to each pore and which depend on the pore sizes. Thus, their

composite spin-echo train will exhibit multi-exponential decay (Figure

2.s).

I4

2.2 NMR Application in Formation Evaluation

NMR relaxation measurements can be extremely useful in the extraction of

petrophysical information if optimally applied. There have been numerous

applications of NMR logging in formation evaluation for the estimation of

hydrocarbon reserves and their producibility (e.g. Kenyon et a1., 1989;

Glorioso et a|.,2003). Currently, the most frequently used applications are:

(1) porosity estimation, (2) permeability prediction, (3) pore size

distribution, (4) irreducible water saturation determination, (5) residual oil

estimation, (6) hydrocarbon typing and oil viscosity estimation (Kenyon,

tee2).

Time

Tlme

Tlme

Time

Time

Figure 2.5 Multi exponential decay character of a porous mediumwith different pore sizes and a single wetting phase (modifiedfrom Coates et al, 1999).

T

T

T

T

O

T

15

2.3 NMR Pore Size Distribution

The pore-size distribution from NMR logging is a significant application in

formation evaluation. The data processing to determine the Tz distribution

is called echo-fitting or mapping, and is a mathematical inversion process.

Through echo- fitting, the echo amplitude as a function of time is mapped

to porosity as a function of Tz (Figure 2.6). Normally, the Tz distribution

of rocks is a continuous function. However, to simplify fitting the echo

train, the mapping process uses a multi-exponential model that assumes

that the Tz distribution consists of discrete relaxation times Tz; with

corresponding components (Þi. The values of Tzr are pre-selected (for

example, 0.5, 1,2,4,8, 16,32,64, 128,256, 512, 1024 ms...), and the

mapping process focuses on determining the porosity components of each

distribution (Coates et al., 1999). The Tz value of a single pore is related to

the surface-to-volume ratio of the pore, when the rock is 100% water

saturated, which is a measure of the size of the pore. Therefore, the Tz

distribution of all pores in the rock sample will represent the pore-size

distribution of the sample (Coates et al., 1999).

M(t) P(r)

Raw Data: Echo Train t Processing Result: T: Distribution Tz

Figure 2.6 Echo train is used in theamplitude (M) versus time (t), until themapped as porosity output, porosity(modified from Coates et al, 1999).

echo fitting as input,T2 distribution can be

(P) versus time (T2),

16

Rocks generally have a distribution of pore sizes, each with their own

value of the S/V. The total magnetization is the sum of the signal coming

from each pore. The sum of the volumes of all the pores is equal to the

fluid volume of the rock. In other words, the total signal is proportional to

porosity and the overall decay is the sum of the individual decays, which

reflects the pore-size distribution. NMR measurements of porosity and pore

size distribution are the key elements of NMR interpretation (Kleinberg et

al., 1994).

Many studies have shown that the NMR pore-size distributions and

distributions obtained from capillary pressure measurements are related

(Marschall et al., 1995). The only difference between them is that the NMR

distribution responds to the size of the pore bodies, whereas the

distribution from capillary pressure measurements responds to the size of

the pore throats for each pressure. The effective surface relaxivity (pe) is

introduced to account for the fact that the distributions respond to different

characteristics of the medium. Figure 2.7 compares both distributions and

shows that when shifting the effective surface relaxivity (pe), the pore size

distribution from mercury injection data closely overlays the NMR T2

distribution. Thus, effective surface relaxivity (pe) is proportional to the

product of intrinsic surface relaxivity (p) and the ratio of pore throat size

to pore body size (Marschall et al., 1995).

The NMR data reveals a pore size distribution for sandstone and carbonate

not only when the zoîe is I00% water-saturated but also when

hydrocarbons are present. NMR data delineates fine-grained sands from

coarse-grained sands (Sen et al., 1990). This information is useful in

determining reservoir quality and depositional environments.

l7

2.4 Capillary Pressure (Pc)

The coexistence of two or more immiscible fluids within the voids of a

porous medium, such as a reservoir rock, gives rise to buoyancy forces

which drive fluid movement. Buoyancy force is created due to the density

difference between the non-wetting phase (hydrocarbon) and wetting phase

(water). The grain-static resistance force to hydrocarbon movement in a

porous rock is termed capillary pressure (Pc).Capillary pressure is

controlled by the radius of the pore throats of the rock, the interfacial

tension between the non-wetting phase and the wetting phase and the

wettability (expressed as the contact angle of hydrocarbon and water

against the pore wall), (Schowalter, 1979). The pressure required to move

hydrocarbon through water-saturated porous rock is called the displacement

pres sure.

Capillary pressure (Pc) measurements are important for the characterization

of a hydrocarbon reservoir. Also, Pc measurements enable calculation of

seal capacity by knowing the displacement pressure of both reservoir and

seal rocks. A plot of capillary pressure vs. saturation can be used to

understand the distribution of fluids in a reservoir. These curves also help

determine the irreducible water saturation of a reservoir rock and the entry

pressure of fluid into a water saturated reservoir. These parameters will

help in the estimation of reserves.

2.4.1.1 Mercury Porosimetry and Pore Size Distribution

Mercury porosimetry or mercury/air capillary pressure curves are

commonly used to measure the distribution of pore throat sizes in a rock

sample. A clean rock sample that may be irregular in shape (e'g', a drill

cutting or a core) is evacuated of air, placed in a low and then a high-

pressure vessel and mercury is introduced into the vessels. The volume of

mercury that goes into the sample is precisely measured while the pressure

is increased incrementally.

18

NMR T2 poresize distribution

\

MICP pore afler ad-iustment(pe)

ba

Throat Radius r (microns) T2 ) 1,000r/2pe (microns) or Relaxation Time T2 (ms)

Figure 2.7 Schematic comparison between NMR T2 distributionand MICP pore size distribution. (a) Shows the similarityhetween two measured curves and (b) shows the excellentcalibration between them after relaxivity adjustment, as

discussed in text, (modified from Coates et al, 1999).

First, the volume of mercury fills the void space in the vessel which will

determine the bulk volume of the sample. Then, with increasing pressure,

some mercury volume will fill the surface irregularities of the sample. This

is known as "conformance".

The first volume of mercury to enter the rock will be at a pressure called

the capillary entry pressure. The displacement pressure is the pressure

required for the mercury to actually enter the largest pores in the rock

(rather than fill surface roughness). This can be expressed by the following

simple equation (Purcell, 1949)

Pur2o ur¡o,"

* (- cos á)(Eq.2.6)r por"

Where,

PHg is the mercury pressure (dyneslcmz)

I9

o is the mercury surface tension (dynes/cm)

0 is the mercury/air contact angle

rpore is the pore throat radius (cm)

A typical value for mercury surface tension (o) is 485 dynes/cm. The

contact angle 0 between clean mercury and sample pores varies with rock

composition and rugosity; however, 140" is generally accepted by industry

(Augsburg et al, 1998).

The relation between the mercury pressure and the pore throat size is

illustrated in the following reversible relationship, Equation (2.6) becomes:

t07 .6r por" Pc (F,q.2.7)

Where rpore is in cm and P" is in psr

This equation treats a pore throat as having a pore radius of a perfect

cylinder, rather than the tortuous shape of most rock pores. The mercury

pressure is increased in small increments and the volume of mercury that

enters is recorded. As the mercury pressure is increased, mercury enters

pores that are accessible via smaller and smaller pore throats. The plot of

mercury pressure versus mercury volume is called the mercurylair capillary

pressure curve. The mercury volume may be normalized by the total pore

volume of the sample to be expressed as saturation. Usually, (1-Sng) is

plotted on the x axis to represent the capillary pressure and saturation of

the wetting phase is plotted on the other (V) coordinate. These

measurements are automated and are done on a routine basis using standard

laboratory MICP equipment.

Additional information can be determined about the pore structure by a

sequence of mercury injection and withdrawal steps. The injection (green)

20

curve in Figure 2.8 represents pores that are filled with mercury during

mercury intrusion to increasingly higher pressures. The withdrawal (blue)

curve represents the mercury that remain as the pressure is reduced to zero.

The hysteresis in saturation during withdrawal process is because of

trapping non-wetting phase (disconnected drops of mercury) in the pore

bodies. The mercury trapped inside the pore space is illustrated in Figure

2.9.

Capillary pressure curves are also affected by the different clay types.

Rocks with similar grain size and sorting but different types of dispersed

clays have different pore structure. The discrete particle clays (e.g.

kaolinite) act as individual grains and have little effect on the permeability

and the capillary pressure curve. Swelling clay such as smectite can affect

the permeability, pore volume and thereby the capillary pressure curve

especially if the rock is exposed to water. Pore lining chlorite and pore

bridging illite decrease the pore throat radius and thus greatly increase the

capillary pressure and reduce the permeability (Jorden and Campbell,

l e 84).

2.4.2 Seal Capacity Determination

Evaluatin g any hydrocarbon accumulation prospect should include full

understanding of seal capacity which refers to the maximum hydrocarbon

column a lithology caî support (Kaldi and Atkinson, 1997). Therefore, seal

capacity increases in fine grained lithology which has smaller pore and

throat sizes. Seal capacity is determined by performing mercury injection

capillary pressure (MICP) analyses on both reservoir and seal samples.

The laboratory (airlmercury) system in MICP analysis should be converted

to the reservoir system (brine/hydrocarbon) before the measurements are

applied to the field situation (discussed in detail in methodology section

3.3.1). The most important part of the MICP measurements in terms of seal

capacity is the displacement pressure (Pd) of both reservoir (r) and seal (s)

2t

rocks. The displacement pressures (Pd) of both type rocks are applied in

the following equation 2.8 to calculate sealing capacity;

H^u*:P, _P.os or

0.433 * (p,- pr")(Eq.2.8)

Where,

Hmax Maximum hydrocarbon column can be sealed (in ft)

P¿. and P¿. : Brine/Hydrocarbon displacement pressure of the seal and

reservoir rock (in psi).

pw and phc : Densities of the reservoir fluids, water and hydrocarbon

(in g I cm3).

2.5 NMR and Displacement Pressure Estimation

NMR T2 measures pore body size while mercury injection capillary

pressure (MICP) measures pore throat size. There is a general relationship

between pore body and throat size in clastic rock and therefore pore to

throat size ratio can be derived. Pore to throat size ratio and relaxivity, in

T2 response, can be combined in one empirical scale factor (called

effective surface relaxivity (pe) earlier) to adjust the T2 distribution. This

adjustment was successfully applied in previous studies by using the

following equation (Figure 2.10);

I _P"(Eq.2.e)

Where,

Pc : Capillary pressure (in psi)

22

Tzs: Surface grain relaxation time (in ms), which dominate Tz response

in 100% water saturation

p" : Effective surface relaxivity factor (combining surface relaxivity and

pore/thro at size ratio)

100

00

80

Ia il

I¿

t

o a )1

aaa alI

I

Í) ,4

u {

StoÉo'€ oo

2t-t)>.äoobà30

20

10

0

1 10 100 lo(I) tomo .l cfxxto

Injection Pressure þsia)

Figure 2.8 Mercury injection and withdrawal capillary pressurecurve in very fined grained sandstone, The green curve representsthe injection (drainage) and the blue one represents withdrawal(imbibition).

Figure 2.9 Sequences of Mercury Injection and withdrawal(modified from Stegemeier, I977).

l¡ì.o" .

jjà

23

Deptlr(¡ttI(B) 0 Gan'vna Ray 150

( gAPr ¡

Cue Enry Pre6sryes

400{¡ e (p6r) ' 04

03 T? at 9895 3000

( rÌ$,

- l8l0 -

- :8_ì5 -

- 1840 -

1845 -

\ )1

)/

/{ (

fl

tì

(

{

t

(

't

:

\¡-\_\¡

\,\/.¿

lì 3

I

Figure 2.10 Correlation between CoreEval entry pressure andcalculated entry pressure at 98% non-wetting phase (NWP)saturation of the T2 distribution in Redman- 1. T2 estimatesdisplacement pressure quite well over on the reservoir interval(modified from Boult et al, 1999).

24

2.6 Geological Overview of the Penola Trough

The Redman Field is located within PEL 32 to the southeast of South

Australia in the Penola Trough of the onshore Otway Basin (Figure 2.ll).It is approximately 2.5 km northwest of the Katnook gas field. The Otway

Basin formed during rifting between Australia and Antarctica in the Late

Jurassic and Early Cretaceous.

The Penola Trough is a garben initiated in the early stage of the rifting of

the Otway Basin (Lovibond et al, 1995). The Casterton Formation and

Crayfish Group were deposited in the Late Jurassic and Early Cretaceous.

In the late Early Cretaceous (Aptian-Albian), the area was uplifted and

erosion took place at the top of the Crayfish Group. During this erosion,

rifting occurred south of the Tartwaup Hingeline axis. The Penola Trough

is dominated by a NW-SE trending half graben, particularly in South

Australia. The major NW-SE trending Kalangadoo fault system bounds the

Penola Trough to the south (Cockshell et al, 1995).

The commercial reservoirs in the Penola Trough are in the Crayfish Group

sandstones. In Redman-1 the primary reservoir is the Pretty Hill Sandstone,

deposited in the Early Cretaceous above the Casterton Formation (Figure

2.12). An unconformity separates the Pretty Hill Formation and Casterton

Formation where an upper boundary is gradational with Laira Formation

(Morton et al, 1995).

The Crayfish Group in PEL 32 consists of interbedded sandstones,

siltstones and mudstones. This interbedded succession indicates a fluvial-

lacustrine environment. The thickness of these sediments is variable

throughout the basin because of the subsidence and faulting process

(Morton et al, 1995). The Crayfish Group is divided into three formations

which are: the Pretty Hill Formation, Laira Formation and Katnook

S andstone.

25

Rêdmâ.llI

N

1

SA

vlc

o

å\,

Ira r iqt

¡t (A A¡ tror¡&

Itlqt

roË

. rll ll.---;----..II

a

tI

a

KII-ôI/IETRES

Figure 2.lI Location and structural map of Otway Basin in South Australia(modified from Redman-1 well completion report, Boral Energy Resourcesreee).

The Pretty Hill Formation is the oldest formation of the Crayfish Group

within the Otway Basin. It extends from the lower Berriasian to Baarmian

in age and occurs from C.australiensis to lower F.wonthaggiensis spore

pollen zones inclusively. The Pretty Hill Formation consists of interbedded

fine to medium grained sandstone with siltstone and shale interbeds.

The Pretty Hill Formation has a variable lithology across the Otway Basin

and consists of sandstones with different degrees of siltstones, clay and

detrital composition (Little, 1996). It has six members in the stratigraphic

column in PEL 32 area which are, from the bottom to top, Australiensis

shale unit, McEachern Sst., Lower Sawpit shale, Sawpit Sst., Upper Sawpit

shale and finally Pretty Hill Sst. (Figure 2.12).

The Laira Formation is Barremian to early Aptian in age and occurs in the

upper F.wonthaggiensis spore pollen zoîe. It consists of siltstone and

claystone deposited in a lacustrine environment with minor fluvial fine-

26

grained sandstone interbeds (Little, 1996). The formation is best developed

in the Penola Trough area with gradational and slightly diachronous lower

boundary with Pretty Hill Sst (Morton et al, 1995). In some areas, the Laira

Formation was deposited unconformablely on basement. The thickness of

this formation is variable from one area to another and the observed

thickness ranges from 132 m to 888 m.

The Laira Formation acts as regional seal to Penola Trough reservoirs. This

formation has the lowest seal potential risk of the Penola Tough strata and

is considered the cap seal for six hydrocarbon accumulation which are

charged to spill point (Jones et aL,2000).

The Katnook Sandstone is the uppermost sandy succession in the Crayfish

Group, above the Laira Formation, and was defined by Morton (1990) as

the equivalent to Unit D of Kopsen and Scholefield's (1990) nomenclature.

It was deposited in the Barremian and upper F'. wonthaggiensis spore

pollen zone. This unit consists of fine to medium sandstone interbedded

with micaceous siltstone. In some areas, it is absent due to erosion or

facies change since it was deposited in a low sinuosity meandering fluvial

to distal braided fluvial environment (Morton, 1990).

The Katnook Sandstone has a variable thickness throughout the Otway

Basin and it thins towards the west and the north. It has a gradational and

strongly diachronous lower boundary with the Laira Formation. The

Katnook Sandstone contains significant hydrocarbon accumulations in the

Penola Trough. The Katnook Field is a gas field, which produces from a

gentle structural dome of Katnook Sandstone and Windermere Sandstone

reservoir. The Eumeralla Formation, a silty claystone, is the cap seal for

this hydrocarbon accumulation.

27

Ma AGE STRAT. LITHOLOGY SPORE POLLENZONES

AR

50

75

t

Et-É,¡¡¡Þ

É,JO-BR

Ë6lu

C blñ*cates

¿.&tu3

P.tuberculatus

N-asperusNIRRÀ¡ìlOA

ÀÉqtill

Ëü

M.divuus

:::.'.:i ii. iiiÉbtt i.ötr Fù.ïi: i' jLbalñci

.n

ou¡()F¡¡JÉ()

t¡lFf

YooqËdtrõEØ

lfoxgr

t.Ilt2t

N.ænech¿s

T.pachyertnus

f: ÞiDler

Adisfocarinatus

ìÉl¡l

OTWAYSUPERGROUP

P.pannoau

u c.panåÐxa

C-st7mtuJ

+l chuslrsíK¡TlrÒé4

Uppêt F.wønthaggf enti'

LowsrF|9anlhøttiensts

t,NJfuallensf s

JF LRwalhercoqsls

OIWAY BASINPEL 32

SIRATIGRAPHY OF IHE PENOTA TROUGH, WESIERN OIWAY BASIN

Fig. 2.12 Stratigraphic column of western (PEL 32) OtwayBasin (modified from Redman-1 well completion report, BoralEnergy Resources 1999).

28

CHAPTER THREEPETROPHYSICAL AND MINERALOGICAL

MEASUREMENTS

3. I Introduction

Redman-1 core was cut in 1998 from the Pretty Hill Sandstone reservoir in

the Penola Trough of Otway Basin. The 18m core (2828.81m - 2846.76m)

consists of a 9m channel facies, a 4.5m bar facies and a 4.5m floodplain

facies. In previous work, conventional core analysis was performed on 30

core plugs in the porous intervals and then more special core analyses

(CoTEVAL) were conducted on a subset of 22 samples. In this study, nine

core plugs were cut in the floodplain facies and two other core plugs were

obtained from the previous dataset. This chapter explains in detail the core

sample selection and preparation and describes each petrophysical and

mineralogical laboratory technique used.

3.2 Sample Selection and Preparation

A total of 11 samples were selected from the floodplain facies in Redman-1

core for analyses. Samples were selected on the basis of grain size and

sorting (Figure.3.1). eight new core plugs (lyr" diameter, 2" letgth) were

cut d.y from the 213 of the core to prevent clay swelling by 'water

adsorption. These core plugs were cut horizontal, parallel to bedding

orientation. All core plugs were dried in an oven at 80o C to dehydrate the

sample.

Off-cut samples were taken from the core for MICP, SEM, XRD, XRF and

thin sections. The following laboratory techniques were performed on these

samples:

Mercury injection capillary pressure (MICP) : 11 samplesa

a Scanning Electron Microscope (SEM) : 11 samples

29

O

o

a

a

X-Ray Diffraction (XRD) : 11 samples

X-Ray Fluorescence (XRF) : 11 samples

Porosity ancl Permeability : 9 core plugs samples

Thin Sections : 3 samples

Porosity and permeability measurements were performed on just 9 core

plug samples. The other two could not be measured because of either

core plug fractures or there was not enough sample to cut a core plug.

Figure 3.1 Redman-1 core image from 2833 to 2841m (2836.81-2844.81m log-depth) where the flood plain facies is dominant. Theproject samples are illustrated here (modified from Redman-1 wellcompletion report, Boral Energy Resources 1999).

å':t¡ *''*{

(otîús

îr) mN

oo

o

ol-ri

o@

o

¡

N6ñÕ('mÞfNowaNÕ¡({{t

-J-

F!q.è

30

aaJ.J Pore Measurement Techniques

Different techniques were used in the mineralogical and petrophysical

description of the I I samples to ensure good characterization of the fine

grained rocks. These measurements include MICP, SEM, porosity and

permeability, XRD, XRF, thin section description and point counting.

3.3.1 Mercury Injection Capillary Pressure (MICP)

Mercury Injection Capillary Pressure is based on the physical principle that

non-wetting liquid, such as mercury, will not penetrate pores until adequate

pressure is applied to force it to enter the pore systems. The Washburn

equation (Equation 3.1) gives the relationship between the applied pressure

and the pore size (Washburn, l92l).

PD: -4 y cos 0 (Eq.3.1)

P applied pressure,

D Pore throat diameter,

y surface tension of mercury

0 contact angle between mercury and the pore wall.

Mercury intrusion volume is calculated continuously while pressure

gradually increases. MICP results are controlled by the size and

distribution of pore throats which characterize different rock types.

The Micromeritics Autopore 94I0 mercury injection porosimeter machine

was used to measure capillary pressure on all the 11 samples (Figure 3.2).

This machine is composed of two systems, one for low pressure runs and

the other for high pressure runs. A sample is initially run in the low

pressure system and followed quickly by the high pressure run to enable

the mercury to penetrate the smallest pore throats.

The low pressure system consists of two sample ports, vacuum pump, a

mercury reservoir and associated mercury fill chamber, an air pump, a

31

pressure sensor and the associated valves (Figure 3.3). The high pressure

system consists of a high pressure port, a pump, a vatiac, pressure sensor

and the associate valves (Figure 3 .4). The high pressure system can

operate to pressure of 60,000 psia (Bentley, 2000). The Autopore 9410

instrument is connected by cable to a desktop computer which controls the

instrument and records all measurements including mercury intrusion,

pressure and sample weight.

All samples were dried and weighed before being loaded into the

penetrometer which was then sealed and placed in the low pressure port of

Autopore 9410 machine. A vacuum pump was used to evacuate the sample

to 0.5 torr. The vacuum was held for approximately five minutes, and the

mercury was pumped from the reservoir to fill the chamber and the

penetrometer. Continuous mercury intrusion and pressure readings were

taken gradually from 2 psia to 40 psia in the low pressure system. The

sample was then returned to atmospheric pressure, removed from the

penetrometer and weighed.

The sample was then placed into the high pressure penetrometer port. The

high pressure run was started at 45 psia and stepwise mercury intrusion and

pressure readings were taken until the pressure reached 60,000 psia. The

pressure was then decreased back to atmospheric pressure with 20 second

equilibration to each pressure incrernent.

MICP data are displayed in excel worksheets and diagrams, Appendix-A.

All mercury saturation data ìwere normalized to take out the conformance

effect which represents the mercury volume required to fill the surface

irregularity of the sample. Then, pore throat size distribution can be

displayed by plotting capillary pressure versus mercury intrusion volume.

The distribution can be displayed as an incremental or cumulative capillary

pressure curve when incremental or cumulative mercury volume are used

respectively. The pore throat diameter was calculated using the Washburn

32

equation (Eq.3.1) since all the terms in that equation are known except

pore throat diameter.

The MICP laboratory measurements should be converted to reservoir

conditions to be applied in any calculation or calibration. The conversion

simply converts the data from airlmercury capillary pressure to a

brine/hydrocarbon capillary pressure. The conversion was performed by

knowing the interfacial tension and the contact angle of both side

airlmercury and brine/hydrocarbon using Equation 3.2 (Purcell, 1949).

Pru,hc = Pco, 4abl' * 0u''"' oor**oot* (Eq.3.2)

Where,

Pc u¡t c : Capillary pressure at reservoir condition (Brine/Hydrocarbon)

Pc u/- Capillary pressure at laboratory condition (Air/Mercury)

o b/hc : Interfacial tension at reservoir condition (Brine/Hydrocarbon)

e b.nc Contact angle (wettability) at reservoir condition

(Brine/Hydrocarbon)

o alm : Interfacial tension at laboratory condition (Air/Mercury)

e alm Contact angle (wettability) at laboratory condition

(Air/Mercury)

JJ

/.

$¡tf'

:¡:.*,1a'

Ftele as ê

poñ8

Hlghpreaaure

Portplug

Mercury -{reservolr

mechan¡smFor plug

PcnotromotcrstGm

Hlgh Þrcs¡urcport

Figure 3.2 Micromeritics Autoporemercury injection porosimeter (modifiedBentley,2000).

94t0from

Figure 3.3 Schematic of low pressure section in MicromeriticsAutopore 9410 mercury injection porosimeter (modihed from Bentley,2000).

Low pressure ports

Pressure sensor

Vacuum pump

34

Volaçt

Variac

Highport

High prêssurepump

Pressuresensor

Figure 3.4 Schematic of high pressure section in Micromeritics Autopore94I0 mercury injection porosimeter (modified from Bentley, 2000).

3.3.2 Scanning Electron Microscopy (SEM)

Scanning Electron Microscopy (SEM) was used to visualize the texture,

mineralogy and pore system of the samples. The tool produces a stream of

electrons with accelerating voltage between 10-30 kv. Once the electron

beam hits the surface of the sample, some electrons reflect as backscattered

electrons (BSE) and some are emitted as low energy secondary electrons

(SE). BSE and SE are collected by a scintillator which sends a pulse of

light when electrons from the sample are detected. This light emitted is

converted to an electrical signal and amplified by the photomultiplier

(Trewin, 1988). These electrical signals are converted to an image and the

contrast in the image is due to the general topography and orientation of

the sample, chemistry of the sample surface and difference in the electrical

properties of the sample.

35

At every depth interval 2 small blocks (0.5cm by 0.5cm) were cut, one

parallel and the other perpendicular to the bedding surface. These 22

samples (11 depth interval) ,were coated with 2nm thick carbon/gold.

Initially a Philips XL20 was used to obtain the pore geometry, clay types

and textural information from secondary electrons (SE). A 10- 15 kv

accelerating voltage was applied to produce better images. Also, X-Ray

diffraction with XL20 was used to identify chemical composition of some

minerals.

An XL30 also was used to acquire higher SE magnification images with

better resolution for particular samples. For polished thin sections, BSE

and SE were used in the XL30 to identify the texture and chemical

differences of three thin sections. The XL30 was also used for other

samples which were covered with epoxy material to determine how far this

material penetrated the samples. A 10-15 kv accelerating voltage was also

used in this tool.

Backscattered electrons (BSE) image were performed on the thin sections

using Philips XL30. Thin sections were polished to obtain better image by

using BSE in Philips XL30. BSE image provide better identification of the

chemical contrast between grains especially when perthite existed in the

samples.

3.3.3 Porosity and Permeability Measurements

Porosity and permeability were determined by routine core analysis. The

porosity analysis was measured using helium whereas permeability was

measured by air on 9 core plugs of Redman-1.

For porosity measurements, the plugs were sealed in a steel chamber

(matrix cup) and a known volume of helium at 100 psi reference pressure

introduced to the cup.From the resultant pressure drop the unknown

volume (i.e. the grain volume), was calculated using Boyle's Law. The bulk

36

volume of every plug was determined by immersed mercury. The difference

between the grain volume and the bulk volume is the pore volume. The

porosity is calculated as the volume percentage of pore space with respect

to the bulk volume. The porosity calculated using this technique is a total

porosity.

Boyle's Law Pr Vr : PzYz

PrVr:Pz(VrfVc-Vg)

(Eq.3.3)

Where, Pr : initial pressureVr : reference cell volumeVc : matrix cup volumeVg : grain volumePz : final pressure

PV:BV-GVAmbient Porosity % : PV IBV x 100

'Where, PV : ambient pore volumeBV : ambient bulk volumeGV : grain volume

For permeability measurements, which were done by Amdel Ltd, the plugs

are placed in a hydrostatic cell at a confining pressure of 400 psig. This

pressure is used to prevent bypassing of air around the sample when the

measurement is made.

During the measurement a known air pressure is applied to the upstream

face of the sample, creating a flow of air through the sample. Permeability

for each sample is then calculated using Darcy's Law through the

knowledge of the upstream pressure and flow rate during the test, the

viscosity of air and the plug dimensions.

37

Ka2000 * BP* p* q* L

(P,' - P])* ¿,(Eq.3.a)

Where, Ka : air permeability (md)BP : barometric pressure (atmospheres)F : gas viscosity (cp)q : flow rate ( cmt ls¡L : sample length (cm)Pr : upstream pressure (atmospheres)Pz : downstream pressure (atmospheres)A : sample cross sectional area (cmz)

3 .4.1 Porecast analysis

Pore geometry is a very important petrophysical characteristic and porecast

analysis is one of the techniques that can be used to evaluate it. The

technique is also useful to estimate the pore to throat size ratio. The

porecast is made by impregnating the sample with blue epoxy resin, making

sure the pore system is penetrated with the epoxy material. The grains or

rock are then dissolved by hydrofluoric acid (in clastic sedimentary rocks)

or hydrochloric acid (in carbonate rocks). This leaves the pore network

preserved as a cast which can be examined using SEM (Trewin, 1988).

The impregnation procedure was originally used for thin sections

preparation. The samples were first impregnated with blue epoxy resin in a

pressure chamber. A vacuum was then applied to remove the air from the

pores. Every few minutes the air was released into a chamber and this

process was repeated for 30 minutes. Finally, the sample was allowed to

be set and the epoxy hardened for 24 hours under room temperature.

The impregnated samples were put in 10M hydrofluoric acid for 4 days

which was effective in dissolving the silicate minerals. All selected

samples were clastic rocks (v. fine Sandstone - fine Siltstone). A large

38

proportion of the sample material was dissolved but some of the bulk

sample remained which allowed observation of some of the consolidated

part of the samples.

All samples were examined using the Philips XL30 scanning electron

microscope (SEM). The Philips XL30 tool has better resolution at higher

magnification which was extremely useful for very fine grained rock.

3.4 Mineralogical Determination Techniques

Four techniques have been used to identify different mineralogy. These

techniques are X-ray diffraction analysis, X-Ray fluorescence, thin sections

and point counting.

3.4.1 X-Ray Diffraction Analysis (XRD)

X-ray diffraction (XRD) is used to identify the bulk mineralogy of

sedimentary rock samples. According to Hardy et al (1988), the best tool to

determine the clay types in very fine grained rocks such as shale is XRD.

The X-ray tool sends a subparallel beam to the powder sample that isrotated at regular speed. Every mineral has different characteristic

diffraction angles and they are recorded when the beam is diffracted to the

receiver. The x-ray diffraction intensity that will be derived from the

sample depends on the differences between grain size, crystallinity,

relative abundance, structure and chemical composition.

XRD analysis was made by CSIRO for all 11 samples. Bulk samples were

pre-ground for 15 seconds in a tungsten carbide mechanical mill to pass

through a 0.5mm sieve. A lg sub-sample was further ground for 10 minutes

in a McCrone micronizing mill under ethanol. The resulting slurry was

oven dried at 60"C then thoroughly mixed in an agate mortar and pestle

39

before being lightly pressed into aluminium sample holders for X-ray

diffraction analysis.

XRD patterns were recorded with a Philips PWl800 microprocessor-

controlled diffractometer using Co Ka radiation, variable divergence slit,

and graphite monochromator. The diffraction patterns were recorded in

steps of 0.05' 2 theta with a 1.0 second counting time per step, and logged

to data files for analysis. Quantitative analysis was performed on the XRD

data using the commercial package SIROQUANT from Sietronics Pty Ltd.

The data were first background subtracted and calibrated for the automatic

divergence slit. The results are normalized to l00o/o, and hence do not

include unidentified or amorphous materials.

3.4.2 X-Ray Fluorescence (XRF)

X-Ray Fluorescence is widely used in the determination of the major and

trace element chemistry of a rock sample. It depends on the excitation of a

powder sample by X-rays. Every element in the sample has different

wavelength characteristic of the X-rays fluorescence which is excited by

the primary X-ray. The concentration of the elements is determined by the

intensity of the X-rays fluorescence (Rollinson, 1993).

XRF analysis was made also by CSIRO for all 11 samples. Approximately

1g of each oven dried sample (105'C) was accurately weighed with 4g of12-22 lithium borate flux. The mixtures were heated to 1050'C in a PtlAu

crucible for 12 minutes then poured into a 32mm Pt/Au mould heated to a

similar temperature. The melt was cooled quickly over a compressed air

stream and the resulting glass disks were analysed on a Philips PW1480

wavelength dispersive XRF system using a dual anode Sc/Mo tube and

algorithms developed in the laboratory.

40

All the minerals were analyzed in oxide form and there no separation

analysis was made for the heavy minerals. Iron content was reported as

Fe2O3 without measuring the Fe2* lFe3* ratio.

3.4.3 Thin Section Petrology

Three samples were selected based on grain size for thin sections. The first

one represented the best seal lithology (massive clayey siltstone "Sample 7

(2841.99m)"), the second one represented a poor seal lithology (very fine

sandstone "Sample 3 (2840.86m)") and the third one comprises clayey fine

siltstone clasts inside fine to medium sandstone "Sample ll (2844.08m)".

Thin sections were prepared by Pontifex and Associate in Adelaide.

Samples were impregnated with blue epoxy resin in a pressure chamber.

Vacuum was then applied to suck the air from the pores. Every few

minutes the air was released into a chamber and this process repeated for

30 minutes. Finally, the sample was allowed to be set and the epoxy

hardened for 24 hours under room temperature.

A thin slice was cut for every sample with a diamond saw. The slice was

ground for around 30 minutes. Extra lapping steps were performed to

produce a highly polished surface which helps to increase resolution in

microscopy (Miller, 1988). A small amount of blue epoxy resin was

applied to the first face and then the glass slide was attached under vacuum

pressure. The second face was cut automatically on diamond-impregnated

saw with a vacuum chuck. The precision lapping machine lapped the slice

to a final 30 prm thickness.

Thin sections were examined under Olympus BH2-UMA petrographic

microscope with an Olympus DP11 camera attached (Figure.3.5).

Petrographic description of thin sections was possible down to the very

fine sandstone samples. Siltstone and finer samples were difficult toexamine. Thin section descriptions focused on the abundant minerals in the

4l

samples. Quartz, albite, orthoclase, biotite, muscovite, clays and accessory

minerals were the main minerals which were described.

3 .4.4 Point Counting

An Olympus BH2-UMA petrographic microscope with pointcounting

equipment attached was used for point counting. To assure better results in

fine grained samples, the procedure used was 300 counts per thin section

with 1 mm vertical interval and 0.65 mm median horizontal interval. Eight

mineral categories were used for point counting because possibility to

distinguish different very fined grains is more difficult. These eight

minerals are quartz, albite, orthoclase, biotite, muscovite, rock fragments,

organic matter and clays.

Figure 3.5 Olympus BH2-UMA petrographic microscope with anOlympus DP 1 1 camera attached and pointcounting equipment used in thinsection analysis.

42

CHAPTER FOURPETROLOGY

4.1 Core Description

This study focuses on the 4.5 m of the floodplain facies which is separated

by channel and bar.facies (Figure 4.I). The interval is predominately

siltstone. Grain size ranges from very fine sandstone to fine siltstone and

the mineralogy from quartz and feldspar rich to clay rich rock. Higher

organic and mica content are observed in the finer grained intervals.

Figure 4.1 shows an intraclast conglomerate which was deposited at the

base of the channel (2844.3m) which represents a sequence boundary. The

intraclasts comprise large clasts (gravels) made up of very fine siltstone

which were transported from previous deposits and mixed with medium

sandstone. The intraclast conglomerate grades rapidly into very fine

sandstone followed by a flood plain interval starting from 2844m with

coarse siltstone interbedded with very fine sandstone (Figure 4.1). Blocky

very fine grained siltstone with high organic content extends to 2843m.

Slight bioturbation is present throughout the flood plain facies. Minor

micro fractures occur at 2843.1 with clay smear along the micro fracture

plane.

Coarsening upward characterizes the interval between 2843 to 2842.2m

with interbedded layers of very fine and very coarse siltstone. Blocky very

fine siltstone makes up the interval between 2842.2 and 284t.lm and

represents a low energy depositional environment. The amount of very fine

sandstone increases upward from 2842.2m until the top of the flood plain

facies, 2840.66m, occurring as interbedded layers within the coarse

siltstone. Occasional light gray lithic clasts with lamination occur between

2841.2 and 2840.8m. A channel facies was deposited above the flood plain

interval between 2840.66 to 2837.6m. Very fine siltstone (clayey siltstone)

43

flood plain sediments with burrows infilled with silt occurs between 2837.6

and 2837.1

Laboratory analyses \¡/ere performed on 11 samples to identify their

textural features and mineralogical composition to complement the

petrophysical data. The location of samples is shown in Figure 4.1 and

Table 4.1. The samples range from clayey siltstone to fine sandstone. Three

thin section samples were prepared and examined using a petrological

microscope. These are interbedded very fine sandstone-coarse siltstone

(Sample 3), massive clayey fine siltstone (Sample 7) and heterolithic

sandstone-siltstone (Sample 11). Figure 4.2 shows the scanned thin

sections. Photomicrographs were taken to illustrate different minerals:

most are taken from the very fine sandstone (Sample 3). The minerals

observed in the fine grained rock interval of the Pretty Hill Sandstone at

Redman-1 can be divided into detrital and authigenic phases and are

described below.

4.2 Detrital Mineralogy

The detrital mineralogy of the fine grained interval of Pretty Hill

Sandstone consists mainly of quartz, feldspar (albite and orthoclase), mica

(biotite and muscovite) and clays (smectite and kaolin). The accessory

minerals include garnet. The different minerals were plotted against depth

using XRD bulk mineralogy and have no distinguishing trend with depth

(Table 4.2; Figttre 4.3).

Quartz and feldspars increase relative to mica and clay contents in the

coarser grained samples Sample 2 (2840.66m), Sample 3 (2840.86m) and

Sample 4 (2S4I.2Im). These samples represent the gradual shift from flood

plain to channel facies. Commonly, the flood plain interval is characterized

by the change from coarse siltstone to fine sandstone and clayey fine

siltstone.

44

zÞ

It

aJftÒoII

ilË

È

È

{F

i

rounded, moderalely well sorted, interbedded

siltslone and sandslone.

Brown blaek, clayey, sub fisslle, argiilaoeous,

slighlly arenaceous, môderâlely hârd s¡lty claystone.

Flood plain rleposil, trace bìolurbation,bunows infilled wlh silt.

Medium grey, fine to rnedium, well sorled bar

facies sandslone. Common cross laminations

near top wlth occasional rounded lithic

fragmenls,

Medium grey brown, mainly medium, sub

angular to sub rounded, moderalely sorled

sandstone, quartz eement, hace lo ocoasional

pale brown clay matri,( and altered feldspar,

trace garnel, æcasional grey lilhics, friable to

lirm, fair to good v¡sual porosity. Channel facies.

0ark brown, very fine sandstone t0 silly

claystone, quarlz cement carbonaceous

microlaminalions, ææsional light grey lithic clasts

with laminations, very poor visual porosity. Flood

plain facìes,

a.a., dark gray, very iìne sandstsne lo çourse

s¡llstone ¡aminaled layers, more bioturbalion.

Dark gray to black, course to very fine siltslone,

mderately hard lo hard, sub lisile, slight

bioturbation. Minor fractures display shale

smear along íracture plane,, Flood plain facies.

Top channcl facies marked by a light grey, very

fine sandstone with argillaæous matrix, Grades

¡nto an rntrâclaslie conglomersle, Sequence

Boundary

Medium biown to brorÁ/n blaek, mainly medium,

sub angular to sub rounded, well sorted

sândstone. quarl¿ cemcrìt, oeeásion¡l to

common dark brown clây matrix fair to good

Green brown, mãìnly angular lo sub

SILT .ätlrirÉ

1m$'.r:e""-!fl "l

e....?..-.

Sedìmentarf Slructures

Physical Biogenic

Sam$e

l/o

LOG SHEET No r or 1

Environment

t-I-t-

T-

l=

t-

5

5

\

4

4I

6

B

5

o

11

r0

ê

l-t-I_

Figure 4.1 Redman- 1 core log for the very fine grained interval(2836.81-2844.8 1 m)

45

Sample

No

Core

Depth

LogDopth Samplo Classif¡cation MICP NMR XRD SEM XRF

Thin

SoctionPorosity and

Pormoability

2833 76 2æ7.57 couse siltstone Yes Yes Yes Yes Yes

2 2æ6 85 2840 66 laminated tne sandstone+iltstone Yes Yes Yes Yes YeS Yes

J 2837 05 2840,86 lamirmted very lure sandstone+ihstone Yes YeS Yes Yes Yes Yes Yes

4 2837 4 2U1.21 laminated coane-ñne siltstone Yes Yes Yes Yes Yes Yes

5 2837 49 2841 3 lammated coarse-ftre siltstone Yes Yes Yes Yes Yes Yes

6 2837.85 2841.6ô rnassive lure siltstone Yes Yes Yes Yes Yes Yes

7 2838.18 2841 99 massive fme siltgtone Yes Yes Yes YêS Yes Yes Yes

I 2839.27 2843.08 massive fine siltstone Yes Yes Yes Yes Yes

I 2839 61 2U342 massive lure siltstone Yes Yes Yes Yes Yes Yes

10 2839 I 2U361 massive lure srlLstone YeS Yes Yes Yes Yes Yes

11 2U0.27 2844.@ inhonrogeneous sandstone-siltstone Yes Yes Yes Yes Yes Yes Yes

Table 4.1 The different analyses were performed on the 1 1 fine lithologysamples of Redman-1 core. Thin sections from three samples (coloredyellow) with different grain size were examined under microscope.

PONTIFEX PONTIFEX(oô) å332 6744 (oa) s3aa 6744

PONTIFEX(o8) 8332 6744

0.5 in

Sample-3(2840.86)

Sample-7(2841 . ee)

Sample-l1(2844.08)

Figure 4.2 Thin section images from samples withdifferences in grain size, sorting and homogeneity.

46

4.2.1 Quartz

Quartz measured from XRD ranges from 9Yo in clay rich samples, such as

Sample 1 (283 7.57m), to 32% in sandier samples, such as Sample 4

(284I.21m). The quartz intensity is between 1800- 6000 counts on XRD

(Figure 4.4 and Appendix-D). Point counting (Table 4.3) indicates similar

percentages as the XRD q:uartz results (Table 4.2) bú point counting is less

accurate than XRD analysis in very fine grained rocks.

The most abundant quaúz type is monocrystalline (Figure 4.5) within the

coarse siltstone and fine sandstone fractions. The quartz, is characterized

by no inclusions, and is interpreted to be of volcanic origin (Little and

Phillips, 1995).

4.2.2 Feldspars

Feldspars range from lgYo to 35o/o percent in the very fine grain interval of

the Pretty Hill Formation based on XRD analysis (Table 4.2) and 18% to

25%o from point counting (Table 4.3). The dominant feldspar type is albite

(32-17%) with minor amounts of K-feldspar (orthoclase) (Table 4.2).

Microcline was recognized in trace amounts in the fine sandstone and may

increase with increasing grain size.

The abundance of plagioclase (Figure 4.6) among the total feldspars

probably reflects a predominantly volcanic provenance. The plagioclase

was probably originally andesine but has since been albitized by digenesis

(Little, 1996). Also, albitization of originally K rich feldspar, which was

reported by Little (1996), may contribute to the high albite content. The

present orthoclase concentration is between 2 and 4 percent. It is slightly

to completely altered to authigenic minerals (Figure 4.7). Orthoclase may

contribute to pore filling clays in the samples. Also, it has some inclusions

of Na-plagioclase distinguishable from original perthitic texture which

47

suggests diagenetic replacement of potassium by sodium (Little and

Phillips, 1995).

Redman-î XRD Bulk Anaþis Re$lts

Sample

No.

Core,

depth

Log.

depth

Quartz

(%)

Albite

t%)

0Íhoclase(%)

Mica

(%)

Chlodte

(%)

Smætite(%)

Kaolin

(%)

Calcito

(%)

Pyrite

(%)

1 2833.76 2ß7.57 I 17 2 ¿o I 32 7

2 2836.85 2840,ffi 24 32 J 10 7 16 6 2

3 2837.ffi 2840,ff 19 2S 4 12 I 22 Ã

4 2837.4 2U1.21 32 28 J 11 7 I 11

5 2837.49 2841.3 24 20 2 22 I 18 4

6 2&37.&5 2841.06 26 20 3 21 I 16 5

7 2838.'18 2841.99 22 20 3 22 I 20 4

I 2839.27 2843.$ 24 23 J 21 7 17 4

I 2839.61 z8/.t.42 23 22 3 21 I 19 4

10 2839.8 2843.61 20 18 2 24 I 22 J 1

11 2U0.27 2844.ffi 15 27 J 16 7 23 2

Table 4.2 XRD bulk mineralogy analysis result shows the increase inalbite and mica content in clayey fine sample in expense of quartz.

Table 4.3 Summary table of point count results from three thin sections.It shows the percentage of the minerals that comprise the rocks. Sample 3

has the most reliable mineral estimation because it is coarse grained andhomogeneous.

Sample

No

Lo9

depth

Quailz

(%)

Albite

(%)

0tlhoclase

(%)

Biolite

(%)

Muscovite

(%)

Rock Fngments

(%)

Organic Matter

(%)

Chlorite

(%)

Smectite

(%)

2840 86 20 667 23 2.333 12 3 333 12.3$ 2.667 18,667

7 2841 99 18.667 21 333 2,ffil 11 0.$3 4,333 3,ffi7 i.66i 24.333

11 2844,08 19.667 13,667 4.333 7,$3 4.mi 4,333 2.333 4,ffi7 39

48

aJ

6E

ss

2841

2841

?841

FroÊ

ïRD.{nalysis

loJt .Jt

2840 66

2840 86

2841 .21

2843.42

2843.61

2844.08

0% 1rl% 2[% 30% 409t 50% 80% I0% E0% g0% 100%

BïlkMimrel Cnftni(oÁ)

Figure 4.3: XRD bulk mineralogy content versus depth

2843 08

s 0uarE

r Albite

¡ Orthoclase

r Chlonte

E Smectite

r Kaolin

¡ Calcile

r PyriTe

¡ Mica

49

{È roat otr^Rfz, sYN+ 466 åLg?É. OAoçreD

35

2t

iì g!1:

& ?55þ tot

1+ tg

ruscovllË.a¡lcuNocHtmE rvntr.uelf ÁaÉL.t +t\iil. itAK{OUNIE.!A

srcÊÕailt{-

oPzt><

Éfo(Jàr¿Øcd,Ë

:1Ê:

! tr11

7 ?s ?tF?4æ

1trI

rmlrlt

t

10.00 20 00 30.m 40.o0 50_0{) 60 00

s

70.00 80m2-Theta Anole (de0)

Figure 4.4 XRD trace showing the main minerals that make-upthis sample: quartz, albite, microcline, mica, clays and chlorite.Quartz has the highest intensity in this sample, >3500 counts.Redman-1, Sample 3 (2840.86m).

t-

a

1Figure 4.5 Cross polarízed photomicrograph of thedominant minerals in the 11 samples: detrital quaúz (Q),feldspars (F) mica (M), altered rock fragment to clay (RF)and lithic grains (L). Monocrystalline qtartz grain (Q) is thedominant qtartz type. Redman-1, Sample 3 (2840.86m).

ít* *

50

Figure 4.6 Cross polarized photomicrograph of Na-Plagioclase (albite) grain with parallel twining (A). Albiteis the dominant type of feldspar in the samples. Redman-1,Sample 3 (2840.86m).

Figure 4.7 Cross polarized photomicrograph of K-feldspar(orthoclase) grain which appears to be altered duringdigenesis-partial. Albitization is likely based on Na-plagioclase inclusions within the grain. Redman-1, Sample3 (2840.86m).

51

4 .2.3 Mica

xRD results indicate that mica forms between 10 - 26 % of the total

mineralogy. Mica content increases when the grain size decreases along

with the quaúz and feldspar content. Point counting (Table 4.3) was done

on the thin sections to determine the percentage of different minerals withemphasis on biotite to muscovite ratio. The XRD results did not identifybiotite whereas the biotite \¡/as more common than muscovite in thinsections (Table 4.3, Appendix-C and Appendix-D).

The discrepancy in the biotite concentration is probably due to diagenetic

alteration which would not have given gharp classical biotite peaks. Areason for the discrepancy between the XRD and the point counting results

is that there was overlap in the XRD of the muscovite and biotite peaks and

they were grouped. The biotites have also been altered by diagenesis, its

birefringence is altered, and in some cases the graines are partiallychloritized. Knowing the biotite percentage is important for the objective

of this study, point counting was performed although it was quite difficultin fine grain rocks.

Biotites are two to three times the muscovite percentages calculated by

point counting (Table 4.3; Figure 4.8). Sample 3 (2840.86m) is the best

sample to obtain reliable mineral estimations from point counting because

Sample 7 (2841.99m) is clayey siltstone (hard to count) whereas Sample 11

(2844.08m) is heterolithic sandstone-siltstone.

Biotite ranges from slightly to highly altered. Biotite flakes have been

squeezed between the quartz and feldspar grains and their orientation is an

indication of the compaction direction and magnitude. It has significantiron content which is likely to affect NMR response (discussed in chapter

6.2).Distorted mica grains were observed in the samples indicatingdeformation via compaction (Figure 4.9). Muscovite is present as finer

52

grains and as elongated flakes which were deposited in the same bedding

direction (Figure 4.10).

PoÍnt Counting

E sample-1 1

M:scovite (2844 08m)

TSamp e-1

(28a1 99m)

Biotite I sample-3(2840.86m)

0 2 4 68101214

percentage $

Figure 4.8 The percentage of biotite and muscovite usingpoint counting on thin sections. Biotite is twice or three timesmuscovite content.

Figure 4.9 Plane polarized photomicrograph of deformedand altered biotite flakes deposited in the bedding directionand every flake has different levels of alteration. The palegreen color increases as the chloritisation increases.Redman-1, Sample 3 (2840.86m).

53

Figure 4.10 Cross polarized photomicrograph of muscovite(M) elongated grain deposited in the same bedding direction.Redman-1, Sample 3 (2840.86m).

4.2.4 Clays

Smectite, chlorite and kaolin are the main clay types which were identified

using XRD analysis within the interval (Figure 4.3). Smectite is the most

abundant clay type with concentration between 16 and 32 % of the total

bulk volume. Kaolin comprises between 2 and 11 percent while the chlorite

(discussed further in chapter 4.4) is between 7 and 9 percent. The increase

in the total clay content indicates the low energy environment.

Smectite is a swelling clay which might reduce porosity, therefore; care

was taken in sample handling to preserve the original pore volume was

examined using SEM (Figure 4.II). Kaolin was not seen in SEM because it

occurs in lower concentration in very fine grained rock. Kaolin can form as

a product of feldspar dissolution. Porosity and permeability would not be

greatly affected by increasing the kaolin percentage.

54

4.2.5 Lithic Grains

The very fine grained rocks of the Pretty Hill Formation have a significant

amount of lithic grains. Point counting indicates 4 to 13 percent lithics in

siltstone to very fine sandstone. Thirteen percent is probably a more

reliable estimation because it was done on very fine sandstone thin section

and the altered nature of the lithic grains can make them hard to identify.

Little and Philips (1995) reported that the origin of lithics in the Pretty

Hill Formation are igneous, metamorphic and sedimentary rocks (Fi$ure

4.I2). However, rock fragments of sedimentary origin were not observed in

the three thin sections examined.

Rock fragments are commonly chemically unstable grains and highly

altered. These unstable grains produce many authigenic minerals through

extensive diagenesis, particularly chlorite. Also, rock fragments are

affected by compaction which changes the grain shape to produce low

permeability pseudo-matrix with indistinct grain boundaries (Figure 4.13).

4.2.6 Accessory Minerals

Garnet occurs in minor amounts in the fine grained rock interval of the

Pretty Hill Formation. It is found as isolated mainly colorless grains

locally concentrated in heavy mineral-rich bands (Figure 4.14). Garnet has

high relief boundaries with no cleavage and the colours vary slightly based

on the chemical composition. The garnets have a high grade metamorphic

origin based on high Mg contents (Tingate,2O05, personal communication).

55

Figure 4.11 sEM image of smectitic clay that cover most of thegrains and occludes the pore system. Redman- l. sample g(2843.42m).

Figure 4.r2 Plane polarized photomicrograph of rock fragment(F) which is partially altered to authigenic clay. Redman-1,Sample 3 (2840.86m).

56

Figure 4.13 Plane polarized photomicrograph of deformedand altered rock fragment grain (RF) with no clear boundariesdue to the compaction process. Redman- 1, Sample 3(2840.86m).

Figure 4.14 plane polarized. photo garnet grain(G) with high relief boundaries. G roughout thePretty Hill Formation and indicate metamorphicsource. Redman-1, Sample 3 (2840.86m).

0.1mm

G

57

4.3 Authigenic Minerals

The most abundant authigenic minerals in the fine grained rocks of the

Petty Hill Formation are chlorite and laumontite. Laumontite is significant

only in one sample (Sample 11), a fine sandstone with poor reservoir

quality.

4.3.1 Chlorite

Chlorite is an abundant authigenic mineral in the Pretty Hill Formation

(Little, 1996). The chlorite concentration in the very fine grained interval

ranges between 7 and 9 percent (XRD analysis). The chlorite content is

considered constant throughout the flood plain facies (no major changes).

Under the microscope, it is pale green to brown color (Figure 4.9). The

chlorite is iron rich as determined from XRD analysis (Appendix-C).

The chlorite occurs as replacement of volcanic lithics in the Pretty HillFormation. The high iron content in diagenetic chlorite was recognized by

Little and Philips (1995) and they proposed a second possible origin related

to biotite alteration.

4.3 .2 Laumontite

Laumontite is a calcium rich zeolite which precipitates as a white pore

filling cement (Figure 4.15) associated with albitization of feldspars. This

authigenic mineral only exits in a fine sandstone (Sample ll (2844.08m))

mixed with fine siltstone clasts, which occur in the poor reservoir quality

rocks. Laumontite is a common authigenic mineral in the reservoir interval

in the Pretty Hill Formation (Little, 1996).

The high laumontite content in the reservoir section is likely to be due to

the original presence of calcic plagioclase. The laumontite formed during

58

the albitization process which altered the calcium bearing-plagioclase to

Na-plagioclase (albite). The calcium released during this reaction formed

laumontite as pore filling cement.

4.3.3 Diagenetic Accessory Minerals

Calcite and pyrite occur as minor components in the samples analyzed.

Calcite was recognized using XRD analysis in Sample 2 (2840.66m), where

it comprises 2 percent. Calcite occurs as a replacement product of other

grains and authigenic minerals in the Pretty Hill Formation. Calcite exists

in the fine-medium sandstone, Sample 2 (2840.66m) which has poor

reservoir quality. The occurrence of carbonates in the reservoir section in

the Penola Trough area was also reported by Little & Phillips (1995).

Pyrite was indicated by XRD only in one sample (Sample 10, 2843.6Im)

comprising 1 percent in XRD, but \¡/as also identified in thin section

(Sample 3, Figure 4.16). Pyrite occurs as cubes or octahedrons and is

commonly associated with organic matter.

59

Figure 4.15 Cross polarized photomicrograph of authigeniclaumontite which occurs as a white pore filling cement (L).Laumotite is a cement commonly associated with albitization ofplagioclase in the Otway Group. Redman- 1, Sample 1 1

(2844.08m).

Figure 4.16 Plane polarized photomicrograph ofpyrite crystals (P), associated with organicRedman-1, Sample 3 (2840.86m).

opaquematter.

60

CHAPTER FIVEPETROPHYSICAL PROPETIES

DETERMINATION

5.1 Introduction

Petrophysical properties of the rocks in this study were determined using

different analytical techniques. The laboratory techniques include porosity

and permeability analyses, mercury injection capillary pressure (MICP),

scanning electron microscopic (SEM) and porecast. Nuclear magnetic

resonance (NMR) was also used to provide information on porosity,

permeability and pore geometry.

This chapter starts with the laboratory and log porosity and permeability

measurements and illustrates which one is more reliable in fine grained

lithologies. Then, capillary pressure and pore throat size data are used to

describe the pore system in the flood plain facies, supported by SEM

observation. NMR data processing and pseudo capillary pressures are then

outlined. Finally, displacement pressures are estimated from the pseudo

capillary pressure curve at different percentages.

5.2 Porosity and Permeability Estimation

Porosity was measured using both laboratory analyses and logging tools. In

the laboratory, core plug porosity was measured by helium and also

calculated using mercury injection capillary pressure. Density, neutron and

Schlumberger' s combinable magnetic resonance (CMR-200) tools (porosity

logs) were run in Redman-1. Comparisons between the results of laboratory

and log porosity measurements are illustrated in Figure 5.1. MICP porosity

has a good correlation with density and total CMR porosity except Sample

S (2843.08m) which has micro fractures. Helium porosity has excellent

6I

match \¡/ith the effective CMR porosity in the flood plain facies. The

neutron porosity log is not reliable in such lithologies and can give higher

or lower measured hydrogen content.

Total CMR porosity ranges between l.l% and 9.2Yo in the siltstone-clayey

siltstone interval (the good seal lithology). The only exception is Sample 8

(2843.08m) which has a micro fractures that affect the porosity

measurement. Porosity increases more than 9.2Yo in the very fine sandstone

which represent poor reservoir quality (Figure 5 . 1). Effective CMR

porosity is much lower than total CMR porosity which characterizes the

very fine grained lithology. The difference between total and effective

porosity decreases over the very fine sandstone interval. Effective CMR

porosity ranges between 0.12% and 2% in siltstone-clayey siltstone

samples.

Permeability was calculated by using the mean TZ model (SDR) from the

CMR tool. It ranges between 0.001 and 0.00006md in the siltstone-clayey

siltstone interval. This suggests a good seal lithology. Permeability

increases when the lithology becomes more sandy (Figure 5.2). The

laboratory measurements for permeability were not reliable and therefore

they are not included for further interpretation (Appendix-D). The results

were too high to be accepted as siltstone-clayey siltstone permeability and

this might be due to alteration of in situ conditions or core handling

artifacts, these samples can be easily fractured. If core plugs have received

fractures, permeability values increase significantly.

From the available effective porosity and permeability data, the flood plain

facies is interpreted as very tight. Since smaller pore and throat sizes have

better sealing potential, these flood plain samples are potentially good

seals.

62

-)e

I

+I

- -] - EffectiveCMR Phi

- t - TotalCMR

- -K - Nuetron

rft- Density

I CoreHelium Phi

+ Hg Phi

+^

Porosity (fraction)

0 0.1 ÍJ.2 03 0.4

2834

2836

âo40-¿ () rJ(J

r- 2 84Ð

EBÊ 2842

2844

2846

zÕ14

Figure 5.1 Different porosity measurements (from Lab & Logs). CMRtotal porosity has similar estimation of MICP porosity and density whilethe effective CMR porosity gives a good match to core helium porosity.Effective CMR porosity, linked to fluid movement, ranges between0.12% and 2Yo in siltstone-clayey siltstone samples.

63

--+- cMR(KSDR)c MR(KSDR)

Permeability (md)

0.00001 0,001 0,1 10 1 000

2835

2836

2837

2838

2839

g 2840J-{È¡

0)

Ê2841

2842

2843

2844

2845

2846

I--t

)---+\\

I

tÒ

I

=r

Jì il(

t€

Figure 5.2 Permeability estimation of flood plain facies rangesbetween 0.001 and 0.00006md in the siltstone-clayey siltstoneinterval.

64

5.3 Capillary Pressure and Pore Throat Size Determination

Mercury injection capillary pressure (MICP) is the primary tool to measure

capillarity. Pressure is applied up to ó0,000 psi which enables mercury to

enter extremely small pores, including seal lithologies. For all samples,

conformance (closure) effect was corrected before applying raw data to any

calibration. Also, data were converted to reservoir condition

(hydrocarbon/brine system) as described in chapter 3.

Mercury/air displacement pressure of Redman-1 samples range between 200

and 500 psi in the good seal lithology (Figure 5.3). In the poor seal

lithology, displacement pressures range between 50 and 200 psi (Figure

5.4). All of the Redman-1 flood plain samples have broad pore throat size

distribution showing no bimodal or trimodal distributions. Sample 8

(2843.08m) has micro fractures which cause the early breakthrough in the

MICP curve. All the MICP data and graphs are attached in appendix A.

The pore throat size of good seal lithologies in Redman-1 samples ranges

between 0.004 and 0.7 microns with median pore throat size around 0.0075

microns (Figure 5.5). Poor seal lithology sample have pore throat sizes that

range between 0.005 and 1 microns with a median pore throat size around

0.015 microns (Figure 5.6).Also, Sample 8 (2843.08m) has a higher

median pore throat size than expected due to micro fractures.

Decreasing pore throat sizes means a more restricted pore system which

increases displacement pressure and sealing potential. Redman-l samples

were chosen in both good and poor seal intervals. Therefore, samples which

have more siltstone and clay are more likely to be better seals while the

increase of sand grains results in poorer seals. The MICP measurement forSample I (2843.08m) was affected by micro fractures.

65

-r- MICP Curve

I

É

-J

10 1 000 1 0000

2843.42(2r3S 6r)

1

100.000

90.000

80.000

70.000

60.000

100

Pc (psi)

àS

E)¡50.000

40.000

30.000

20.000

10.000

0.000

Figure 5.3 Mercury injection capillary pressure (MICP) curve of agood seal lithology sample in Redman-1. Displacement pressure isaround 400 psi. Redman-1, Sample 9.

Figure 5.4 Mercury injection capillary pressure (MICP) curve of poorseal lithology sample in Redman-1. Displacement pressure is around 50psi. Redman-1, Sample 3.

-.*-.MlCP Curve

100

s0

80

70

60ès

;50I

40

30

20

10

0

Ç

7

2e40.86f289? 05 l

10 1 000 1 00001 100

Pc (psi)

66

Median pore size {microns} = 0.0075RÈ'1ïan-1 Sâmple 2li1'l tìtjm

i2837 85m l

l¿ 0t0

lil

lil

ilt

\ )

ilt

1!0t0

lnnfn

0)

E

Poo-

U ULU

6 0t0

¿ 0c0

r 0t0

0 0E0

oa

õ=*:3o Dislribut¡on of Pore Thrcat S¡ze (m¡cro,m.l

Figure 5.5 Pore throat size distribution in good seal lithology. Medianpore throat size is 0.0075microns. Redman-1, Sample 6.

Medlan pore slze (mlcrons) = 0.0152Red ma n 1 km ple 2840 86m

(2ô37 05nr)

í¡E

E[lLoIL

tn nnn

I 000

U UUU

7 000

6 000

5 0û0

4 t00

3 000

2 000

1 000

0 000

o

o o

Distribution of Pore Throat Size (micro.m.)

\\I

I il

Il1

t, fv til

I .t

il iltr

Ä

Figure 5.6 Pore throat size distribution in poor seal lithology. Medianpore throat size is 0.0152microns. Redman-1, Sample 3.

67

5.4 T2 Distribution Processing

Nuclear magnetic resonance (NMR) at Redman-1 well was processed in

GeoFrame using different stacking levels. One, three and five stacking

levels were used. Increasing stacking level increases the signal to noise

ratio at the expense of vertical resolution. The CMR tool acquired rolling

average readings, with readings every 3.6 inches (9.I4 cm). The output

value was averaged with the previous and the following readings when the

stacking level was greater than one (Figure 5.7).

One, three and five averaging levels of T2 distribution were converted to

ASCII files from GeoFrame. All the incremental curves were displayed as

incremental T2 distribution for comparison of results. The signal to noise

ratio increases and is more consistent when the stacking level increases

(Figure 5.8 A-C). The NMR tool usually has high level of noise in very

fine grained rocks which might broadens T2 distribution. Therefore,

stacking level of five was chosen in the calibration to MICP curve in this

study.

Figure 5.7 Illustration of different stacking levels (one, three and five)were used in CMR data processing

o

o(A(l)ú(€C)

q)

O.(l)â

Throe averagi:rg level

Five avsraging ler'el

68

\./^ \

\\,^.\ -\A \

^\^\X\\ \ /t \ l\

\

û45

0.4

0,35

03

025

a2

015

tl 1

0.05

0û3

A

o

a 300 3000

lncremental Ta Distribution(l averagirrg level)

30

T2 Relaxation Time (ms)

\\

t*\ \

-/-- k -/x /\,tr J-^\\

050¿5

o4035

0.3

025

o20,15

01

005

0o.3

B

o

È

3 3000

Incremental Tz Distribution(3 æraging level)

30 300T2 Relanation Time (ms)

\'þ\ I

\\ ) /\\\ì/ ù // \/ \11 Y \/\

-\

u5

0 dls

04

035

03425

0?

015

01

005

t

c

oË

a

03 ? 300 3000

Incremental Ta Distribution(5 averaging lcvel)

30T2 Relocarion Time

Figure 5.8 A-C Incremental T2 distributions of different depthintervals for every stacking level (one, three and five). Signal tonoise ratio increases with increasing stacking level.

69

5.5 Tz Pseudo Capillary Pressure Curves

The standard T2 distribution was displayed as an incremental distribution

while mercury injection capillary pressure (MICP) was displayed as a

cumulative distribution. Therefore, the T2 incremental distribution was

converted to a cumulative distribution before any adjustment was made to

the relaxation time or amplitude. Then, clay bound water which exists on

the surface and within the structure of the clay aggregates was taken out of

the cumulative T2 distribution. Finally, the two T2 curve axes, relaxation

time and amplitude, were converted to capillary pressure and water

saturation respectively.

5.5.1 Corrected Cumulative T2 Distribution

NMR incremental T2 distributions need some modification to be calibrated

to MICP curves. First of all, the incrementalT2 distribution was converted

to a cumulative distribution by adding the amplitude value for every

incremental time to the sum of all previous amplitudes. This process was

repeated for every depth interval (Figure 5.9).

The T2 relaxation time responds to the hydrogen content in the pore space

of the matrix. Thus, T2 response measures any hydrogen which exists in

the effective pore system (e.g. interparticle pores) and also ineffective pore

system (e.g. micro porosity and clay bound water). Sandstone reservoir

rocks have a small amount of ineffective porosity which might be

eliminated by the applied effective relaxivity (scale) factor. The NMR T2

response in seal lithologies (clay-rich rocks) includes the response from

fluids between clay aggregates and bound water on clay surfaces (Figure

s. 10).

The surface area associated with clays is much larger than aîy other

sediment and the magnitude depends on clay types. Smectite has the

70

highest surface area and kaolin has the lowest. Consequently, clay bound

water in clay-rich rocks contributes significantly to the T2 distribution.

Swelling clay, such as the smectite group, has additional storage spaces for

brine or water inside the clay structure. The water inside the clay structure

might be included in the T2 distribution if it has significant amount of

water which could contribute to the relaxation mechanisms. On the other

hand, mercury injection capillary pressure (MICP) measures only the

effective (connected) pore system of the clay-rich rock. This includes the

interaggregate pore system and capillary bound water.

Because of this, the clay bound water content should be removed from the

T2 distribution in order to calibrate it to MICP data. This was achieved by

subtracting the effective porosity from the total porosity to calculate the

clay bound water (CBW) percentage at every depth (Figure 5.11). There is

high clay bound water content in all the flood plain samples. The CBW

percentage ranges between 53Yo and gÙyo, a high portion of the total T2

distribution. The amount of clay bound water porosity can be used to

determine the clay bound water portion (amplitude) of T2 distribution by

using the following relation (Eq.5.1) for every T2 distribution curve.

clay Bound water Portion -û"'**ZAmplitude100

(Eq. s . 1)

Where,

ú"u, : Percentage of the clay bound water porosity

lAmptitude: S:uuli,of T2 distribution Amplitudes

To build a cumulative T2 distribution free of the clay bound water, the clay

bound water (CBV/) amplitude portion was subtracted from every

incremental value in the T2 distribution curve (Figure 5.12). That means

that every value on cumulative T2 distribution was suppressed by the value

of CBV/ amplitude in each sample.

7T

il

)

I

iltL

/ T

/r ilt

ilt/

t: Iil

ilt illt il

01 r 000 1 0000

oG

2

È

Cumulative T2 Distribution(5 averag¡ng level)

10 100

T2 Relxation Time

4.5

4

3.5

3

2.5

1.5

1

0.5

0

Figure 5.9 Converted cumulative T2 distribution from incrementalcurves for every depth sample.

slow Molecula r diffusion f ast

Tz (ms) Tz (ms)

Figure 5.10 T2 distribution is the sum of the fluid content of bothinteraggregate pores and on the clay surfaces. At normal loggingspeeds, the logging tool can not differentiate between them.

Signa ibutionSi d¡ n

72

Clay Bound Water (CBV/) Percentages

0 2tCBlÀl'Contant %o

40 60 80 100

c¡

ã

æ.37.52

2t40.72

¿o4u ót

2841.17

2841.33

2841.63

2841 94

2É43 00

2843.46

2É43.61

2844.O7

Figure 5.11 Clay bound water content for every depthwas calculated by subtracting effective porosity fromtotal porosity. Then, CBW content can be easily takenfrom T2 response.

Figure 5.12 Cumulative T2 distribution after taking out the claybound water (CBW) for every depth sample.

il .i

ftl -I

7

I I J

,/

ililt

Cumulative Tz Distribution without CBW(5 aueraging level)

I

r8

16

1,4

12

1

08

06

04

a.2

0

1 000 1 n00û01 10 100

T: Relaxation Time

(¡)€

I

naIJ

5.5.2 Creating Pseudo Capillary Pressure Curves

Converting the two axes of T2 distribution to their equivalent capillary

pressure is essential to build pseudo capillary pressure curve. The two axes

of T2 distribution were converted from relaxation time and amplitude to

capillary pressure and water saturation respectively.

Water saturation was calculated by considering the maximum cumulative

amplitude to be I00% for each curve. Then, the water saturation for every

cumulative amplitude value can be calculated by the following relationship

(Eq.s.2);

(Eq.s.2)

The relaxation time of T2 distribution extends logarithmically from 0.3ms

to 3000ms. The short relaxation time represents small pores, thereby, high

capillary pressure. In contrast, long relaxation time represents large pores

and low capillary pressure. The relationship between relaxation time and

capillary pressure is reversible (8q.5.3). The effective surface relaxivity

(p") is proportional to the product of intrinsic surface relaxivity (p) and the

ratio of pore throat size to pore body size.

I P"(Eq. s.3)

Where,

Pc : Capillary pressure (in psi)

Tzs: Surface grain relaxation time (in ms), which dominates the

Tz response in 100% water saturation

pe : Effective surface relaxivity factor (combines surface relaxivity and

pore/throat size ratio)

water saturation (l00yo) = - !*llitude value * 100

' MaximumAmplitudeValue

74

By knowing the relaxation time values for each curve, capillary pressure

can be calculated when the appropriate effective surface relaxivity factor

(p") is known. The Pretty Hill Sandstone effective surface relaxivity factor

(p"), is 1200 as the best fit (constant) for reservoir lithologies. However,

this value does not work for flood plain samples. Therefore, the correct

factor had to be estimated using trial and error technique. The best

effective surface relaxivity factor (p") for all 1 1 flood plain facies samples

was found to be 2300. A reliable factor is essential to build a valid

calibration.

The water saturation axis was converted to a non-wetting phase saturation

by subtracting from 100. This was done in order to calibrate to mercury

injection capillary pressure (MICP) data. The 11 pseudo capillary pressure

curves which were converted from T2 distribution are illustrated in Figure

5.13 (detailed results in Appendix-B). Note that the 2840.66m curve has a

different trend than the other curves and it can be considered as an outlier.

This is because it is a fine sandstone and also it was affected by the

response from the coarse sand interval in between the flood plain section

(due to 5 stacking level).

Figure 5.13 Pseudo Capillary pressure curves from T2 distribution. 11

curves are for flood plain facies samples in Redman-1. The high entrypressure is recognized for all samples except for 2840.66m which isaffected by nearby coarser samples.

75

-êg\

ostl-

R'{)âo

(¡)

FI

oz

100 o@

m-@

t0.000

70.000

õ0.0@

5().il]o

40 000

s ooo

20 ofn

10,@

OW

ilt

ttl{ I/ tIlr

ilt |wt ¡

îl l][/L t

llt Itríilf lll,-

iiþ-t|r

l'

Pseudo Capillary Pressure Curves

10000 1@ 000lmoCapillary Prcssurc (p si)

IODO 000 1moo,ff]o0.roo

5.6 Pseudo Pc and MICP Curves Calibration

Full curve calibration of pseudo capillary pressure and mercury injection

capillary pressure (MICP) curves was done for the 11 samples. The best

effective surface relaxivity factor (p") used for pseudo capillary pressure

was 2300 for all samples. Figures 5.14 to 5.24 shows the full curve

calibration for every depth sample. An obvious mismatch between the two

curves was recognized for most of the samples. Selecting a different

effective surface relaxivity factor (p") for each sample was not practical to

build a reliable relationship.

Some samples have consistent variation between the pseudo capillary

pressure plots and the measured capillary pressure curves such as Sample 1

(Figure 5.14,5.15,5.17 and 5.2I). Most of the samples exhibit deviation at

the beginning of the curve with low pressure and non-wetting saturation (0-

30% Snw) such as Sample 3 (Figure 5.16,5.18, 5.19,5.20,5.22,5'23 and

5.24). The disparity of the pseudo capillary pressure curve increases where

NMR T2 distribution represents two lithologies, shale and sand.

Consequently, the deviation of the pseudo capillary pressure curve is more

dominant when the median pore size of the sample increases (Figures 5.14-

24). Calibration is poor when median pore throat size is grater than

0.013microns. It gets better, at high pressure equivalent to extremely small

pore sizes.

The most important part of the capillary pressure curve in conventional

seal capacity determination is the displacement pressure. Pseudo capillary

pressure curves have a mismatch from the actual MICP curve in the low

pressure region (large pore throats). This means pseudo capillary pressure

curves will not be reliable in estimating displacement pressure when using

a simple effective surface relaxivity factor (p"). Pseudo capillary pressure

curves have good calibration to MICP above 30% non-wetting phase (N\MP)

saturation (small pores) in most of the samples except Sample 8

(2843.08m) which is influenced by micro fractures.

76

5.7 Estimation of NMR Displacement Pressure

Displacement pressure can be estimated from MICP curves by determining

the capillary pressure at l0% non-wetting phase (NWP) saturation

(Schowalter, 1979). The 20Yo and 35% NV/P saturations were examined as

well because displacement pressure in seal lithologies is higher than in the

reservoir lithologies that Schowalter studied. Therefore, displacement

pressures were selected at I0o/o,20o/o and 35% NWP saturations from both

pseudo capillary pressure and mercury injection capillary pressure (MICP)

curves (Table 5.1). This technique assumes both curves are representing

one lithology. Displacement pressures plotted against depth for the 11

samples are shown in Figures 5.25-27.

The best calibration between the three saturation percentages used is the

20Yo saturation. Agreement between the curves at 3 5Yo is the worst. While

it improves at 10o/o, they are not close enough. Other saturation percentages

were also examined but little improvement was seen. Statistically,

correlation coefficient for the lÙyo, 20% and 35% saturations were

analyzed (Figure 5.28-30). It is obvious from the correlation coefficients

that the 20Yo satvation values give the best estimation of the displacement

pressures. The correlation coefficient at the 20o/o saturation is 0.59 but it isnot good enough to rely on to estimate displacement pressure. Moreover,

the standard deviation distribution of the 20% saturation shows the

difference between MICP and pseudo capillary pressure values around the

mean (Figure 5.31). MICP values are distributed well around the mean

which increase the statistical confidence of the measurements whereas

pseudo capillary pressure values are skewed.

20% NWP saturation of T2 pseudo capillary pressure curve

Mean : 667.79

Standard Deviation : 478.38

77

20% NWP saturation of mercury injection capillary pressure curve

Mean : 627.93

Standard Deviation : 290

Pseudo capillary pressure deviation from the true displacement pressure

increases when median pore size increases which indicates more sand

contribution. Sample I (2837.57m), Sample 2 (2840.66m), Sample 3

(2840.86m) and Sample 4 (284I.2Im) have large median pore sizes,

representing two lithologies (shale and sand), and the greatest deviation

occurs within those samples. Also, Sample 8 (2843.08m) has the higher

median pore size but this is influenced by micro fractures which contribute

to the mismatch. This deviation might be due to one reason or many factors

may contribute to the poor match. Sample 2 (2840.66m) which can be

considered an outlier was taken out to improve the calibration but the

correlation was statistically worse. This decline may be due to the

inadequate sample numbers. The different factors which may influence

NMR response and subsequently T2 distribution are discussed in detail in

the following chapter.

+Log -- Lab Media¡rpore size :O.0151 microns 2837.5irn

10076m)

s0

ÐBo-3 70

Ld

E60!Cdv) 50òa'E 40q)

F30¿ö20z

10

o'to 't 00 1000

CapillarSr Pressure (trrsi)

1 0000

I

I c

{

Figure 5.14 T2 pseudo capillary pressure and MICP calibration curves.The mismatch increases with an increase in pore size especially at largepore sizes (low non-wetting phase saturation). Redman-1, Sample 1.

78

+Log ---'Lab Media¡r pore si-ze : O.O173 microns 2a40(2836 85m)

Õ.Ed!rd

c/)bD

'_Ë+o

0z

100

90

ao

70

60

50

40

30

20

10

oo_1 1 1() 100

C apillar¡r Pressure (¡r si)1 00() 1 ()()00

+Log *--Lab Media¡r pore size : 0.0152 rnicrons 284O.8óm(2437 O5m)

10()

90Ðao.9 To

¿dts6tld./) 5|OÞD

E40oã3.,E20z,' 10

oo.1 1 10 loo

Capillar¡r Pressure ftrsi)1 000 1 0000

244t.2<243'7 .4m)

1 00()0

Medianpore si-ze : O.Ol39 microns

10Capillaqr Pressure ftrsi)

1()0

so

ùsoEzo=Ë60Ð.5 so

òD-= 40

#30s2cz10

o1 0001 100o.1

+ Log '-*- Lat¡

--7-

/

Figure 5.15-I7 T2 pseudo capillary pressure and MICP calibrationcurves. The mismatch increases with an increase in pore sizeespecially in large pore sizes (low non-wetting phase saturation).Redman-1, Sample 2-4.

79

+ Log ." Lal)

oßÊ¿Ëv)bÐ

o

z

100

90

80

70

60

50

40

30

20

10

o

L

Ir

Mediarr pore si-ze : 0.0066 microns

1() 100 1 000 10o.1Capillar5r Pressure ftrsi)

1

(243'7 49m)2a4t

za4l

1 0()()()

Media¡r pore si-ze : O.OO75 rnictons

10

õJMJ100

so

ùaoé,o7ÕEË605äsoòD.E 40+€30È20oz 10

o1()0

C apillar5r Pressure (gr si)10 0()1

+Log -.'Lal)

a .,1

+Log Lab

î

I)

\ê AOo\

ã70a€6t¡€EoaÈP 40l=-o 3(!#å2o,ozlo

o

lrdetli¿r¡r pore siiae O.OlO1 trticrt¡rls

1000()10 100 I OOO

Ca-pilla.:¡r Prcssurc (psi)1

244t.9

10f)

90

Figure 5.18-20 T2 pseudo capillary pressure and MICP calibrationcurves. The mismatch increases with an increase in pore sizeespecially in large pore sizes (low non-wetting phase saturation).Redman-1, Sample 5-7. 80

+Log *-'Lab Median pore size : O.O454 rnicrons 2443(2439 2'7m)

100

9(]

SBoE70E6(]ÉÞ.E 5o

s4r,.E-iD 30Èt20oz10

o1 10 1(]0

Capillar¡r Pressure fu si)1 f}0() 1 0(}00

+Log'-.--Lab Median pore size : û.0097 rnicrons 244(283 9 6 1m)

100

í] goo\

Y80ã-É 70<d

E6tlñ?o uo-E 40o

ã3.,É2c'z

10

o1 10 100 1 000 1 (}()00

Capillary Pressure (psi)

ti

Log Lcb

o\

o

Ed4)Ð'ÈoÞAO7,

100

90

ao

70

60

50

40

30

20

10

o

T

s

/

hrlerlial pore size : O.O 118 misrons

10(¡Capilla:¡r Pressure (psi)

1 000 'roooo'to1

2843.61rn(2839 Bm)

Figure 5.21-23 T2 pseudo capillary pressure and MICP calibrationcurves. The mismatch increases with an increase in pore sizeespecially in large pore sizes (low non-wetting phase saturation).Redman-1, Sample 8-10. 81

2844-OSrn(2a4o 21m)

1 ()(}(]0

Median pore si-ze - O.O 1l 1 rnicrons

1(}

't oo

90o\ ao

Ëro=Ë60åsoàD.É 40*O

Iãz.oz10

o100

Capillar5r Pressrrre (psi)1 ()()()

+Log - Lab

t

Figure 5.24 T2 pseudo capillary pressure and MICP calibrationcurves. The mismatch increases with an increase in pore sizeespecially in large pore sizes (low non-wetting phase saturation).Redman- 1, Sample 1 1.

1Oth Percentile 20th Percentile 35th Percentile

Sample No. Logdepth MICP Pd Log Pd MICP Pd Log Pd MICP Pd Log Pd

I 2æ7.57 349.960316 88.46153846 699.430æ03 1 04.5454545 1 01 4.357099 766.6666667

2 2840.66 102.1413394 0.s6666667 247.6',t8æ07 8.433333333 596.æ20942 9.92'1568627

3 2840.86 70.9331 8349 9.æ'1568627 207.7145783 10.54166667 595.7243681 876.1904762

4 28ø.1.2'.1 350.071 31 71 9.921568627 597.7202794 1 226.6ô6667 841.85093 1 533.333333

5 28/.1.3 599.2173885 1022.2n2. 1219.991462 1226.666ô67 1748.029778 1 226.666667

6 28/.1.66 597.7806977 876.1904762 840.208æ55 ß22.n222 1454.æ4213 1 226.666667

7 2841.SS 4æ.67009æ 876.1904762 704.89741 33 1V22.72W2 1 21 6.840578 't226.666667

8 2843.08 247.40237'14 88.46153846 247.4023714 95.83333333 302.139n72 127.n77778

I 28/.3.42 490.5684369 127.7n7778 841.4947431 876.1m4762 1219.9823æ 1022.n222.

10 2843.61 4'19.9183764 766.6666667 701.@18384 876.1904762 1014.96901 1 226.666667

11 2844.08 350.4921375 1 0.54166667 599.77'16912 876.1904762 1 01 3.880778 1022.2222

Table 5.1 Comparison table for displacement pressures (Pd) in everysample at different saturations (ltJv/o, 20Yo and 35%) for MICP curvesand NMR T2 pseudo capillary pressure curves.

82

0.1 1

Itth Pucentile Cutoff

D isplacmert Presswe (psi)

10 100 100[ 1[0t0

283s

Ë rr4r

f, rB41-ûI ?84?

l.l

l0J I

/il.ilt

2844

?t45

t lr'lICP r¡'Log

Figure 5.25 The displacement pressure at l0% non-wetting phase saturation for both T2 pseudo Pc and MICPcurves. T2 pseudo Pc estimation gives poor agreementwith the MICP values.

io,l I/il+. t

Iililil 1ilil illl

ililt +

4

Ïil

ililil { il ilil

lll'.I lil ilt I

ililil ilt

I I

lt

I I

I

ililI llil II

ililil ilill ilttT

Ii

I ¡I

ililil ilt llil

83

1fl

f fth Percennle üut+ff

Displacement Pressrre (prr)

1 10 100 1tm 1mm

l{.Ht.r

1-l

+rÊ.{,1

H

?t3¡

?83t

2fi39

284t

?t41

2fi42

2t43

2t44

2fi45

Figure 5.26 The displacement pressure at 20%o non-wettingphase saturation for both T2 pseudo Pc and MICP curves. T2pseudo Pc estimation at this saturation is the best to predictthe MICP displacement pressures but it is not good enough toaccurately predict MICP values.

ilililil]iltililt!iltilililil]iltillililtrilililil]iltililililil

lllllllllllllllllililmilililillllliltilililH¡llllllllllllirlll

lllllllillililililt

]l

lIln

]t

illillill

ilil

ilil

ilil

ill]il]tilililil

+ llEF I I ,l¡!

84

l5tfi lercenfile tut+ff

Displacement Fressrre fu rt)

1I t1 1 [][?ffir

?ff3ff

t,lr.

d+{+lÊ.ilJ

Ë

2ff3s

?f4[

?ff41

¡1Ð,1î¿UT*l

?ff44

1Ërl¡1¿UI¿

2845

+ llEF. I oLol

Figure 5.27 The displacement pressure at 35o/o non-wettingphase saturation for both T2 pseudo pc and MICP curves. T2pseudo Pc at this saturation is not accurate enough to predictdisplacement pressures.

I

i

l

';

T Þ

ilililt

i

)

I

1ilil

I

ililil ilililt

t

llililt]ilililffiililt

iltiilililt

tilil]tl

ililt

lit

It

/

ililil]ilil11ililt

1ililllllililil1ilil

ilililmilt

ililt

]ilil1]]ilt

I

ililmt

1ilil

ililt]ililt

85

I Ottr Percentil e Correl ation Coeffrcient

U

Gt

ÞAU

E

700

6nn

500

400

300

200

100

tì

û ?u0 4tt0 6uu 800 1û0rì

TZ Pseudo Capillary PressureVat¡e

1?ttu

Figure 5.28 The correlation coefficient at 10% non-wetting phasesaturation for the two data sets (MICP and Pseudo Pc) is low.

Figure 5.29 The correlation coefficiont at 20Yo non-wetting phasesaturation for the two data sets (MICP and Pseudo pc) is consideredthe best in this study but it is not good enough to accurately predictMICP values.

t2 Oth Perc entile Correlation Coefli cient

1400

1 ?00

lUUU

800

600

400

?00

0

0 200 1200 1400

o

dÞtE

400 800 00u 1f100

TZ Pseudo Capillary Pressrxe Value

+ 357o

- Lins âr (35o/o)3 -5th Percentile C orrelation Coeflicient

2û00

1 800

1 t001400

1 2û0

1 00t80[

600

4û0

?00

0

U

È

O.¡l

E

200uû 500 100[ 1 500

T2 Pseudo Capillary Presslre Vahre

Figure 5.30 The correlation coefficient at 35% non-wetting phasesaturation for the two data sets (MICP and Pseudo pc) is too low andmakes the worst correlation.

standa¡il Deviation Distributions of 20th MICP/T2 pc percentile cutoff

It/lCr )l <=112 38 ItllCP: X <-'126 83

E5%1.4

1.2

T1C)Ê

o.e

B o.s

6¡P 0.4

0l

-1 2 3

Yulues in Thousituls

MCP: I{ear=627.9218J=rl1I

-l_

a

T2 P¡. Mer.:507 ?603

Figure 5.31 Standard deviation distributions of the 20Yo non-wettingphase saturation for MICP and pseudo capillary pressure curves.MICP values distribute very well around the mean unlike the pseudocapillary pressure values.

87

CHAPTER SIXDISCUSSION

6.1 Introduction

Nuclear magnetic resonance (NMR) does not effectively estimate

displacement pressure in fine grained lithologies in the Pretty HillFormation. In addition, the high standard deviation value, 478.3g, in the

2% NWP saturation of pseudo capillary pressure shows wide spread around

the mean which indicates a high level of uncertaiirty.

This part of the study investigates and discusses the reason(s) thatcontribute to T2 distributions giving poor estimates of displacementpressures. These reason(s) might be mineralogical or petrophysical or bothor due to sampling errors. The mineralogical reasons will be discussed firstfollowed by the petrophysical and sampling ones.

6.2 Discussion

Nuclear Magnetic resonance (NMR) response is sensitive to the moleculardiffusion of the pore fluid if the applied magnetic field is not

homogeneous. The fluid possesses a gradient over a length scale thatcorresponds to the dephasing length of the spins. The NMR tool controlsthis gradient by applying pulse sequences known as the cpMG technique.However, the magnetic susceptibility between the solid phase and the fluidin the pore space can lead to significant magnetic field inhomogeneities

which are called internal magnetic field gradients (Hurlimann, 199g).

This internal field gradient is caused mainly by the presence ofparamagnetic ions such as iron, nickel and manganese which are frequentlyfound in clays (Kleinberg eÍ. al, 1994). The effect of the internal field on

88

the NMR response depends on its magnitude and the applied magneticfield. If these gradients are equal to or larger than the applied toolgradient, interpretation of NMR data becomes more complicated. Moreover,

the magnitude of internal field gradient increases in small pore sizes and

high iron-rich clay content (Appel et al, 1999). The Redman-1 flood plainsamples are characterized by small pore size system and high concentration

of iron which suggests the existence of internal magnetic field gradients.

XRD analysis and thin sections show high content of chlorite and biotiteminerals in the flood plain facies of Redman-1 core. XRD result shows thatChlorite, (Mg, Al, Fe)12 [(Si, Al)8020] (OH)16, is between 7 and, g

percent of the total bulk sample in all 11 samples. Point counting indicates

that Biotite, K2 (Mg, Fe+2)6-4 (Fe+3, Al, Ti)0-2 [si6-5 Al2-3 o20l (oH,F)4, is between 7 and 12 percent. Approximately 12 to 20 percent of the

bulk samples are composed of iron-rich minerals.

Iron oxide content expressed as Feror(Fe'* lFe3* was not measured) was

measured using XRF technique and varied between 5.2 and 7.16 percent(Table 6.2.1). Iron in the Pretty Hill Formation is characterized by itsreduced status. The Fe2* lFe3* ratio was found (previous studies) 0.7 in the

Pretty Hill Sandstone and 0.9 in the otway Group Sandstone (Tingate,

personal communication). The high concentration of iron would raise itseffect directly on NMR response and subsequently the conversion to pseudo

capillary pressure.

Internal magnetic field gradients in iron-rich clays have been reported by

many authors (zhang et al, 1998; 200r;2003; Appel et al, 1999 and Shafer

et al, 1999). Researcher attention has focused mainly on chlorite since it isthe most common iron-rich mineral found in sandstone reservoirs. Zhang

et al (2001) examined the internal magnetic field gradient of both chlorite(iron-rich clay) and kaolinite (iron-free clay) fluid slurries to measure the

magnetic field difference. Different chlorite fluid slurries were used and

they produced maximum magnetic field gradient values that are

89

significantly higher than the kaolinite fluid slurries (Table 6.2.2). Also, the

internal magnetic field gradient values for chlorite fluid slurries are much

greater than the applied gradient of the logging tool. The internal magnetic

field gradients are proportional to the volume of magnetic susceptibilitiesbetween grains and pore fluids (Kleinberg and Vinegar, 199ó).

Depth si02 4t203 Mgo Fe2O3 CaO Na20 K20 T¡02 P205 MnO so3m m % to olo ñ

2t37.57 53.27 22.75 2.38 0.76 2.æ 4.93 1.40 0.17 0.ø2840.66 63.8'l 't 7.80 1.54 1.01 3.æ 3.26 o.82 0.13 0.0ô

2840.86 61.62 19.05 1.77 0.86 2.97 377 0.89 0.13 o.o7

2U't.21 65.40 15.62 1.68 o.72 2.65 282 080 0.13 0.06

2U1.9 6224 't7.70 1.92 o.92 2.O7 3.84 0.93 o.12 0.06

2U1.æ 63 35 17.3',1 1.81 0.92 2.æ 3.84 0.93 0.13 0.06

2841.99 6221 17 88 1.79 1.06 2.ú2 406 0.97 o.14 0102U3.ß 62.93 't7.55 't.78 0.80 2.æ 3.95 0.94 0.'t4 0.08

28/.3.42 62.85 17.79 1.84 0,86 2.28 3.96 0.91 0.'t4 0.08

28/.3.61 60 5'l 18.68 1.98 0.98 1.Y2 4.24 't.01 o.14 0.æ

2444.æ 62.54 17.98 1.73 0.69 2.67 3,85 1ù2 0.13 007

Table 6.1 xRF analysis results with the paramagnetic cationshighlighted. Feror( Fe'* I Fe3* ratio was not determined) has highconcentration in flood plain Redman- 1 samples and traces of themanganese.

A physical model was proposed for a chlorite-coated sandstone by Zhang et

al (2001). Chlorite flakes can be viewed as forming microchannels

perpendicular to the pore walls and therefore every mircopore opens to a

macropore (Figure 6.1(a)).The magnetic fields in the macropores,

micropores, and chlorite flakes were modeled between two planes whichwere parallel to the magnetic field lines. Field lines are concentrated inside

the chlorite flake because of the paramagnetic characteristics of chloritewhile the magnetic field strength is weakened in the micropore (Figure6.1(b)). Also, the magnetic field difference increases between macropore

and micropore when the size of the microchannel becomes small relative to

90

the size of the chlorite flake. This can occur in very restricted pore systems

as in the Redman-1 flood plain samples.

The chlorite model can also be applied to biotite since it too, is an iron richmineral with platey characteristics (Figure 6.2). Biotite is rich in ironwhich increases the magnetic susceptibilities between the solid phase and

fluids.

Thus, the Redman-1 flood plain interval is expected to possess internalfield gradients due to magnetic susceptibilities since it has highconcentration of chlorite and biotite. Also, the very confined pore system

of the rock interval suggests high internal magnetic field gradients. The

magnitude of these gradients is not known in the flood plain interval of the

Pretty Hill Formation at Redman-1, as measuring the magnitude of thisinternal field gradient is beyond the scope of this study. However, the

internal field gradient is likely to affect the NMR response and thus

increase the diffusion effect in the total T2 relaxation. Consequently,

molecular diffusion relaxation rate dominates the total T2 relaxation inequation 2.1.lf surface relaxation rate does not dominate T2 relaxation, the

T2 time distribution does not represent pore size distribution.

(Eq.2. 1)e),.,.:[t), .

I rt), .

[rt ),

Elrine I{exane Soltrol

Chlorite 7TT 7to 7toI{aolinite 1.8 4.O 3.7

Table 6.2 Maximum Internal field gradients (Gauss/cm)chlorite and kaolinite slurries. Chlorite has a much highergradient than kaolrnite (Zhang et al, 2001).

for differentinternal field

91

macroporeflake

microchannel

(a)

Bo

Bf

(b)

Figure 6.1 Model of magnetic field for chlorite coatedsandstone. (a) chlorite flakes forming microchannels open tomacro pore. (b) magnetic fields of macrop ore, Bo, micropore,B, , and inside chlorite flake, B " (Zhang et al, 200I).

.E,t-

t

Figure ó.2 SEM photo shows biotite has platey structurewhich increases the internal magnetic field gradients.

*---'-- *l> prrrt)et

i,lwr)2ti Ìt

;-*:É11**-*

lo 0 kv li 0 1lÌti42.\V 1ìl){rt M;rqn

92

Relaxivity, which is the ability of the surface to relax hydrogen nuclei, isalso affected by the presence of magnetic minerals. Iron and manganese,

which are the paramagnetic ions in the samples, increase NMR surface

relaxivity parameter p by an order of magnitude or more. This variation isas great as the variation observed for pore size distribution in natural

geological material (Roberts, et al 1995).

All Redman-1 flood plain samples have high iron concentration which can

be considered as constant. If the iron content varies between samples, then

the effective surface relaxivity factor (p") should also be changed to

account for the variability (Figure 6.3). Given the consistency of the iron

content, it may be possible to use the same effective surface relaxivityfactor (p") for the whole interval. However, the increase in the effective

surface relaxivity factor (p") shortens T2 distribution and pore size.

Therefore, this adjustment leads to underestimation of the actual pore size

of the sample and also the converted capillary pressure. The

underestimation level is proportional to the concentration of the iron in the

samples. Redman-1 samples have high iron concentrations and therefore

unrealistic reduction of pore size and capillary pressure is likely to occur

(Figure 6.4).

The nuclear magnetic resonance (NMR) response represents the hydrogen

content in the pore space. The hydrogen exists mainly between grains or

inside any kind of voids in the clastic or carbonate sediments. In clays,

there is potentially hydrogen in bound water on the clay surface itself. The

amount of surface space depends on the clay type. The smectite group or

swelling clay has high surface area to volume ratio while kaolin has low

surface area to volume. Changing clay type concentration will change

surface aÍea to volume ratio which is the base of NMR pore size

quantification. Therefore, NMR response will vary based on the clay type

present and not because of the variations in pore size distribution.

93

Redman-1 samples are rich in smectite (swelling) clay, between 8-32 % ofthe total volume. This high concentration of swelling clay might be

responsible of significant amount of hydrogen on its surface that does not

represent pore space between clay aggregates. Also, swelling clays can

store hydrogen inside the smectite structure when it has high enough water

saturation. In this study, this amount was estimated to be the difference

between total and effective porosity and subtracted from T2 response.

Because of the large amount of hydrogen which can be stored in such rock,

the clay bound water cutoff (less than 1ms) might not be the solution tosuch problem. Therefore, it is believed that the pore geometry in such

swelling-clay rocks is not well represented by NMR response.

Hence, T2 distribution position in clay-rich rocks such as the Redman-1

samples is affected by the difference and concentration of clay types.

Consequently, changing T2 distribution position is not a function of inter

aggregate pore size changes only but also of the large surface area ofclays. Swelling clay (largest surface area) in particular can increase the

deviation of T2 response which can not be able to represent the effective

pore geometry of the sample (comparing to MICP measurements). Redman-

1 samples have significant amount of smectite which contribute to the

skewness of T2 distribution.

The NMR response is basically porosity or pore body size measurement

while mercury capillary pressure is pore throat size measurement. From a

petrophysical point of view, the T2 distribution is calibrated to capillarypressure curves based on the hypothesis that there is relationship between

pore throat size and pore body size especially in clastic rocks. The

sandstone pore system has low pore to throat size ratio and pore throat size

can be estimated by knowing pore body size. Therefore, the effective

surface relaxivity factor (p") was introduced to T2 distribution to take into

account for pore to throat size ratio as well as relaxivity.

94

The difference between MICP drainage and imbibition curves is an

indication of the pore to throat size ratio. The pore to throat size ratio

increases in very fine grained rocks such as Redman-1 samples (appendix

A). The Redman-1 flood plain samples have very confined pore system and

pore throat sizes are very small, between 0.1 to 0.004microns (appendix

A). Blue epoxy resin, which was used to prepare thin sections, was not able

to enter the pore system of the rock even after etching, confirming the

small size of the pore throats in there samples (Figure 6.5). Therefore, the

large difference between pore and throat size measurements might be too

high to be adjusted by the effective surface relaxivity factor (p").

The Redman- 1 flood plain samples are clay rich samples which are

characterized by isolated pores with very small patchy connected throats

(Figure 6.6). Thus, the difference between pore body and pore throat size is

too high and variable which means they can not be estimated from each

other. Therefore, the two measurements, T2 distribution and MICP

distribution, are difficult to compare in very fine sediments (clay richrocks).

A question raised here is why does the deviation in capillary pressure

estimation increase with median pore size. An answer may be that internal

magnetic field gradients occur more readily in the bigger pore sizes. Brine,

associated with internal gradients, in very small pores relaxes at a very

early stage which does not give much time for protons to be affected by the

internal magnetic field.

Finally, a better sampling selection of one lithology would help the study a

lot in representing shale lithology effectively. More samples of only shale

lithology will give a robust statistical correlation.

95

Mirerals)

Chlorite

o Glaur

t

rnite

Kaolinitea

Illiteo

oKaolinite

hon effect on Relaxivity

10

5

4

3

2

a)'I

ê.

1

0

0 5 2t 25 3015

Fe2O3 (utelo)

Figure 6.3 Iron oxides of different clay types and their effect onNMR surface relaxivity (modified after Matteson, et al 1998).

IronLow Iron

0l l0

olJ+J

ÈE

10.0 100 0 1000 0 10000 0

Figure 6.4 High iron concentration and clay type changes can affectT2 distribution position. T2 distribution is shortened by the increasein iron concentration.

96

,¿

; t. r'.4:*:""-,.*€ 'I O llrn

? 7ìãtæ'""

a

Et--

&,, o'

F.

---

"ë

a'. =t

Ât;c V lr¡:ol M,tt,¡lrI0OkVllO t¡fi'/lx

l)¡rl wl)1,1 .) t ,l

.!;i.

{

.?

/;

Figure 6.5 SEM photo of epoxy resin (E), on the left side, whichcould not enter pore space of very fine sandstone sample

Figure ó.6 SEM photo of clayey siltstone with high pore to throatsize ratio. Note isolated pores with poor connected throats.

t€.þutk"

{!;

\&d %

Þ

tì"rÞ

Þ

'-fu.*"

I

l"\{'j..í; il

r\t,. \,' :;lr.ìÞ l'1'rtr¡f O l) h.V :ì iì I :ltì4 ?v

!, ¡trrr

r.

_f, æf

97

7.1

CHAPTER SEVENCONCLUSIONS AND

RECOMMENDATIONS

Conclu sions

The best estimated displacement pressures from the down-hole T2

pseudo capillary pressure curve is the 20% non-wetting phase

saturation which has a correlation coefficient of 0.59 in flood plain

samples. However, this correlation is not high enough to reliably

determine seal capacity from CMR 200 data in low porosity

formations.

Down-hole T2 pseudo capillary pressure differs more from laboratory

mercury injection capillary pressure (MICP) curve in large pores

(low pressure). The difference increases with increasing pore size

and calibration improves in smaller pores. The standard deviation ofthe 20o/o NWP saturation in T2 pseudo capillary pressure curve is

much higher than MICP curve.

20% NWP saturation of T2 pseudo capillary pressure curve

Mean : 667.79 psi

Standard Deviation : 478.38 psi

20% NWP saturation of mercury injection capillary pressure curve

Mean : 627.93 psi

Standard Deviation : 290 psi

The Redman-1 flood plain samples are iron-rich rocks, as XRD and

XRF analyses show, due to the presence of minerals such as chlorite

and biotite. Thus, increased magnetic susceptibility characterizes

o

a

a

98

a

a

a

o

a

Redman- 1 flood plain samples because of the difference inparamagnetic ions between the solid phase and fluids.

Internal magnetic field gradients are generated due to the magnetic

susceptibility. NMR relaxation is affected by the internal fieldgradient and the effect is proportional to the internal fieldmagnitude. The magnitude of the internal field gradient depends on

the paramagnetic ions concentration which is high in Redman-1 flood

plain samples.

NMR response was dominated by the diffusion relaxation mechanism

of the measurements in the large pores. As a result, the NMR

response assumption of a pore size distribution in not valid in iron-

rich rocks with small pores.

The calibration improves in fine pores because hydrogen nuclei relax

quickly by surface relaxation mechanism. Therefore, the internal

magnetic field gradients do not influence the NMR response in

extremely small pore sizes.

Surface relaxivity varies as a function of heavy mineral

concentration (such as iron content of samples). Therefore, the

effective surface relaxivity factor (p,) should be calibrated to the

heavy mineral concentrations.

Clay type and concentration can affect NMR T2 distribution position

because every clay type has different surface area. Swelling clays

have large surface areas which can affect surface to volume ratio and

thereby, change NMR response.

Pore to throat size ratio can be included in the effective surface

relaxivity factor ( p") in sandstone lithology. Pore to throat size ratio

a

99

could be more difficult to estimate in very fine pores because the

ratio increases as pore size decreases.

7 .2 Recommendations

Calibrating NMR T2 distribution to mercury injection capillary

pressure curve should be re-examined in fine grained lithologies with

low paramagnetic ion content. Understanding clay type concentration

and pore to throat size ratio can be useful when magnetic

susceptibility and the high relaxivity effect are eliminated.

The magnitude of the internal magnetic field gradients should be

measured using magnetic susceptibility logging tool or NMR

laboratory technique on Redman- 1. The effect on NMR response may

be predicted in the seal and reservoir intervals by recognizing the

magnitude of the internal field gradients for the whole core.

NMR measurements should be acquired down-hole over the Pretty

Hill Formation with echo spacing as small as possible to minimize

the diffusion effect. Also, the static field gradient of NMR tool

should be as high as possible to control the internal magnetic fieldgradients.

Investigation of pore to pore throat size ratio by using MICP

drainage and imbibition curves in iron-free seal lithology should be

carried out as they might help to constrain the relationship between

the T2 and MICP displacement pressures.

Hg samples should always be taken at least Yz the stacking interval

resolution distance away from the edge of the lithology to be

compared with the NMR response.

a

a

o

100

a Finally, CMR 200 tool might not be adequate to sample micro facies

individually because of its poor resolution and acquisition. Higher

signal to noise ratio data needs to be used in such study to provide

more reliable T2 distribution without distortion.

101

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Miller, J., 1988, Cathodoluminescence microscopy. In M. Tucker, ed.,Techniques in Sedimentology: Blackwell Scientific Publications, 6, p.174-1 90.

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Roberts, S.P., Macdonald, P.J. and Tritchard, T., 1995, A bulk andspatially resolved NMR relaxation study of sandstone rock plugs: Journalof Magnetic Resonance, v.11ó, no.2, p.189-195.

Rollinson, H.R., 1993, Using geochemical data: Evaluation, presentation,interpretation: Prentice Hall, Pearson Education Limited.

Schowalter, T.T., 1979, Mechanics of secondary hydrocarbon migration andentrapment: AAPG Bulletin, v.63, p.723-760.

Seevers, D.O., 1966, A nuclear magnetic method for determining thepermeability of sandstone, paper L, in 7th annual logging symposiumtransactions: Society of Professional Well Log Analysts, 14 p.

Sen, P.N., Straley, C., Kenyon, W.E., and Whittingham, M.S., 1990,Surface-to-volume ratio, charge density, nuclear magnetic relaxation, andpermeability in clay-bearing sandstones: Geophysics, v. 55, no. l, p. 6I-69.

Shafer, J.L., Mardon, D. and Gardner, J., 1999, Diffusion effects on NMRresponse of oil & water in rock: Impact of internal gradients, paper SCA-9916, in International Symposium of the Society of Core Analysts:Montpellier, France.

Stegemeier, G.L., 1977, Mechanisms of entrapment and mobilization of oilin porous media, in, Shah, D.O. and Schechter, R.S., eds., Improved oilrecovery by surfactant and polymer flooding: New York, Academic Press,p.55-91.

10s

Timur, 4., 1968, Effective porosity and permeability of sandstonesinvestigated through nuclear magnetic principles, paper K, in 9th annuallogging symposium transactions: Society of Professional Well LogAnalysts, 18 p. Later published in 1969: The Log Analyst, v. 10, no. I,January-February, p. 3-1 1.

Trewin, N., 1988, Use of the scanning electron microscope inSedimentology, in M. Tucker, ed., Techniques in Sedimentology: BlackwellScientific Publications, 8, p.229-27 3.

Vavra, C.L., Kaldi, J.G. and Sneider, R.M., 1992, Geological Applicationsof Capillary Pressure: A Review: AAPG Bulletin, v.76, p. 840-850.

Washburn, E. W., 1921, The Dynamics of Capillary Flow: Physical Review,v.17,p.273-283.

Zhang, G.Q., Hirasaki, G.J. and House, W.V., 1998, Diffusion in internalfield gradients, paper SCA-9823, in International Symposium of theSociety of Core Analysts, 10p.

Zhang, G.Q., Hirasaki, G.J., and House, W.V., 2001, Effect of internalfield gradients on NMR measurements: Petrophysics, v.42, no. 1, p. 37-47.

Zhang, G.Q., Hirasaki, G.J. and House, W.V., 2003, Internal field gradientsin porous media: Petrophysics, v.44, no. 6, p.422-434.

106

APPENDIX - A

Mercury Inj ectionCapill ary Pressure Data Results

r07

Sample REDMAN-1,D12837.57m log-depthQS33.76m core-deptþ

Sample WeightPen. Weight:Assembly Weight:Hg SurfaceTension:Pen. Volume:Stem Volume:Hg Density:Hg volumeSample volumeBulk densitySkeletal volumeGrain densityPorosityo/o Intrusion

3.733063.5670251.2720

485.000015.21970.380013.5335

2.2960

2.51958.871938.0000

Intrusion

(From

Data

Pressure

Summary

Volume

Area

Diameter

Diameter

Diameter

Density

8.8719

Used

61000 psia)2 to

:Total

Total

Median

Median

Average

Bulk

Apparent

Porosity

Stem

Intrusion

Pore

Pore

Pore

Pore

Density

(skeletal)

Volume

(Volume)

(Area)

(4vlÐ2.296

0.0386

13.048

gllr,L

2.5t95

38

mLlg

m"lg

0.0151

0.0061

0.0118

%

pm

lrmpm

o//o

dmL

108

REDMAN- 1, D / 2837 . 5 7 m log-depth (2833 .7 6m core-depth) Drainage'12 000

10 000

8 000

6\(:)

o

C)!oÀ

6.000

4 000

2 000

0.000

o oooDistribution of Pore Throat Size (micro.m.)

|1il ||l

il|t ||t

|ilt

\

I

|( lllI ||t il|l

o/n intrusion of: = 38.0000penelromeler gem

REDMAN- 1, D I 2837 .57 m log-depth (2833 .7 6m core-depth)

CALCULATED VALUESporodty %grain dendtygmdcc

= 8.87192.5195

100

90

80

s70o'.É 60(ÉBa! 50(^

!¿oo!(.)à30

a

J

ot

(

aa

a a I

aa aloo

,. j

1000

Injection Pressure psia

'1000001 100 10000

20

10

0

109

Sample REDMAN-l,Dl2840.66mlog-depth(2836.85mcore-deptþ

Sample WeightPen. Weight:Assembly Weight:Hg SurfaceTension:Pen. Volume:Stem Volume:Hg Density:Hg volumeSample volumeBulk densitySkeletal volumeGrain densityPorosityo/o Intrusion

10.543061.7200230.2290

48s.000015.88131.100013.5335

2.s048

2.67386.318424.0000

Intrusion

(From

Total

Total

Median

Median

Average

Bulk

Apparent

Porosity

Stem

Data

Pressure

Intrusion

Pore

Pore

Pore

Pore

Density

(skeletal)

Volume

Summary

Volume

Area

Diameter

Diameter

Diameter

Density

6.3t84

Used

61000 psia)2 to

(Volume)

(Area)

(4vtA)

2.s048

0.0252

7.377

glmL

2.6738

24

mLlg

mtlg

0.0173

0.0081

0.0t37

pm

pm

pm

o//o

glmL

%

110

REDMAN-1, D12840.66m log-depth (2836.85m core-depth) Drainage

o\o

-ão

oÊÀ

'10 000

I 000

I 000

7 000

6 000

5.000

4 000

3.000

2.000

1 000

0 000

o oo o o

oDistribution of Pore Throat Size (micro.m.)

I

l/ I

I

\I

= 6.31842.6738

CALCULA'IED VALUESporodty %grain dendty gmdccREDMAN-1, D12840.66m log-depth (2836.85m core-depth)

1U% intrudon of

24.0000penetrometer dem

Ia I

a t

aa a /a( a ) aaa a

/

J .J

+ >4

100 1000 10000 100000

Injection Pressure psia

10

0

1

'100

90

80

s70'.= 60d!Iô50(t)Þ340(.)

0)

230

20

111

Sample REDMAN-I, D/2840.86m log-dpeth(2ï37.05m core depth)

Sample V/eightPen. Weight:Assembly WeightHg SurfaceTension:Pen. Volume:Stem Volume:Hg Density:Hg volumeSample volumeBulk densitySkeletal volumeGrain densityPorosityo/o Intrusion

lntrusion

(From

4.782063.3830248.6480

48s.0000Is.21910.380013.5335

2.5386

2.74217.438837.0000

2

Data

Pressure

Intrusion

Pore

Pore

Pore

Pore

Density

(skeletal)

Volume

Summary

Volume

Area

Diameter

Diameter

Diameter

Density

7.4388

Used

61000 psia)to

Total

Total

Median

Median

Average

Bulk

Apparent

Porosity

Stem

(Volume)

(Area)

(4vtA)2.5386

0.0293

9.42

mLlg

m'lg

0.0152

0.0071

0.0124

pm

pm

pm

o,//o

glmL

2.7427

37 %

glmL

tt2

REDMAN-I, D/2840.86m log-dpeth (2837.05m core depth) Drainage

Õ\o

)o

IoÞ.

l0 000

I 000

8.000

7.000

6.000

5 000

4 000

3 000

2 000

't.000

0 000

ooo

o o oeDistribution of Pore Throat Size (micro.m.)

Iil

It

V

t, tI I

lH

Ä

REDMAN-1, D12840.86m log-dpeth (2837.05m core depth)

CALCULAIED VALUESporosity %

grain dendty gmJcc= 7.4388

27427

100

80

s70.E60dlr(Û50

rl)

5¿ot)Eso

20

10

0

% intrudon ofpenetrometer dem

= 37 0000

'100 1000 10000 100000

aa

I)

a) /

( a

Ia

o

I

a( a )

J

ttú

tI

Injection Pressure psia

113

Sample REDMAN-1,D12841.21m log-depth(2837.40m core-deptþ

Sample WeightPen.'Weight:Assembly Weight:Hg SurfaceTension:Pen. Volume:Stem Volume:Hg Density:Hg volumeSample volumeBulk densitySkeletal volumeGrain densityPorosity% Intrusion

s.631063.6870246.2570

48s.0000t5.21970.380013.5335

2.624s

2.7692s.226r30.0000

Intrusion

(From

Total

Total

Median

Median

Average

Bulk

Apparent

Porosity

Stem

Data

Pressure

Intrusion

Pore

Pore

Pore

Pore

Density

(skeletal)

Volume

Summary

Volume

Area

Diameter

Diameter

Diameter

Density

5.226r

Used

61000 psia)2 to

(Volume)

(Area)

(4vlA)

2.6245

0.0199

6.833

glmL

2.7692

30

mLlg

ñle0.0139

0.0076

0.0117

pm

pm

pm

o//o

e/^L

%

tt4

REDMAN- 1, D I 28 4 l.2l m log-depth (2837 .40m core-depth) Drainage

12 000

l0 000

I 000

6 000

4 000

2 000

0 000

6\o

o

oÈoêr

o

Distribution of Pore Throat Size (micro.m.)o

o

Il l|il

||t

|ilt

It ||tI

lil

Il ||t ||l

REDMAN- l, D I 28 41 .21 m log-depth (2837 .40m core-depth)CALCULA-IED VALUESporodty %

grain dendty gmdcc= 5.2261

2.7692'100

90

80

s70.E606tIñ50

(/)

:l40o!(.)

Eso

20

10

0

1

% intrudon ofpe netro m ete r

10

dem 30 0000

100 1000 100000

aa I

I

a

a Ia

(

ra{ a aaa a

tJ

lnjection Pressure psia

10000

115

Sample REDMAN -l Dl284l.3m log-depth (2837 .49mcore-depth)

Sample WeightPen. Weight:Assembly Weight:Hg SurfaceTension:Pen. Volume:Stem Volume:Hg Density:Hg volumeSample volumeBulk densitySkeletal volumeGrain densityPorosityo/o Intrusion

5.5ó40

63.7290

245.6430

48s.0000rs.2t970.380013.533s

2.5417

2.69895.8226

34.0000

Intrusion

(From

Total

Total

Median

Median

Average

Bulk

Apparent

Porosity

Stem

Data

Pressure

Intrusion

Pore

Pore

Pore

Pore

Density

(skeletal)

Volume

Summary

Volume

Area

Diameter

Diameter

Diameter

Density

5.8226

Used

61000 psia)2 to

(Volume)

(Area)

(4vlÐ2.54t7

0.0229

12.74r

glmL

2.6989

34

mLlg

m'lg0.0066

0.0048

0.0072

o,//o

pm

pm

pm

o,//o

glmL

116

il1ilil|l il| il|t

ill| illlt |ilt

|Jt llIt

I IlI illlt il|t l|ll

\l]il |1il

REDMAN- I D I 2841 .3m log-depth (2837 .49 m core-depth) Drainage

25.000

20.000

1 5.000

10.000

5.000

0.000

o\o

o!oêi

o

Distribution of Pore Throat Size (micro.m.)

o ooo

= 5 82262.6989

CALCULA'IED VALUESporodty %grain dendty gmJccREDMAN- 1 D I 28 4 | .3m log-depth (2837 .49 m core-depth)

a

)

a)

aa)

)

¡oÒa( r

{

^^,4 + 1

IU% intrudon of

34.0000penetro mete r Sem

1000

Inj ection Pressure psia

10

0

1 100 10000 100000

100

90

80

s70'ir 60(d!

cC 50U)

l¿o()!(.)

à30

20

tt7

Sample REDMAN-I D12841.66m log-depth (2837.85m core-depth)

Sample WeightPen. Weight:Assembly Weight:Hg SurfaceTension:

Pen. Volume:Stem Volume:Hg Density:Hg volumeSample volumeBulk densitySkeletal volumeGrain densityPorosityo/o Intrusion

4.92t064.7960236.ssso

48s.0000t4.32060.380013.5335

2.4694

2.61635.6157

29.0000

Intrusion(From

Total

Total

Median

Median

Average

BulkApparent

Porosity

Stem

Data

Pressure

Volume

Summary

Volume

Area

Diameter

Diameter

Diameter

Density

5.6157

Used

61000 psia)2 to

Intrusion

Pore

Pore

Pore

Pore

Density(skeletal)

0.0227

11.945

mLlgrrf le0.007s

0.0054

0.0076

(Volume)

(Area)

(4vtA)2.4694

pm

pm

pm

o,//o

d^L2.6t63

29 %

glmL

118

REDMAN- I D I 2841.66m log-depth (2 837. 85m core-depth) Drainage

14 000

12 000

10 000

I 000

6 000

4 000

2 000

0 000

6\

oJo()roÀ

o

Distribution of Pore Throat Size (micro.m.)

ooo

|il

|il

||t lIt

|il |il It

= 5.6157

mdcc

CALCULATED VALUBporosity %rain de

% intrusion ofpenetrometer dem

REDMAN-1 Dl284l.66mlog-depth (2837.85m core-depth)

¿

aÒ

?

a

oaa

a

I

100 10000 100000

Injection Pressure psia

10

0

1 1000

100

90

80

s70o'.É 60cúÈdi! 50(n

ã¿ooko

20

l19

Sample REDMAN - I D I 2841.99m lo g-depth (2 8 3 8. 1 8m core-deptþ

Sample WeightPen. V/eight:Assembly Weight:Hg SurfaceTension:Pen. Volume:Stem Volume:Hg Density:Hg volumeSample volumeBulk densitySkeletal volumeGrain densityPorosity

% Intrusion

Intrusion(From

4328063.6r00250.8910

485.000015.2t970.380013.s335

2.5441

2.69475.5865

25.0000

2

Total

Total

Median

MedianAverage

BulkApparent

Porosity

Stem

Data

Pressure

Intrusion

Pore

Pore

Pore

Pore

Density(skeletal)

Volume

Summary

VolumeArea

Diameter

Diameter

Diameter

Density

s.s865

Used

61000 psia)to

o,//o

(Volume)

(Area)

(4vlÐ2.s44t

0.022

9.69

glmL2.6947

mLlgm'lg0.0101

0.0061

0.0091

elmL

pm

lrm

lrm

25 %

t20

REDMAN-l D/284l.99mlog-depth (2838. 1 8m core-depth) Drainage

16.000

'14 000

ilt ilt

It It

It.\1

ilt ilt

lil Il Il

l2 000

ô\o)o

o

Êr

10 000

I 000

6 000

4,000

2 000

0 000

o oo

oDistribution of Pore Throat Size (rnicro.m.)

REDMAN-l Dl2841.99m log-depth (2838. I 8m core-depth)

CALCUI.AIED VALUBp oros¡ty %grain dendty gmdcc

5.586526947

100

90

80

70ê\

od

âd

C'

ãoo

60

50

40

30

20

10

0

1

%¡ntrudon ofpenetometer Sem

10

25 000c

100 1000 10000 100000

Injection Pressure psia

aa

a

¿

aI

a

(a I

too(

aa I

aa a IJ

11 2

Sample REDMAN- l, D I 2843.08m lo g-dep th (2839 .27 m core-deptþ

Sample Weight

Pen. Weight:AssemblyWeight:Hg SurfaceTension:

Pen. Volume:

Stem Volume:

Hg Density:

Hg volume

Sample volume

Bulk density

Skeletal volume

Grain density

Porosityo/o Intrusion

Intrusion(From

2.6190

62.0990

98.7550

48s.0000

3.5475

0392013.5335

2.s366

2.8567

lt.206r30.0000

2

TotalTotal

Median

Median

Average

BulkApparent

Porosity

Stem

Data

Pressure

Intrusion

Pore

Pore

Pore

Pore

Density

(skeletal)

Volume

Summary

VolumeArea

Diameter

Diameter

Diameter

DensityII.206IUsed

61000 psia)to

o//o

(Volume)

(Area)

(4vlA)2.5366

0.0442

8.713

glmL2.8s67

mLlgm'lg0.0454

0.0096

0.0203

glmL

pm

pm

pm

30 %

122

RE DMAN- 1, D/2843.08m log-depth (2839.27 m core-depth ) Drainage

ê\o)ooo

40.000

35.000

30.000

25.000

20.000

1 5.000

I 0.000

5.000

0.000

Il It

It lil It It

lI ilt

Ilt ilt

ilil

Ilt ill It It

i iÄ ilt

TI

,þI trr

il|l Ilo

oo ooDistribution of Pore Throat Size (micro.m.)

CALCUTAIED VALUESporodty %

grain densjty gmdcc11.20612.8567REDMAN- l, D/2 843. 0 8m log- depth (2839 .27 m core-depth)

%intruSon ofpenetrometer Sem

10

30.0000

a ra( a ¡ao a( a

-/,

I

1000

Injection Pressure psia

1 100 '10000 100000

100

90

80

-70

€60d

ãd50(/)

È.340o

20

'10

0

r23

Sample REDMAN-1D12843.42mlog-depth(2839.6lmcore-deptþ

Sample Weight

Pen. Weight:

Assembly Weight:Hg SurfaceTension:

Pen. Volume:

Stem Volume:

Hg Density:

Hg volume

Sample volume

Bulk density

Skeletal volume

Grain density

Porosity

% Intrusion

Intrusion(From

5.2250

63.4250

246.8070

485.0000

15.2197

0.3800

13.5335

2.5419

2.7096

6.1875

33.0000

2

DataPressure

IntrusionPore

Pore

Pore

Pore

Density(skeletal)

Volume

Summary

VolumeArea

Diameter

Diameter

Diameter

Density

ó.1 875

Used

61000 psia)to

TotalTotal

Median

Median

Average

BulkApparent

Porosity

Stem

(Volume)(Area)

(4vlA)2.5419

0.0243

10.834

glmL2.7096

mLlgm'lg0.0097

0.0064

0.009

glmL

%

pm

pmpm

o/,/o

JJ

t24

REDMAN-l Dl2843/2m Iog-depth (2839.61m core-depth) Drainage

14 000

ill

T tl It

t\ It

llt\ It It

, lll It Il

Il \ Il ill

II ill Il Il

12 000

10 000

ê\o

to

goÀ

I 000

6 000

4 000

2 000

0 000

o oo

Distribution of Pore Throat Size (rnicro.m.)

CALCULA]ED VALUESporodty %

g ra ¡n d ens¡ty g mgc c =

6 187527096

% intrudon ofpenetrometer Sem

aa

I)

a

aa /

a a

a( a ¡ôf II

/

/

REDMAN-l D/284342m log-depth (2839.6 hn core-depth)

Injection Pressure psia

'100

90

20

10

0

'l 100 1000 10000 100000

80

-70ô\É.9 60d3d50

cnà¿40o

t25

Sample

Date

Sample WeightPen. Weight:Assembly Weight:Hg SurfaceTension:Pen. Volume:Stem Volume:Hg Density:Hg volumeSample volumeBulk densitySkeletal volumeGrain densityPorosity% Intrusion

REDMAN- l, D I 2843.61m log-depth (2 8 3 9. 80m core-depth)

l2lrs12004

5.06s063.5540246.0360

485.0000rs.21970.380013.5335

2.4002

2.59837.623242.0000

Summary

61000 psia)

Intrusion(From

Total

Total

Median

Median

Average

BulkApparent

Porosity

Stem

Data

Pressure

Volume

Intrusion

Pore

Pore

Pore

Pore

Density(skeletal)

Volume

Area

Diameter

Diameter

Diameter

Density

7.6232

Used

(Volume)

(Area)

(4vlA)2.4002

0.0318

12.908

el^L2.5983

42

mLlgtf lg0.0118

0.00s3

0.0098

%

2 to

pm

pm

pm

o//o

glml-

126

il

II

)

II

fl I lt

I il il

\

t\.

REDMAN- l, D I 2843.61m log-depth (2 839. 80m core-depth) Drainage

6\o

o()ko

Þr

10 000

9 000

8 000

7 000

6 000

5 000

4 000

3 000

2 000

1 000

0.000

oo

o o o ooo

Distribution of Pore Throat Size (micro.m.)

REDMAN-1, Dl2843.6lmlog-depth (2839.80m core-depth)

CALCULAÏED VALUSporodty %

gra¡n dendty gmdcc= 7.6232

2.5983

100

90

80

s70E.E60d

i! 50(t)h340()à30

20

10

0

10% intrudon of

42.0000penetrometer Sem

100 1000 100000

olÔ)

{

a ) /a I

aaa ar I

Ix

Injection Pressure psia

10000

t27

Sample

Date

Sample WeightPen. Weight:Assembly Weight:Hg SurfaceTension:Pen. Volume:Stem Volume:Hg Density:Hg volumeSample volumeBulk densitySkeletal volumeGrain densityPorosityo/o Intrusion

REDMAN- l, D I 284 4.08m lo g-dep th (28 40.27 m c ore-depth)

r2lts12004

5.688063.5870244.2790

48s.0000ts.21970.380013.5335

2.4854

2.6s656.438839.0000

Summary

61000 psia)

Intrusion(From

Total

Total

Median

Median

Average

BulkApparent

Porosity

Stem

Data

Pressure

Intrusion

Pore

Pore

Pore

Pore

Density(skeletal)

Volume

Volume

Area

Diameter

Diameter

Diameter

Density

6.4388

Used

(Volume)(Area)

(4vlA)2.48s4

%

0.0259

10.155

dmL2.6565

39

mLlgttf le0.0111

0.0065

0.0102

%

2 to

pm

pm

pm

glmL

128

REDMAN- 1, D2 844. 0 8m log-depth (2840.27 m core-depth) Drainage

14 000

It

It

I ltl

t

x

^ltl K Il

l2 000

10.000

o\(.)É

=oÈ

Ê.

8 000

6 000

4.000

2 000

0 000

oIo

o o oooDistribution of Pore Throat Size (micro.m.)

= 6.43882 6565

CALCULATED VALUESporodty %grain dendty gmdcc

39.0000% intrudon ofpenetrometer dem

REDMAN-1, D2844.08m log-depth (2840.27mcore-depth)

ooo

)

ta

aI

ar a ) loÔ t

I

II

,4 .a

Injection Pressure psia

100 '1000 10000 100000'l

100

90

80

s70'E 60(d!¿É 50(/)

5¿ooÈo

20

10

0

t29

APPENDIX - B

NMR Data Results

130

+ NMR

f

I

I

a

90

(o' 80o\

870Ëoo!(úv) soò0

'E 40o

a30.oZ20

10

I 000010

100

0

2837.57m(2833.76n)

100 1000

Capillary Pressure (psi)

Capillary Pressure (psi) Non-wetting Saturation (%)

0.767 0.0000.794 0.0000.823 0.0190.855 0.0400.889 0.0410.926 0.0410.967 0.0417.667 o.0428.032 0.0428.433 0.0438.877 0.0439.370 0.o449.922 0.04410.542 0.04476.667 0.04582.143 0.88688.462 7.74895.833 14.681104.545 19.2041 15.000 21.590127.778 23.678766.667 27.831876.1 90 35.7921022.222 47.8761226.667 63.1 661533.333 80.0612044.444 96.8353066.667 100.0006133.333 100.0007666.667 100.000

I1aJ

2840(2836.8sm)

1 0000

100

90

S80

E70(Ë

EoocËv) soÒo

'E 40C)

ã30220

10

0

10 100

Capillary Pressure þsi)10000.1 1

+NMR

Capillary Pressure (psi) Non-wettinq Saturation (%)0.767 0.0000.794 0.0000.823 0.0010.855 0.7840.889 2.5700.926 5.4830.967 9.1 507.667 12.9688.032 16.3018.433 18.8168.877 20.8549.370 23.4859.922 28.07510.542 35.54876.667 45.70582.143 56.93488.462 66.57095.833 72.016104.545 72.8791 15.000 73.008127.778 73.009766.667 73.009876.1 90 75.0781022.222 82.5741226.667 94.5861533.333 100.0002044.444 100.0003066.667 100.0006133.333 100.0007666.667 100.000

132

+NMR

L

:S' Bo

8zo'.ÉidEooÐ(gcn sool)

È40!)

130220

10

00.1 1000 100001

2840.86m(2837.0sm)

100

90

10 100

Capillary Pressure (psi)

Capillary Pressure (psi) Non-wettinq Saturation (%0.767 0.0000.794 1.1340.823 2.5340.855 3.9920.889 4.9030.926 5.1780.967 5.5007.667 5.8128.032 6.0588.433 6.2238.877 6.3629.370 6.5919.922 9.80010.542 16.73876.667 23.34782.143 26.65188.462 27.49195.833 27.913104.545 27.914115.000 27.914127.778 27.914766.667 27.915876.1 90 29.2371022.222 35.8431226.667 46.6091533.333 59.9472044.444 74.3983066.667 88.9316133.333 100.0007666.667 100.000

t33

2847.21m(2837.4m)

1 0000

100

90

380870goo]r,5 soò0.E 40

#30F20zlo0

1 10 100

Capillary Pressure (psi)

1 0000.1

+ NMR

r

¿

Gapillary Pressure (psi) Non-wetting Saturation (%

0.767 0.000o.794 8.5190.823 8.5190.855 8.5200.889 8.5200.926 8.5210.967 8.5217.667 8.5228.032 8.5228.433 8.5238.877 8.5239.370 8.5249.922 8.52410.542 13.03676.667 13.81482.143 13.81488.462 13.81595.833 13.815104.545 13.8161 15.000 13.816127.778 13.817766.667 13.817876.1 90 13.8181022.222 14.0791226.667 16.6521533.333 29.3062044.444 47.1783066.667 66.4066133.333 84.3487666.667 99.542

r34

+ NMR

a

I

I

t

100

90

s80É70o'trfË 605Þ#50à0É' 40

g30È20z

10

oo.1 I 000 1 00001

2841.3m(2837.49m)

10 100

Capillary Pressure (psi)

Pressure Non-wettinq Saturation (%)

0.767 0.0000.794 3.4010.823 3.4020.855 3.4020.889 3.4030.926 3.4030.967 3.4047.667 3.4048.032 3.4058.433 3.4058.877 5.9289.370 5.9289.922 5.92910.542 6.47476.667 6.47582.143 6.47688.462 6.47695.833 6.477104.545 6.4771 15.000 6.478127.778 6.478766.667 6.479876.1 90 6.4791022.222 8.1211226.667 19.0501533.333 36.7842044.444 57.0733066.667 76.5706133.333 93.0747666.667 100.000

135

2841.66m7.85rn)

1 0000

100

90s:80õö70Ë60!

åsoò0.E 40

F30¿20

10

0

1 10 100

Capillary Pressure þsi)

1 000

+ NMR

¿

II

I

Capillary Pressure (psi) Non-wetting Saturation (%)0.767 0.0000.794 0.5570.823 0.5580.855 0.5580.889 0.5590.926 0.5590.967 0.5607.667 0.5618.032 0.5618.433 0.5628.877 1.9659.370 2.5499.922 2.54910.542 2.55076.667 2.55082.143 2.55188.462 2.55295.833 2.552104.545 2.553115.000 2.553127.778 2.554766.667 2.554876.190 2.5551022.222 10.8251226.667 26.7721533.333 46.6062044.444 66.6003066.667 83.9016133.333 96.8847666.667 100.000

136

+ NMR

I

+

100

90

\o 80

Å70oil 60I)5soS40.E

{c) 30áå20zro

o10 I 000 100001

284I.99rn(2838.1 8m)

100

Capillary Pressure þsi)

Capillary Pressure (psi) Non-wetting Saturation (%)0.767 0.0000.794 0.0740.823 0.0740.855 0.0750.889 0.0750.926 0.0760.967 0.0767.667 0.0768.032 0.0778.433 0.0778.877 0.0779.370 0.0789.922 0.08410.542 0.08476.667 0.08582.143 0.15688.462 2.27395.833 4.286104.545 4.287I 15.000 4.287127.778 4.287766.667 4.288876.1 90 6.5811022.222 14.4651226.667 27.0811533.333 42.7132044.444 59.5183066.667 75.9266133.333 90.7727666.667 100.000

137

2843.08mQ839.2'7m)

1 0000

100

90

S80870g60á1J

.E 50

.it 40Ë-9 30Êå20

10

0

1 10 100

Capillary Pressure (psi)

1 000

--¡- NMR

II

)

Capillary Pressure rps Non-wetti nq Saturation %

0.767 0.0000.794 0.0010.823 0.0020.855 0.0030.889 0.0040.926 0.0050.967 0.0067.667 0.0078.032 0.0088.433 0.0098.877 0.0109.370 0.0129.922 0.01310.542 0.08576.667 o.16782.143 0.72488.462 6.22195.833 14.867104.545 22.8471 15.000 28.471127.778 32.966766.667 38.256876.190 46.5291022.222 59.2511226.667 75.9091533.333 95.2012044.444 100.0003066.667 100.0006133.333 100.0007666.667 100.000

138

2843.4(2839.61m)

I 0000

1

âgoo\Y80o'¡ 70cdt<E60(Ë

?o uo

'.= 40oÞ30

I

820z10

0

00

1 00010 100

Capillary Pressure þsi)1

---r-NMR

)t

t

Gapillary Pressure (psi) Non-wetting Saturation (%0.767 0.0000.794 0.0000.823 0.0010.855 0.0010.889 0.0020.926 0.0020.967 0.0037.667 0.0038.032 0.0048.433 0.0048.877 0.0059.370 1.8689.922 1.95010.542 1.95076.667 1.951

82.143 1.951

88.462 1.95295.833 2.017104.545 2.5911 15.000 5.024127.778 8.539766.667 1 3.1 95876.1 90 19.3851022.222 27.4851226.667 37.7191533.333 50.1 072044.444 64.3983066.667 79.9936133.333 95.9227666.667 100.000

r39

+ NMR

\oo\

(ËIf

!

(tÒ0ñ

0)

BAoZ

100

90

80

70

60

50

40

30

20

10

0

/

I

100001000101

(2839.8m)2843.61

100

Capillary Pressure þsi)

)apillarv Pressure (psi) Non-wetti ng Saturation (%)0.767 0.0000.794 0.3060.823 0.3060.855 0.3070.889 0.3070.926 0.3080.967 0.3087.667 0.3098.032 0.3098.433 0.6868.877 5.1289.370 5.581

9.922 5.581

10.542 5.58276.667 5.58282.143 5.58388.462 5.58395.833 5.584104.545 5.584115.000 5.848127.778 6.275766.667 7.577876.190 12.3971022.222 20.5041226.667 31.5871533.333 45.1 832044.444 60.6133066.667 76.9026133.333 92.8147666.667 100.000

140

2844(2840.27m)

I OOOO

100

90

o\ 80

.E 70uE60!,5 soÒ0

.g 40+l

.Ð

À,Þ 20A

10

oloo

CapiTlary Pressure (psi)I OOO1 lo

+NMR

¿

r

Capillary Pressure (psi) Non-wetting Saturation %l0.767 0.0000.794 1.1020.823 1.8200.855 1.9760.889 1.9760.926 1.9770.967 1.9787.667 1.9788.032 1.9798.433 1.9798.877 2.2549.370 2.6769.922 5.54010.542 8.84776.667 10.40982j43 10.41088.462 10.44195.833 10.529104.545 10.5951 15.000 10.637127.778 10.754766.667 12.349876.190 18.7281022.222 29.6661226.667 44.2501533.333 61.1162044.444 78.6903066.667 95.34561 33.333 100.0007666.667 100.000

1 14

APPENDIX - C

XRD Analysis Results

142

Redman.l XRD Bulk Analysis Results

core-depth logdepth Quartz Albite Orthoclase Mica Chlorite Sm€ct¡te Kaolin Calcite Pyrite

2C43.76 2&}7.57 I 17 2 26 I ,5¿ 7

2836.85 2840.66 24 32 10 7 16 t) 2

2837.05 2840.86 19 29 4 12 I 22 5

2æ7.4 2841.2'l 32 28 J 11 7 I 11

2837.45 2841.3 24 20 2 22 I 18 4

2837.85 2841.ñ 26 20 3 21 8 '16 5

2tl:l8.18 2841.99 22 20 J 22 I 20 4

2839.27 2843.m 24 23 2 2'l 7 17 4

2839.61 284.3.42 23 22 J 21 I 19 4

2&t9.8 2843.61 20 18 2 24 s 22 1

2UO.27 2U4.æ 15 27 16 7 29 2

I(RD Analysís

)tìì 7^

28:_ì6 85

1837 rr5

18,r 7 4

2837 49

2837 65

E Ouadz

I Albite

g Orthoclase

tr Mca

I Chlorile

¡ Smect¡to

I Kaolin

tr Calcttê

¡ FlTrite

)È:18 18

r'È39 27

tE39 61

ls39 I

28411 :7

4 ilc/o 6C r¡i

Bulk Nllneral Content (o;o)

2 0?ô :lC o¡ãU LIJt

143

I zzss I 8S5

3 188

1 501

I 817

2 280

1 43tI 781

257

I

2128 r 9s1

MICRQCI.INE,

2¿51

2382

? 736

¡+è l0¡15 OUARTZ. sYNS- 466 ALBITÉ. ORDERED

1,+- r6,r KAOL|N|TE-íA

1S- 9J2F 26a2t> 701l3- t35

MUSCOVTTE-2MlcLtNocHLoRE-tllilE,

Redma n-1, 2837.57 m log-depth(2833.76m core-depth)

{ 4867 812

4 151

160183

18

15

812xatt

Eog(J>'6c(l)E6

10 00 20 00 40.00 50 00

2 -Theta Angle (deg)

30.00

30 00 40 002 -Theta Angle (deg)

60 00 70.00 80 0c

80 00

ooX

,6coc

Redman-1, 2840.66m log-depth(2836.85m core-depth)

I

4 ?.53

I

r¿ S0I

'ï'P't

I

7 084

3 188

1 817

2 2,94 2 128

345

CALCfTE, AYNKAOLINITE.IA

1 5422 45t

1 980tùþYll:as tgôl I

I 73il1 671

'1 660

8YN

500 1¿t

46.1ø¡t9' 46619.9;¡?6.2fit29.70113.1355.580l,[- 16.1

10.00 20 00 50 00 60 00 70 00

144

2 12A

I 817

2 456

1 9802?ß4

22ñ I

46- SYN

!¡-6.2tr1

1 542

I 502I 671

r 662

l4- t64 KAOL|N|TE-1A

29-'t3-

Redman-1, 2840.86m log-depth(2837.05m core-depth)

4 252

7 082

6 385

oox

=U'cos

1

10.00 20.00 30.00 40.00 50.00

2 -Theta Angle (deg)

40.00 50.00

2 -Theta Angle (deg)

60.00 70.00 80.00

80.00

1

oox

oo

=aCoc.

Redman-1, 2841.2In log-depthQ$7.aîn core-depth)

109/

i,T9ô8 6 350

I

I

14-164 KAOL|N|TE-tA

3 187

1 542

2 t28

1 S80

t l9ta

46-

29-13"

1 817

2 451

1 501 1 453

2 280

s-46ô

6.26:t{9-

I 671

1 658

10.00 20.00 30.00 60.00 70.00

t45

3 187

2 128

1 980

I 817

2 456

2 240

2ââ33

ll :os:

1 542

I

1!ñe1 501

1 453

r4-164 KAOL|NtTE-tA

48- 1045 oUARTZ, SYN0.468 ALSIfE, ONDEñED

2S-13.

Redman-1, 2841.3m log-depth(2837.49m mre-depth)

4 252

6345

7 065

I

I

ooxat

CJoo

=u,coC,

ooX

10.00 20-00 40.00 50.00

2 -Theta Angle (deg)

40.00 50.00

2 -Theta Angle (deg)

60.00 70.00 80.00

lt

80.00

30.00

Redman-1,(2æ7.

4254

9747 085

6 387

2841.66m log-depth85m core-depth)

3 r88

'i 54?

2 128

1 501 I d53

I 81S

2 451

2 2801 980 1 671

16m

SYN

749

14-164 KAOLtNTTE-1A

,t 9-6-2S-l3-

10.00 20.00 30.00 60.00 70.00

t46

Redman-1, 2841.99m log-depth(2838.18m core-depth)

a 252

7 083

3 187

2 128

1 453

I 817

2 456

1 60ô

z sob 22ao

1al3

I

48- SYN9-486

14- 164 KAOL|N|TE-1A

1 540

1 501I ô711 656

2S-13-

ooX

=ací)c

ooxØc=oC)

10.00 20.00 30.00 40.00 50.00

2 -Theta Angle (deg)

40.00 50.00

2 -Theta Angle (deg)

60.00 70.00 80.00

TI

80.00

14-164 KAOL|N|TE-1A

3 lB7

2 1281 611

1 502

û- 2&r25.13-

1 8171 542

2 45ô

2 9Ð7

631166t

I

46. 104!is- 466ls-

22n2 234

Redman-1, 2843.08m log-depthQ$9.27m core-depth)

4 252

f

7 083

6 385

10.00 20.00 30.00 60.00 70.00

147

3 188

1 817

1 542

1 672

lrsa¡

SYN

KAOL N TE-IA

2 4572 124

2 2€01 9802fgtú

1 757

135164

13-14-

1 500t d53

46.

19-

Redman-1, 2843.42m log-depth(2839.61m core-depth)

7 085

4 253

ooxat

C=o()

1

oox

=Øc(¡)

c

10.00 20.00 30.00 40.00 50.00

2 -Theta Angle (deg)

40.00 50.00

2 -Theta Angle (deg)

60.00 70.00 80.00

80.00

3 t87

2 1282

I1 m&3

KAOLINITE-,IAPYRITE

1 811

2.260rul otu

1 S80 1 6712.A.ô33

1 180 , r/u053

l0(546-466s.

2&t6-2S-13-

I 542

1 501

14- 16442-13ÁÐ

Redman-1, 2843.61m log-depth(2839.80m core-depth)

1 252

6 423

f o85

I

10.00 20.00 30.00 60.00 70.00

148

14.164 KAOLTNFE-rA

3 188

I 819

2 128

1 980

29-13-

f99e 1 4552

sqzl tuI

1s-2fß6-

2 5662 457

2 933

2 857

46- í0¡ISQUARTZ, sYN0-466 ALBITE, ORDËRED

Redman-1. 2844.08m log-depth(24O.27m core-depth)

4 253

7 085

6 400

ooxttc.fo(-)

=at,coc

10,00 20.00 30.00 40.00 50.00

2 -Theta Angle (deg)

60.00 70.00 80.00

149

APPENDIX - D

XRF Analysis, Point Counting &Porosity and Permeability

Results

150

Redman.l XRF Results for Main Minerals in Fine Grained Rock Samples

Core-Depth Log.Depth sio2 4t203 Mso Fe203 CaO Na2O K20 Tio2 P205 MnO so3

m m oh % Yoottø

o/o oa oh otm ola oÂ

Yo

2833.76 2837.57 53.27 22.75 2.38 6.60 0.76 2.00 4.93 1.40 0.'t7 0.07 0.u4

2836.85 28/0.ú 63.81 17.80 f.il 5.45 't.01 3.30 3.26 0.82 0.'t3 0.06 0.m

28:ì7.05 2840.86 61.62 '19.05 1.n 5.65 0.8tì 2.97 3.77 0.89 0.13 0.06 0.07

2t37.40 2841.21 ô5.40 15.62 1.68 7.16 0.72 2.65 2.82 0.80 0.13 0.08 0.m

2837.45 28/.'l.N 62.24 17.70 1.v2 6.09 0.92 2.07 3.84 0.93 012 0.06 0.06

2æ7.85 28/.'i.,ffi 63.35 't7.3',l, 1.81 5.83 0.92 2.09 3.84 0.93 0.13 0.06 0.06

2838.18 284,l.99 62.21 17,88 't.79 5.67 1.æ 2.02 4.06 0.97 0.14 0.06 0.10

2t39.27 28/3.ß 62.93 't7.55 1.78 5.57 0.80 2.30 3.95 0.94 0.14 0.06 0.08

2839.61 28r'3.42 62.85 17.79 'l.u 5.94 0.86 2.28 3S 0.91 0.14 0.07 008

2&!S.m 28/,3.6',1 60.51 18.68 1.98 5.98 0.98 1.92 4.24 1.01 0,14 0.06 0.73

28/,0.27 2U4.ß 62.54 17.98 1.73 5.21 0.69 2.67 3.85 1.02 0.13 0.06 0.07

XRF analysis

Content (7o)û% 20% 40% 60% 8û% 1 0f1%

2 83/_57

2 840_66¡ s¡o2

r 4t203

tr [4gO

D Fe203

I CaO

E Nazo

r K20

trTi02

r P205

! MnO

tr so3

2 840.86

2841 -21tn0,)

ñV)

2 841 .30

2841_t¡6

2 841 -SS

2 843.08

2 843.61

2 844.08

I1 5

Poinú Counttlng Results for Sarnple 284{).86rn (log-depúh)

Type No Mineral Types Total No. Percerntage o/o

1 Quartz 62 20.672 Albite 69 23.OO3 Orthoclase 7 2.334 Biotite 36 't2.oo5 Muscovíte 10 3.336 Rock Fragments 37 12.337 Organ¡c Matter a 2-67a Clor¡te 15 5-OO

9 Smect¡te 56 1a.67

Total = 30()

Point Counttlng Results for Sanrple 2841.99rn (log-depth)

Type No. Mineral Types Total No. Percentage 0,6

1 Quartz 56 18.672 Albite 64 21 .333 Orthoclase I 2.674 B¡otite 33 1 1.OO

5 Muscovite t9 633o Rock Fragments IJ 4337 Organ¡c Matter 't1 3.67a Clorite 7.67I Smectite 73 24.33

Total = 300

Polnt Counûing Results for Sarnple 2844.O8rn (log- depúh)

Type No Mineral Types Total No- Percentage 9o

1 Quartz 59 19.672 Albite 41 13.673 Orthoclase 13 4.334 B¡otite 22 7.335 Muscovite 14 4.676 Rock Fragments 13 4.437 Organic Matter 7 2_43a Clorite 14 4.67I 'Smectite '1 17 39.OO

Total 3o(l

t52

Porosity Results of Laboratory Measurement

WellName: Redman # I

Recalibrate PI 799.8 P2 433. I Vl (Calc) 131.8ó696

Sample No Core-Depth Log-Depth P1 P2 Bulk Volume (cc) weiglrt G) V3 (Calc) Porosity (þI 2836.8-i 2er'.0.66 799.8 564,-5 -r8.90 149.9 54.91 3.76

2 2837.0s 2840.86 799.9 565.8 58.20 r49.08 54.56 1.91

J 2437.4 284.t.2I '799.8 518 4l.8ó 106.21 71.74 4.66

4 2537.49 2U1.3 800.2 558.4 54.80 t42.24 57.10 0.46

_5 283 7.85 2U1.66 800 573 7 60.18 155.27 52.O2 0.91

Recalibrate P1 800.5 D' 433.70 Vl (Calc) I 32.01

6 2838,18 2U1.99 800 2 5óó.-f 58.0ó 149. ll 54.46 1.49

2839.61 28¡.3.42 800, l 509.7 36.7'.| 94,89 J5,27 0.91

8 2839.8 2U3.61 800.4 538.4 48.03 I 23.58 64.24 1.29

9 zgto.27 2&14.08 800.2 560 55.98 143.82 56.62 1.70

Permeability Ræults of Laboratory Measurement

WellName: REftnan # I

Sarnple No Core-Dqth L¡g-Deptl Gauge Press PlugLcrgth PlugBllk Air Viscosity Qb (cc/Ð PmnmbilityKa (mD) KlinkmbaKl(mD)

(m) (m) (Atnù (cm) Vrll(cc) (cp)

I 2Eó,85 2840,óó 5 51.12 58.90 0,018 0.22 35.49 2990

2 2ts7.05 2840,86 5 51.96 58.20 0.01 8 1.25 210.83 197.9

J zgtiA ïUt.2l 5 3?.31 41 86 0.018 0.418 50.54 43.49

4 28ß1.49 2841.3 5 53.78 54.80 0.018 1.09 209.18 l9ó.00

5 2837.85 2U1,66 I 51.92 60.18 0.01 I 0.21 111.U2 158.32

ó 2838.18 z8/t.99 I 50.61 58 06 0.018 0.15 120.31 109.05

7 289.ól 2U342 I 36,13 36.17 0.018 0.38 49.05 42.13

8 2ß9.8 2843,61 I 47 38 48.03 0 018 0$ 501 33 495.04

9 2U0.21 2844,08 I 50.51 55.98 0.018 0.12 19.90 16,19

1s3