porosity and permeability in tight rock - core · porosity and permeability in tight rock 10 wade...

POROSITY AND PERMEABILITY IN TIGHT

ROCK

THESIS – PART B

By: Wade Jenkins

Student No. s164065

Supervisor:

Dr. Daria Surovtseva

Co-Supervisor:

Mehrdad Rezazadeh

School of Engineering & Information Technology

Faculty of Engineering, Health, Science and the Environment

Charles Darwin University

Darwin

October 2015

Porosity and Permeability in Tight Rock 2 Wade Jenkins

Student name: Wade Jenkins

Supervisor: Daria Surovtseva

Thesis topic: Porosity and Permeability in Tight Rock

Abstract

Keywords: Porosity, permeability, core analysis, porous solids, SEM, tight rock,

photomicrography, Big Lake field, Cooper Basin

Porosity and permeability analyses were performed on a tight sand core sample from the Big Lake

field, Cooper Basin, South Australia. This process involved the design and construction of an

experimental system capable of measuring these characteristics. Permeability analysis involved a

comparison of the data obtained from this system with the data modelled by a pre-existing theoretical

anisotropic stress-dependant permeability model, engineered specifically for the Big Lake field

region. Whilst the results were greater in magnitude than expected for a tight stand reservoir, the trend

in data did fit the trend expected by data modelled for this region. Porosity analysis involved

comparing two methods of photomicrography; optical microscope and SEM imaging. These analyses

focused on pore structure, including porosity and pore size distribution. It was concluded that the

sample is on the more porous side of the ‘tight’ reservoir rock, with an average porosity of 10.22% and

a pore size distribution comprised primarily of typically more permeable micro-fractures, explaining

why the sample is more permeable than expected. Overall it was determined that SEM

photomicrography is more useful as a pore analysis tool than it’s optical microscope counterpart,

primarily due to the higher magnification capabilities. These findings contribute to improving the

relatively new and developing field of tight rock characteristics analysis within Australia.


Table of Contents

Abstract ....................................................................................................................................... 2

1. Introduction ............................................................................................................................ 5

1.2 Scope ................................................................................................................................. 6

2. Literature Review ................................................................................................................... 8

2.1 Porosity Analysis .............................................................................................................. 8

2.1.1 Bulk Volume .............................................................................................................. 9

2.1.2 Pore Volume ............................................................................................................. 11

2.1.3 Photomicrography Methods ..................................................................................... 13

2.2 Permeability Analysis ..................................................................................................... 17

2.2.1 Gas ............................................................................................................................ 17

2.2.2 Liquid ....................................................................................................................... 20

2.3 Permeability Modelling .................................................................................................. 21

2.4 Experimental Setup ......................................................................................................... 24

2.5 System Components ....................................................................................................... 31

2.5.1 Pumps ....................................................................................................................... 31

2.5.2 Core Holders ............................................................................................................ 31

3. System Design ...................................................................................................................... 33

3.1 Components .................................................................................................................... 34

3.1.1 Core Holder .............................................................................................................. 35

3.2 Confining Pressure Delivery System .............................................................................. 36

3.3 Porosity System .............................................................................................................. 37

3.4 Permeability System ....................................................................................................... 38

3.5 Future Variations ............................................................................................................ 39

4. The Sample ........................................................................................................................... 40

5. System Test Procedure and Analysis.................................................................................... 42

5.1 Porosity Analysis ............................................................................................................ 42

5.1.1 Results and Analysis .................................................................................................... 44

5.2 Permeability Analysis ..................................................................................................... 46

5.2.1 Results ...................................................................................................................... 46

5.2.2 Analysis .................................................................................................................... 47

6. Photomicrography Porosity Analysis ................................................................................... 50

6.1 Optical microscope ......................................................................................................... 50

6.2 SEM ................................................................................................................................ 53


6.2.1 Pore Size Distribution .............................................................................................. 59

6.3 Comparison of Methods .................................................................................................. 63

8. Conclusion ............................................................................................................................ 64

7. Recommendations ................................................................................................................ 65

9. References ............................................................................................................................ 66

10. Appendix ............................................................................................................................ 68

Appendix A – Permeability Testing Raw Data .................................................................... 68

Appendix B – Photomicrography Images for Sample Bottom 2 .......................................... 69

Appendix C – SEM Images for Sample Top 2 Point 1 ......................................................... 71

Appendix D – Pore Size Distribution Data ........................................................................... 74


1. Introduction

As fossil fuel reserves in conventional reservoirs begin to deplete, the extraction of gas from

unconventional reservoirs becomes a far more attractive proposition. These reservoirs are

often much less profitable due to some combination of lower depth, dispersion of the product

over a larger area or expensive extraction procedures. Tight rock reservoirs are one such type

of unconventional reservoir, defined in 1970 by the US Government as rock with an expected

permeability of less than one millidarcy (mD) (Holditch, 2006). This means that pores within

the rock formations are either very small or very poorly connected, leading to a low rate of

fluid flow. Whilst tight rock gas production has been practiced world-wide for over 30 years

(Department of Mines and Energy, 2015), it is relatively new to Australia due to the available

quantities of conventional gas and other, cheaper unconventional reservoirs such as coal seam

gas, still available in the region. Other unconventional reservoirs include coal seam and shale

reservoirs, their properties can be seen in the Figure 1 below (Australian Petroleum

Production & Exploration Association, 2015)

Figure 1 - Conventional and unconventional reservoir properties (Australian Petroleum Production & Exploration

Association, 2015)

Two key parameters of reservoir analysis are porosity, defined as “the percentage of the soil

or rock volume that is occupied by pore space, void of material” (Carter, 2002), and

permeability, defined as “the capacity of a porous rock, sediment, or soil for transmitting a


fluid” (Carter, 2002). These characteristics are indicative of a viability of a reservoir; porosity

for example, details the potential space within the rock available to hold resources such as oil

or gas. With some analysis, this information can be used to estimate the quantity and

concentration of gas within the reservoir. Similarly, permeability is indicative of the potential

flow rate obtainable when extracting from the reservoir, which then determines the viability

of extraction and whether a reservoir stimulation procedure is necessary for a commercially

viable flow rate. One such commonly used stimulation procedure is hydraulic fracture

stimulation (often referred to as ‘fraccing’). The fraccing process involves “pumping fluid

under pressure into the reservoir to prop open small cracks and openings that allow more gas

to be released” (DEHP, 2014). This fluid is typically comprised of mostly a water and sand

mixture, with some other chemical additives that impede infrastructure corrosion and bacterial

growth (DEHP, 2014).

There are a number of different methods that may be employed to measure porosity and

permeability characteristics, some of which are identified, discussed and implemented in this

thesis.

1.2 Scope

The purpose of this thesis is to determine the worth of photomicrography methods of porosity

analysis, and the validity of a theoretical model developed for the dependence on reservoir

pressure of permeability in tight rock reservoirs. Charles Darwin University does not currently

have any systems capable of measuring porosity and permeability at high pressure. In order to

obtain experimental data, a system that is capable of measuring these two attributes as

accurately as possible, given the appropriate time and budget constraints, has been designed

and constructed. Tests have been performed using this system to assist with the validation of

this model

Photomicrography analysis methods have been performed for pore characteristics analysis.

Conclusions regarding the validity and overall usefulness of these methods for tight rock core

analysis have been made.

The thesis is split into two sections. The first covers a literature review of currently used

systems and testing methods. This information was subsequently used to design the

aforementioned system capable of measuring both porosity and permeability to industry level

standards. The second section covers the commission and testing of the system and results of

the photomicrography porosity analysis, as well as conclusions regarding these methods, the

developed system and the theoretical model.


Primary Objectives

Develop a system capable of measuring porosity and permeability at high pressure to

industry level standards.

Commission tests using this system to measure sample permeability at a range of inlet

pressures.

Commission tests of two photomicrography methods, to determine sample porosity

and pore size distribution.

Evaluation of photomicrography analysis methods in regard to the applicability to

tight rock samples.

Evaluation of experimental system.

Evaluation of the anisotropic stress-dependant permeability model developed by

Razazadeh et al.

Secondary Objectives

Commission porosity tests using the developed system and compare with

photomicrography analysis results.

System refinement and calibration.

Constraints

Only one tight rock sample is available for testing.

Only readily available materials and equipment will be used, with the exception of the

core holder.

Only the two photomicrography methods available for use are considered (optical

microscope and scanning electron microscope).


2. Literature Review

2.1 Porosity Analysis

Porosity analysis has two main parameters of interest that are typically used in further

applications; pore volume (VP) and porosity (φ). Pore volume is the actual volume of pores

within the sample and porosity is the ratio of empty space to total volume. From the American

Petroleum Institute’s (API) Recommended Practice for Core Analysis (1998), these values

can be calculated using equations 1 and 2 below using bulk volume (VB), which is the total

volume of the sample, and grain volume (VG), which is the actual volume of the mass of the

sample.

𝑉𝑃 = 𝑉𝐵 − 𝑉𝐺 (1)

𝜑 = (𝑉𝐵 − 𝑉𝐺)/𝑉𝐵 (2)

The two parameters can be related using equations 3 and 4 below.

𝜑 = 𝑉𝑃/(𝑉𝑃 + 𝑉𝐺) (3)

𝜑 = 𝑉𝑃/𝑉𝐵 (4)

Pore volume, bulk volume and grain volume can all be determined experimentally using

various methods, the most common of which are listed in the following table from API’s

Recommended Practice for Core Analysis (1998).


Table 1 - Methods for determining bulk, grain and pore volume (API, 1998)

2.1.1 Bulk Volume

The Archimedes Buoyancy methods, as outlined by API (1998), apply the Archimedes

Principle to determine the bulk volume of a sample via fluid displacement (most commonly

mercury). The amount of mercury displaced can be related to bulk volume by the following

equation.

𝑉𝐵 =𝑀𝑎𝑠𝑠 𝑜𝑓 𝑚𝑒𝑟𝑐𝑢𝑟𝑦 𝑑𝑖𝑠𝑝𝑙𝑎𝑐𝑒𝑑

𝐷𝑒𝑛𝑠𝑖𝑡𝑦 𝑜𝑓 𝑚𝑒𝑟𝑐𝑢𝑟𝑦 (5)

A typical setup for this method of measurement using mercury as the displaced fluid, shown

in Figure 2 below, uses a mercury-filled container that rests upon an electronic scale. A core

sample is then lowered and forced into the mercury until it is completely immersed. Due to

the Archimedes Principle, which states that buoyancy force is equal to the weight of the

displaced fluid, the weight changed on the scale is directly related to the amount of mercury

(or other fluid) displaced. For other fluids, it is often not necessary to force the core sample

into the fluid as these are normally much less dense and viscous. In this case the sample can

be hung from a balance over the body of liquid and the submerged sample weight can be

found and compared with the initial sample weight to determine the bulk volume.

Additionally, using a fluid with lower interfacial tensions increases the probability of the fluid


penetrating the sample when submerged. This can be avoided by fully saturating the sample

with another fluid prior to testing or preventing penetration via a sleeve or another similar

coating. The main advantage of this method is the accuracy of measurements given proper

operating procedure, even for irregularly shaped samples (API, 1998). If mercury is used,

samples are not damaged in any way and thus may be used in subsequent testing provided that

no mercury penetration occurred. One disadvantage of this method of measurement is that it

requires an additional separate system to be assembled that potentially uses sizeable volumes

of mercury as the immersion fluid if possible. It also requires extra preparation for highly

permeable or structurally deteriorated samples to prevent mercury penetration or for all

samples if using another fluid (API, 1998). Even with these precautions, the risk of sample

damage is always present since pores large enough for mercury penetration can be present in

even low permeability samples due to pore size anomalies.

Figure 2 - Example mercury porosimeter (API, 1998)

Another method for measuring bulk volume is simply to use callipers. This method involves

measuring sample dimensions at five different positions on the sample at minimum, then

using an average value for each dimension to calculate the bulk volume. A correction factor

can be determined by comparing calliper results to displacement results, which may be used

to increase the accuracy of subsequent measurements given similar core shape and structure.

Using callipers is an incredibly simple and fast method that poses no risk to core integrity

and, although it is slightly less accurate than other methods, repeated measurements can

potentially be within 0.15 cm3 for bulk volume given proper care to technique (API, 1998).

However, this technique is not suitable for irregularly shaped samples or samples with

inconsistent surfaces, as the accuracy is based upon averaging multiple measurements that are


representative of sample geometry. It should be noted that API do not recommend this

method if pore volume is to be determined from bulk volume and grain volume (see equation

1), as the lesser accuracy of callipers will have a more adverse effect than if used to calculate

porosity from a measured pore volume (equation 4).

2.1.2 Pore Volume

The first commonly used method for pore volume determination is Boyle’s Law Single Cell

method (API, 1998). This method involves applying a low confining pressure to the sample,

typically using a core holder with a liquid jacket, in order to simulate sample pressurisation

within a reservoir. The reference chamber is filled with a gas, typically helium or nitrogen,

where the initial conditions (temperature and pressure) are recorded. This gas is then allowed

to flow into the sample chamber. An example diagram of a typical setup for this method can

be seen below.

Figure 3 - Example system for Boyle's Law Single Cell analysis (API, 1998)

Once pressure equilibrium has been reached, the previously mentioned parameters are once

again recorded. Given that the system is of a known volume, the following equation derived

from Boyle’s Law using mass balance can be applied to determine the pore volume of the

sample (API, 1998).

𝑃1𝑉𝑟

𝑍1𝑇1𝑟+

𝑃𝑎(𝑉𝑃+𝑉𝑑)

𝑍𝑎𝑇1=

𝑃2(𝑉𝑟+𝑉𝑃+𝑉𝑑+𝑉𝑣)

𝑍2𝑇2 (6)

Where:

P1 = absolute initial reference volume pressure.

P2 = absolute expanded pressure.

Pa = absolute atmospheric pressure initially in sample.

Z1 = gas deviation factor at P1 and T1.


Z2 = gas deviation factor at P2 and T2.

Za = gas deviation factor at Pa and T1.

T1r = absolute temperature of reference volume at P1.

T1 = absolute temperature of sample pore volume at Pa.

T2 = absolute temperature of reference volume and sample after P2 has stabilized.

Vr = reference chamber volume.

VP = sample pore volume.

Vv = valve displacement volume

Vd = dead volume

The valve displacement volume, dead volume and reference chamber volume are all

determined during the calibration process prior to testing. In an isothermal system, the

equation can be simplified further:

𝑉𝑃 =𝑉𝑟(

𝑃1𝑍2𝑃2𝑍1

−1)−𝑉𝑣

1−𝑃𝑎𝑍2𝑃2𝑍𝑎

− 𝑉𝑑 (7)

High pressure porosity testing can also be performed using this method, however the

subsequent porosity calculation must take into account that fact that bulk volume and pore

size will decrease due to the sample compressing and reducing pore size as the applied

pressure increases. There are two methods that allow for this; the first assumes that the

decrease in bulk volume is equal to the decrease in pore volume (equation 8) and the second

assumes that grain volume remains constant regardless of confining pressure (equation 9)

(API, 1998).

𝜑 = 𝑉𝑃/(𝑁𝑜𝑛 − 𝑠𝑡𝑟𝑒𝑠𝑠𝑒𝑑 𝑉𝐵 − ∆𝑉𝑃) (8)

𝜑 = 𝑉𝑃/(𝑉𝑃 + 𝑉𝐺) (9)

The main advantages of this method are that pore volume is rapidly determined and that

permeability testing can be performed immediately afterwards since the sample is already

loaded and will remain clean and dry. The ability to test porosity at a user determined

confining pressure allows for the accurate determination of porosity at reservoir conditions

that can be tailored to suit each individual reservoir. Also, the use of an inert gas such as


helium removes the risk of any reaction with the sample. However, the accuracy of this

method is heavily dependent upon the correct calibration for the system’s dead volume. The

surface of the sample must also be consistent to allow for the sleeve within the core holder to

tightly fit otherwise there is a risk of error due to gas collecting between the core sample

surface and the sleeve. Also, in low permeability samples, it may take extended periods of

time to reach pressure equilibrium which increases the likelihood of temperature and

atmospheric pressure changes, further complicating calculations.

The other commonly used method for pore volume calculation is the liquid saturation method

(API, 1998). This method saturates a dried and cleaned sample in a liquid of known density at

high pressure (typically 2000 to 3000 psi) in order to completely saturate the pores of a

sample. Weight measurements are taken of the dry and saturated sample and the sample

immersed in fluid. The sample pore volume can be determined using equation 10 and the

porosity using equation 11 below.

𝑉𝑃 =𝑆𝑎𝑡𝑢𝑟𝑎𝑡𝑒𝑑 𝑊𝑒𝑖𝑔ℎ𝑡−𝐷𝑟𝑦 𝑊𝑒𝑖𝑔ℎ𝑡

𝐷𝑒𝑛𝑠𝑖𝑡𝑦 𝑜𝑓 𝑆𝑎𝑡𝑢𝑟𝑎𝑛𝑡 (10)

𝜑 =

𝑆𝑎𝑡𝑢𝑟𝑎𝑡𝑒𝑑 𝑊𝑒𝑖𝑔ℎ𝑡−𝐷𝑟𝑦 𝑊𝑒𝑖𝑔ℎ𝑡𝐷𝑒𝑛𝑠𝑖𝑡𝑦 𝑜𝑓 𝑆𝑎𝑡𝑢𝑟𝑎𝑛𝑡

𝑆𝑎𝑡𝑢𝑟𝑎𝑡𝑒𝑑 𝑊𝑒𝑖𝑔ℎ𝑡−𝐼𝑚𝑚𝑒𝑟𝑠𝑒𝑑 𝑊𝑒𝑖𝑔ℎ𝑡

𝐷𝑒𝑛𝑠𝑖𝑡𝑦 𝑜𝑓 𝐼𝑚𝑚𝑒𝑟𝑠𝑖𝑜𝑛 𝐿𝑖𝑞𝑢𝑖𝑑 (11)

This advantage of this method is that it does not require any specialised equipment such as a

core holder to perform; only a vacuum to prepare the sample and a high pressure vessel

capable of holding a sample and liquid saturant. This also means that multiple cores can be

tested at the same time, increasing the efficiency of large scale porosity testing. However, the

testing itself is an extremely long process; up to 18 hours for the evacuation process as part of

the sample preparation for low permeability samples and at least 4 hours for the pore flooding

process. Additionally, following testing the sample must be evacuated and dried again if it is

to be used in further testing. There is also a risk of liquid loss during the weighing process if

sample porosity is too high or immersion liquid density is too low.

2.1.3 Photomicrography Methods

As an alternative, or, in addition to experimental methods, photomicrography, an image taken

at enhanced magnification, methods of analysis can be used. These methods involve

interpreting results from various methods of enhanced imaging, such as the two available at

Charles Darwin University; optical microscope and Scanning Electron Microscope (SEM)

imaging. These methods allow for the possibility for a number of porosity characteristics to

be determined such as porosity, pore dimensions and connectivity. Which characteristics can


be determined will depend upon both the sample and the method used, though some methods

tend to be more reliable than others.

The first photomicrography method available is optical microscope imaging. This method

involves using an optical microscope and appropriate imaging equipment to capture images of

the sample at high magnification. Dependent upon the sample, polishing of the surface may

be required to obtain a suitable surface finish that allows for the majority of the image to be in

focus. This method is a simple method, perfect for initial analysis due to the simplicity and

speed of the process, as well as the low risk of the activity comparative to other methods.

Optical microscope images can be used to look at surfaces of images in full colour, which can

be analysed to potentially determine a number of pore characteristics such as pore size, shape,

count and distribution. This information relies on sufficient magnification to successfully and

clearly capture the sample pores. Other, somewhat unrelated information can also be captured

using this method, such as material composition via identification of the various different

colours and textures of the material within the sample.

The other photomicrography method available is SEM imaging. Unlike optical microscope

imaging, the methodology for testing is longer, more restrictive and comparatively expensive.

The specifics of the procedure will vary based upon the equipment. In general terms, the

procedure begins with ensuring that the sample will fit onto the sample holder inside of the

SEM’s test chamber which may require cutting the sample into smaller pieces. The sample(s)

must then be affixed inside of the test chamber and depressurised to a high vacuum. Once this

is achieved, the electron beam can initiate and analysis begins. Like most photomicrography

methods, SEM analysis purely examines the surface of the sample and its’ characteristics,

though potentially at much higher magnification than other methods and in more detail due to

improved focusing and contrasting capabilities. However, SEM images are greyscale due to

the nature of the analysis, which restricts analysis based upon colouration.

Optical microscope and SEM photomicrography were utilised by Spencer (1898) in his

“Review of Characteristics of Low-Permeability Gas Reservoirs in Western United States”.

This paper is primarily a summary of a large quantity of tight rock related reservoir

engineering and geologic research that had recently been conducted in Western United States

during the 1980s. This research centres on the comparisons of the two types of tight gas

reservoirs, low porosity and high porosity, in a number of areas including confining stress

sensitivity, capillary pressure in relation to permeability, water saturation and more. Optical

microscope photomicrographs were used to illustrate the different material compositions and

pore sizes and distributions that can be found in both conventional and tight gas wells. These


images utilised a low viscosity blue epoxy to impregnate sample pores, this makes the pores

clearly visible including the less obvious micro-fractures that add considerably to porosity and

permeability due to their elongated and interconnected nature. SEM photomicrographs were

also used by Spencer in two instances; to display the difference in appearance, size and

location between natural and artificially induced micro-cracks and to display authigenic clay

within tight rock pore spaces. The high magnification and high contrast provided by the SEM

provides highly visible images that clearly display the subject of the images. Given that this

paper is a summary, no detailed methodology for the analyses performed is present. As such,

it is difficult to suggest how these images were utilised other than purely as a display piece

that could be used to verify the results of the composition, porosity and permeability analyses

performed. However, the usefulness of these image types as a visual tool and verification aid

is clearly displayed.

SEM photomicrography was also utilised in Walls 1982 paper “Tight Gas Sands –

Permeability, Pore Structure, and Clay”. As the title suggests, this study involved testing the

gas permeability of a suite of cores from Spirit River in western Alberta and two from Cotton

Valley in east Texas. Using these samples, Darcy’s Law was validated for applications within

the microdarcy range. Investigation into the importance of clay to permeability was also

undertaken utilising thin-section, x-ray diffraction and SEM studies to analyse pore structure

and clay presence as well observing the effects of varying pressure, saturation and salinity.

SEM analysis played a key role in this paper as it was utilised to investigate pore structure

and geometry, as well as the identification of the presence of clay, as well as the type of clay.

This was achieved by closely analysing the structure of the clay formation, where it has

formed as well as images were also utilised as a verification aid during the various studies

performed, such as to confirm the effect that the potassium chloride solution had upon the

sample clay content. Should the sample display a presence of clay, a similar investigation into

the clay content and formation could prove useful in accurately describing the pore structure.

The use of photomicrography methods was further showcased in Randolph and Soeder’s

(1987) paper “Porosity, Permeability, and Pore Structure of the Tight Mesaverde Sandstone,

Piceance Basin, Colorado”. During this study, special core analysis was performed on 44 core

plugs of tight Mesaverde group sandstone, from the Piceance Basin in North-Western

Colorado, United States. Core plugs were dried in a relative-humidity oven and subject to a

number of porosity and permeability test procedures using the Society of Petroleum

Engineers’ Computer Operated Rock Analysis Lab. These tests included porosity to gas at

initial reservoir confining stress, permeability as a function of pore pressure and permeability


confining stress dependence. Subsequently, samples were sliced from 19 of the 44 core plugs,

for use in the photomicrograph examination methods that comprise the petrography for these

samples. Thin sections of these samples were analysed under an optical microscope. Like

Spencer, Randolph and Soeder impregnated the pore space of the optical samples with a blue

dyed epoxy as a visual aid in identifying the extent of pore space and features. Some of the

more polished samples underwent SEM photomicrography analysis. The petrographic

analysis was focused particularly on analysis of pore geometry and potential gas flow

pathways throughout the samples, particularly since these properties had already been

somewhat deduced from the permeability data obtained using these samples. Of particular

interest are the substances that are present within pore space, such as various clays, dolomite

and quartz crystals. The in depth analysis of the pore geometry and connectivity performed as

part of this study are incredibly thorough and provide useful information regarding reservoir

potential. Although it may stand to hasten the analysis given an indication of the resulting

data is already known, as proven by this paper, the existence of porosity data from prior

analysis is clearly demonstrated as unnecessary as the results of a photomicrograph analysis

can stand on their own. In particular, examining which pore type the majority of gas actually

flows through and how they are connected is vital in determining core permeability

characteristics.


2.2 Permeability Analysis

2.2.1 Gas

For gas permeability, the industry standard technique is the confining stress method for axial

steady-state flow (API, 1998). This method utilises a core holder to apply a confining stress to

the sample (typically from a pressurised jacket surrounding the sample) whilst a gas is

injected into the sample. A sample system can be seen below in Figure 4.

Figure 4 - Example system for confining stress method

For this method, the sample is first pressurised using the hydraulic oil delivery system to fill

the jacket surrounding the sample with hydraulic oil, or another fluid of similar properties, in

order to simulate reservoir conditions. Next, the working fluid (for this method, a standard gas

such as nitrogen is typically used) is injected into the system via the working fluid delivery

system. As this fluid flows through the system, gas pressure, temperature and flow rate are

recorded for use in the determination of sample gas permeability kg from the following

equation.

𝑘𝑔 =2𝐶2𝜇𝑃𝑟𝑞𝑟𝑧𝑚

𝐶1𝑧𝑟𝐺𝑓(𝑃1−𝑃2)(𝑃1+𝑃2) (12)

Where:

C1 and C2 = Conversion constants

µ = Gas viscosity

Pr = Reference pressure (absolute)


qr = Flow rate at reference pressure

zm = Gas correction factor at mean pore pressure

zr = Gas correction factor at reference pressure

P1 = Injection pressure (absolute)

P2 = Outflow pressure (absolute)

Note that mean pore pressure is defined as 𝑃𝑚 =𝑃1+𝑃2

2

The geographic factor Gf can be calculated using sample diameter and length in the following

equation

𝐺𝑓 =𝜋𝐷2

4𝐿 (13)

Permeability testing for gas fluids must take the gas slippage into consideration which can be

done using the Klinkenberg effect. Klinkenberg permeability can be determined by taking

multiple permeability measurements at different pore pressures, then plotting them versus the

reciprocal of the mean pore pressure. The Klinkenberg permeability is equal to the intercept

of the line of best fit (API, 1998).

This method is the current industry standard and as a result there are significant amounts of

pre-existing data held across the industry. There are a number of reasons for this, such as

using an inert gas as the working fluid leaves the sample clean and guarantees composition

integrity. Also, a large range of confining pressures may be used, allowing analysis for a wide

variety of applications. This method can have problems measuring permeability in low

permeability sample (less than about 0.1 mD) due to the extended time required for the flow

to become steady state. Also, core plug ends must be cut completely square otherwise uneven

applied pressure distribution can occur potentially resulting in sample damage.

Another method for determining steady-state permeability using gas is the probe permeability

method. The procedure involves sealing the probe against the surface of the sample and

injecting pressurised gas into the sample through a small orifice. Since the sample is only

sealed around the gas injection orifice, the outflow pressure of the gas is equal to atmospheric

pressure. Gas pressure, temperature and flow rate are measured for use in the calculation of

sample permeability. An example setup can be seen in Figure 5 below.


Figure 5 - Example probe permeameter (API, 1998)

Permeability can be determined using equation 12 where P2 = Pa, Pr = P1 and Gf = G0ri. G0 can

be determined from readily available plots of G0 versus ro/ri, where ri is the inner probe seal

radius and ro is the outer probe seal radius.

This method is useful as a portable, on site analysis since the only requirement is a good seal

between the probe and the sample which does not require a lab. Since only a small section of

the sample is tested with each trial, this method can be used to determine spatial permeability

variation. This also means that multiple tests at different locations on a sample will be

required for a full permeability analysis. A confining stress is not applied using this method,

meaning permeability results are incredibly optimistic and likely do not represent

permeability within the reservoir.


2.2.2 Liquid

Steady-state liquid permeability can be measured using the imposed differential pressure

method (API, 1998). This method is very similar to the confining stress for axial steady-state

flow method, using an almost identical system that illustrated in Figure 4. The fully saturated

sample is first placed inside of the core holder which is then compressed by the confining

pressure. A degassed liquid is then delivered to the sample using a pump which, due to liquid

viscosity being significantly higher than gas viscosity, causes a sizable pressure drop across

the sample. The liquid permeability of the sample can be determined using the following

equation.

𝑘𝑙 =𝐶2𝑞𝜇

𝐶1𝐺𝑓(𝑝1−𝑝2) (14)

This method does not require the samples to be dried prior to testing, which is a potentially

damaging process, since the sample must be saturated prior to testing. On the same note,

further tests that require the sample to be saturated can be performed immediately following

the conclusion of permeability testing. Liquid permeability does not require gas slippage

correction and pressure differentials are typically much higher due to the higher fluid density,

both of these factors simplify the testing process. However, this also makes the analysis of

low permeability samples incredibly difficult and some liquids will require the use of

expensive corrosion resistant equipment. Also, temperature control must be handled much

more attentively to avoid viscosity change and thermally induced volumetric expansion which

will alter volumetric flow rate.


2.3 Permeability Modelling

The sample to be tested is from the Big Lake gas field in South Australia’s Cooper Basin. A

study 2014 titled “Development of a Stress Dependant Permeability Model in Tight Gas

Reservoirs: A Case Study in Cooper Basin, Australia” by Rezazadeh et al was based upon the

same gas field. This study involved the development of an anisotropic stress dependant

permeability model and comparing it to the Palmer-Mansoori model developed in 1988. The

Palmer-Mansoori model was developed to model pore volume compressibility primarily in

coalbeds, which have high compressibility due to extremely low pore space, though the model

can be used with other bodies of similar properties. This recently developed model is

primarily concerned with the change in permeability experienced within a reservoir as

reservoir pressure drops over the production life of the reservoir due to gas extraction. This

effect is even more pronounced in tight sandstone reservoirs, such as those present in the Big

Lake field, due to tendency for micro-fractures to be the primary avenue of gas flow through

the rock. The model is based upon modelling the effective stress within the reservoir to

determine the permeability. This requires geotechnical knowledge of the area, including the

fracture compressibility, Poisson’s ratio and Biot’s coefficient in order to accurately model

the reservoir. Using this information, the change in anisotropic permeability due to reservoir

pressure change can be determined using equations 15-17 below.

𝑘ℎ𝑚𝑖𝑛

𝑘0,ℎ𝑚𝑖𝑛= exp[3𝑐𝑓(𝛼∆𝑝)] (15)

𝑘ℎ𝑚𝑎𝑥

𝑘0,ℎ𝑚𝑎𝑥= exp [3𝑐𝑓(

𝑣

1−𝑣𝛼∆𝑝)] (16)

𝑘𝑣

𝑘0,𝑣= exp [3𝑐𝑓(

𝑣

1−𝑣𝛼∆𝑝)] (17)

Where:

kh = Permeability values in the directions of minimum horizontal stress, maximum horizontal

stress and vertical stress.

ko = Previous permeability values in the directions of minimum horizontal stress, maximum

horizontal stress and vertical stress.

cf = Fracture compressibility

α = Biot constant

Δp = Pore pressure change


Using geographic data available from a 2012 study of the Cooper Basin and a hydraulic

fracturing simulation as well as production data for the reservoir, the newly developed model

was simulated and compared with the Palmer-Mansoori model for the same data. The results

for production are shown in Figure 6 below.

Figure 6 – Comparison of anisotropic model with Palmer-Mansoori model (Rezazadeh et al, 2014)

As can be seen, Razazadeh et al’s model is more representative of the historical data.

However, multiple sets of data are required to validate a model. This thesis will involve

utilising the data obtained from the tight sandstone sample from the Big Lake field to further

validate this model. The data detailed in this paper, as well as the previously highlighted

equations, will serve as a simple point of reference for the permeability data obtained using

the developed porosity and permeability system. Figure 7 shows the variation in permeability

over the production life, which can be combined with Figure 8 to determine the change in

reservoir pressure that corresponds with each permeability change. These pressure changes

can be tested with the available sample from the same area to assist with the validation of this

model. It is also of interest to note that this model was specifically tailored to conditions

present within the tight sandstone reservoirs of Big Lake gas field. Specifically, the strike-slip

fault present within the Big Lake field is considered within the model and the formulae are

also simplified to take advantage of the almost uniaxial stress conditions that have been

measured for this field.


Figure 7 - Permeability change over well production life (Rezazadeh et al, 2014)

Figure 8 - Changing in reservoir pressure over production life (Rezazadeh et al, 2014)


2.4 Experimental Setup

At a basic level, system design for porosity and permeability measurement is somewhat

standard given that the testing is based upon core flooding. Typically, the set up involves the

pumping of a fluid into a sample holder to either flood the sample for porosity testing or

permeate through the sample, out of the sample holder and into a flow meter for permeability

testing. If a confining pressure is required the sample is often enclosed within a sleeve to

which pressure can be applied using second fluid, confine the sample evenly. Pressure,

temperature, flow rate and mass change are all measured using appropriate instrumentation

for use in the calculation of the permeability constant and porosity. Many variations can be

made to the basic system design to accommodate specialist test requirements such as the

addition of a gas or liquid analyser if fluid composition is desired or multiple inlets for testing

fluid mixtures or staged delivery such as testing condensate recovery.

A good example of a simple, single purpose design was used in the study “Measurement and

revised interpretation of gas flow behavior in tight reservoir cores” by Li et al (2009). The

focus of the study was to investigate how variable backpressure affects permeability and gas

slippage in tight rock reservoirs, which only required the use of a basic gas permeameter

system (shown in Figure 9 below). The notable differences from the previously described

standard system in chapter 2.2.1 are the use of a high pressure nitrogen cylinder with a

pressure regulator instead of a pump to control gas input and a back pressure regulator located

after the core holder to maintain pore pressure and to reduce the pressure downstream to

within the operation range of the gas flow meter. This substitution simplifies the system by

reducing the equipment requirement, making it ideal for gas permeability. It could also be

used for porosity testing, with some minor modifications such as the addition of temperature

sensors, although this was not within the scope of the intended design as porosity was

determined separately using a different system in this study. However, with this type of fluid

intake system, the pore pressure will be limited to below that of the fluid storage pressure,

which may not be sufficient for high pressure testing. Furthermore, the use of a gas flow

meter eliminates the possibility of using fluids that are liquid under normal conditions.


Figure 9 - Li et al (2009) system

A similar setup was also used in another study by Dong et al (2012) on “Permeabilities of

tight reservoir cores determined for gaseous and liquid CO2 and C2H6 using minimum

backpressure method”. This study investigated the variances between liquid and gas

permeability via the use of carbon dioxide and ethane in both gas and liquid form. The system

used, shown in Figure 10 below, was almost identical to the aforementioned system; the only

difference is the use of a pump to increase gas pressure beyond vapour pressure for liquid

permeability testing. It also utilised a gas flow meter which could be used for both gas and

liquid permeability since the pressure reduction after the backpressure regulator returns the

liquid carbon dioxide and ethane to a gaseous state. This configuration is also very flexible as

the use of a pump allows the use of a large range of pore pressures that are not reliant on gas

storage pressure, as well as the use of liquefied gas for liquid permeability testing. However,

the use of a gas flow meter restricts the usage of liquids to liquefied gases that can be returned

to gas form for analysis; it does not allow the use of other commonly used liquids such as

brine.


Figure 10 - Dong et al (2012) system

Permeameter systems can be tailored for highly specialised testing, such as the system used

by Al-Abri et al (2002) in their study; “Mobility ratio, relative permeability and sweep

efficiency of a supercritical CO2 and methane injection to enhance natural gas and condensate

recovery: Coreflooding experimentation”. This study investigated the use of supercritical

carbon dioxide and methane mixtures of varies concentrations to increase gas condensate

recovery, as well as the effects on natural gas microscale displacement efficiency and stability

following an injection of pure supercritical carbon dioxide. These highly specialised

experiments required a setup capable of first flooding a core plug with natural gas condensate,

then injecting the SCCO2/methane mixture at a pressure of 5900 psi whilst also maintaining a

temperature of 95 degrees Fahrenheit to simulate the conditions of a local reservoir. In order

to accommodate for these needs, the system design, shown in Figure 11, incorporated

multiple, individually controlled, piston accumulators containing natural gas condensate and

the SCCO2/CH4 mixture, of which the pressures are controlled by water injection via a pump.

The temperature of the system was controlled by playing all flow related components

(accumulators, piping, core holder and pressure and backpressure regulators) within a forced

air oven and confining pressure is applied via a hand pump. A flow meter and CO2 analyser

are utilised to analyse the gas after breakthrough in order to determine condensate recovery,

gas composition and CO2 content in the effluent gas. This setup also provides full digital


control for the entire core flooding process which minimises error as well as allows for the

safe operation of the system whilst at high temperatures. This system is incredibly flexible as

it is capable of high pressure and temperature analysis, as well as allowing for the individually

controlled delivery of two fluids which is essential to a variety of applications such

condensate displacement and recovery testing. However, temperature control and multiple

fluids are not used during standard porosity and permeability testing, making the system

unnecessarily complex and expensive for the more common applications. Also, the setup is

only usable with gas or liquefied gas given the instrumentation on the effluent flow lines. A

later study, also by Al-Abri and Amin (2010), titled “Phase Behaviour, Fluid Properties and

Recovery Efficiency of Immiscible and Miscible Condensate Displacements by SCCO2

Injection: Experimental”, used a slightly revised version of the same system (see Figure 12).

This revised version incorporated a separator into the effluent flow line located before the gas

analysis instrumentation as well as repositioning the gas analysis instrumentation to inside of

the temperature-controlled oven. The separator enables the system to effectively analyse the

liquid-gas mixture that results from three phase permeability testing.

Figure 11 - Al-Abri et al (2002) system


Figure 12 - Al-Abri et al (2010) system


Experimental systems can be designed to cater for testing that requires the analysis of both

gas and liquid effluent, such as the system used in a study by Akhlaghinia et al (2014) on

“Experimental investigation of temperature effect on three-phase relative permeability

isoperms in heavy oil systems”. Their study involved testing two and three-phase

permeability for a fluid system of heavy oil, water and carbon dioxide and a comparison of

the results with simulated data. The system, as seen in the following figure, offers a number

of methods for injecting fluid into the core sample. The first is the transfer cylinder which is

used to inject the heavy oil, driven by a syringe pump using water as the hydraulic drive. The

second is a standard gas injection system via a high pressure storage cylinder, which can be

set to inject into either the transfer cylinder to mix with the heavy oil or directly into the core

holder by adjusting the valves along the piping. The third is the piston pump which, via

adjusting valves, can inject water into the transfer cylinder or directly into the core holder.

Multiple phase testing typically produces a mixture of liquid and gas effluent that must be

analysed to determine relative permeability, which must be accommodated in the system

design. The fluid exiting the core holder is passed through a separator located after a

pneumatic controlled backpressure regulator, where the liquid component is collected in a test

tube for later analysis. The gas component continues overhead, downstream to a gas flow

meter and exits the system through a ventilation hood. Also, since the experiment required

testing at three different temperatures, the transfer cylinder, core holder and instrumentation

was placed inside a temperature controlled air bath. The main advantage of this system is the

versatile fluid injection system which allows each fluid to be injected individually or

combined within the transfer cylinder prior to injection. The ability to control system

temperature as well as analyse both liquid and gas flow adds to the flexibility of the system

and the variety of applications for it. However, these features are not necessary for single

phase permeability testing.


Figure 13 - Akhlaghinia et al (2014) system


2.5 System Components

2.5.1 Pumps

As outlined by the American Petroleum Institute (1998), pump choice is very important for

methods that are very sensitive to small changes in pressure or flow rate or inconsistent flow.

Liquid fluids in particular require constant pressure and flow rate to minimise temperature

related viscosity change and thermally induced volumetric expansion. Positive displacement

pumps are ideal since they are capable of constant, controlled fluid injection for the volume of

the stroke. In some cases the pump stroke volume may be too small to reach steady-state flow

conditions. In these cases API recommend a multiple pump setup to continue constant fluid

delivery (API, 1998).

2.5.2 Core Holders

Three types of core holders are predominantly used in porosity and permeability testing;

Hassler, hydrostatic (also known as bi-axial) and tri-axial. Hassler type core holders are core

holders that apply a radial pressure to the sample (Core Lab Instruments 2012). Figure 14

shown below is a diagram of the RHC series Hassler type core holder by Core Lab

Instruments, which. The sample is placed within an impermeable sleeve inside of the sample

chamber, Hassler core holders utilise a fixed size sample chamber with spacers to allow the

fitting and testing of undersized core samples (Core Lab Instruments, 2012). A radial

confining pressure is applied to the sample by pressurising the chamber surrounding the

sample with a fluid via the confining pressure ports. The working fluid is then delivered to the

sample via the distribution plug; the sleeve surrounding the sample ensures that all fluid flows

through the sample. One advantage of this style of core holder is that core samples can be

interchanged without disassembling the entire core holder and draining the confining fluid

(Vinci, 2014). This is possible due to the fixed sample size sample chamber, which allows the

sleeve to remain in place whilst the core holder is being loaded.

Figure 14 - RCH series Hassler core holder (Core Lab Instruments, 2012)


Hydrostatic core holders, also known as bi-axial core holders, apply a common confining

pressure both radially and axially to the sample (Core Lab Instruments, 2012). This is

achieved via the floating distribution plug that allows for the confining fluid to surround one

end of the core sample as well as the radius, as seen below. The floating plug allows for a

variable sample chamber size without the use of spacers to fit undersized samples, since the

pressure fits the plug to the sample. A bi-axial pressure distribution is preferable to radial only

as it more accurately simulates the pressure distribution that the sample would experience

within a reservoir.

Figure 15 - HCH series bi-axial core holder (Core Lab Instruments, 2012)

Tri-axial core holders are core holders that allow the individual control of radial and axial

confining pressures. Some models even allow for specific control of the axial pressure applied

to each side of the sample. As can be seen in the following figure, separate axial and radial

pressure chambers are utilised to achieve this. Like Hassler core holders, tri-axial core holders

also use a fixed size sample chamber, with spacers for variable sample length, which allows

for samples to be interchanged without draining the confining fluid (Core Lab Instruments,

2012).

Figure 16 - TCH series tri-axial core holder (Core Lab Instruments, 2012)


3. System Design

The system, shown in Figure 17 below, was used to measure sample porosity and

permeability experimentally. Porosity measurements have been performed by measuring the

sample bulk volume with callipers and the pore volume via the Boyle’s Law Single Cell

method, outlined in section 2.1.2. Permeability has been determined using the confining stress

method for axial steady-state flow, outlined in section 2.2.1. These methods are detailed

further in 3.3 and 3.4. This system is unique in that it has the capability to perform both

porosity and permeability measurements without any further additions. It is also very flexible

in that future additions will require little work to connect and commission.

Figure 17 - Experimental system design


3.1 Components

As can be seen in figure 17, the following instrumentation and equipment were used;

WIKA S-10 Pressure transducers for pressure measurement, rated for 0-600 bar, to

allow pressure data to be recorded electronically.

Pyrosales GPA Resistance temperature detectors (RTD’s) measure the flow

temperature on the intake and outflow of the core holder as per supervisor

recommendation based on previous experience with RTD’s.

The flow meter is float type analogue gas mass flow meter. Ideally, a digital flow

meter would be used for the previously mentioned electronic data collection.

However, the expected range of flow rates was not known prior to testing, so the

purchase of a digital flow meter could not be justified.

A positive displacement pump to inject the working fluid and the gas booster to

control hydraulic oil injection, as per API recommendation, since it offers a higher

level of control over the working fluid than the gas booster.

A Gas booster pump is used to pressurise hydraulic oil within the jacket surrounding

the core sample and apply a confining pressure. The gas booster can be used to

continuously feed working fluid into the pump if required after the desired confining

pressure is achieved.

Pressure regulator

Piping is ¼“ Swagelok.

Tee’s and other fittings are all ¼” Swagelok.


3.1.1 Core Holder

The core holder chosen for use in this system is the HCH series hydrostatic core holder by

Core Lab Instruments with specifications as per the table below. A hydrostatic model was

chosen based upon the desire to simulate the hydrostatic conditions within reservoirs. Also,

after discussion with specialists from both Core Laboratory Instruments and Vinci

Technologies, it was discovered that the design of Hassler model core holders do not allow

for pressures exceeding 5000 psi (approximately 35.5 MPa). This system needs to provide a

maximum confining pressure of at least 50 MPa, making the Hassler type models unsuitable

for use. The core holder features two inlet ports for multiple fluid injection systems or to

evenly distribute a single fluid and two pressure taps that allow for the analysis of pressure

drop at intervals along the sample. Interchangeable parts that allow the use of 1” diameter

core samples are also available to increase setup versatility. The stainless steel wetted parts

will limit this system to the use of non-corrosive liquids, as the extra cost for corrosion

resistant Hastelloy wetted parts could not be justified. It is important to note that there is one

key limitation imposed by this hydrostatic core holder; due to the configuration of the

mechanism for separating the sample from the confining fluid, it is not possible to test the

sample with a pore pressure that exceeds the confining pressure. Given that confining

pressure will significantly surpass the pore pressure in the testing planned for this system, this

poses no concern, but it should be noted for future applications nonetheless.

Table 2 - Core holder specifications

Specifications

Core diameter 1.5” or 1”

Maximum core length 2.5”

Maximum confining pressure 50 MPa (7252 psi)

Maximum pore pressure 46.2 MPa (6700 psi)

Maximum working temperature 200oC

Body material Stainless steel

Wetted material Stainless steel

Sleeve Viton rubber 70 durameter

Confining pressure ports 2

Inlet ports 2 x ¼” Swagelok

Outlet ports 1 x ¼” Swagelok

Pressure taps 2


3.2 Confining Pressure Delivery System

Confining pressure is applied to the sample via delivery of hydraulic oil into the jacket

surrounding the sample within the core holder. Gas is transferred from the storage cylinder

into the gas booster where it is used as a hydraulic drive to propel the hydraulic oil into the

system via a piston within the hydraulic oil cylinder. As seen in Figure 18 below. The desired

confining pressure can be achieved via sufficient pressurisation of the gas.

Figure 18 – Confining pressure delivery system


3.3 Porosity System

Porosity is determined using the sections of the system shown below in Figure 19. The

method of analysis used is outline in section 2.1.2. The core can have a confining pressure

applied, as outline in section 3.2. The pressure vessel within the piston pump tank will act as

the initial gas reference chamber, where initial values of gas pressure and temperature are

recorded. The valve separating the reference chamber from the sample can then be opened,

allowing the gas to flood the pores of the sample. Once pressure equilibrium has been

reached, which can be determined using the pressure readings from inside of the core holder

and the pump pressure reading, final values can then be recorded. Using equation 6, pore

volume can be determined and hence porosity can be determined using equation 9.

Figure 19 - Porosity system


3.4 Permeability System

Single-phase gas permeability is determined using sections of the system shown in Figure 20

below. Permeability is determined using the confining stress method for axial steady-state

flow outlined in section 2.2.1. Confining pressure is applied to the core using the process

outlined in section 3.2. The piston pump is used to inject fluid into the sample at a

predetermined constant pressure. As the gas passes through the core and out into the system,

pressure, temperature and flow readings are taken at the required points in the process, as

determined by the method of analysis. These are the pressure in the inlet and outlet of the core

holder, the temperature of the fluid and the flow rate at the outlet, as well as the pressure at

which this flow rate was taken. Using this information as well as the geometry of the core

sample, permeability can be calculated using equation 12.

Permeability using liquid can also be determined using the imposed differential pressure

method as discussed in section 2.2.2. In order to determine liquid flow rate the effluent flow

must be collected and weighed then compared with the flow time. The process is almost

identical to the procedure for gas permeability analysis, using equation 14 to determine the

permeability.


Figure 20 - Permeability system

3.5 Future Variations

There are many future variations that can be made to this system to accommodate for various

testing procedures, as the base system is very versatile. For example, placing the core holder

and related instrumentation inside of a temperature controlled oven would allow for

temperature controlled tests. A secondary fluid injection system can be connected to the

second inlet port on the core holder to allow for mixture or multiple fluid testing. The addition

of a separator to the effluent flow line would allow for multiple phase testing.


4. The Sample

The sample tested is a core 1.5 inches (38.1mm) in diameter and 2 inches (50.8mm) in length.

This plug is a piece of a larger plug excavated from approximately 3000m below surface level

from a tight gas reservoir within the Cooper Basin’s Big Lake gas field in South Australia,

illustrated on the map in Figure 21 below (Google Earth, 2015).

Figure 21 - Location of Big Lake field, Cooper Basin, South Australia (Google, 2015)

Upon receiving the sample, the non-uniform ends were cut away from the sample in order to

produce the cylindrical core plug shown in Figure 22 below. The sample is light grey in

colour, with some slight variations in surface colouration that are visible to the naked eye,

though the more distinct variations are likely residue from the cutting process. It is solid,

consistent and smooth to the touch. It has remained relatively clean and dry despite the humid

Northern Territory weather by keeping the sample within a sealed container during transport

and stored within air-conditioned rooms when possible.


Figure 22 - Sample from Big Lake field, Cooper Basin

The offcuts produced from each end of the sample were retained, to be cut further into smaller

test samples for the photomicrography porosity analysis methods. This allows these tests that

require smaller samples to be performed without inhibiting the ability to perform further

testing on the core plug. An image showing these smaller samples can be seen below.

Figure 23 - Smaller samples sliced from offcut prepared for SEM analysis


5. System Test Procedure and Analysis

5.1 Porosity Analysis

The sample should be clean and dry prior to testing. If necessary, samples can be dried using a

drying oven (humidity, vacuum or air forced, depending upon the sample) and cleaned via

flushing the pores with inert gas flow through the sample.

Ideally testing would occur at an overburdened pressure comparable to that experienced by

the rock within the reservoir. An equation to calculate this can be derived from the

fundamental equations shown below.

𝑃 =𝐹

𝐴, 𝐹 = 𝑚𝑔, 𝑚 = 𝑝𝑉, 𝑉 = 𝐴ℎ

𝑃 =𝑚𝑔

𝐴

𝑃 =𝑝𝑉𝑎

𝐴

𝑃 = 𝑝𝑔ℎ (18)

Where:

P = Pressure (Pa)

p = Density of overlying rock (kg/m3)

g = Gravitational acceleration (m/s2)

h = Depth below surface (m)

Using the data achieved by the gravity inversion model developed by Geoscience Australia

(2009), it is possible to determine the density of the ground above depth where the sample

was taken from. A summary of this data is shown in Figure 24 below (Meixner, 2009). Note

that since the exact location where the drilling occurred is not known, the density selected for

this calculation is an approximate across the entire range of data taken in order to best

represent the largest number of possible locations.


Figure 24 - North-South sediment density, Cooper Basin (Meixner, 2009)

At a depth of 3000m, the average material composition above this depth is approximately

2250m of Eromanga Basin sediment with a mean density of 2300 kg/m3 and 750m of Cooper

Basin sediment with a mean density of 2500 kg/m3. These values characterise most of the

available data, given that there is only a small southern area where other, higher density rock

bodies are present at 3000m depth.

Ideally, gravitational acceleration as a function of depth would be used to accurately represent

the effect of gravity upon the overhead deposit. In this case, the value for standard gravity of

9.80665 ms-2

was used since this is only an approximation and the depth is relatively low

compared to the radius of the earth, meaning that the variation of gravity due to depth is

expected to be minimal. This is reflected in Anderson and Dziewonski’s “Preliminary

Reference Earth Model”, shown in Figure 25 below, which illustrates there is slight increase

in gravitational acceleration throughout the earth’s mantles. Given that the 3000m depth does

not even reach the mantles since it is still within the crust layer of the earth, the change in

gravitational acceleration can be assumed as minimal. Further, as the local gravity at surface

level for the Cooper Basin is slightly below the value of standard gravity at 9.79 ms-2

, the

small increase in gravitational acceleration due to depth would likely be more than

compensated for.


Figure 25 - Gravitational acceleration within the Earth's radius (Anderson and Dziewonski, 1981)

Using the aforementioned data and equation 18, pressure within the reservoir at sample depth

can be estimated.

𝑃 = 𝑝𝑔ℎ

𝑃 = (0.75 × 2300 + 0.25 × 2500) × 9.80665 × 3000

𝑃 = 69.137 𝑀𝑃𝑎

The estimated confining pressure of 69.137 MPa is substantially above the system’s 50 MPa

maximum. Therefore testing should be performed as close to the maximum confining pressure

of the system as possible in order represent reservoir conditions as accurately as possible

given the apparent discrepancy.

Bulk volume was measured using the calliper method outlined in section 2.1.1. This method

can only be used for a sample of somewhat uniform shape, since manually measuring physical

dimensions of a non-uniform shape is difficult and inaccurate.

5.1.1 Results and Analysis

First, the sample bulk volume was determined using calliper measurements. Measurements

were taken at multiple locations with 5 measurements for the sample length and 8

https://upload.wikimedia.org/wikipedia/commons/5/50/EarthGravityPREM.svg


measurements for the sample diameter. The average dimension measurements and the

difference between the highest and lowest measurements are shown in Table 3 below.

Table 3 - Sample dimensions

Parameter Length (mm) Diameter (mm)

Average magnitude 49.32 39.14

Maximum variance 0.50 0.50

Using these dimensions, the cylinder bulk volume was calculated to be 59.33mm3 using the

standard formula for the volume of a cylinder. Given the small variance in length and

diameter, approximating the volume as a standard cylinder is accurate. Also, an interesting

characteristic of the sample that was discovered during the measurement procedure is that the

sample is not perfectly square. There is a slight U-shaved curvature along the length of the

sample, as shown in the image below. Note that this did not appear to have any effect on the

diameter of the sample and given that the sleeve is Viton rubber, it is flexible enough to

conform to the shape of the sample without causing sealing issues. This curvature may result

in a slightly reduced bulk volume given that this is calculated from end to end length and

localised diameter measurements which would not capture a curvature such as this.

Figure 26 - Sample Curvature

Due to time constraints, porosity testing using the designed core analysis system to determine

pore volume was unable to be performed. It is recommended that this is revisited during

future works, allowing sample porosity to be determined experimentally.


5.2 Permeability Analysis

As with porosity testing, the sample should first be dried and cleaned prior to testing. In this

case, the sample was cleaning via nitrogen gas flow through the sample from the various

previous system tests such as leak tests and instrumentation checks.

Tests were commissioned at a confining pressure of approximately 70 bar. These tests used a

range of inlet pressures ranging from 3 bar, up to 9 bar, to simulate the decreasing of reservoir

pressure over the lifetime of a well. The tests were performed using the confining stress for

axial steady-state flow as outlined in section 2.2.1, which is detailed specifically for this

system in section 3.4.

Testing was performed without the use of the program written to obtain instrumentation

measurements and perform the permeability calculation. Ultimately, due to time constraints it

was not possible to connect and commission the electrical side of the system as well as

perform testing. Measurements were taken using an individual monitor for the pressure

transducers and a multimeter for the RTD’s current signal. These readings were recorded after

steady-state had been achieved, to be calculated manually. The result of this is that fewer data

points are available since they had to be recorded manually rather than logged automatically

in specified intervals. This means that the data is less accurate in general given that anomalies

in readings are less likely to be identified and removed or smoothed out. In an attempt to

mitigate this, each instrument was monitored for some time period and an average reading

was recorded.

An important design oversight became apparent immediately prior to testing; that the current

configuration of the system is unable to measure the temperature of the fluid at reference

pressure. This was temporarily addressed by removing the RTD on the inlet stream, and

relocating it to downstream from the flow meter. A more permanent solution should be

considered for use in future testing.

5.2.1 Results

The table below summarises the results obtained from permeability testing. The raw

instrumentation data, which is included in Appendix A, was input into equation 12 to

calculate the permeability of the sample for each test.


Table 4 - Permeability Test Results

Test no. Confining pressure (bar) Inlet pressure (bar) Permeability (mD)

1 70.073 9.1872 3.0297

2 70.073 7.0653 2.7303

3 70.073 4.9872 2.5879

4 70.073 3.0880 2.2153

A confining pressure of 70.073 bar was maintained for all tests in order to provide a

consistent testing environment for the varying inlet pressures. Note that this is significantly

lower than the capability of the system, outlined in section 3.1.1, and the recommended test

pressure to simulate reservoir conditions stated in section 5.1. This pressure was used since

the system displayed signs up leakage with pressures exceeding 100 bar and, since gas was

used as the confining fluid rather than the recommended hydraulic oil, a lower pressure such

as this reduces the likelihood of confining fluid infiltrating the sample chamber.

5.2.2 Analysis

Upon observing the results, the most immediate point of interest is that the sample

permeability for this series of tests is above the maximum permeability typically observed in

tight rock (1 mD as per the US Government definition). Considering that the data used in

Rezazadeh et al’s modelling is in the range of 0.1 mD, there is definitely a discrepancy here

from the expected norm of samples from this region. There are a number of possible reasons

for this, given that the permeability is still in the millidarcy range, the most likely being that

this sample was fractured at micro-pore level during one of the many activities it has been

subject to, including; excavation, extraction, transportation, storage and trimming. It is also

possible that the error is system related, as it is a new, unproven system that has not

undergone any calibration procedure and, at this stage, is still in the process of fully realising

some aspects of the design.

The results also clearly show that there is a proportional relationship between inlet pressure

and permeability, as a decrease in inlet pressure results in a decrease in sample permeability.

Given the magnitude of this change, the results tend to agree with the conjecture that micro-

fractures provide the majority of permeable pore networks in tight sandstone reservoirs, as

such a drastic change only result from previously available fluid pathways closing entirely

rather than simply reducing in size as standard pore types tend to favour. This further evident

in the conforming of results to the model, which was developed on the principle of such

inferences.


Despite the discrepancy in actual versus expected results, comparison with the model is still

possible as it is a trend based modelling system rather than magnitude based. In theory, a

sample from the region should still conform to the trends of that region, such as predominant

permeating flow through micro-fractures, which may result in a similar decrease in

permeability due to reservoir pressure decrease. In order to allow for a comparison of this data

with the data modelled for this gas field by Rezazadeh et al, the permeability ratio for each

permeability was determined, using the permeability of the highest inlet pressure test as a

basis. Given that the inlet pressure in these tests is significantly less than the reservoir

pressure used for the model, the permeability will be compared with the ratio of current inlet

pressure to maximum inlet pressure. This assumes that permeability trends at high inlet

pressure are similar to that at low inlet pressure. If the same confining pressure was used, this

assumption may not be valid, since the extreme pressure differential would force shut all

micro-fractures and other pressure vulnerable pore pathways. Figure 27 below shows the

comparison the permeability ratio and inlet pressure ratio, the ratio of current value versus

maximum value, for both the experimental results and Rezazadeh et al’s modelled results.

These ratios are a ratio of the current value over the maximum value for permeability and

pressure respectively.

Figure 27- Permeability ratio versus pressure ratio for modelled and experimental results

0

0.2

0.4

0.6

0.8

1

1.2

0 0.2 0.4 0.6 0.8 1 1.2

Pe

rme

abili

ty r

atio

Pressure ratio

Permeability Ratio Versus Pressure Ratio for Modelling and Experiemental Results

Modelled

Experimental


As can be seen, the experimental data and modelled data ratios are relatively close, despite the

large discrepancy in actual value. Note that the modelled data that the experimental data has

been compared with is the maximum horizontal permeability change modelled, referred to as

the Ky permeability ratio in Figure 7, chosen due to the prominence of the typically more

permeable micro-fracture pores in this sample. This suggests that either the experimental data

collected is correct and representative of the region, or that it is offset by a factor that is

eliminated when ratios are utilised. In addition to this, given that the model is based upon

some previous data from the same field, the system and model both producing consistent data

is an indicator that they are both likely to be accurate to some extent. This is a highly positive

result for the unproven, uncalibrated system, as it was not expected to perform in an accurate

manner at this stage in development. Despite these results, further testing of both the system

and the model should take place in order to verify their accuracy, as a single instance of

testing is not enough to prove validity.


6. Photomicrography Porosity Analysis

6.1 Optical microscope

The first of the photomicrography methods performed was an analysis using Charles Darwin

University’s optical microscope and image capture arrangement. The samples tested were the

smaller samples slice from the offcuts of the main core sample; 6 triangular samples were cut

from both the bottom and top offcuts, for a total of 12 samples. 4 of these samples (the two

outside pieces from each offcut) were too small, thin, or broken to analyse, leaving a total of 8

samples to test. These 8 samples were cut to an appropriate size such that they could also be

used in the size-restricted SEM analysis. The left side of each sample was marked with a dot

and numbered to ensure sample structure can be recorded and maintained. Note that it was

opted not to attempt to impregnate pore space with a coloured epoxy for visual aid, as seen in

both Spencer’s (1989), and Randolph and Soeder’s (1987) studies. There were two reasons for

this; the first is that the samples used for optical microscope analysis were to be reused for

other analyses such as SEM photomicrography. Therefore it is preferable to leave the samples

as pristine as possible to avoid any potential issues arising during further testing procedures.

This had another benefit in that it allows for a direct comparison between methods on a

sample by sample basis if required. The second reason is that there were no facilities available

that could facilitate such a procedure, meaning that a method of impregnation would have to

be developed and tested to ensure a thorough dispersion of the dyed epoxy throughout the

sample’s pores. Should the epoxy not reach all of the pores, the dye may misrepresent the

pore space within the sample, which would obfuscate analysis more than than no colouration

at all.

As surface finish left by the blade that cut the samples was deemed to be on par with or

superior to the surface obtainable via polishing, the first step of the analysis was to determine

which magnification best illustrated sample pore characteristics. This magnification was then

to be used for all subsequent analyses. The first magnification used was 50 times

magnification on sample 2 from the bottom offcut, as illustrated below in Figure 28 below.


Figure 28 - Photomicrography 50 times magnification sample image

Whilst larger pores are clearly visible, smaller pores are indiscernible from the rest of the

rock. Given that the sample is tight rock, it is expected that the vast majority of pores will be

smaller meaning that 50 times magnification is not acceptable for this analysis. The next

magnification used was 100 times magnification, the highest magnification this optical

microscope is capable of. Figure 29 below shows the same sample at 100 times magnification.

Figure 29 – Photomicrography 100 times magnification sample image


Smaller pores are definitely more discernible at 100 times magnification and, given that this is

the highest magnification that the microscope is capable of, 100 times magnification will be

used for the analyses of all samples.

Following this conclusion, images of 3 locations on each of the 8 samples were taken. Most of

the locations required multiple images at different focuses to accurately capture the entire

surface due to the heavy blurring and focusing issues that arise due to changes in topography.

As an example, the images for sample bottom 2, point 2 are shown in Appendix B.

Despite the improvement in visibility over 50 times magnification, the pores are still too small

to analyse. In the darker regions, such as the top right area in Figure 29 above, it is difficult to

differentiate the pores from the rock surface. The lack of consistent focus across an entire

image adds to the difficulty of analysis, since multiple images must be analysed for a single

location.

Images were analysed using Adobe Photoshop to determine porosity by colour shading. By

determining the colour and shade of the pores and the colour palette function, it is possible to

determine how much of the image is that shade or darker and thus the percentage of pores in

the image. However, due to the previously mentioned issues with colouration and focusing,

this analysis method proved unsuitable for these images. Given that pore size is so small at

this level of magnification, manual measurement with any semblance of accuracy is also

unfeasible.

Since the two methods of analysis available were unusable with the optical microscope

images, it can be concluded that optical micrscope photomicrography is not an accurate tool

for this approach to detailed pore analysis. It certainly has other applications that were out of

scope of this thesis, such as sample composition. This sample was clearly comprised of

multiple materials, the most obvious being the darker, dull material that most of the sample is

comprised of, and the brighter, reflective material visible in some parts of the sample. This

reflective material is likely some kind of crystal formation, such as quartz, given the prismatic

nature of the reflected and, in some instances, refracted light. Without the ability to see the

formations in further detail or another method of analysis, little can be concluded. It is also

useful to as a method to easily view the surface finish of the sample, as this can be vital to a

higher magnification analysis procedure such as the SEM that followed. It was also useful in

that the images taken clearly displayed that the core was consistent in structure and surface

finish at a higher level across all of the samples, which is an important material structure

characteristic and relevant to sample porosity.


6.2 SEM

Following the conclusion of the optical microscope image analysis, SEM photomicrography

was performed on the samples. 7 of the 8 samples were able to be tested; sample bottom 2

was too large to fit safely into the SEM test chamber and, due to time constraints, was unable

to be resized and tested.

The testing procedure first involved mounting the samples on a dish that is then affixed inside

of the SEM test chamber. The samples were attached to the mounting cylinders using

adhesive patches, which were then fixed to the sample dish via screws. The sample disk slid

and locked into an indentation inside of the sample chamber (see Figure 30 below), to ensure

that the samples do not move during the insertion and depressurisation process, as well as

during testing.

Figure 30 - Samples prior to SEM testing

Once the sample dish has been secured, the final step before analysis was to insert the samples

into the microscope and depressurise the chamber. Extra care had to be taken to ensure that

the chamber correctly sealed when depressurisation began, especially since a high vacuum (a

maximum pressure of 100 mPa) was required for the SEM analysis as per manufacturer’s

recommendations.

SEM analysis began with selecting magnifications and electron beam properties that are best

suited for illustrating the sample’s pore characteristics. The lowest magnification available

was 50 times reality for the top samples and 70 times reality for the slightly thicker bottom


samples, since magnification is limited by proximity of the sample to the electron beam

filament. The initial accelerating voltage was set to 13 kV, which provided a poor amount of

contrast between pores and sample surface. The accelerating voltage was increased in steps of

3 kV up to the maximum 30 kV that the SEM was able to produce, which also produced the

optimum images for analysis. This accelerating voltage was used for all subsequent SEM

images. An image of sample top 2 at 50 times magnification with an accelerating voltage of

30 kV can be seen below in Figure 31.

Figure 31 - Sample Top 2, point 1, 50 times magnification, 30kV accelerating voltage

Pores are easily visible in the lower magnification images due to the excellent depth of focus

produced by the SEM which relates elevation to brightness; with the raised sections of the

surface having a white to lighter grey shading, the mid-level surface having a darker grey

shade and pores (or what appear to be pores) being almost black.

Whilst this depth of focus allows for easy analysis, it is important to ensure that what are

believed to be pores actually are pores. In order to do this, higher magnifications were used to

investigate the images and pores further, such as the image of 1000 times magnification,

shown in Figure 32 below. In this imagine it is clearly visible which of the dark regions in the

initial image are actual pores and which are possibly due to the surface finish left by the blade

during the cutting process. To further investigate, images of multiple magnifications of the

same location were taken; 100, 250, 1000, 2500 and 4000 times magnification. Using this

process of evaluation for pores, it was possible to determine the correct parameters for pore


colouration. Note that this was specific to each location, since colour shading was different

for each location due to the adjustments to brightness and contrast that are required for a

serviceable image. This process was repeated for 3 locations on each sample, for each of the 7

samples able to be analysed using the SEM. An example of these images for the first point of

analysis on sample top 2 can be seen in Appendix C.

Figure 32 - Sample Top 2, point 1, 1000 times magnification

Now that correct pore colouration is known for each location, the colour shading procedure

used for the analysis of the optical microscope images was applied to the SEM images. Using

this method, the percentage of pixels that comprise the pores at each location on the sample

was determined, and hence the porosity of the sample at each location. The results for each

suite of samples are summarised in Tables 5 and 6 below.


Table 5 - SEM images porosity analysis results for top offcut samples

Sample Location Porosity (%) Average Porosity

(%)

Top 2 1 7.95 10.51

2 11.5

3 12.08

Top 3 1 8.05 9.53

2 11.38

3 9.17

Top 4 1 7.14 9.28

2 9.53

3 11.16

Top 5 1 12.32 10.75

2 10.28

3 9.66

Average 10.02

Table 6 - SEM images porosity analysis results for bottom offcut samples

Sample Location Porosity (%) Average Porosity

(%)

Bottom 3 1 6.21 10.76

2 14.94

3 11.12

Bottom 4 1 13.65 11.11

2 8.01

3 11.67

Bottom 5 1 13.53 9.61

2 8.47

3 6.84

Average 10.49

The average porosity across the 7 samples was 10.22% with a deviation of approximately

±1% for each sample, which is slightly above the 10% porosity that classifies a reservoir as

“tight” as per the US Government’s 1970 definition (Holditch, 2006). It is likely that the

porosity of the samples were in fact less than 10%, given that it was very difficult to select the

exact shade of dark grey that defined a pore and changing the shade slightly often resulted in a

variation in the order of several percentage of the image area. Even so, it is likely that this


sample is of the more porous variety of tight rock, which suggests the potential for higher

storage capacity than other, less porous tight gas wells.

One interesting detail to note is that the bottom samples consisted of a 0.47% higher average

porosity valve than the top samples. This may seem fairly insignificant, but considering the

magnitude of the porosity for these samples, this is an approximate 5% increase in porosity

over the top samples. This suggests that pore distribution within the reservoir may be

considerably non-uniform given that samples merely centimetres apart are showing a porosity

5% variance. Nevertheless, 5% difference is still somewhat insignificant, predominantly so

given that the method of analysis used is particularly predisposed to the aforementioned

errors. Further analysis is required for any conclusive statements to be made.

The SEM images were able to confirm the presence of crystals within the sample

composition, shown in Figure 33 below, though not quite as common as was suggested by the

optical microscope images. Ultimately sample composition analysis is outside of the scope of

this thesis, though it is still of worth to note that SEM imaging can be utilised for such a

purpose if required.

Figure 33 - Crystal presence at sample bottom 4, point 1

It should be noted that SEM images used were of basic quality since the high and ultra quality

image settings in the SEM software were subject to heavy blurring and distortion as well as

blinding bright spots, an example of which can be seen in the figure below. Basic quality


images with manual focus and contrast adjustments to improve handling proved to be more

than serviceable for use in the analysis.

Figure 34 - Sample image showing the bright spots and blurring issues experienced on higher quality image settings


6.2.1 Pore Size Distribution

A pore size distribution was also performed on the images captured by SEM. The procedure

involved utilising the scale provided by the SEM software on each image to determine the

approximate size of each pore. This was performed following the general porosity analysis of

the SEM images via analysing the 250 times magnification SEM photomicrographs that had

previously undergone pore area identification using Adobe Photoshop. This magnification

was used since it was sufficiently large to allow the measurement of individual pores whilst

still showing as much of the sample as possible in each image. Every area identified as a pore

was measured in pixels, which was then scaled to reality via comparison with the scaled

imbedded into each image by the SEM software and approximated into an area. These results

where categorised into 5 size ranges that cover the range of commonly found pore sizes.

These sizes and the average count and percentage of each size across the 3 points analysed on

each sample are shown in Tables 7 and 8 below for the top and bottom sample respectably.

Comprehensive data covering pore sizes for each SEM image is available in table form in

Appendix D.


Table 7 - Pore size distribution results for top offcut samples

Sample Area (µm2) Count Percentage

Top 2 <65 25.67 56.20

60-260 11.67 25.55

260-590 5.67 12.41

590-1050 1.33 2.92

>1050 1.33 2.92

Top 3 <65 49.67 70.62

60-260 13.67 19.43

260-590 5.67 8.06

590-1050 0.67 0.95

>1050 0.67 0.95

Top 4 <65 37.50 70.09

60-260 10.50 19.63

260-590 4.50 8.41

590-1050 1.00 1.87

>1050 0.00 0.00

Top 5 <65 37.67 71.97

60-260 9.00 17.20

260-590 4.33 8.28

590-1050 1.33 2.55

>1050 0.00 0.00

Top Average <65 49.67 70.62

60-260 13.67 19.43

260-590 5.67 8.06

590-1050 0.67 0.95

>1050 0.67 0.95


Table 8 - Pore size distribution results for bottom offcut samples

Sample Area (µm2 ) Count Percentage

Bottom 3 <65 40.33 63.35

60-260 15.67 24.61

260-590 5.33 8.38

590-1050 2.00 3.14

>1050 0.33 0.52

Bottom 4 <65 42.67 63.68

60-260 18.33 27.36

260-590 4.33 6.47

590-1050 1.67 2.49

>1050 0.00 0.00

Bottom 5 <65 49.00 59.27

60-260 23.00 27.82

260-590 8.67 10.48

590-1050 1.33 1.61

>1050 0.67 0.81

Bottom Average <65 44.00 61.88

60-260 19.00 26.72

260-590 6.11 8.59

590-1050 1.67 2.34

>1050 0.33 0.47

As can be seen in the results tabulated above, the vast majority of pores (above 50% of the

pores for every sample) had an area of less than 65 µm2. As that the sample is tight sandstone,

a pore size distribution favouring smaller pores is not unexpected. Also, this category

included micro-fractures given they have a long but thin structure compared to other small

pores, which means a small pore area. Given the tendency for tight sandstone to develop

micro-fractures as the primary avenue of permeable flow, as noted by Rezazadeh et al, the

high count of micro-fractures are particularly of note. From the permeability analysis, it is

known that this sample has permeability slightly above that of the standard ‘tight’ range. The

high micro-fracture count, suggested to be pore type most predisposed to providing permeable

pathways in tight sandstone, may explain why a higher permeability was observed than

typically seen in tight sandstone from the Big Lake field.


The next smallest size range, 60-260 µm2, was the second most frequently observed,

consisting of between 17-27% of the pore count. Although this is not reflected as significantly

in the averaged data, the 60-260 µm2 range varied quite considerably, falling as low as

11.86% of the pore count for Top 2 Point 3 and to a maximum of 32.35% for Bottom 3 Point

3. This suggests that pore size distribution is not necessarily uniform within each localised

area, but is somewhat uniform throughout the larger sample. The larger size ranges (590-1050

µm2 and >1050 µm

2) varied similarly from location to location. Though it can be concluded

that there is definitely a presence of these larger pore sizes, their occurrence is somewhat

inconsistent despite the uniform material composition. This suggests that perhaps these larger

pores are the result of the removal of the material dividing several close proximity smaller

pores, potentially due to the core plug extraction process or, more likely, as a result of the

slicing of these samples from the offcut. It would be wise to examine the samples again with

the intent of searching for these larger pore size distribution irregularities before drawing any

conclusions, since the relatively large size of these pores in comparison with the size of the

images create the potential for skewed results.

Another interesting characteristic can be seen when the data from the top and bottom samples

are compared; the top samples have on average of 8.74% more pores within the <65.5 µm2

range and the bottom samples have an average of 7.29% more pores within the 60-260 µm2

range, shown in Table 9 below. This suggests that the pore size distribution is non-uniform

across even such a small sample section from this reservoir, meaning that it is possible that

some areas of the reservoir will have higher fossil fuel storage potential than others due to

larger pores in that area, which could impact upon well design, placement and productivity.

This single digit percentage shift to a slightly larger pore size is reflected in the 0.47% higher

average porosity value of the bottom samples. These two findings combined suggest that the

sample pore distribution is likely non-homogeneous, which could be confirmed in the future

by the testing of more samples from this region.

Table 9 - Comparison of pore size distribution results between top and bottom samples

Area (µm2) Top (%) Bottom (%) Difference

<65.5 70.62 61.88 8.74

60-260 19.43 26.72 -7.29

260-590 8.06 8.59 -0.54

590-1050 0.95 2.34 -1.40

>1050 0.95 0.47 0.48


6.3 Comparison of Methods

Both methods have some merit for use in porosity analysis, depending upon the scope of the

analysis. Whilst an optical microscope’s coloured images have some applications, overall, for

pore characteristics analysis such as pore structure and size distribution, SEM

photomicrography is vastly superior it’s optical microscope counterpart. This is mainly due to

the ability to magnify the images far beyond the capabilities of optical microscope, which is

required when analysing pores of such a small size. The ability to manipulate image

brightness and contrast such that pores are clearly visible is also very useful in simplifying the

identification of pore area, though a similar result can be obtained for the optical microscope

by using a coloured epoxy. Given the simplicity of testing with an optical microscope, this

method still has a secure place as a preliminary examination, or if colouration is required such

as in a material composition analysis.

Ultimately, both of these photomicrography methods are based upon analysing the surface of

the sample, which is only a small piece of the larger puzzle. This limitation means that any

conjecture regarding what happens below the surface, such as comments regarding pore

interconnectivity for example, is purely educated speculation.


8. Conclusion

Porosity and permeability analyses were performed on a core sample from the Big Lake field,

Cooper Basin, South Australia. Permeability testing involved measuring permeability at data

obtained was compared with a theoretical model, engineered specifically for the Big Lake

field region. Whilst the results were greater in magnitude than expected for the region, the

trend in data did fit the trend expected by data modelled for this region. Further

experimentation to validate the results of both the experimental system and the theoretical

model is required.

Porosity analysis involved comparing two methods of photomicrography; optical microscope

and SEM imaging. These analyses focused on pore structure, including porosity and pore size

distribution. It was concluded that the sample is on the more porous side of the ‘tight’

reservoir rock, with a porosity of greater than the standard definition and a pore size

distribution comprised primarily of micro-fractures, which suggests and explanation regarding

the higher than expected sample permeability. Overall it was determined that SEM

photomicrography is more useful as a pore analysis tool, primarily due to the higher

magnification capabilities.


7. Recommendations

Future work should prioritise finalising the development and calibration of the system.

Primarily, correcting the oversight previously outlined regarding the lack of temperature

recording instrumentation at the reference pressure. Following this, instrumentation should be

configured to automatically record data and perform porosity and permeability calculations,

as per the original system design. The system should also be leak tightened and calibrated,

which will involve testing a sample with this system and another, calibrated system. Results

can be compared and a calibration factor developed to improve the accuracy of the system.

Once this has been achieved, the tests performed in this thesis should be repeated using the

same sample to confirm the validity of the results and the theoretical model. Tests should be

performed at a higher confining and inlet pressure where possible to ensure cohesiveness

between modelled and experimental data. Porosity testing using the developed procedure

should also occur, the results of which can be compared with the photomicrography results to

evaluate the validity of the conclusions made based upon the surface pore structure for the

entire sample.

If possible, more samples from the Big Lake field should be obtained and tested in a similar

manner. This would assist in evaluating the characteristics of the entire field rather than a

localised section, which could prove useful in evaluating the theoretical model as well as

providing data across the region.


9. References

Akhlaghinia M, Torabi F, Chan C 2014, ‘Experimental investigation of temperature

effect on three-phase relative permeability isoperms in heavy oil systems’, Fuel,

Volume 118, 15 February 2014, Pages 281-290, ISSN 0016-2361, via ScienceDirect.

Al-Abri A, Amin R 2010, ‘Phase Behaviour, Fluid Properties and Recovery Efficiency

of Immiscible and Miscible Condensate Displacements by SCCO2 Injection:

Experimental Investigation’, Journal of Transport in Porous Media, Volume 85, Issue

3, December 2010, Pages 743-756, ISSN 0169-3913, via Springer.

Al-Abri A, Sidiq H, Amin R 2002, ‘Mobility ratio, relative permeability and sweep

efficiency of supercritical CO2 and methane injection to enhance natural gas and

condensate recovery: Coreflooding experimentation’, Journal of Natural Gas Science

and Engineering, Volume 9, November 2012, Pages 166-171, ISSN 1875-5100, via

ScienceDirect.

American Petroleum Institute 1998, ‘Recommended Practice for Core Analysis’,

Journal of Recommended Practice, volume 40, 2nd

edn., via supervisor.

Anderson D, Dziewonski A 1981, Preliminary Reference Earth model, Physics of the

Earth and Planetary Interiors, Volume 25, Issue 4, 1981, Pages 297-356, ISSN 0031-

9201, via ScienceDirect.

API – see American Petroleum Institute.

Australian Petroleum Production & Exploration Association 2015, ‘Quality of

Reservoir’, Australia, http://www.shale-gas.com.au/about-natural-gas/natural-gas-

reservoirs/

Carter JF 2002, Definition of Terms, US Geological Survey, United States,

http://pubs.usgs.gov/ha/ha747/pdf/definition.pdf.

Core Laboratory Instruments 2012, ‘Core Holders for Routine & Special Core

Analysis’, Core Lab Instruments, OK, USA, via consultant.

DEHP – see Department of Environment and Heritage Protection

Department of Environment and Heritage Protection 2014, ‘Fraccing’, Queensland

Government, QLD, Australia, https://www.ehp.qld.gov.au/management/non-

mining/fraccing.html.

Department of Mines and Energy 2015, ‘Natural gas from shale and tight rocks’,

Government of Western Australia, WA, Australia,

http://www.dmp.wa.gov.au/15136.aspx.

http://www.shale-gas.com.au/about-natural-gas/natural-gas-reservoirs/

http://www.shale-gas.com.au/about-natural-gas/natural-gas-reservoirs/

http://pubs.usgs.gov/ha/ha747/pdf/definition.pdf

https://www.ehp.qld.gov.au/management/non-mining/fraccing.html

https://www.ehp.qld.gov.au/management/non-mining/fraccing.html

http://www.dmp.wa.gov.au/15136.aspx


Dong M, Li S, Li Z 2009, ‘Measurement and revised interpretation of gas flow

behavior in tight reservoir cores’, Journal of Petroleum Science and Engineering,

Volume 65, Issues 1–2, March 2009, Pages 81-88, ISSN 0920-4105, via

Sciencedirect.

Dong M, Li S, Li Z, Yao J 2012, ‘Permeabilities of tight reservoir cores determined

for gaseous and liquid CO2 and C2H6 using minimum backpressure method’, Journal

of Natural Gas Science and Engineering, Volume 5, March 2012, Pages 1-5, ISSN

1875-5100, via ScienceDirect.

Google Earth 6.2 2015, ‘28.232519149S, 140.279647827E’, Google,

http://www.google.com/earth/index.html

Holditch S 2006, ‘Tight Gas Sands’, Journal of Petroleum Technology, Volume 8,

Issue 6, June 2006, Society of Petroleum Engineers, DOI 10.2118/103356-JPT, via

OnePetro.

Meixner T 2009, ‘Cooper Basin region now in 3D’, Ausgeo News, Issue 96,

December 2009, Geoscience Australia, Australian Government,

http://www.ga.gov.au/ausgeonews/ausgeonews200912/cooper.jsp.

Palmer I, Mansoori J 1998, ‘How Permeability Depends on Stress and Pore Pressure

in Coalbeds: A New Model. Society of Petroleum Engineers’, SPE Reservoir

Evaluation and Engineering, Volume 1, Issue 6, December 1998, Pages 539-544, via

Randolph P, Soeder D 1987, ‘Porosity, Permeability and Pore Structure of the Tight

Mesaverde Sandstone, Piceance Basin, Colorado’, Society of Petroleum Engineers,

SPE Formation Evaluation June 1987, via OnePetro.

Rezazadeh M, Haghighi M, Pokalai K 2014, ‘Development of a Stress Dependant

Permeability Model in Tight Gas Reservoirs: A Case Study in Cooper Basin,

Australia’, SPE International, via co-supervisor.

Spencer C 1989, ‘Review of Characteristics of Low-Permeability Gas Reservoirs in

Western Unite States’, Volume 73, No. 5, Pages 613-629, via OnePetro.

Vinci Technologies 2014, ‘Core Holder – Technical and Commercial Proposal’, Vinci

Technologies, France, via consultant.

Walls J 1982, ‘Tight Gas Sands – Permeability, Pore Structure, and Clay’, Journal of

Petroleum Technology, November 1982, via OnePetro.

http://www.google.com/earth/index.html

http://www.ga.gov.au/ausgeonews/ausgeonews200912/cooper.jsp

10. Appendix

Appendix A – Permeability Testing Raw Data Table 10 - Raw permeability data

Test

no.

Confining

pressure (kPa)

Inlet pressure

(kPa)

Outlet pressure

(kPa)

Reference

pressure (kPa)

Sample

temperature (K)

Reference

temperature (K)

Flow rate

(m^3/s)

1 7007.3 918.72 918.66 10.43 293.2 281 2.18E-06

2 7007.3 706.53 706.45 10.21 289.7 281 2.08E-06

3 7007.3 498.72 498.61 10.27 287.6 281.1 1.95E-06

4 7007.3 308.8 308.62 9.99 285.3 281.4 1.72E-06

Appendix B – Photomicrography Images for Sample Bottom 2

Figure 35 - Photomicrography sample bottom 2, point 2

Figure 36 - Photomicrography sample bottom 2, point 2 focus 2


Figure 37 - Photomicrography sample bottom 2, point 2 focus 3


Appendix C – SEM Images for Sample Top 2 Point 1

Figure 38 - SEM Sample Top 2, point 1, 50 times magnification



Appendix D – Pore Size Distribution Data

Table 11 - Pore Size Distribution Data Table

Sample Point Area (um^2) Count Percentage

Top 2

1

<65.5 21 63.64

60-262 7 21.21

262-590 4 12.12

590-1050 1 3.03

>1050 0 0.00

2

<65.5 27 60.00

60-262 10 22.22

262-590 6 13.33

590-1050 1 2.22

>1050 1 2.22

3

<65.5 29 49.15

60-262 18 30.51

262-590 7 11.86

590-1050 2 3.39

>1050 3 5.08

Average

<65.5 25.67 56.20

60-262 11.67 25.55

262-590 5.67 12.41

590-1050 1.33 2.92

>1050 1.33 2.92

Top 3

1

<65.5 53 81.54

60-262 9 13.85

262-590 3 4.62

590-1050 0 0.00

>1050 0 0.00

2

<65.5 38 66.67

60-262 7 12.28

262-590 8 14.04

590-1050 2 3.51

>1050 2 3.51

3

<65.5 58 65.17

60-262 25 28.09

262-590 6 6.74

590-1050 0 0.00

>1050 0 0.00

Average

<65.5 49.67 70.62

60-262 13.67 19.43

262-590 5.67 8.06

590-1050 0.67 0.95

>1050 0.67 0.95


Top 4

1

<65.5

60-262

262-590

590-1050

>1050

2

<65.5 38 69.09

60-262 10 18.18

262-590 5 9.09

590-1050 2 3.64

>1050 0 0.00

3

<65.5 37 71.15

60-262 11 21.15

262-590 4 7.69

590-1050 0 0.00

>1050 0 0.00

Average

<65.5 37.5 70.09

60-262 10.5 19.63

262-590 4.5 8.41

590-1050 1 1.87

>1050 0 0.00

Top 5

1

<65.5 48 80.00

60-262 8 13.33

262-590 4 6.67

590-1050 0.00

>1050 0.00

2

<65.5 40 70.18

60-262 12 21.05

262-590 4 7.02

590-1050 1 1.75

>1050 0.00

3

<65.5 25 62.50

60-262 7 17.50

262-590 5 12.50

590-1050 3 7.50

>1050 0.00

Average

<65.5 37.67 71.97

60-262 9 17.20

262-590 4.33 8.28

590-1050 1.33 2.55

>1050 0 0.00

Top Average

<65.5 37.63 67.84

60-262 11.21 20.21

262-590 5.04 9.09

590-1050 1.08 1.95

>1050 0.50 0.90


Bottom 3

1

<65.5 35 68.63

60-262 10 19.61

262-590 4 7.84

590-1050 2 3.92

>1050 0 0.00

2

<65.5 45 62.50

60-262 15 20.83

262-590 8 11.11

590-1050 3 4.17

>1050 1 1.39

3

<65.5 41 60.29

60-262 22 32.35

262-590 4 5.88

590-1050 1 1.47

>1050 0 0.00

Average

<65.5 40.33 63.35

60-262 15.67 24.61

262-590 5.33 8.38

590-1050 2 3.14

>1050 0.33 0.52

Bottom 4

1

<65.5 52 59.09

60-262 26 29.55

262-590 7 7.95

590-1050 3 3.41

>1050 0 0.00

2

<65.5 40 70.18

60-262 13 22.81

262-590 4 7.02

590-1050 0 0.00

>1050 0 0.00

3

<65.5 36 64.29

60-262 16 28.57

262-590 2 3.57

590-1050 2 3.57

>1050 0.00

Average

<65.5 42.67 63.68

60-262 18.33 27.36

262-590 4.33 6.47

590-1050 1.67 2.49

>1050 0.00 0.00

Bottom 5

1

<65.5 51 57.30

60-262 28 31.46

262-590 6 6.74

590-1050 2 2.25

>1050 2 2.25


2

<65.5 55 58.51

60-262 26 27.66

262-590 13 13.83

590-1050 0 0.00

>1050 0 0.00

3

<65.5 41 63.08

60-262 15 23.08

262-590 7 10.77

590-1050 2 3.08

>1050 0 0.00

Average

<65.5 49.00 59.27

60-262 23.00 27.82

262-590 8.67 10.48

590-1050 1.33 1.61

>1050 0.67 0.81

Bottom Average

<65.5 44.00 61.88

60-262 19.00 26.72

262-590 6.11 8.59

590-1050 1.67 2.34

>1050 0.33 0.47

porosity and permeability in tight rock - core · porosity and permeability in tight rock 10 wade...

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