Investigations into Crude Oil Properties and Rock-Fluid
Interactions for Offshore Australian Basins
Thivanka Dedigama
BSc. Eng.
The University of Adelaide
Australian School of Petroleum
This thesis is submitted for the degree of
Master of Engineering Science
October 2007
Abstract
Wettability has been identified as a significant parameter in the prediction of relative
permeability. This work is an attempt to identify the best framework for quantification of
wettability based on crude oil and rock properties. This research has been carried out in
two parts to look at the effects of crude oil and rock properties separately. Firstly, crude oil
classification and characterisation systems have been developed based on True Boiling
Point (TBP) distillation. Thereafter, the link between rock properties and wettability has
been investigated. A new wettability indicator based on the end-points from relative
permeability experiments is also proposed.
TBP distillation is a widely used batch distillation process for the characterisation of crude
oils, traditionally mainly for marketing and refining purposes. The shape of these curves is
dependant on the volatility of components in a given crude oil. As such, these curves give a
“footprint” of the composition of crude oils. A new method of characterising crude oils
based on the shape of TBP distillation curves is proposed. A gamma distribution is used to
characterise the TBP distillation curve, and the parameters of the fitted distribution are
used as characterisation parameters. The proposed method has been found to describe
experimental data very well with two parameters, and as such offers a practical approach in
terms of classifying crude oils. Ranges of values for the characterisation parameters for
different types of crude oil have been identified for a large set of TBP data. The
characterisation parameters can be correlated with a number of crude oil properties. As an
alternative, it is shown how crude oil cut fractions may be classified with the aid of a
ternary diagram, and the link between this approach and the characterisation parameters
introduced above is demonstrated.
Wettability of oil reservoirs is governed by the interaction of rock surfaces with reservoir
oil and brine. The impact of mineralogical factors on wettability for a data set from the
Bonaparte basin, offshore Australia has been considered. The USBM index and AmottHarvey index are two widely used measures of wettability. The ratio of relative
permeability oil and water end point, and crossing point of relative permeability curves
have also been recognised as indicators of wettability. The link between relative
permeability end points and wettability has been investigated, and a scaled ratio that better
quantifies wettability has been proposed
Thivanka Dedigama
i
Table of Contents
Abstract
i
Table of Contents
ii
Declaration
iv
Acknowledgements
v
List of Figures
vi
List of Tables
ix
Introduction
1
Rock and Oil Characterisation
4
2.1
Classification of crude oils
4
2.1.1 Characterisation of crude oils in terms of geochemical parameters
4
2.1.2 Characterisation of crude oils in terms of whole crude properties
7
2.1.3 Characterisation of crude oils in terms of assay properties
10
2.2
Wettability
12
2.2.1 Effects of crude oil properties on wettability
14
2.2.2 Rock properties and wettability
18
Crude Oil Characterisation
20
3.1
True boiling point distillation
22
3.2
TBP curve characterisation
29
3.2.1 Gamma distributions
29
3.2.2 Methodology
31
3.2.3 Results
34
3.3
Cut Fraction characterisation
46
3.3.1 Methodology
46
3.3.2 Results
50
3.3.3 Link with characterisation parameters
54
Quantification of Wettability
57
4.1
Relative permeability endpoints
59
4.2
Rock properties affecting wettability
69
Thivanka Dedigama
ii
Relative Permeability Computer Program
75
Summary and Conclusions
76
References
78
Appendix A – Fitted TBP Curves for Table 3.1 Samples
82
Appendix B – Additional Fitted TBP Data
95
Thivanka Dedigama
iii
Declaration
This thesis contains no material which has been accepted for the award of any other degree
or diploma in any other universities or tertiary institutions and, to the best of my
knowledge and belief, contains no material previously published or written by any other
person, except where due reference has been made in the text.
I give consent to this thesis, when deposited in the University Library, being available for
photocopying and loan.
Thivanka Dedigama
Thivanka Dedigama
iv
Acknowledgements
Firstly I would like to thank my supervisor, Professor Peter Behrenbruch, of the Australian
School of Petroleum, University of Adelaide, for giving me the opportunity to undertake
this master of engineering programme. I would also like to thank him for his guidance,
encouragement and continuous support of my work. My thanks to Peter and Vanessa for
their friendship and support. I would also like to thank my co-supervisor, Professor
Hemanta Sarma for his encouragement and support throughout my studies.
I would like to acknowledge the Industry Sponsors of this research, BHP Billiton,
ChevronTexco, Santos and Woodside Energy, for their financial support, which enable me
to undertake this programme. I would also like to thank them for providing data for
analysis and permission to publish data and results. Their ongoing interest in this research,
feedback on direction and guidance was invaluable and is gratefully acknowledged.
Many thanks to the staff and students of the Australian School of Petroleum for their
support and encouragement throughout my time at the school, and for making me feel
welcome and part of the ASP family. In particular, I would like to thank the administration
staff of the school; Maureen, Yvonne, Janet, Ian, Aphrodite and, Eileen. Special thanks to
my fellow postgraduate student in the research group, Hussam and Shripad, for many
hours spent discussing our research as well as other interesting topics.
My sincere thanks to the Master and staff of Kathleen Lumley College for providing a
comfortable, convenient and friendly place to live during my studies. Thanks also to the
many residents, past and present, of the College for their company and friendship.
I would like to thank Greg Horton and members of the Programme Management Team,
Petroleum Engineering Department, Santos Ltd. for letting me work part-time in their
group, to support my studies. I was able to gain valuable insight into petroleum
engineering operations and project management during this time.
Finally I would like to thank my parents and brother for their continuous encouragement
and emotional support. I gratefully acknowledge their financial support towards my
studies. Their constant encouragement has been a critical factor in the completion of this
thesis.
Thivanka Dedigama
v
List of Figures
Figure 2.1: Ternary diagram showing a typical live crude oil composition.
9
Figure 2.2: Paraffins, naphthenes and aromatics (PNA) composition classification
ternary diagram (from Tissot and Welte, 1984 - p 440).
11
Figure 2.3: Schematic representation of relative permeability curves for water and oil
wet rock plugs.
16
Figure 3.1: TBP distillation curves for a selection of samples listed in Table 3.1.
Numbers correspond to Sample No in Table 3.1.
Figure 3.2: TBP distillation curves for Griffin crude oil.
24
27
Figure 3.3: Probability density functions and cumulative probability distributions for
the gamma distribution with varying values of α and β.
30
Figure 3.4: Gamma distribution fitted TBP distillation curves for Griffin crude oil.
33
Figure 3.5: Gamma distribution fitted TBP distillation curves for Cossack crude oil.
35
Figure 3.6: Gamma distribution fitted TBP distillation curves for Gippsland crude oil.
36
Figure 3.7: Gamma distribution fitted TBP distillation curves for Laminaria crude oil.
37
Figure 3.8: Gamma distribution fitted TBP distillation curves for North West Shelf
condensate.
38
Figure 3.9: Predicted vs. experimental values for API gravity using equation 6 for the
24 samples listed in Table 3.1. The UOP K factor for the outlying samples
has been annotated.
43
Figure 3.10: Predicted vs. experimental values for API gravity using equation 6 for
paraffin base crude oils (UOP K factor >12.2).
44
Figure 3.11: Predicted vs. experimental values for API gravity using Equation 6 for
non-paraffin base intermediate crude oils (UOP K factor between 11.5 and
12.2). The UOP K factor for the outlying samples has been annotated.
Thivanka Dedigama
45
vi
Figure 3.12: Cut fraction temperature definitions used by three companies based on
data published on their respective web pages.
47
Figure 3.13: Composition of temperature cut fractions (Modified after Hunt, 1996. p
45).
49
Figure 3.14: Proposed cut fraction ternary diagram showing the 24 samples listed in
Table 3.1.
51
Figure 3.15: Proposed cut fraction ternary diagram with areas corresponding to crude
oil types identified.
52
Figure 3.16: Relationship between gamma distribution characterisation parameters and
cut fraction ternary diagram.
53
Figure 3.17: Constant API gravity lines for paraffin base crude oils (UOP K factor
>12.2).
55
Figure 3.18: Constant API gravity lines for non-paraffin base crude oils (UOP K factor
<12.2).
56
Figure 4.1: Relative permeability index vs. USBM wettability plot for data in Table
4.1.
60
Figure 4.2: RQI vs. PG plot for Skua 4 SCAL data from Table 4.1.
63
Figure 4.3: Relative permeability index vs. USBM wettability plot for selected points
from data in Table 4.1.
64
Figure 4.4: Proposed relative permeability endpoint index vs. USBM wettability for
selected points as highlighted in Table 4.2
67
Figure 4.5: Comparison between SCAL plug porosity and thin section visual porosity
for integrated samples from Table 4.3.
Figure 4.6: Effect of pyrites content on wettability for samples from Table 4.3
71
72
Figure 4.7: Effect of authigenic clay content on wettability for samples from Table 4.3 73
Figure 4.8: Effect of total clay content on wettability for samples from Table 4.3
74
Figure A.1: Gamma distribution fitting for sample No. 1 from Table 3.4.
82
Thivanka Dedigama
vii
Figure A.2: Gamma distribution fitting for sample No. 2 from Table 3.4.
83
Figure A.3: Gamma distribution fitting for sample No. 3 from Table 3.4.
83
Figure A.4: Gamma distribution fitting for sample No. 4 from Table 3.4.
84
Figure A.5: Gamma distribution fitting for sample No. 5 from Table 3.4.
84
Figure A.6: Gamma distribution fitting for sample No. 6 from Table 3.4.
85
Figure A.7: Gamma distribution fitting for sample No. 7 from Table 3.4.
85
Figure A.8: Gamma distribution fitting for sample No. 8 from Table 3.4.
86
Figure A.9: Gamma distribution fitting for sample No. 9 from Table 3.4.
86
Figure A.10: Gamma distribution fitting for sample No. 10 from Table 3.4.
87
Figure A.11: Gamma distribution fitting for sample No. 11 from Table 3.4.
87
Figure A.12: Gamma distribution fitting for sample No. 12 from Table 3.4.
88
Figure A.13: Gamma distribution fitting for sample No. 13 from Table 3.4.
88
Figure A.14: Gamma distribution fitting for sample No. 14 from Table 3.4.
89
Figure A.15: Gamma distribution fitting for sample No. 15 from Table 3.4.
89
Figure A.16: Gamma distribution fitting for sample No. 16 from Table 3.4.
90
Figure A.17: Gamma distribution fitting for sample No. 17 from Table 3.4.
90
Figure A.18: Gamma distribution fitting for sample No. 18 from Table 3.4.
91
Figure A.19: Gamma distribution fitting for sample No. 19 from Table 3.4.
91
Figure A.20: Gamma distribution fitting for sample No. 20 from Table 3.4.
92
Figure A.21: Gamma distribution fitting for sample No. 21 from Table 3.4.
92
Figure A.22: Gamma distribution fitting for sample No. 22 from Table 3.4.
93
Figure A.23: Gamma distribution fitting for sample No. 23 from Table 3.4.
93
Figure A.24: Gamma distribution fitting for sample No. 24 from Table 3.4.
94
Thivanka Dedigama
viii
List of Tables
Table 2.1: Biological markers as source and paleoenvironmental indicators (from
Hunt, 1996. p107).
6
Table 2.2: Ranges of values of wettability measures for a given wettability regime
(taken from Anderson, 1986 b).
15
Table 2.3: ‘Rules of thumb’ established by Craig (1971) for evaluating wettability
based on relative permeability characteristics.
Table 3.1: List of TBP distillation data (source: Chevron, 2005).
17
23
Table 3.2: Detailed TBP distillation data for Griffin crude oil (source: BHP Billiton,
2005).
26
Table 3.3: Gamma distribution fitting for Griffin crude oil TBP distillation data.
32
Table 3.5: UOP K factor values for paraffin, naphthene and intermediate base crude
oils (from Nelson, 1958).
40
Table 3.4: Gamma distribution fitting results for the samples listed in Table 3.1.
41
Table 3.6: Boiling temperature cut fractions selected for plotting the proposed cut
fraction ternary diagram.
48
Table 4.1: Skua 4 SCAL data (source: Core Laboratories, 1991).
58
Table 4.2: Proposed endpoint ratio.
66
Table 4.3: Laminaria 2 SCAL data with integrated mineralogy from thin section point
count data.
70
Table B.1: Results from additional gamma distribution fitting.
96
Table B.1: Results from additional gamma distribution fitting (Cont’d).
97
Table B.1: Results from additional gamma distribution fitting (Cont’d),
98
Thivanka Dedigama
ix
Chapter 1 - Introduction
Chapter 1
Introduction
This research was undertaken in support of a larger research project aimed at developing
an improved definition of relative permeability for better recovery efficiency prediction.
Two major goals of this work are to review existing crude oil characterisation systems and
to identify the best system for characterising crude oils, and to review the existing
wettability quantification systems and identify suitable measures of wettability.
Petroleum has an organic origin based on the accumulation of plant and animal matter and
the action of heat and pressure over a long period of time on this biological material. This
has led to the varied compositions and properties of crude oils, which can be analysed in
terms of various characterisation systems: geochemical parameters, which attempt to relate
the crude oil to its organic source; whole crude (live oil) properties; assay properties such
as PNA fractions or cut fractions from TBP distillation. Each of these characterisation
systems is composed of a number of possible frameworks for the classification of crude
oils. These characterisation systems are also important for decision making at some stage
of exploration, production and refining of crude oil. Various frameworks for the
classification and presentation of data under each characterisation systems have been well
established based on the needs of the users of this information. However, there is a lack of
understanding as to how the characterisation systems are interrelated.
This research proposes a new characterisation system for crude oil based on TBP
distillation results. This new characterisation system is shown to be flexible and applicable
to a wide array of reservoir fluid types. It also offers a very practical approach in terms of
classifying crude oils. A new ternary plot, also based on distillation cut fractions is
introduced as an alternative to the conventional Paraffins, Naphthenes and Aromatics
(PNA) diagram.
Wettability has been defined as “the relative attraction of one fluid for a solid in the
presence of other immiscible fluids” in Honarpour et al. (1986). Craig (1971) defines
wettability as “the tendency of one fluid to spread on or adhere to a solid surface in the
Thivanka Dedigama
1
Chapter 1 - Introduction
presence of other immiscible fluids”. Hydrocarbon bearing reservoirs are systems where
the rock grain surfaces act as the solid and oil water and gas make up the immiscible fluids.
Wettability in a hydrocarbon bearing reservoir is a function of the interactions between the
solid rock surface, oil phase and reservoir brine phase. As such, the distribution of fluids in
porous reservoir rock, and consequently the flow of those fluids, is strongly influenced by
wettability. The wettability of reservoir rocks can be broadly classified into three types –
water-wet, oil-wet and intermediate. Fractional wettability is a situation where some pores
are oil-wet and others are water-wet, and is also referred to as spotted or Dalmatian
wettability. Mixed wettability is a special case of fractional wettability where the
preferentially oil-wet surfaces form a continuous path through the larger interconnected
pores, while the smaller pores remain water-wet. There are a number of experimental
procedures available for the measurement of wettability. Contact angle measurement, and
the Amott-Harvey and USBM indices are direct experimental measures of wettability.
Other measures based of special core analysis results such as relative permeability data
have also been defined.
This research has reviewed available wettability quantification systems and investigated
the link between relative permeability endpoints and wettability. A new relative
permeability endpoint ratio that better quantifies wettability is proposed. Additionally, an
attempt has been made to correlate mineralogical elements with wettability.
Chapter 2, the next chapter of this thesis, contains a review of crude oil characterisation
systems available and details common frameworks for classification under each system.
This is followed by a review of rock and oil properties identified in the literature as having
an impact on wettability. Chapter 3 discusses True Boiling Point (TBP) distillation as a
characterisation framework for dead crude oils and describes the proposed new
characterisation framework. A graphical characterisation framework using TBP distillation
results and ternary diagrams are also proposed in this section. Chapter 4 presents results
from investigations into quantification of wettability and describes a new scaled relative
permeability ratio for measurement of wettability. This chapter also presents some results
from attempts to correlate wettability with mineralogical factors. Chapter 5 contains a
brief description of the development of a computer program able to predict relative
permeability, integrating various results of research for the entire group (of four
Thivanka Dedigama
2
Chapter 1 - Introduction
researchers). Chapter 6 contain a summary of the work carried out and conclusions drawn
from this work. Finally, Chapter 7 sets out a list of references cited in this thesis.
Thivanka Dedigama
3
Chapter 2 – Rock and Oil Characterisation
Chapter 2
Rock and Oil Characterisation
Key literature on different frameworks available for classification of crude oils under the
three characterisation systems is reviewed below. Literature dealing with reservoir rock
wettability and its quantification and measurement, as well as the linkage between crude
oil and rock properties with wettability is also briefly summarised.
2.1
Classification of crude oils
Petroleum is a complex mixture of hydrocarbons that naturally occur in reservoir rocks.
The term encompasses liquid hydrocarbons referred to as crude oil, natural gas and solid
hydrocarbons such as tars. The chemical constituents of petroleum and the techniques
available to separate and analyse the various compounds are discussed, for example in
Waples (1985). Major fractions of petroleum crude oil are saturated hydrocarbons
including straight chained, branched and cyclic hydrocarbons, simple aromatic
hydrocarbons and small sulphur bearing compounds, resins and very large aromatic
asphaltene compounds.
As previously mentioned, there are three broad characterisation systems for crude oil, and
a number of frameworks are available under each system. Each framework is based on a
set of measurable parameters. There is a specific data requirement and a specified data
analysis procedure for these parameters. The characterisation systems in common use are
described below.
2.1.1
Characterisation of crude oils in terms of geochemical parameters
Geologists and geochemists use certain parameters as a framework to identify and
characterise crude oil, for crude oil-source rock correlation and to measure the degree of
evolution (Tissot and Welte, 1984). A range of geochemical parameters is available for the
characterisation of crude oil. Of these parameters, biological markers, stable carbon
isotopes and light hydrocarbons were considered in this research for the purpose of
Thivanka Dedigama
4
Chapter 2 – Rock and Oil Characterisation
identifying the most suitable framework for geochemical characterisation. Each of the
above frameworks is further discussed in the following sections.
A subset of the compounds present in petroleum has been identified as biomarkers, a term
abbreviated from biological markers. These are defined as “chemical compounds derived
form specific biological precursors” (Waples, 1985 p206). It is possible to trace the origin
of a given biomarker to its original biological precursor, thus allowing them to be used as
indicators of the organic source of the petroleum. Some of the biomarkers present in
petroleum and their source material or depositional environment are summarised in Table
2.1. Most of the biomarkers listed in Table 2.1 can be easily identified and quantified by
gas chromatography (GC). Ratios of various compounds can also be used as parameters in
classification. Examples include the ratios of Pristane and Phytane to n-C17 and n-C18
respectively and to each other (Hunt, 1996). Biomarkers offer a fairly accurate framework
for oil source correlation. However, this is usually based on the comparison of test results
for a range of different biomarker compounds, and tends to incorporate parameters from
other frameworks as well. Such analyses tend to be somewhat qualitative in nature.
The second framework for geochemical characterisation is based on stable carbon isotopes.
Atoms of an element having the same number of protons in their nuclei, but having
different numbers of neutrons are known as isotopes. Most isotopes are unstable and decay
into lighter elements. The decay rates of these unstable isotopes can be used for dating
certain ancient material. However, these unstable isotopes are unimportant in petroleum
geochemistry and the technique used relies on stable isotopes, and is based on the fact that
there are very small differences in the rates at which isotopes of a given element will react.
Major stable isotopes used in petroleum geochemistry are 12C and 13C. Light isotopes tend
to react faster than heavier ones. This is called the kinetic isotope effect. In the reactions
involved in the generation of hydrocarbons from kerogen, the generated hydrocarbons will
become richer in the light isotope (12C) and the remaining material will be richer in the
heavy isotope (13C).
Thivanka Dedigama
5
Chapter 2 – Rock and Oil Characterisation
Table 2.1: Biological markers as source and paleoenvironmental indicators (from
Hunt, 1996. p107).
NOTE: This table is included on page 6 of the print copy of the
thesis held in the University of Adelaide Library.
Thivanka Dedigama
6
Chapter 2 – Rock and Oil Characterisation
The difference in isotope content is usually reported as the ratio between the heavy and
light isotopes (Waples, 1985). Sofer (1988) defined a parameter called the “canonical
value” (CV) based on stable carbon isotopes that is also useful for geochemical
classification of oils. A detailed discussion of the use of stable carbon isotopes for oil-oil
and oil-source correlation is given in Waples (1985). Using stable carbon isotope analysis
on its own is of fairly limited use in oil source correlation, but the analysis is more useful
in the determination of source rock maturity. However, when combined with biomarker
analysis, isotope markers are very useful and have good resolution in the identification of
the organic source of crude oils. Overall, for the purpose of the current research, the
applicability of this framework on its own is somewhat doubtful.
The third framework for geochemical characterisation of crude oils is based on the use of
light hydrocarbons pioneered by Mango (1997). He proposed that the light hydrocarbons
(C1 to C9) in petroleum originate from a catalytic process which would be specific to a
given kerogen type. This work has led to the definition of a number of ratios based on the
quantities of C7 compounds (isoheptanes and dimethylpentanes) in crude oil, which can be
used to correlate oils to the kerogen type form-which they evolved. However, it is not
possible to differentiate between oils that evolved from different source rocks having the
same kerogen type. He also specified a number of other parameters based on C7
compounds that could be of use in geochemical classification. Ten Haven (1996) has
further elaborated on the applications, advantages and drawbacks of this technique.
Regardless of the above disadvantages, this framework has the advantage that it is a simple
technique for classification that can be used independently of any parameters of other
frameworks.
2.1.2
Characterisation of crude oils in terms of whole crude properties
The second characterisation system available for crude oil classification is based on whole
crude properties. A whole crude oil or live crude oil is the petroleum fluid containing
volatile compounds (gasses) such as CO2, N2 and CH4 dissolved in solution at reservoir
conditions. These gasses usually come out of solution when the reservoir pressure
decreases during production. Measurement of PVT properties and classification of live
Thivanka Dedigama
7
Chapter 2 – Rock and Oil Characterisation
crude oils based on the content of volatile components is, for example, discussed in Danesh
(1998). Live crude properties are of interest to reservoir engineers, who use these
properties to understand fluid flow in the reservoir and to predict changes in reservoir
conditions with time (e.g. reservoir pressure), during depletion (production). Such
properties are also of interest to production engineers, typically at reduced pressures.
The usual framework of classification is to consider a live crude oil based on three
fractions based on volatility. An example of this, based on the work by Cronquist (2001), is
to classify a live crude oil as follows.
•
the C1 fraction + N2
•
the C2 to C6 fraction + CO2 and
•
the C7+ fraction.
Ternary diagrams are used to display this data. A typical live crude oil analysis based on
the above fractions is shown in Figure 2.1. This plot has been modified from the original
by Cornquist (2001) to include some additional data points. In this analysis the C7+ fraction
has been used to define the type of reservoir fluid. Reservoir fluids with a C7+ fraction of
typically less than 11% are defined as volatile oils (including gas condensates). Light oils
are defined as having a C7+ fraction between 11% and 32% while heavy oils are those with
C7+ fractions between 32% and 74%. Other fraction ranges may also be useful in this
analysis and this has been investigated as part of this study.
Thivanka Dedigama
8
Chapter 2 – Rock and Oil Characterisation
NOTE: This figure is included on page 9 of the print copy of the
thesis held in the University of Adelaide Library.
Figure 2.1: Ternary diagram showing a typical live crude oil composition.
(Modified after Cronquist, 2001)
Thivanka Dedigama
9
Chapter 2 – Rock and Oil Characterisation
2.1.3
Characterisation of crude oils in terms of assay properties
The third characterisation system for crude oils is based on refinery assay properties. Crude
oil assay properties are of most interest to petroleum refiners, who need to know the
quantities of distillate fractions that a particular crude will produce during distillation.
They also need to know the physical and chemical properties of successive fractions
(Tissot and Welte, 1984). A conventional assay report will contain information regarding
the quantities and compositions of the important distillate fractions such as gasoline,
naphtha, kerosene, gas oil, lubricating oil and residue. Additional whole crude parameters
such as API gravity, pour point, UOP K factor, wax content and sulphur content are also
quoted. Crude oil marketers, involved in pricing of crude oils also have a need to obtain
detailed cut fractions and other assay properties.
One possible framework for classification of crude oils based on assay properties is given
in Tissot and Welte (1984). They propose a classification based on the relative content of
paraffins, naphthenes and aromatics (PNA) in a crude oil, displayed on a ternary diagram.
They have applied this technique to a large number of samples and have shown how this
ternary diagram can be used for oil-oil and oil-source correlations. An example of their
classification technique is shown in Figure 2.2.
Another possible framework for this analysis is based on boiling point temperature cut
fractions. True Boiling Point (TBP) distillation is one of the most common experimental
procedures employed in ascertaining assay properties of a crude oil. The method for
performing a TBP distillation experiment is described in ASTM D 2892 (American Society
of Testing and Materials, 1999a) and ASTM D 5236 (American Society of Testing and
Materials, 1999b) and the procedure can be used to analyse hydrocarbon mixtures,
including crude oils, condensates and petroleum fractions. However, the method cannot be
used to analyse liquefied petroleum gasses, very light naphtha fractions and fractions with
Initial Boiling Points (IBP) greater than 400°C. The TBP experiment is performed by
distilling a sample of crude oil or petroleum fraction in a standardised fractionating column
that is subject to specified operating conditions. Distillation is carried out at atmospheric
pressure from the IBP to about 210°C vapour temperature, and at reduced pressure beyond
this point. Distillation under a partial vacuum avoids cracking of the more complex
components at elevated temperatures.
Thivanka Dedigama
10
Chapter 2 – Rock and Oil Characterisation
NOTE: This figure is included on page 11 of the print copy of the
thesis held in the University of Adelaide Library.
Figure 2.2: Paraffins, naphthenes and aromatics (PNA) composition classification
ternary diagram (from Tissot and Welte, 1984 - p 440).
Thivanka Dedigama
11
Chapter 2 – Rock and Oil Characterisation
Samples of distillate are collected at specified temperature cut points. Mass and density of
each fraction is measured, and the distillation yield by mass is calculated. Volumetric yield
can be estimated with mass and density data. Vapour temperatures measured at reduced
pressure are translated to Atmospheric Equivalent Temperature (AET). Distillation can
usually be continued up to an AET of approximately 400°C. Final results of such
experiments are TBP curves in mass and/or volume yield versus boiling temperature
expressed in AET.
The shape of a TPB curve is dependant on the type and quantities of hydrocarbon
compounds that make up the mixture being analysed. As such, this curve uniquely
describes a given crude oil in terms of its chemical makeup. As an indicator of a crude oil’s
compositional makeup, a TBP distillation curve has the added advantage that it is
commonly generated for marketing purposes on all crude oils. This data is also easily
accessible, as crude oil marketing companies publish this data in the public domain.
2.2
Wettability
As with its definition, there are a number of experimental procedures available for the
measurement of wettability. Details of available methods for wettability measurement are
given in Cuiec (1987) and Honarpour et al. (1986). However, three quantitative methods
for the measurement of wettability are in use today (Anderson, 1986b). They are contact
angle measurement, the Amott-Harvey method and the USBM method.
In contact angle measurement, the oil-water contact angle is measured on a polished rock
surface, usually using simulated reservoir fluids (Craig, 1971). In this method, the
wettability of the rock is determined by the value of the contact angle of two fluid phases
in contact with a solid surface. There is some ambiguity as to the exact cut-off values of
contact angle for different wettability types. Surface roughness of a rock causes the
apparent contact angle to be different to the measured value for a polished homogenous
crystal surface. Additionally, the presence of adsorbed organic layers on reservoir rock
surfaces renders experimental result invalid. As such, this method of evaluating wettability
Thivanka Dedigama
12
Chapter 2 – Rock and Oil Characterisation
is of limited use. The Amott-Harvey and USBM wettability indices measure the average
wettability of a rock core sample. In the Amott-Harvey method (Boneau and Clampitt,
1977 and Trantham and Clampitt, 1977) the core is first centrifuged under brine and then
oil to initial water saturation. It is then subject to spontaneous imbibition followed by
forced imbibition by centrifuge of first water and then oil. The test follows the fact that the
preferentially wetting fluid will spontaneously imbibe into the core more than the nonwetting fluid. The major drawback of the Amott-Harvey method is its insensitivity for
samples having neutral wettability, as spontaneous imbibition of either phase is negligible
for such samples. The USBM method (Donaldson et al., 1969) uses the ratio of work
required to each fluid to displace the other. The preferentially wetting phase will require
less energy to displace the non-wetting phase. Unlike the Amott-Harvey method, the
USBM method is sensitive to samples having neutral wettability. One shortcoming of the
USBM method is that pressure measurement ranges are limited to 10 psig on drainage and
imbibition cycles, which tends to be insufficient for low permeability samples. These
methods give better results for the measurement of wettability of actual reservoir core
plugs with real reservoir fluids.
Classification of the type of wettability is based on the measured value. The range of
values for each method for a given wettability regime are summarised in Table 2.2 (taken
from Anderson, 1986 b). It has been pointed out that the Amott-Harvey index is insensitive
when used for cores with intermediate wettability (Anderson, 1986b). As such, the USBM
method is more suitable for quantitative measurement of wettability. Water-wet plugs will
result in USBM index values close to 1 while oil-wet plugs will have index values close to
–1. An USBM index close to zero indicates neutral wettability. There have been some
recent publications on new methods of evaluating wettability (for example Kewen and
Horne, 2003).
It is also possible to infer wettability based on the results of relative permeability
experiments. Relative permeability of a fluid flowing in a multi-phase porous medium
describes the ability of that fluid to flow, with the other fluids present. It is therefore clear
that wettability will have a strong impact on relative permeability results. Figure 2.3 shows
a schematic representation of relative permeability curve shapes for water-wet and oil-wet
plugs. Craig (1971) proposed a set of criteria for evaluating wettability based on relative
permeability characteristics. These ‘rules of thumb’ are summarised in Table 2.3.
Thivanka Dedigama
13
Chapter 2 – Rock and Oil Characterisation
Honarpour et al. (1986) reported that residual oil saturation and connate water saturation
can be used as indicators of wettability in some cases. Corey Exponents are a measure of
the curvature of relative permeability curves, and as such are a function of relative
permeability endpoints. Goda and Behrenbruch (2004) have shown that Corey Exponents
can be indicative of wettability and have developed a modified matrix for estimation of
wettability regimes based on Corey Exponents. The ratio of water permeability at residual
oil saturation to oil permeability at initial water saturation is another wettability indicator
in use (Honarpour et al., 1986). Behrenbruch (2000) has applied this concept as a
wettability indicator for the Buffalo Field, offshore northern Australia.
2.2.1
Effects of crude oil properties on wettability
It was initially thought that all reservoirs were water-wet. However, field observations and
laboratory data have established the existence of oil-wet reservoirs. It is now thought that
reservoir rocks are water-wet before the migration of oil, and after migration, the
characteristics of the reservoir change, depending on the type of crude oil and rock
mineralogy, due to rock surface and oil interactions. There are diverse views about the
properties of crude oils which may have an impact on wettability. Crude oil properties that
can possibly affect reservoir wettability have been identified as the following: asphaltene,
resins, sulphur and nitrogen content, and total acid and base numbers (Cuiec, 1984). After
testing a large group of cores with restored wettability Cuiec was unable to identify any
correlation between the acid or base content, aromatics, resins, nitrogen or sulphur, and
goes on to point out that, of these, asphaltene content probably has the greatest impact on
wettability. This leads to the conclusion that no single property of crude oil can account
fully for wettability, but rather that a combination of factors are at play.
Thivanka Dedigama
14
Chapter 2 – Rock and Oil Characterisation
Table 2.2: Ranges of values of wettability measures for a given wettability regime
(taken from Anderson, 1986 b).
NOTE: This table is included on page 15 of the print copy of the
thesis held in the University of Adelaide Library.
Thivanka Dedigama
15
Chapter 2 – Rock and Oil Characterisation
Figure 2.3: Schematic representation of relative permeability curves for water and oil wet
rock plugs.
Thivanka Dedigama
16
Chapter 2 – Rock and Oil Characterisation
Table 2.3: ‘Rules of thumb’ established by Craig (1971) for evaluating wettability
based on relative permeability characteristics.
NOTE: This table is included on page 17 of the print copy of the
thesis held in the University of Adelaide Library.
Thivanka Dedigama
17
Chapter 2 – Rock and Oil Characterisation
Rock-crude oil interactions that influence wettability are summarised in Cuiec (1987) as
follows.
•
No effect was noted from the crude oil fraction boiling at less than 360°C
•
No effect was noted from the light acidic or basic compounds in crude oil
•
The asphaltene fraction has an important effect.
•
Intermediate boiling fractions could have an important effect, depending on the
rock type.
Surface-active agents are a class of compound found in crude oils that are believed to have
an impact on wettability. It is thought that these compounds are polar or resinous
compounds containing nitrogen, oxygen and sulphur. Porphyrin compounds containing
metals (Ni, V, Fe, Cu, Zn, Ti, Ca, Mg) can also have surface-active properties. Such
compounds are to be found throughout the molecular weight distribution of a crude oil but
are mainly concentrated in the heavy end (Anderson, 1986a). Cuiec (1987) points out that
further research is needed to identify the exact compounds involved in rock wettability
alteration.
The mechanisms involved are also not clearly understood. Legens et al. (1998) have
investigated the effects of organic acids on the wettability of carbonate rocks. Their work
proposes a mechanism by which organic acids can become chemically adsorbed on to
carbonate rock surfaces, thus altering their normally water-wet characteristics. This
supports the observation that the vast majority of carbonate reservoir rocks are oil-wet.
2.2.2
Rock properties and wettability
As mentioned above, chemistry of the rock mineral surface also has an important role to
play in wettability. The ability of polar compounds in crude oil to bind to the rock surface
determines how the wetting properties of the rock will behave in contact with oil. When all
surface adsorbed impurities are removed, most common minerals (e.g. quartz, carbonates,
sulphates) are strongly water wetting. Adsorption of polar compounds on mineral surface
can change the surface to oil wetting.
Thivanka Dedigama
18
Chapter 2 – Rock and Oil Characterisation
In summary, Cuiec (1984) has observed such phenomena and the following may be
highlighted.
•
Wettability is controlled by surface chemistry of minerals (e.g. basic compounds
can be chemically adsorbed on to silicate surfaces by van der Waal’s forces).
•
Carbonates tend to adsorb polar compounds more actively compared to silicates,
and are thus in general more prone to being oil wet. It has been widely reported that
carbonate reservoir rocks are more oil-wet than sandstone rocks (Anderson, 1986a).
The work by Legens et al. (1998) further bears out this fact.
•
Specific surface area of rock can have an impact can have an impact on wettability.
•
Clays tend to absorb polar compounds, becoming preferentially oil wet.
•
Some minerals are naturally non-water wet
Thivanka Dedigama
19
Chapter 3 – Crude Oil Characterisation
Chapter 3
Crude Oil Characterisation
There are a number of characterisation systems available for crude oils. Each of these
characterisation systems is important for decision making at some stage of exploration,
production and refining/marketing of crude oil. These systems can be broadly grouped as
follows.
•
Geochemical parameters
•
Whole crude (live oil) properties
•
Assay properties
One of the aims of this research was to find a crude oil characterisation framework suitable
for identifying factors influencing wettability. The frameworks available in literature have
been reviewed in the previous chapter. These frameworks were evaluated to determine
their suitability for this exercise. For this purpose, the criteria for selection of the most
suitable framework for each have were defined as follows.
•
Ability to capture wettability causing factors
•
Availability of data for analysis
•
Ease of data manipulation for the given framework
•
Clarity of presentation of the final results ease of comparison of results with the
other crude oil characterisation systems
The data needed for the classification of crude oils based on geochemical parameter for
each of the above frameworks for offshore Australian crude oils is generally available in
papers published on this topic. For example a detailed geochemical characterisation of oils
in the Bonaparte Basin (offshore northern Australia) is given in Preston and Edwards
(2000). Another source of data would be from published and unpublished reports of
Geoscience Australia (GA). Data for live crude oil classification is available from PVT
analysis reports. This data is usually available with the field operators and would have to
be acquired through them. Crude oil assay data is available in assay reports. These are
Thivanka Dedigama
20
Chapter 3 – Crude Oil Characterisation
produced by oil marketing companies and are widely available in the public domain. They
are also periodically reported in industry journals such as the Oil and Gas Journal.
The previously mentioned criteria were applied to select the most suitable framework for
crude oil classification, from those discussed. Crude oil assay data was identified as the
most widely available source of characterisation data. In this grouping, TBP distillation
data was found to be more extensively available than PNA data. PNA analysis is only
performed on more detailed assay reports, while most basic assay reports contain at least
partial TBP data. An extensive database of TBP distillation results was collected from the
Internet as part of this research. Therefore, a characterisation system using TBP distillation
data was considered.
Thivanka Dedigama
21
Chapter 3 – Crude Oil Characterisation
3.1
True boiling point distillation
TBP distillation is one of the most common experimental procedures employed in
ascertaining assay properties of a crude oil. Many researchers have investigated the use of
TBP distillation data as a basis for characterisation of crude oils. Miquel et al. (1992)
recognised that TBP distillation data was the most commonly available information
regarding the volatile behaviour of hydrocarbon mixtures. They proposed a
characterisation system based on optimally selecting pseudo-fractions from the TBP
distillation curve. Haynes and Matthews (1991) proposed a continuous approach to use
TBP distillation data for heavy end characterisation of live crude oils in vapour-liquid
equilibrium calculations. Specific cut fractions (part of the TBP range), used in the
generation of petroleum products directly influence pricing of crude oils. The other major
use of this data is in deciding refinery processes needed to refine a given crude oil. TBP
distillation can also be used as a method to isolate a specified fraction from a crude oil for
testing. The currently available applications of TBP distillation data for crude oil
characterisation are fully discussed in Riazi (2005).
The procedure for conducting TBP distillation experiments is described in section 2.1.3.
Some TBP distillation data and other assay properties published on the Internet by
Chevron (Chevron, 2005) are listed in Table 3.1. The list covers a range of crude oil types
from gas condensates to heavier oils, containing samples from across the world, and
constitutes one of two major data sets used in the current study. Figure 3.1 shows the TBP
curve shapes for a selection of crude oils from this set of data. Three marker crude oils
(Brent, West Texas Intermediate (WTI) and Tapis) are also shown for comparison.
TBP distillation data from detailed assay reports was used to initially establish the validity
of the proposed methodology. These were from the following detailed assay reports for
Australian crude oils published on the BHP Billiton web site (BHP Billiton, 2005): Griffin
Crude Oil, Gippsland Crude Oil, Cossack Crude Oil, Laminaria Crude Oil and North West
Shelf Condensate
Thivanka Dedigama
22
Chapter 3 – Crude Oil Characterisation
Table 3.1: List of TBP distillation data (source: Chevron, 2005).
NOTE: This table is included on page 23 of the print copy of the
thesis held in the University of Adelaide Library.
Thivanka Dedigama
23
Chapter 3 – Crude Oil Characterisation
Figure 3.1: TBP distillation curves for a selection of samples listed in Table 3.1. Numbers
correspond to Sample No in Table 3.1.
Thivanka Dedigama
24
Chapter 3 – Crude Oil Characterisation
Typical detailed TBP distillation data for Griffin crude oil is shown in Table 3.2, as an
example. The corresponding TBP distillation (cumulative weight fraction distilled vs.
temperature) curve is shown in Figure 3.2 (a). This curve can be converted in to a weight
fraction density curve, as shown in Figure 3.2 (b). The weight fraction density can be
calculated as follows.
Weight fraction density
of the ith fraction
=
Δyact
ΔT
………………………(3.1)
where,
Δy act = y act ,i − y act ,i −1
ΔT = Ti − Ti −1
y act ,i cumulative weight fraction of the ith fraction
Ti
boiling temperature of ith fraction
Thivanka Dedigama
25
Chapter 3 – Crude Oil Characterisation
Table 3.2: Detailed TBP distillation data for Griffin crude oil (source: BHP Billiton,
2005).
NOTE: This table is included on page 26 of the print copy of the
thesis held in the University of Adelaide Library.
Thivanka Dedigama
26
Chapter 3 – Crude Oil Characterisation
1.0
(a)
Cumulative Mass Fraction
0.8
0.6
API Gra vity
55.1
Total Sulphur
0.02 wt%
K Factor
12.2
0.4
0.2
0.0
0
100
200
300
400
500
600
0.007
(b)
Mass Fraction Densi ty
0.006
0.005
0.004
API Gra vity
55.1
Total Sulphur
0.02 wt%
K Factor
12.2
0.003
0.002
0.001
0.000
0
100
200
300
400
500
600
Boiling Temperature (°C)
Figure 3.2: TBP distillation curves for Griffin crude oil.
Thivanka Dedigama
27
Chapter 3 – Crude Oil Characterisation
The scatter seen on the experimental differential curve may be attributed to two factors.
The first is due to the lumping of compounds with close boiling points. The other is due to
fluctuations in conditions during the experiment. The variation is highlighted in the weight
fraction density curve, as this is a differential form of the cumulative weight fraction
distribution, which is somewhat smoother by comparison.
A mathematical characterisation of TBP distillation curves would be useful for a number
of applications. These include physical property predictions, heavy end characterisation for
EOS modelling and crude oil valuation. In addition, the comparison of shapes of different
TBP curves would allow for the identification of groupings of oils based on composition.
Another application may be related to the pricing of crude oils.
The following sections detail a method of mathematically characterising a TBP distillation
curve using the two-parameter gamma distribution. The fitting methodology used is
described and results for a range of crude oil types have been presented, demonstrating the
flexibility of the proposed methodology. A correlation between the fitting parameters and
API gravity is also shown. A new classification system based on temperature cut fractions
from TBP distillation data, using a ternary diagram is introduced as an alternative to the
PNA (Paraffins, Naphthenes and Aromatics) ternary diagram (Tissot and Welte, 1984).
Thivanka Dedigama
28
Chapter 3 – Crude Oil Characterisation
3.2
TBP curve characterisation
3.2.1 Gamma distributions
The gamma distribution has been widely used as a mathematical function to characterise
hydrocarbon mixtures. The main focus of this work has been to describe the molecular
weight distribution with the objective of predicting phase behaviour of hydrocarbon
mixtures. Whitson (1983) proposed a continuous approach for modelling the molecular
weight distribution of a hydrocarbon mixture using the three-parameter gamma distribution
for Equation of State (EOS) modelling. Cotterman et al. (1985) also used the threeparameter gamma distribution to describe the molecular weight distribution of hydrocarbon
mixtures for process design applications.
The use of probability distribution functions (PDF) for modelling properties of continuous
mixtures, such as boiling point, molecular weight and specific gravity are described in
Riazi (2005). The PDF for a gamma distribution can be described using the following twoparameter form (Hastings and Peacock, 1975).
x
xα −1e β
p(x ) = α
β Γ(α )
where,
………………………(3.2)
x
-
variable (0 ≤ x < ∞ )
α
-
shape parameter (α > 0)
β
-
scale parameter (β > 0)
Γ(α)
-
the gamma function with parameter α, given by,
∞
Γ(α ) = ∫ e u du
α −1
−u
………………………(3.3)
0
The cumulative probability function is given by,
x
P( X ≤ x ) = ∫ p( x )dx
………………………(3.4)
0
Probability density functions and cumulative probability distributions for different values
of parameters α and β are shown in Figure 3.3, indicating the diversity of shapes that can
be accommodated.
Thivanka Dedigama
29
Chapter 3 – Crude Oil Characterisation
Figure 3.3: Probability density functions and cumulative probability distributions for the
gamma distribution with varying values of α and β.
Thivanka Dedigama
30
Chapter 3 – Crude Oil Characterisation
From the definition of the two-parameter gamma distribution, the mean, standard deviation
and mode of the distribution are as follows.
Mean
=
Standard Deviation
=
Mode
=
αβ
β α
β (α − 1)
3.2.2 Methodology
The two parameters (α and β) of the gamma distribution described previously are varied to
obtain the best fit with experimental data. The data in Table 3.2 for the detailed assay of
Griffin crude oil is used as an example. The predefined function GAMMADIST in MS
Excel is used to generate the cumulative weight fraction data and weight fraction density
data. The goodness of fit is measured using the coefficient of determination (R2) and the
Root Mean Square Error (RMSE). The Solver plug-in from MS Excel is used to tune α and
β to the optimum values while minimising RMSE.
The application of the methodology described above for the Griffin crude oil (Table 3.2
and Figures 3.2 (a) and 3.2 (b)) is shown in Table 3.3. The fitted values of α and β are 2.90
and 63.48 respectively, and the cumulative weight fraction and weight fraction density
curves are shown in Figures 3.4 (a) and 3.4 (b), respectively. The RMSE and R2 are
calculated using Equations (4) and (5), respectively.
∑ (y
n
RMSE =
i =1
act
2
∑ (y
i =1
n
act
∑ (y
i =1
where,
2
………………………(4)
n
n
R = 1−
)
− y pred
− y pred
)
2
………………………(5)
− yact )
2
act
y act
y pred
-
actual (experimental) value
-
predicted value from model
y act
-
mean of actual values
n
-
number of points
Thivanka Dedigama
31
Chapter 3 – Crude Oil Characterisation
Table 3.3: Gamma distribution fitting for Griffin crude oil TBP distillation data.
Experimental
Temperature
T (°C)
19
60
70
80
90
100
110
120
130
140
150
160
165
170
180
190
200
210
220
230
240
250
260
270
280
290
300
310
320
330
340
350
360
390
400
410
430
450
480
500
530
530 +
Cumulative Cumulative
weight
weight
percentage
fraction
3.1
9.0
11.8
13.9
17.1
23.0
26.9
32.4
37.1
40.3
45.3
48.8
50.6
52.8
56.2
59.7
63.5
67.3
71.1
72.4
73.7
76.6
78.9
80.6
82.4
84.4
86.6
87.7
88.8
89.9
91.3
92.3
93.4
94.5
95.6
96.3
97.3
98.1
98.8
99.1
99.5
100.0
yact
0.031
0.090
0.118
0.139
0.171
0.230
0.269
0.324
0.371
0.403
0.453
0.488
0.506
0.528
0.562
0.597
0.635
0.673
0.711
0.724
0.737
0.766
0.789
0.806
0.824
0.844
0.866
0.877
0.888
0.899
0.913
0.923
0.934
0.945
0.956
0.963
0.973
0.981
0.988
0.991
0.995
1.000
Predicted
Weight
fraction
density
Cumulative
weight
percentage
Weight
fraction
density
(yact - ypred)2
(yact - yact)2
0.0016
0.0014
0.0028
0.0021
0.0032
0.0059
0.0039
0.0055
0.0047
0.0032
0.0050
0.0035
0.0036
0.0044
0.0034
0.0035
0.0038
0.0038
0.0038
0.0013
0.0013
0.0029
0.0023
0.0017
0.0018
0.0020
0.0022
0.0011
0.0011
0.0011
0.0014
0.0010
0.0011
0.0004
0.0011
0.0007
0.0005
0.0004
0.0002
0.0002
0.0001
ypred
0.005
0.081
0.114
0.150
0.189
0.231
0.274
0.317
0.361
0.404
0.446
0.487
0.507
0.527
0.564
0.600
0.633
0.665
0.695
0.722
0.748
0.771
0.793
0.813
0.831
0.848
0.863
0.877
0.890
0.901
0.912
0.921
0.930
0.950
0.956
0.961
0.969
0.976
0.983
0.987
0.991
0.0007
0.0030
0.0035
0.0038
0.0041
0.0042
0.0043
0.0044
0.0043
0.0043
0.0042
0.0040
0.0039
0.0038
0.0037
0.0035
0.0033
0.0031
0.0029
0.0027
0.0025
0.0023
0.0021
0.0019
0.0018
0.0016
0.0015
0.0013
0.0012
0.0011
0.0010
0.0009
0.0008
0.0006
0.0005
0.0005
0.0004
0.0003
0.0002
0.0002
0.0001
0.00070
0.00008
0.00002
0.00012
0.00034
0.00000
0.00002
0.00004
0.00010
0.00000
0.00004
0.00000
0.00000
0.00000
0.00000
0.00001
0.00000
0.00006
0.00027
0.00000
0.00011
0.00003
0.00002
0.00005
0.00005
0.00002
0.00001
0.00000
0.00000
0.00001
0.00000
0.00000
0.00002
0.00003
0.00000
0.00001
0.00002
0.00003
0.00002
0.00002
0.00002
0.39017
0.31994
0.28905
0.26691
0.23487
0.18116
0.14949
0.10998
0.08102
0.06382
0.04106
0.02810
0.02239
0.01629
0.00877
0.00344
0.00043
0.00030
0.00307
0.00467
0.00662
0.01218
0.01779
0.02261
0.02835
0.03548
0.04425
0.04900
0.05399
0.05923
0.06624
0.07148
0.07749
0.08373
0.09022
0.09447
0.10072
0.10586
0.11047
0.11247
0.11517
0.00228
3.57276
Thivanka Dedigama
32
Chapter 3 – Crude Oil Characterisation
1.0
Cumulative Mass Fraction
(a)
0.8
Actual
Actual
0.6
API Gra vity
55.1
Total Sulphur
0.02 wt%
K Factor
12.2
0.4
0.2
α
2.90
β
63.5
R2
0.999
Fitted
Fitted
0.0
0
100
200
300
400
500
600
0.007
α
Mass Fraction Densi ty
0.006
0.005
0.004
(b)
2.90
β
63.5
R2
0.999
API Gra vity
55.1
Total Sulphur
0.02 wt%
K Factor
12.2
Actual
Actual
0.003
Fitted
Gamma
0.002
0.001
0.000
0
100
200
300
400
500
600
Boiling Temperature (°C)
Figure 3.4: Gamma distribution fitted TBP distillation curves for Griffin crude oil.
Thivanka Dedigama
33
Chapter 3 – Crude Oil Characterisation
For the above example,
RMSE =
R2 = 1 −
0.00228
= 0.0075
41
0.00228
= 0.9994
3.57276
These values, together with a visual comparison of the fitted curve to the actual data,
indicate that a good fit has been achieved. RMSE was selected as the parameter for
optimisation (i.e. minimisation of error) using the Solver as it showed a better convergence
when compared to the results obtained by using R2 (i.e. aiming for an R2 value of 1.0). This
fitting is independent of the initial values used for α and β. The fitted curve shows a good
visual agreement with experimental data points and R2 values of 0.99 or better in all cases.
Use of the three-parameter gamma distribution for this characterisation was also evaluated
as part of this work. However, it was found that the inclusion of an additional parameter
did not significantly improve the fit obtained. Moreover, the ability of the fitting technique
used to optimise three parameters and arrive at a unique solution was found to be
questionable.
3.2.3 Results
The same methodology described above has been applied to other detailed samples. Fitted
distributions obtained for four other detailed TBP distillation curves are shown in Figures
3.5, 3.6, 3.7 and 3.8. As can be seen from these graphs, the methodology described has the
ability to fit experimental TPB curves very well for a range of oil types.
Thivanka Dedigama
34
Chapter 3 – Crude Oil Characterisation
1.0
Cumulative Mass Fraction
(a)
0.8
Actual
Actual
0.6
API Gra vity
48.2
Total Sulphur
0.04 wt%
K Factor
12.1
0.4
0.2
α
2.03
β
115.7
R2
0.998
Fitted
Fitted
0.0
0
100
200
300
400
500
600
0.006
Mass Fraction Densi ty
0.005
0.004
α
2.03
β
115.7
R2
0.998
API Gra vity
48.2
Total Sulphur
0.04 w%
K Factor
12.1
0.003
(b)
Actual
Actual
Fitted
Fitted
0.002
0.001
0.000
0
100
200
300
400
500
600
Boiling Temperature (°C)
Figure 3.5: Gamma distribution fitted TBP distillation curves for Cossack crude oil.
Thivanka Dedigama
35
Chapter 3 – Crude Oil Characterisation
1.0
Cumulative Mass Fraction
(a)
0.8
Actual
Actual
0.6
API Gra vity
47.1
Total Sulphur
0.12 wt%
K Factor
12.0
0.4
0.2
α
1.97
β
119.9
R2
0.995
Fitted
Fitted
0.0
0
100
200
300
400
500
600
0.006
Mass Fraction Densi ty
0.005
0.004
α
1.97
β
119.9
R2
0.995
API Gra vity
47.1
Total Sulphur
0.12 wt%
K Factor
12.2
0.003
(b)
Actual
Actual
Fitted
Fitted
0.002
0.001
0.000
0
100
200
300
400
500
600
Boiling Temperature (°C)
Figure 3.6: Gamma distribution fitted TBP distillation curves for Gippsland crude oil.
Thivanka Dedigama
36
Chapter 3 – Crude Oil Characterisation
1.0
Cumulative Mass Fraction
(a)
0.8
Actual
Actual
0.6
API Gra vity
59.4
Total Sulphur
0.02 wt%
K Factor
12.2
0.4
0.2
α
1.86
β
81.6
R2
0.998
Fitted
Gamma
0.0
0
100
200
300
400
500
600
0.007
0.010
α
0.009
Mass Fraction Densi ty
0.006
(b)
1.86
0.008
β
81.6
0.005
R2
0.998
0.007
API Gra vity
59.4
0.004
Total Sulphur
0.02 wt%
0.005
K Factor
12.2
Actual
Actual
Actual
0.006
0.003
Fitted
Gamma
Gamma
0.004
0.003
0.002
0.002
0.001
0.001
0.000
0.000
00
100
100
200
200
300
300
400
400
500
500
600
600
Boiling Temperature (°C)
Figure 3.7: Gamma distribution fitted TBP distillation curves for Laminaria crude oil.
Thivanka Dedigama
37
Chapter 3 – Crude Oil Characterisation
1.0
Cumulative Mass Fraction
(a)
0.8
Actual
Actual
0.6
0.4
API Gra vity
60.3
Total Sulphur
<0.01 wt%
K Factor
11.9
0.2
α
2.47
β
54.2
R2
0.999
Fitted
Fitted
0.0
0
50
100
150
200
250
300
350
Mass Fraction Densi ty
0.009
α
2.47
0.008
β
54.2
0.007
R2
0.999
0.006
0.005
API Gra vity
60.3
Total Sulphur
<0.01 wt%
K Factor
11.9
(b)
Actual
Actual
0.004
Fitted
Fitted
0.003
0.002
0.001
0.000
0
50
100
150
200
250
300
350
Boiling Temperature (°C)
Figure 3.8: Gamma distribution fitted TBP distillation curves for North West Shelf
condensate.
Thivanka Dedigama
38
Chapter 3 – Crude Oil Characterisation
The partial TBP distillation curves listed in Table 3.1 consist of weight fraction data
collected at eight temperature cut points ranging from 80°C to 570°C. Other basic assay
properties such as API gravity, sulphur content and UOP K factor are also available. The
UOP K factor (or Watson characterisation factor) is defined as follows (Nelson, 1958)
K=
where,
3
TB
………………………(7)
S
TB
-
average molal boiling temperature in degrees Rankine
S
-
specific gravity at 60°F
The value of the K factor allows the characterisation of the base of a crude oil as paraffin
base, naphthene base or intermediate base. The ranges of K factor values corresponding to
each base are listed in Table 3.5. The same methodology described above for detailed
assay results was used to fit a gamma distribution to each of these curves. The results are
shown in Table 3.4. As with the detailed assay results, good fitting can be observed in all
cases. These results also demonstrate the flexibility of the new methodology. As mentioned
previously and shown in Figure 1, the 24 samples in Table 3.1 cover a wide range of crude
oil types. Represented are oils with API gravity ranging from 19.2 (for Kuito crude oil, a
heavy oil from Angola) to 61.2 (for North West Shelf Condensate, a gas condensate). In
addition, the list contains crude oils with a wide distribution of total sulphur content (0.0 to
1.55 wt.%), UOP K factor (11.6 to 12.7) and pour point (-54°C to 38°C). For this diverse
group of oils, the methodology is able to fit gamma distributions with the coefficient of
determination (R2) being better than 0.99 in each case. A visual comparison of the fitted
and actual curves confirms the excellent fit. The graphical results of this fitting for each of
the above examples are shown in Appendix A. Sample 24 in Table 3.4 (Hamaca crude oil
from Venezuela) is a synthetic crude oil. The proposed methodology is able to fit this oil
reasonably well even though the TBP curve displays a slight bimodal nature.
A further set of 77 partial TBP results have also been analysed as described above. The
tabular and graphical results for this analysis are given Appendix B.
Thivanka Dedigama
39
Chapter 3 – Crude Oil Characterisation
As noted in section 2.1.3, the TBP experiment is unable to analyse fractions boiling
above 400°C. Therefore, TBP data for heavier crude oils does not include the heavier
end, and the cumulative mass fraction does not reach 1.0. For example refer to crude
oil TBP curves for Kuito (Figure A.1), Cabinda (Figure A.2), Doba Blend (Figure
A.5) and Duri (Figure A.15). In these cases, the methodology is able to fit the
available portion of the curve well, but the data does not extend to a cumulative mass
fraction of 1.0. Sensitivity analysis was performed to see if this methodology is able
to extrapolate the heavier end, beyond experimental data.
Sensitivity analysis was carried out using the full distillation data for Griffin crude oil
(Table 3.2). Griffin crude oil was selected as the TBP data for this sample extends to a
cumulative mass fraction of 0.995. The analysis was performed by progressively
ignoring the upper section of the TBP curve and fitting a gamma distribution to the
remaining lower section. For this case, it was found that the lower 60% of the curve
was sufficient to extrapolate and match the full curve. This indicates that the proposed
gamma distribution fitting methodology has some potential to be used as an
extrapolation tool for TBP data that does not extend over the full compositional range.
Further work is needed to refine the use of this methodology for extrapolation of TBP
curves.
Table 3.5: UOP K factor values for paraffin, naphthene and intermediate base crude
oils (from Nelson, 1958).
NOTE: This table is included on page 40 of the print copy of the
thesis held in the University of Adelaide Library.
Thivanka Dedigama
40
Chapter 3 – Crude Oil Characterisation
Table 3.4: Gamma distribution fitting results for the samples listed in Table 3.1.
Gamma Distribution Fitting
Parameters
Whole Crude Properties
Crude Oil
Date
Of
Assay
Gravity Specific
Sulphur
K Factor
(°API) Gravity
(wt%)
Pour
Point
(°C)
Alpha
Beta
R2
1
Kuito
2000
19.2
0.9390
11.6
0.68
-32
4.21
109.9
1.000
2
Cabinda
1996
32.8
0.8610
12.3
0.13
16
2.42
182.2
0.996
3
Nemba
2001
39.3
0.8282
12.2
0.19
-4
2.22
147.2
0.997
4
Palanca
2001
38.4
0.8329
12.1
0.17
10
2.54
136.6
0.996
5
Doba Blend
2002
20.5
0.9308
12.0
0.16
-4
6.09
88.7
0.999
6
Kitina
2002
37.7
0.8365
12.2
0.09
7
2.18
170.1
0.997
7
N'kossa
2002
46.8
0.7936
12.4
0.03
-21
1.95
139.3
0.997
8
Bonny Light
2000
35.8
0.8459
11.9
0.14
-18
3.30
93.4
0.994
9
Escravos
2000
34.4
0.8528
11.7
0.15
7
3.20
100.5
0.996
10
Pennington
1995
35.0
0.8497
11.7
0.08
10
4.56
66.0
0.998
11
Barrow Island
1987
38.0
0.8346
11.7
0.05
-54
3.67
69.0
0.999
12
Cossack
1998
48.1
0.7877
12.0
0.04
-18
2.23
103.7
0.998
13
NWS Codensate
1997
61.2
0.7344
12.3
0.01
-51
2.42
54.7
1.000
14
Thevenard Island
2001
41.3
0.8189
NA
0.02
NA
4.87
50.6
0.998
15
Duri
1994
20.8
0.9293
12.0
0.20
10
4.07
142.0
0.998
16
Minas
1997
35.3
0.8482
12.6
0.09
38
3.32
131.6
0.998
17
Kutubu Light
1999
45.1
0.8014
12.2
0.04
-1
2.15
112.9
0.995
18
Nanhai Light
2000
40.1
0.8246
12.4
0.06
32
3.54
97.9
0.994
19
Benchamas
2002
43.0
0.8108
12.7
0.04
32
2.43
144.2
0.994
20
Tantawan
2000
43.3
0.8094
12.5
0.05
16
2.81
111.7
0.999
21
Hibernia
2000
35.9
0.8454
11.6
0.34
13
2.37
154.4
0.998
22
Rincon
1999
36.0
0.8447
11.8
0.37
-4
2.77
128.0
1.000
23
Medanito
1994
35.2
0.8486
12.2
0.41
-1
2.49
146.0
0.999
Hamaca
2003
26.0
0.8987
12.1
1.55
-40
3.04
139.2
0.996
24
(synthetic)
Thivanka Dedigama
41
Chapter 3 – Crude Oil Characterisation
An attempt was made to correlate the gamma distribution characterisation parameters (α
and β) with API gravity. Various functional forms were tested against the available data.
The form that gave the best approximation to the experimental data was as follows.
API Gravity
=
a(β (α −1) + b )
c
………………………(6)
where,
α and β
-
gamma distribution characterisation parameters
a, b and c
-
constants
The functional form of Equation (6) was selected as the term β(α−1) corresponds to the
modal temperature of the fitted distribution. As such the expectation was that there would
be a relationship between the modal temperature and API gravity. The constants a, b and c
were tuned to get the best possible fit to the experimental data by using the Solver plug-in
from MS Excel. The results for prediction of API gravity using Equation (6) are compared
to experimental values for all 24 samples in Figure 3.9. Values for constants a, b and c, and
the coefficient of determination (R2) and RMSE for this data are shown on the graph.
Although there is reasonable agreement between experimental and predicted values, the
degree of scatter on the graph is high, with indication of a secondary trend. This aspect was
further investigated in order to determine the reasons for this scatter, in terms of other
available crude oil properties. The scatter observed on Figure 3.9 appeared to be related to
the UOP K factor and outlying points have been annotated on that figure.
As evident from Fig. 9, samples with a UOP K factor greater than 12.2 cluster as a separate
trend, distinct from the main trend. This group corresponds to crude oils having a paraffin
base. Therefore, an attempt was made to predict the API gravity of crude oils with a
paraffin base (K factor greater than 12.2) using the functional form of Eqn. 6 and tuning
the constants a, b and c to match the experimental data. Results from this API gravity
prediction for paraffin base crude oils are compared to experimental values in Figure 3.10.
It can be seen that there is very good agreement between the predicted and experimental
values. An attempt was then made to predict API gravity for the non-paraffin base crude
oils (K factor less than 12.2) using the same functional form and tuning a, b ad c to get the
best match with experimental data. Results are shown in Figure 3.11, indicating that, with
the exception of three scattered point, there is also very good agreement between the
predicted and experimental API gravities.
Thivanka Dedigama
42
Chapter 3 – Crude Oil Characterisation
70
60
12.3
12.2
Predicted values (°API)
50
NA
11.7
12.5
40
12.7
12.1
30
12.4
11.6
12.6
20
a
10
26,042
b
166.8
c
-1.104
R2
0.8760
RMSE
3.26 °API
0
0
10
20
30
40
50
60
70
Experimental values (°API)
Figure 3.9: Predicted vs. experimental values for API gravity using equation 6 for the 24
samples listed in Table 3.1. The UOP K factor for the outlying samples has
been annotated.
Thivanka Dedigama
43
Chapter 3 – Crude Oil Characterisation
70
60
Predicted values (°API)
50
40
30
20
a
10
26,042
b
251.1
c
-1.044
R2
0.9990
RMSE
0.36 °API
0
0
10
20
30
40
50
60
70
Experimental values (°API)
Figure 3.10: Predicted vs. experimental values for API gravity using equation 6 for
paraffin base crude oils (UOP K factor >12.2).
Thivanka Dedigama
44
Chapter 3 – Crude Oil Characterisation
70
60
Predicted values (°API)
50
12.2
40
12.1
30
11.6
20
a
10
26,042
b
167.3
c
-1.107
R2
0.9841
RMSE
0.92 °API
0
0
10
20
30
40
50
60
70
Experimental values (°API)
Figure 3.11: Predicted vs. experimental values for API gravity using Equation 6 for nonparaffin base intermediate crude oils (UOP K factor between 11.5 and 12.2).
The UOP K factor for the outlying samples has been annotated.
Thivanka Dedigama
45
Chapter 3 – Crude Oil Characterisation
3.3
Cut Fraction characterisation
3.3.1 Methodology
One possible framework for classification of crude oils, based on assay properties is given
in Tissot and Welte (1984). They propose a classification based on relative quantities of
paraffins, naphthenes and aromatics (PNA) contained in a particular crude oil, displayed on
a ternary diagram. They have applied this technique to a large number of samples and
show how this ternary diagram can be used for oil-oil and oil-source correlations. An
example of their classification technique is shown in Figure 2.2. One draw back of this
PNA framework is that data for plotting the ternary diagram is not readily available. An
assay report would normally only contain the PNA break down for the lighter fraction
(usually boiling at less than 200°C) of the crude oil. As such, it is necessary to perform a
separate analysis to populate this type of ternary plot. The latter is perhaps one reason that
this terminology appears to be used relatively infrequent.
For the above-mentioned reasons, a new ternary plot based on assay properties is proposed,
utilising three boiling temperature cut fractions from TBP distillation. It should be noted
that boiling temperature ranges for petroleum fractions vary regionally and also among
companies. Definitions used by three companies are shown in Figure 3.12, based on data
published on the respective company web sites. This figure indicates that, although
companies use slightly different definitions for individual cut fractions, there is some
agreement among them. Three cut fractions were selected to correspond to distinct refinery
products. The cut fractions were also picked to have a good distribution of points on the
resulting ternary plot for various types of crude oils available, ranging from gas
condensates to heavy oils. Table 3.6 shows the three cut fractions that were selected for the
purpose of drawing the new ternary plot.
Alternate cut fractions were tested as part of this work. However, the cut fractions selected
represent the best choice, in that they broadly correspond to refinery products (as indicated
on Table 3.6) and produce a good distribution of points on the ternary plot as seen on
Figure 3.14.
Thivanka Dedigama
46
Chapter 3 – Crude Oil Characterisation
Figure 3.12: Cut fraction temperature definitions used by three companies based on data
published on their respective web pages.
Thivanka Dedigama
47
Chapter 3 – Crude Oil Characterisation
Table 3.6: Boiling temperature cut fractions selected for plotting the proposed cut fraction
ternary diagram.
Cut fraction no.
Temperature range
Main distillation products
1
IBP to 200°C
Naphtha
2
200°C to 350°C
Kerosene and Gas Oil
3
350°C +
Atmospheric Residue
Weight fractions are read by interpolating cumulative weight fractions distilled at 200°C
and 350°C from the distillation curve. The three weight fractions, adding up to 1.0, are
then calculated as follows.
IBP to 200°C fraction
=
W200
200°C to 350°C fraction
=
W350 – W200
350°C + fraction
=
1 – W350
where,
W200
-
cumulative weight fraction distilled at 200°C in
W350
-
cumulative weight fraction distilled at 350°C
Figure 3.13 shows the link between cut fractions selected above and chemical composition
for a naphthenic crude oil (Hunt, 1996). Vertical dashed lines indicate the selected
fractions. It can be seen that there is some degree of correspondence between the selected
fractions and composition elements. The IBP to 200°C fraction is richer in normal and isoparaffin, while the 350°C + fraction is richer in heavy aromatics and nitrogen, sulphur and
oxygen (NSO) compounds.
Thivanka Dedigama
48
Chapter 3 – Crude Oil Characterisation
NOTE: This figure is included on page 49 of the print copy of the
thesis held in the University of Adelaide Library.
Figure 3.13: Composition of temperature cut fractions (Modified after Hunt, 1996. p
45).
Thivanka Dedigama
49
Chapter 3 – Crude Oil Characterisation
3.3.2 Results
The proposed cut fraction ternary diagram, with the samples from Table 3.1 plotted, is
shown in Figure 3.14. It can be seen from the plot that there is a good distribution of points
on this ternary diagram, considering the varied fluid types plotted. This is further
demonstrated using the second TBP results dataset consisting of 77 samples. These results
are shown in Appendix B. Based on all data analysed, it has been possible to identify some
broad areas on this plot corresponding to crude oil type (i.e. condensates, volatile oils and
heavy oils). These areas are shown in Figure 3.15, superimposed on the plot of Figure 3.14.
The location of a crude oil on the proposed ternary diagram is governed by the relative
quantities of each fraction produced, being a function of the shape of the TBP distillation
curve. As such, there is a unique relationship between the two-parameter gamma
distribution characterisation system described previously and this ternary diagram. This
can be verified by identifying the relationship between points on the ternary plot and the
characterisation parameters (α and β). Figure 3.16 shows constant α and constant β lines
superimposed on the ternary plot. It can be seen that a given pair of α and β lines have
only one intersection. A given TPB curve shape (as defined by its α and β values) will be
uniquely located on the ternary plot.
Thivanka Dedigama
50
Chapter 3 – Crude Oil Characterisation
Figure 3.14: Proposed cut fraction ternary diagram showing the 24 samples listed in Table
3.1.
Thivanka Dedigama
51
Chapter 3 – Crude Oil Characterisation
Figure 3.15: Proposed cut fraction ternary diagram with areas corresponding to crude oil
types identified.
Thivanka Dedigama
52
Chapter 3 – Crude Oil Characterisation
Figure 3.16: Relationship between gamma distribution characterisation parameters and cut
fraction ternary diagram.
Thivanka Dedigama
53
Chapter 3 – Crude Oil Characterisation
3.3.3 Link with characterisation parameters
It has also been possible to identify the distribution of API gravity on the new cut fraction
ternary diagram. As described previously, a good correlation has been obtained between
the gamma distribution characterisation parameters (α and β) and API gravity for paraffin
and non-paraffin base crude oils. This relationship, based on the functional form of Eqn. 6,
was used to calculate constant API gravity lines on the ternary diagram.
Eqn. 6 can be rearranged as follows.
( )
⎧G
⎨ a
β =⎩
1
c
− b ⎫⎬
⎭
where,
(α − 1)
………………………(8)
α and β
-
gamma distribution characterisation parameters
G
-
API gravity
a, b and c
-
constants
This formulation allows the β value corresponding to a given α value to be calculated for a
specified API gravity. As shown previously, a given set of α and β values results is a
unique point on the ternary diagram. As such, a constant API gravity locus on the ternary
diagram can be established by varying α and calculating β, using Eqn. 8. This equation
represents a modified form of Eqn. 6, and as such the previously calculated values for the
three constants (a, b and c) are no longer valid. Therefore, the constants need to be
recalculated from the original α, β and API gravity data for the new form shown in Eqn. 8.
Figure 3.17 shows constant API gravity lines for the paraffin base crude oils from Table
3.1 (as defined by having a UOP K factor >12.2). It can be seen that the data points match
well with the predicted constant API gravity curves. Constant API gravity lines for nonparaffin base crude oils (as defined by having a UOP K factor <12.2) are shown in Figure
3.18, indicating good agreement between the data points and predicted curves. A
comparison of the two sets of constant API gravity curves is shown in Figures 3.17 and
3.18, indicating that the two sets are significantly different. It can be concluded that the
distribution of crude oils according to API gravity on the cut fraction ternary diagram is
dependant on their base.
Thivanka Dedigama
54
Chapter 3 – Crude Oil Characterisation
Figure 3.17: Constant API gravity lines for paraffin base crude oils (UOP K factor >12.2).
Thivanka Dedigama
55
Chapter 3 – Crude Oil Characterisation
Figure 3.18: Constant API gravity lines for non-paraffin base crude oils (UOP K factor
<12.2).
Thivanka Dedigama
56
Chapter 4 – Quantification of Wettability
Chapter 4
Quantification of Wettability
Measures of wettability have been investigated as part of this research. The focus of this
work was to better understand the relationship between wettability and relative
permeability curves. There has been a need to have a reliable system of wettability
quantification that is independent of direct experimental measurement, as part of the
overall research project. As discussed previously, the shapes of relative permeability
curves are strongly influenced by wettability. This is borne out by the work of Goda and
Behrenbruch (2004). The starting point for this work was the ratio of water permeability at
residual oil saturation to oil permeability at initial water saturation used by Behrenbruch
(2000). This ratio has been found to describe wettability with a high degree of success.
Further research has been carried out to evaluate the performance of other ratios of relative
permeability endpoints.
The evaluation of relative permeability endpoint ratios in this research is based on a set of
Special Core Analysis (SCAL) data from the Skua field, Bonaparte basin, Offshore
Australia. The location, structure and reservoir description of the Skua field are described
in Fairway Exploration Consultants (1991). The data set is made up of 10 plugs for which
relative permeability endpoints and USBM wettability have been established for the same
plug in the fresh state. Coring was carried out with a 100,000 ppm NaCl brine, and the
same brine has been used for storage and plug preparation (Core Laboratories, 1988). Four
of the plugs have been re-tested after cleaning with hot solvents, drying and restoration of
wettability by aging. Most of the samples show intermediate wettability (i.e. USBM index
slightly positive or slightly negative), with two samples (89 and 108) showing strong
water-wetting behaviour. Restoration of wettability from the fresh state has lead to the
plugs moving more towards neutral wettability. The data used is summarised in Table 4.1.
Relative permeability endpoints have been evaluated for all 14 points independently. As
such, each sample can be used for comparison of endpoints with USBM wettability,
regardless of whether it is fresh state or restored state.
Thivanka Dedigama
57
Chapter 4 – Quantification of Wettability
Table 4.1: Skua 4 SCAL data (source: Core Laboratories, 1991).
NOTE: This table is included on page 58 of the print copy of the
thesis held in the University of Adelaide Library.
Thivanka Dedigama
58
Chapter 4 – Quantification of Wettability
4.1
Relative permeability endpoints
Behrenbruch (2000) has used the following ratio of permeability to oil and water at
residual saturations (Equation 1) as a wettability indicator.
k o max
k w max
Where,
komax
-
kwmax -
…………………(1)
maximum permeability to oil at initial water saturation
maximum permeability to water at residual oil saturation
As demonstrated in Figure 2.3, this ratio is related to USBM wettability. Honarpour et al.
(1986) have described the use of the inverse of the above ratio as a wettability indicator.
Based on that result, it can be expected that the ratio in Equation 1 will have a value of
close to one for oil-wet plugs and a value greater than 3.0 for water-wet plugs. The data
shown in Table 4.1 has been used to verify this relationship. A plot of the traditional
(Equation 1) relative permeability ratio is shown in Figure 4.1. The ratios plotted on this
graph are given in Table 4.2. The arrows on Figure 4.1 indicate the movement of samples
that have been cleaned and had their wettability restored by aging. It can be seen that some
of the points lie on a positively sloping trend against USBM wettability (referred to as the
main trend and indicated by the dotted blue line on Figure 4.1). This result is according to
expectation, based on behaviour of relative permeability curves with changing wettability.
The remainder of points are scattered and do not appear to fall on any reasonable trend.
The apparent negative trend shown by the latter points is counterintuitive and the reasons
causing some points to fall outside the expected trend were investigated.
It has been mentioned that the coring took place with a water-based mud, but no
information on mud additives was available. It is possible that the water-based mud caused
some alteration in core wettability from the reservoir state. The exact nature of these
alterations is difficult to predict, especially considering that the exact nature of the drilling
mud is unknown. Cleaning and wettability restoration is carried out to remove any
potential effect of contamination. This procedure involves boiling plugs in solvents and
gradual drying, removing any adsorbed polar compounds on the rock crystal surfaces, thus
rendering the plug strongly water-wet. Subsequent aging in contact with reservoir fluids
restores the adsorbed polar compounds and attempts to re-establish the original wettability.
Thivanka Dedigama
59
Chapter 4 – Quantification of Wettability
Figure 4.1: Relative permeability index vs. USBM wettability plot for data in Table 4.1.
Thivanka Dedigama
60
Chapter 4 – Quantification of Wettability
The degree of wettability restoration depends on a number of factors, including types of
fluids used and aging time. These factors and their impact on wettability restoration are
fully discussed in Anderson (1986b) and Cuiec (1984). The impact of wettability
restoration on the points in Figure 4.1 is discussed below.
Samples 93 and 93R show that alteration of wettability during cleaning and aging has not
been affected, as both samples plot on the main trend on Figure 4.1. In this case the plug
appears to have become very slightly water wet after aging. Sample 108 falls outside the
main trend on Figure 4.1. However, after restoration the retested sample (sample 108R)
falls onto the main trend. This indicates that the wettability of sample 108 was altered in
some way during coring, but was subsequently corrected by cleaning and aging. Samples
10R and 12R have been cleaned and then had their wettability restored by aging under
reservoir fluids before wettability measurement. Both samples have diverged from the
main trend on Figure 4.1 after restoration. This indicates that the aging process appears to
have somehow ‘damaged’ the sample and failed to restore the original wettability. Sample
10R has moved from a moderate oil wet state (for sample 10) to a slightly water wet
condition. On the other hand, sample 12R has moved from moderate water wet (for sample
12) to slightly oil wet. This could be due to some plug alteration caused during aging that
resulted in an unnatural wettability change, and could explain why these two samples fall
outside the main trend on Figure 4.1 after wettability restoration. It should be noted that
many of the plugs contain clay and it is believed that this condition would be a contributing
factor in some of the observed inconsistencies.
The remaining outliers are samples 6, 34 and 85. Sample 85 has very low permeability (8.7
md), while having reasonable (20.2%) porosity. The plug description indicates that there is
poor sorting and traces of carbonates and pyrites. The very low absolute permeability
indicates that this sample is from a zone that is not representative of the lithotype of the
other samples, and as such has been excluded from further consideration. This
discrimination is based on hydraulic flow zones (HFZ) analysis proposed by Behrenbruch
and Biniwale (2005). They describe the use of a plot of Reservoir Quality Index (RQI) vs.
Porosity Group (PG) (defined based on conventional core properties) to identify groupings
of core plugs based on depositional environment. Each depositional environment (or HFZ)
is characterised by a Flow Zone Indicator (FZI). The RQI vs. PG plot for data used in this
Thivanka Dedigama
61
Chapter 4 – Quantification of Wettability
analysis (from Table 4.1) is shown in Figure 4.2. It should be noted that this plot only
shows the points for the 10 fresh state samples, as the restored state samples have identical
conventional core properties and as such, overlie the original points on this plots. This plot
shows that sample 85 belongs to a very different flow zone (with FZI of 0.8) and is not
representative of the remaining plugs. Samples 6 and 34 are of reasonable quality and
belong to the same flow zone as the majority of samples. Sample 34 has an oil
permeability endpoint that is almost three times higher than the water endpoint. This
indicates that the plug should be very strongly water wet, while the wettability
measurement indicates that the plug is in fact slightly oil wet. Similarly for sample 6, the
relative permeability endpoints indicate that the sample should be strongly water wet,
while measurement shows it to be only slightly water wet. The reason for such divergence
is not clear, but could be due to some contamination by drilling mud during coring.
Based on the above factors, Samples 6, 10R, 12R, 34, 85, and 108 have been excluded
from further analysis. The ratio values (from Equation 1) for the remaining eight points are
plotted against USBM wettability on Figure 4.3. There is a broadly linear, positively
sloping trend evident from this plot. The line of best fit is also indicated. The degree of
correlation between the two variables is not very high, as indicated by the R2 value of
0.428. This inturn, leads to the conclusion that while the ratio in Equation 1 is able to
describe wettability to a certain extent, other factors are also involved. This observation led
to an investigation of other SCAL parameters that could be correlated with wettability.
Thivanka Dedigama
62
Chapter 4 – Quantification of Wettability
Figure 4.2: RQI vs. PG plot for Skua 4 SCAL data from Table 4.1.
Thivanka Dedigama
63
Chapter 4 – Quantification of Wettability
Figure 4.3: Relative permeability index vs. USBM wettability plot for selected points from
data in Table 4.1.
Thivanka Dedigama
64
Chapter 4 – Quantification of Wettability
Anderson (1986b) has pointed out that residual oil saturation (Sor) is a strong function of
wettability. As such, it was decided that formulations incorporating Sor as a scaling factor
for Equation 1 should be investigated to determine their suitability for wettability
quantification. As the USBM wettability index is a dimensionless parameter, another
saturation term is required in any formulation used. The irreducible water saturation (Swir)
was selected for this purpose. It is widely accepted that Swir is weak function of wettability,
and its inclusion is this analysis is as a normalising parameter for Sor.
The proposed scaled endpoint ratio is shown in Equation 2.
k o max 1 − S or
×
k w max 1 − S wir
Where,
komax
-
…………………(2)
maximum permeability to oil at initial water saturation
kwmax -
maximum permeability to water at residual oil saturation
Sor
-
residual oil saturation
Swir
-
initial water saturation
The ratio in Equation 2 has been selected based on insights into wettability gained from
ongoing research to improve the definition of relative permeability. Calculated results for
the ratio in Equation 2 above are shown in Table 4.2.
Thivanka Dedigama
65
2314.1
2314.7
2315.2
2343.2
2345.6
2346.8
2346.8
2351.3
2351.3
2313.5
2314.7
2315.2
2321.9
2344.4
10
12
81
89
93 R
93
108 R
108
6
10 R
12 R
34
85
m
Depth
8
Plug ID
0.395
0.232
0.118
0.156
0.153
fraction
Swir
Thivanka Dedigama
0.264
0.168
0.406
Fresh State
Fresh State
0.425
0.219
Restored State 0.097
Restored State 0.170
Fresh State
Fresh State
Restored State 0.070
Fresh State
Restored State 0.107
Fresh State
Fresh State
Fresh State
Fresh State
Fresh State
Comments
2.8
186
140
85
193
767
1040
387
1700
29
434
95
38
79
md
ko max
0.120
0.088
0.243
0.190
0.078
0.179
0.242
0.104
0.112
0.145
0.070
0.102
0.095
0.112
fraction
Sor
1.07
63
51
32
109
286
635
533
1800
27
300
101
72
75
md
-0.049
-0.146
-0.096
0.171
0.147
0.67
0.433
0.034
-0.07
0.82
0.38
0.273
-0.338
0.169
USBM
kw max Wettability
2.62
2.95
2.75
2.66
1.77
2.68
1.64
0.73
0.94
1.07
1.45
0.94
0.53
1.05
Outlier
Outlier
Outlier
Outlier
Outlier
Outlier
3.71
1.96
2.59
2.30
3.45
4.00
1.33
0.88
0.94
1.52
1.75
0.96
0.57
1.10
Plot Status
Chapter 4 – Quantification of Wettability
Table 4.2: Proposed endpoint ratio.
66
Chapter 4 – Quantification of Wettability
Figure 4.4: Proposed relative permeability endpoint index vs. USBM wettability for
selected points as highlighted in Table 4.2
Thivanka Dedigama
67
Chapter 4 – Quantification of Wettability
It is clear from a visual comparison between Figures 4.3 and 4.4 that there is a significant
improvement in correlation when using the proposed (Equation 2) ratio. All points with the
exception of Sample 81 move closer to the linear trend when the new ratio is applied.
Sample 108R shows the most improvement. It moves to a point on the linear trend on
Figure 4.4, while it is the furthest outlier on Figure 4.3. The line of best fit is indicated on
Figure 4.4. This has an R2 value of 0.693 for the fit, which indicates a much improved
correlation between variables. The line of best fit indicated on Figure 4.4 correlates USBM
wettability with the new ratio. This relationship can be expressed with the following
functional form (Equation 3).
⎛ k o max 1 − S or ⎞
⎜
⎟ = a ⋅W + b
×
⎜k
⎟
1
−
S
⎝ w max
wir ⎠
…………………(3)
where,
W
-
wettability in USBM index
komax
-
maximum permeability to oil at initial water saturation
kwmax -
maximum permeability to water at residual oil saturation
Sor
-
residual oil saturation
Swir
-
initial water saturation
a, b
-
constants
Based on the available data set, the constants a and b have values of 0.905 and 0.939
respectively. The functional form of Equation 3 can be used as predictive tool for
wettability when relative permeability endpoints are known. The rearranged form for
wettability prediction is shown in Equation 4 below.
⎛ k o max 1 − S or ⎞
⎜
⎟−b
×
⎜k
⎟
1
S
−
⎝
w max
wir ⎠
W=
a
…………………(4)
Constants a and b need to be re-evaluated using the same data set
Thivanka Dedigama
68
Chapter 4 – Quantification of Wettability
4.2
Rock properties affecting wettability
An attempt to quantify the impact of mineralogical factors on wettability based on the
available data has also been made. SCAL data from the Laminaria field has been used for
this analysis. The Laminaria field is located in the Bonaparte basin, Offshore Australia.
The data set (presented in Table 4.3) consists for 12 plugs from Laminaria 2 core for which
wettability has been measured as part of SCAL testing (Core Laboratories, 1996). Core
plug mineralogical descriptions from this report are also included in Table 4.3. In addition
a series of mineralogical point counts from thin section analyses are available for this core.
The first step in this analysis was to integrate between the SCAL plugs and point count thin
sections. This was done by matching up the SCAL plug depth with thin section depth.
SCAL plug porosity was compared to visual porosity for each integrated thin section as an
additional check. Results of this comparison are shown in Figure 4.5. This plot indicates
that there is broad agreement between porosity for the SCAL plugs and thin sections.
However, a perfect integration between the two sets of data is not possible.
Once integrated, the data presented in Table 4.3 was used to evaluate the effect of
mineralogy on wettability. Plots of three selected mineralogical factors (pyrites, authigenic
clays and total clay respectively) against USBM wettability are shown in Figures 4.6, 4.7
and 4.8. The dashed line on each plot indicates the overall trend of data. As described in
Chapter 2, pyrites and clays have been identified as mineralogical factors that could
influence oil wetting behaviour in normally water-wet quartz sandstone rock. As seen in
each of these plots, the overall trend in each case is as expected. For example Figure 4.6
shows that plugs with higher pyrite content are oil wet and vice versa.
The high degree of scatter in these plots could be attributed to the error caused by ascribing
mineralogical properties of a single thin section to a SCAL core plug. The data available
for this study is insufficient to establish if mathematical correlations are possible between
the above mineralogical parameters and wettability. However it is clear that the
relationships identified in literature broadly hold true for this data set. A larger set of
wettability and mineralogical data is needed to establish whether correlations are possible.
Thivanka Dedigama
69
3,217.9
3,249.6
3,269.4
3,290.3
3,256.5
3,208.5
3,215.4
3,205.6
3,231.1
3,275.1
3,280.6
3,252.2
15
23
27
33
c1-15
c1-37
c1-5
c1-81
c2-138
c2-156
c2-70
mSS
7
Plug ID
Plug Depth
Thivanka Dedigama
395
3
2
1
18
2080
76
360
1037
196
2117
884
md
Absolute
Permeability
18.3
14.8
13.9
9.7
16.8
16.6
19.2
10.4
13.2
19.9
17.4
15.9
%
φ
-0.138
-1.062
-0.798
-0.818
-0.494
-0.199
-0.044
-0.131
0.091
-0.168
-0.039
-0.065
USBM
Wettability
Fine
Fine
Very Fine
Fine
Course
Course
Fine
Course
Medium
Fine
Course
Medium
Grain Size
Detrital clay +
Kaolin
Kaolin
Detrital clay +
Kaolin
Detrital clay +
Kaolin
Quartz
overgrowth
Quartz
overgrowth
Quartz
overgrowth
Quartz
overgrowth
Moderately well
sorted
Well Sorted
Moderately well
sorted
Moderately well
sorted
Kaolin
Kaolin
Kaolin
Quartz
overgrowth
Quartz
overgrowth
Quartz
overgrowth
Well Sorted
Moderately well
sorted
Well Sorted
18.3
10.3
13.1
11.6
Kaolin
Quartz
overgrowth
Well Sorted
10.2
Quartz
overgrowth
13.4
Moderately well
sorted
Kaolin
Quartz
overgrowth
16.6
8.7
15.7
13.5
14.6
14.6
Visual
Porosity
Well Sorted
Detrital clay +
Kaolin
Kaolin
Quartz
overgrowth
Well Sorted
Moderately well
sorted
Clays
Cementation
Sorting
Plug Description from SCAAL Report
0.9
4.0
1.1
1.1
1.7
1.4
1.7
4.9
0.6
0.6
0.9
2.6
Pyrites
2.3
1.1
0.3
Detrital
Clays
0.6
2.3
2.9
0.6
1.4
1.4
1.7
0.3
0.3
3.1
0.3
0.6
Authigenic
Clays
Point Count Data (%)
1.1
Mica
0.6
4.6
4.0
0.6
1.4
1.4
2.0
0.3
0.3
3.1
0.3
0.6
Total Clays
Chapter 4 – Quantification of Wettability
Table 4.3: Laminaria 2 SCAL data with integrated mineralogy from thin section point
count data.
70
Chapter 4 – Quantification of Wettability
Figure 4.5: Comparison between SCAL plug porosity and thin section visual porosity for
integrated samples from Table 4.3.
Thivanka Dedigama
71
USBM Wettability
0.0
-1.2
1.0
2.0
3.0
4.0
5.0
6.0
-1.0
-0.8
-0.6
-0.4
-0.2
0.0
0.2
Chapter 4 – Quantification of Wettability
Pyrites %
Figure 4.6: Effect of pyrites content on wettability for samples from Table 4.3
Thivanka Dedigama
72
USBM Wettability
0.0
-1.2
1.0
2.0
3.0
4.0
-1.0
-0.8
-0.6
-0.4
-0.2
0.0
0.2
Chapter 4 – Quantification of Wettability
Authigenic Clays %
Figure 4.7: Effect of authigenic clay content on wettability for samples from Table 4.3
Thivanka Dedigama
73
USBM Wettability
0.0
-1.2
1.0
2.0
3.0
4.0
5.0
-1.0
-0.8
-0.6
-0.4
-0.2
0.0
0.2
Chapter 4 – Quantification of Wettability
Total Clays %
Figure 4.8: Effect of total clay content on wettability for samples from Table 4.3
Thivanka Dedigama
74
Chapter 5 – Relative Permeability Program
Chapter 5
Relative Permeability Computer Program
As part of the aim of the overall research project, a computer program has been developed
to perform calculations using the new relative permeability methodology. This package
uses Microsoft Excel® as an interface and is based on a series of VBA macros. This
software has gone through a number of development and testing stages, with functionality
being added on in stages. Some of the features of this package are summarised below.
•
Generate relative permeability curves from endpoints only
•
Fit experimental relative permeability data, and select points to be used for fitting,
for both steady-state and unsteady-state experiments
•
Anchor the fitted curve at Swir and/or kromax
•
Perform corrections to standard fits, giving better results
•
Export calculated results
•
Import previously exported results for recalculation
•
Perform Corey fitting on experimental or predicted data
•
Perform Sor correction for Corey fitting
This package is being continuously upgraded and further enhancements are being
undertaken.
Thivanka Dedigama
75
Chapter 6 – Summary and Conclusions
Chapter 6
Summary and Conclusions
This research has focused on two broad themes, with the aim of better understanding the
various factors influencing the rock-oil interactions governing wettability of oil reservoirs.
This work has been carried out in support of a research group looking at an improved
relative permeability characterisation system. A summary of the outcomes of this research
is given in the following sections, followed by some conclusions and recommendations for
future directions that may be of interest.
A system for the characterisation of crude oils, based on TBP distillation curve data using a
two-parameter form of the gamma distribution from statistics, has been described. The new
methodology has been tested against a broad selection of crude oil types and is able to fit
the cumulative TBP distillation curve with a high degree of correlation in all cases. The
proposed methodology has been shown to be useful as a tool to interpolate between data
points on partial TBP data sets. The potential to use this methodology to extrapolate TBP
curves has also been investigated. The gamma distribution characterisation parameters (α
and β) have been related to API gravity using a single functional form, having different
constants depending on the base of the crude oil as defined by the UOP K factor.
A cut fraction ternary diagram for crude oils has also been introduced as an alternative to
the traditional PNA diagram. It is possible to display a wide range of crude oil types on the
cut fraction ternary diagram. This ternary diagram has the advantage that the data required
to populate it is easily available from crude oil assay reports. The distribution of crude oils
according to their API gravity on the cut fraction ternary diagram has also been described.
It has been shown that this distribution is a function of the base of a crude oil as defined by
the UOP K factor. The relationship developed suggests that API gravity is a function of the
modal boiling temperature of a crude oil.
While developing the crude oil characterisation system described above, a number of
alternative uses for this system were identified. It has been shown that the characterisation
parameter introduced can be successfully correlated with API gravity and UOP K factor.
Thivanka Dedigama
76
Chapter 6 – Summary and Conclusions
An extension of this work would be to correlate these parameters with other useful dead oil
properties such as viscosity and pour point. Such correlations would be of significant use
in many different applications. Another interesting application of the two parameter oil
characterisation system would be in crude oil valuation. The value of a crude oil is usually
linked to a marker crude oil by some comparison of their properties. TBP distillation data
is one of the key inputs to all conventional valuation systems. It would be possible to use
the characterisation parameters as a tool to compare a crude oil of interest to a marker
crude oil and hence derive a relationship for the price on the unknown oil.
A new relative permeability endpoint ratio for prediction of wettability has been proposed.
This ratio shows superior performance in wettability prediction when compared to the
more traditional ratios in use. A relationship between the new ratio and USBM wettability
has been proposed. Though it is believed that the functional form of this relationship is a
general one, it has not been possible to verify this, due to the scarcity of suitable data.
More research using a broader data set of wettability and relative permeability will be
needed to conclusively establish this relationship.
The relationship between wettability and rock mineralogy has also been investigated. This
work has shown that there is a relationship between measured USBM wettability and clay
and pyrite content. This is in line with what is already known about the surface chemistry
of these minerals. The lack of more suitable data has limited this work being extended to
other minerals of interest. A more extensive data set could also facilitate the development
of simple relationships that would act as ‘rule of thumb’ for a first pass approximation of
rock wettability, without performing measurements.
Thivanka Dedigama
77
Chapter 7 – References
Chapter 7
References
American Society of Testing and Materials, 1999a. ASTM D 2892: Distillation of Crude
Petroleum (15-Theoretical Plate Column). Annual Handbook of ASTM Standards,
Vol 05.01.
American Society of Testing and Materials, 1999b. ASTM D 5236: Distillation of Heavy
Hydrocarbon Mixtures (Vacuum Potstill Method). Annual Handbook of ASTM
Standards, Vol 05.01.
Anderson, W. G., 1986a. Wettability Literature Survey – Part 1: Rock / Oil / Brine
Interactions and the Effects of Core Handling on Wettability, Journal of
Petroleum Technology, Oct. 1986, pp 1125-1144.
Anderson, W. G., 1986b. Wettability Literature Survey – Part 2: Wettability Measurement,
Journal of Petroleum Technology, Nov. 1986, pp 1246-1262.
Behrenbruch, P., 2000. Waterflood Residual Oil Saturation - The Buffalo Field, Timor Sea,
SPE 64282, presented at The Asia Pacific Oil and Gas Conference and Exhibition,
Brisbane, Australia, Oct. 16 - 18.
Behrenbruch, P. and Biniwale, S., 2005. Characterisation of Clastic Depositional
Environments and Rock Pore Structures Using The Carman-Kozeny Equation:
Australian Sedimentary Basins. Journal of Petroleum Science and Engineering,
Elsevier, 47, pp 175 - 196.
Biniwale, S. and Behrenbruch P., 2004. The Mapping of Hydraulic Flow Zone Units and
Characterization of Australian Geological Depositional Environments. SPE
88521, presented at The Asia Pacific Oil and Gas Conference and Exhibition,
Perth, Australia, Oct. 18 -20.
BHP Billiton, 2005. BHP Billiton (online). Available from:
http://globaloil.bhpbilliton.com/ (accessed on 4 April 2005).
Thivanka Dedigama
78
Chapter 7 – References
Boneau, D.F., and Clampitt, R.L., 1977. A Surfactant System for the Oil-Wet Sandstone
Of the North Burbank Unit. Journal of Petroleum Technology, May 1977, pp 501506.
Chevron, 2005. Chevron (online). Available from:
http://www.chevron.com/crudemarketing/ (accessed on 4 April 2005).
Core Laboratories, 1988. Skua #4 Special Core Analysis (Unpublished report dated 19th
October 1988).
Core Laboratories, 1996. An Advanced Rock Property Study of Selected Rock Samples
from Laminaria #2 (Unpublished report dated November 1996).
Cotterman, R. L., Bender, R. and Prausnitz, J. M., 1985. Phase Equilibria for Mixtures
Containing Very Many Components - Development and Application of
Continuous Thermodynamics for Chemical Process Design. Ind. Eng. Chem.
Process Des. Dev., Vol 24, pp 194-203.
Craig, F. F., 1971. The Reservoir Engineering Aspects of Waterflooding. SPE Monograph
Series, Vol 3, Society of Petroleum Engineers.
Cronquist, C., 2001. Estimation and Classification of Reserves of Crude Oil, Natural Gas,
and Condensate, Editions Technip, Institut Français du Pétrole, France.
Cuiec, L., 1984. Rock / Crude Oil Interaction and Wettability: an Attempt to Understand
their Interrelation, SPE Paper 13211.
Cuiec, L., 1987. Wettability and Oil Reservoirs, North Sea Oil and Gas Reservoirs, The
Norwegian Institute of Technology, pp 193-207.
Danesh, A., 1998. PVT and Phase Behaviour of Petroleum Fluids, Elsevier Science B.V.,
Netherlands.
Donaldson, E. C., Thomas, R. D. and Lorenz, P. B., 1969. Wettability Determination and
its Effect on Recovery Efficiency, Society of Petroleum Engineers Journal, March
1969, pp13-20.
Donaldson, E.C. and Thomas, R. D., 1971. Microscopic Observations of Oil Displacement
in Water-Wet and Oil-Wet Systems, SPE Paper 3555.
Thivanka Dedigama
79
Chapter 7 – References
ExxonMobil, 2005. ExxonMobil (online). Available from:
http://www.prod.exxonmobil.com/crude_oil/ (accessed on 4 April 2005).
Fairway Exploration Consultants, 1991. Hydrocarbon Potential of the North West Shelf
Australia – Appendix 1: Oil Field Information, Fairway Exploration Consultants,
Surrey, UK.
Goda, H. and Behrenbruch, P., 2004. Using a Modified Brooks-Corey Model to Study OilWater Relative Permeability for Diverse Pore Structures, SPE 88538, presented at
the SPE Asia-Pacific Annual Technical Conference and Exhibition, Perth,
Australia, 18 - 20 Oct.
Hastings, N. A. J. and Peacock, J. B., 1975. Statistical Distribution. Butterworth & Co.
(Publishers) Ltd, London.
Haynes, H. W. and Matthews, M. A., 1991. Continuous-Mixture Vapour-Liquid Equilibria
Computations Based on True Boiling point Distillation. Ind. Eng. Chem. Res.,
Vol 30, pp 1911-1915.
Honarpour, M., Koederitz, L. and Harvey, A. H., 1986. Relative Permeability of Petroleum
Reservoirs, CRC Press Inc., Florida USA.
Hunt, J. M., 1996. Petroleum Geochemistry and Geology – 2nd Edition, W. H. Freeman
and Company, USA.
Kewen, L. and Horne, R. N., 2003. A Wettability Evaluation Method for both Gas-LiquidRock and Liquid-Liquid-Rock Systems, SPE Paper 80233.
Legens, C., Toulhoat, H., Cuiec, L., Villieras, F. and Palermol, T., 1998 Wettability
Change Related to the Adsorption of Organic Acids on Calcite: Experimental and
Ab Initio Computational Studies, SPE Paper 49319.
Mango, F. D., 1996. The Light Hydrocarbons in Petroleum: a Critical Review, Organic
Geochemistry, Vol. 26, No. 7/8, p 417-440.
Miquel, J., Hernandez, J. and Castells, F., 1992. A New Method for Petroleum Fractions
and Crude Oil Characterization. SPE Reservoir Engineering, May 1992, pp 265270.
Nelson, W. L., 1958. Petroleum Refinery Engineering – 4th Edition, McGraw-Hill Book
Company Inc, New York.
Thivanka Dedigama
80
Chapter 7 – References
Preston, J. C. and Edwards, D. S., 2000. The Petroleum geochemistry of Oils and Source
Rocks from the Northern Bonaparte Basin, Offshore Northern Australia, APPEA
Journal 2000, p257.
Riazi, M.R., 2005. Characterization and Properties of Petroleum Fractions – 1st Edition,
American Society of Testing and Materials, USA.
Salathiel, R. A., 1973. Oil Recovery by Surface Film Drainage in Mixed Wettability
Rocks, Journal of Petroleum Technology, 1973, pp 1216-1224.
Sofer, Z., 1988. Stable Carbon Isotope Composition of Crude Oils: Application to Source
Depositional Environments and Petroleum Alteration, Bulletin of the American
Association of Petroleum Geologists, Vol. 68, p31-49.
ten Haven, H. L., 1996. Applications and Limitations of Mango’s Light Hydrocarbon
Parameters in Petroleum Correlation Studies, Organic Geochemistry, Vol. 24, No.
10/11, pp 957-976.
Tissot, B. P. and Welte, D. H., 1984. Petroleum Formation and Occurrence - 2nd Edition.
Springer-Verlag, Berlin Germany.
Trantham, J.C. and Clampitt, R.L., 1977 Determination of Oil Saturation after
Waterflooding in an Oil-Wet Reservoir The North Burbank Unit, Tract 97 Project.
Journal of Petroleum Technology, May 1977, pp 491-500.
Waples, D. W., 1985. Geochemistry in Petroleum Exploration, D. Reidel Publishing
Company, Dordrecht, Holland.
Whitson, C. H., 1983. Characterizing Hydrocarbon Plus Fraction. Society of Petroleum
Engineers Journal, August 1983, pp 683-694.
Thivanka Dedigama
81
Appendix A
Appendix A
Appendix A – Fitted TBP Curves for Table 3.1 Samples
Figure A.1: Gamma distribution fitting for sample No. 1 from Table 3.4.
Thivanka Dedigama
82
Appendix A
Figure A.2: Gamma distribution fitting for sample No. 2 from Table 3.4.
Figure A.3: Gamma distribution fitting for sample No. 3 from Table 3.4.
Thivanka Dedigama
83
Appendix A
Figure A.4: Gamma distribution fitting for sample No. 4 from Table 3.4.
Figure A.5: Gamma distribution fitting for sample No. 5 from Table 3.4.
Thivanka Dedigama
84
Appendix A
Figure A.6: Gamma distribution fitting for sample No. 6 from Table 3.4.
Figure A.7: Gamma distribution fitting for sample No. 7 from Table 3.4.
Thivanka Dedigama
85
Appendix A
Figure A.8: Gamma distribution fitting for sample No. 8 from Table 3.4.
Figure A.9: Gamma distribution fitting for sample No. 9 from Table 3.4.
Thivanka Dedigama
86
Appendix A
Figure A.10: Gamma distribution fitting for sample No. 10 from Table 3.4.
Figure A.11: Gamma distribution fitting for sample No. 11 from Table 3.4.
Thivanka Dedigama
87
Appendix A
Figure A.12: Gamma distribution fitting for sample No. 12 from Table 3.4.
Figure A.13: Gamma distribution fitting for sample No. 13 from Table 3.4.
Thivanka Dedigama
88
Appendix A
Figure A.14: Gamma distribution fitting for sample No. 14 from Table 3.4.
Figure A.15: Gamma distribution fitting for sample No. 15 from Table 3.4.
Thivanka Dedigama
89
Appendix A
Figure A.16: Gamma distribution fitting for sample No. 16 from Table 3.4.
Figure A.17: Gamma distribution fitting for sample No. 17 from Table 3.4.
Thivanka Dedigama
90
Appendix A
Figure A.18: Gamma distribution fitting for sample No. 18 from Table 3.4.
Figure A.19: Gamma distribution fitting for sample No. 19 from Table 3.4.
Thivanka Dedigama
91
Appendix A
Figure A.20: Gamma distribution fitting for sample No. 20 from Table 3.4.
Figure A.21: Gamma distribution fitting for sample No. 21 from Table 3.4.
Thivanka Dedigama
92
Appendix A
Figure A.22: Gamma distribution fitting for sample No. 22 from Table 3.4.
Figure A.23: Gamma distribution fitting for sample No. 23 from Table 3.4.
Thivanka Dedigama
93
Appendix A
Figure A.24: Gamma distribution fitting for sample No. 24 from Table 3.4.
Thivanka Dedigama
94
Appendix B
Appendix B
Appendix B – Additional Fitted TBP Data
Thivanka Dedigama
95
Thivanka Dedigama
Africa
Africa
Africa
Africa
Africa
Africa
Africa
14
15
16
17
18
19
20
Middle East
Africa
13
Middle East
Africa
12
26
Africa
11
25
Africa
Middle East
Africa
9
10
Middle East
Africa
8
24
Africa
7
23
Africa
6
Africa
Africa
5
Middle East
Africa
4
22
Libya
Africa
3
21
Gabon
Africa
2
Dubai
Abu Dhabi
Abu Dhabi
Abu Dhabi
Abu Dhabi
Nigeria
Nigeria
Nigeria
Nigeria
Nigeria
Nigeria
Nigeria
Libya
Libya
Libya
Libya
Libya
Gabon
Egypt
Congo
Angola
Algeria
Algeria
Algeria
Africa
1
Country
Region
Sample
No.
Dubai
Zakum
Umm Shaif
Murban
El Bunduq
Qua Lboe
Pennington
Forcados Blend
Escravos
Brass river
Bonny Medium
Bonny Light
Zueitina
Sarir
Ed Sider
Bu Attifel
Brega
Amna (high pour)
Gamba
Anguille
Gulf of Suez Blend
Emeraude
Cabinda
Zarzaitine
Hassi Messaoud
Arzew Blend
Name
32.5
40.1
37.6
39.4
38.5
37.4
37.7
30.5
36.2
43.0
26.0
37.6
39.6
36.5
37.0
40.6
40.4
36.1
31.8
32.0
31.5
23.6
32.9
42.0
44.7
1.68
0.98
1.38
0.74
1.12
0.11
0.08
0.18
0.16
0.08
0.23
0.13
0.23
0.14
0.45
0.10
0.21
0.15
0.11
0.74
1.40
0.50
0.15
0.08
0.13
0.10
(Wgt%)
°API
44.3
Total
Sulphur
API
Gravity
-20.6
-15.0
-15.0
-15.0
-10.0
10.0
2.8
-15.0
10.0
-20.6
-20.6
-15.0
12.8
26.0
45.0
39.0
-1.1
23.9
23.0
3.0
4.4
-36.0
18.3
-9.0
-52.0
-29.4
(°C)
Pour
Point
6.4
35.7
38.5
5.0
2.8
2.9
36.0
43.6
38.0
32.9
60.7
36.0
42.2
10.0
5.6
3.2
3.6
13.7
36.7
0.0
-
7.8
78.6
3.4
2.0
0.0
(cSt)
Viscosity
3.00
2.20
2.26
2.36
2.48
2.68
4.13
3.88
2.77
3.32
5.07
2.61
2.67
2.57
3.13
2.83
2.11
2.03
3.93
1.93
2.51
1.99
1.95
2.20
2.18
2.40
Alpha
132.3
173.4
176.2
159.5
134.8
127.6
77.9
94.1
127.5
87.5
75.5
135.9
157.8
163.1
125.4
145.9
187.5
245.4
120.6
247.6
183.0
297.9
274.7
158.7
134.9
129.8
Beta
0.987
1.000
0.999
1.000
0.997
1.000
0.995
0.999
0.999
0.989
0.999
0.999
0.998
0.999
0.993
0.998
1.000
0.999
0.999
0.999
0.998
1.000
0.997
0.999
0.999
0.999
R2
Appendix B
Table B.1: Results from additional gamma distribution fitting.
96
Thivanka Dedigama
Middle East
Middle East
Middle East
Middle East
Middle East
Middle East
Middle East
Middle East
Middle East
Middle East
Middle East
Middle East
Middle East
Middle East
Middle East
Middle East
Middle East
30
31
32
33
34
35
36
37
38
39
40
41
42
43
44
45
46
North America
Northern Europe
Northern Europe
Northern Europe
49
50
51
52
Middle East
Middle East
29
North America
Middle East
28
48
Middle East
27
47
Region
Sample
No.
UK
Norway
Norway
USA
Trinidad
UAE
Saudi Arabia
Saudi Arabia
Saudi Arabia
Saudi Arabia
Saudi Arabia
Oman
Neutral Zone
Neutral Zone
Neutral Zone
Neutral Zone
Kuwait
Iraq
Iraq
Iran
Iran
Iran
Iran
Iran
Iran
Iran
Country
Beryl
Statfyord
Ekofisk
North Slope
Trinidad Blend
Mubarek
Arabian Medium (Zuluf)
Arabian Medium
Arabian Light (Berri)
Arabian Light
Arabian Heavy
Oman
Ratawi
Hout
Eocene
Burgan (Wafra)
Kuwait
Kirkuk
Basrah
Sirip Blend
Sassan
Iranian Light
Iranian Heavy
Fereidoon Blend
Darius
Cyrus
Name
39.6
38.2
36.6
26.8
33.6
37.0
30.7
30.8
38.8
33.4
28.2
32.8
23.5
35.3
18.6
23.3
31.2
35.9
33.9
27.1
33.9
33.5
30.8
31.0
33.9
0.36
0.27
0.21
1.04
0.23
0.62
2.51
2.40
1.10
1.80
2.84
1.25
4.07
1.40
4.55
3.37
2.50
1.95
2.08
2.45
1.91
1.40
1.60
2.60
2.45
3.48
(Wgt%)
°API
19.0
Total
Sulphur
API
Gravity
-53.9
-6.7
20.0
-20.6
13.9
-12.2
-40.0
-15.0
-34.4
-34.4
-34.4
-26.1
-9.4
-17.8
-28.9
-20.6
-17.8
-36.0
-9.4
-33.0
-20.6
-28.9
-20.6
-23.3
-17.8
-23.3
(°C)
Pour
Point
2.9
4.4
42.5
83.0
-
3.2
10.7
9.4
3.8
6.1
18.9
55.0
-
6.0
-
-
58.7
4.6
60.8
20.4
44.2
6.4
9.8
-
-
-
(cSt)
Viscosity
2.00
2.16
2.59
2.94
4.19
2.32
2.37
2.44
2.53
2.55
2.16
2.72
2.61
2.46
3.32
3.65
2.38
2.27
3.79
1.90
2.39
2.31
2.25
2.13
2.14
2.80
Alpha
196.9
187.4
148.3
157.0
84.4
161.1
194.3
176.3
152.7
164.1
238.5
155.4
189.4
160.5
162.7
130.3
186.1
170.8
100.4
273.8
150.0
185.5
200.4
225.8
202.9
207.5
Beta
0.997
1.000
0.990
0.999
0.998
1.000
0.997
0.998
0.999
0.997
1.000
0.998
0.999
1.000
0.999
0.997
0.994
0.995
0.991
1.000
0.990
0.999
0.999
0.998
0.999
0.998
R2
Appendix B
Table B.1: Results from additional gamma distribution fitting (Cont’d).
97
Thivanka Dedigama
Northern Europe
Northern Europe
Northern Europe
Northern Europe
Northern Europe
Russia
South America
South America
South America
South America
South East Asia
South East Asia
South East Asia
South East Asia
South East Asia
South East Asia
South East Asia
South East Asia
South East Asia
South East Asia
South East Asia
South East Asia
South East Asia
South East Asia
53
54
55
56
57
58
59
60
61
62
63
64
65
66
67
68
69
70
71
72
73
74
75
76
77
Region
Sample
No.
Indonesia, Iran
Malaysia
Indonesia
Indonesia
Indonesia
Indonesia
Indonesia
Indonesia
Indonesia
Indonesia
Indonesia
Indonesia
Indonesia
China
Brunei
Venezuela
Venezuela
Mexico
Ecuador
Russia
UK
UK
UK
UK
UK
Country
Walio Export Mix
Tembungo
Tarakan (Pamusian)
Sepinggan
Poleng
Minas (Sumatran Light)
Melahin
Kerindingan
Jatibarang
Handil
Duri
Attaka
Arjuna
Taching
Seria Light
Lagomedio
Bachequero
Reforma (Cactus Reforma)
Ecuador crude (Oriente)
Romashkinskya
Thistle
Piper
Ninian
Montrose
Forties
Name
35.4
37.4
18.1
37.9
43.2
35.2
24.7
21.6
28.9
30.8
20.6
43.7
37.7
33.0
38.8
32.6
16.8
33.0
30.4
32.4
37.4
35.6
35.1
41.9
0.68
0.04
0.15
0.10
0.19
0.09
0.27
0.30
0.11
0.09
0.21
0.07
0.12
0.04
0.05
1.23
2.40
1.56
0.87
1.61
0.31
0.92
0.41
0.23
0.28
(Wgt%)
°API
36.6
Total
Sulphur
API
Gravity
-6.7
-3.9
-45.6
-9.4
-9.4
32.2
-12.2
-12.2
43.3
35.0
13.9
-34.4
26.7
35.0
15.6
-26.1
-23.3
-16.4
-6.7
-28.9
4.4
-9.0
7.2
-6.7
-1.1
(°C)
Pour
Point
4.9
2.0
26.2
2.2
1.8
-
5.7
21.9
-
5.9
1844.0
1.4
37.7
137.9
33.0
55.0
1.4
6.8
61.8
50.1
4.6
7.0
6.9
-
40.7
(cSt)
Viscosity
2.31
4.46
11.71
5.01
2.13
2.78
8.09
9.91
2.58
3.75
4.37
2.66
2.28
3.34
3.15
2.20
2.93
2.69
2.91
2.52
2.45
2.15
2.22
1.69
2.25
Alpha
170.6
64.2
29.9
57.9
127.4
177.8
38.2
34.0
273.4
108.9
137.0
102.7
166.9
167.8
96.4
209.7
209.5
151.4
145.0
165.9
161.1
201.4
194.7
234.0
185.8
Beta
0.991
0.995
0.995
1.000
0.999
1.000
1.000
0.999
0.996
0.990
0.995
0.992
0.998
0.997
0.999
0.998
0.992
0.992
0.997
0.997
0.989
0.998
0.999
0.995
0.999
R2
Appendix B
Table B.1: Results from additional gamma distribution fitting (Cont’d),
98