58
Characterisation of Crude Oil Components, Asphaltene Aggregation and Emulsion Stability by means of Near Infrared Spectroscopy and Multivariate Analysis by Narve Aske Thesis Submitted in Partial Fulfilment of the Requirements for the Degree of DOKTOR INGENIØR Department of Chemical Engineering Norwegian University of Science and Technology Trondheim, June 2002

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Characterisation of Crude Oil Components,

Asphaltene Aggregation and Emulsion Stability

by means of Near Infrared Spectroscopy

and Multivariate Analysis

by

Narve Aske

Thesis Submitted in Partial Fulfilment of the

Requirements for the Degree of

DOKTOR INGENIØR

Department of Chemical Engineering

Norwegian University of Science and Technology

Trondheim, June 2002

I

Preface

This thesis is submitted in partial fulfilment of the requirements for the degree of dr.ing. at

the Norwegian University of Science and Technology, NTNU. It consists of five papers that are

based on work performed at Statoil R&D Centre from January 2000 to June 2002. In addition,

some of the work is a result of a shorter stay at Norsk Hydro Research Park in Porsgrunn,

Norway.

After finishing my siv.ing. degree in chemical engineering in 1998, I was introduced to the

field of surface and colloid chemistry by joining the research programme Flucha II (Fluid

Characterisation at Elevated Pressures and Temperatures). The programme is supervised by

Professor Johan Sjöblom, and is a joint event between Statoil R&D Centre and Department of

Chemical Engineering at NTNU in Trondheim. The programme is financed by The Research

Council of Norway and the oil industry, represented by Statoil, Norsk Hydro, TotalFinaElf and

ABB. The sponsors have acted as a reference group by evaluating the programme progress

twice a year. The project has involved three doctoral students and one post doc.

In addition to the five papers presented in this thesis, a sixth paper has been accepted for

publication in Journal of Dispersion Science and Engineering. Parts of the work contained in

this thesis were presented at the 3rd International Conference on Petroleum Phase Behavior

and Fouling in New Orleans, USA in March 2002. I have also contributed to an invited series

paper in a special issue of Advances in Colloid and Interface Science on the occasion of

Professor Overbeek’s 90th birthday.

II

Acknowledgements

I would like to express my sincere gratitude to

Professor Johan Sjöblom, for taking on the challenging task of teaching a process engineer the

fine art of surface and colloid chemistry, and not at least for his skills in “Finnish

teambuilding”.

Dr. Harald Kallevik, for his valuable contributions to this thesis, and also for his genuine

interest and insightful comments to my work.

Dr. Harald Førdedal, for first introducing me to the fascinating, yet sometimes very

frustrating, world or asphaltene chemistry, and Dr. Robert Orr, for interesting discussions on

interfacial rheology and for taking good care of me during my stay in Porsgrunn.

Trond Erik Havre, Inge Harald Auflem and the rest of my Flucha-colleagues, for participating

in quite a few “fruitful” coffee breaks, and for providing some invaluable memories from

Trondheim by night.

Statoil R&D Centre, for providing access to not only office space and laboratory facilities, but

also for letting me “exploit” some of their highly skilled employees during my work. The rest

of the Flucha-sponsors and The Research Council of Norway are thanked for their financial

support.

Tove, for her love and support, and simply for being the best reason in the world to go home

from work from time to time.

III

Abstract

Effective separation of water-in-crude oil emulsions is a central challenge for the oil industry

on the Norwegian Continental Shelf, especially with the future increase in subsea and even

down-hole processing of well-fluids. The mechanisms and properties governing emulsion

stability are far from fully understood, but the indigenous surface-active crude oil components

are believed to play a major role.

In this work a thorough physico-chemical characterisation of a set of crude oils originating

from a variety of production fields has been performed. Crude oil properties responsible for

emulsion stability were identified by use of multivariate analysis techniques like partial least

squares regression (PLS) and principal component analysis (PCA). Interfacial elasticity along

with both asphaltene content and asphaltene aggregation state were found to be main

contributors to emulsion stability. Information on a crude oils ability to form elastic crude oil-

water interfaces was found to be especially crucial when discussing emulsion stability.

However, measured values of interfacial elasticity were highly dependent on asphaltene

aggregation state.

Several experimental techniques was utilised, and partly developed, for the crude oil

characterisation. A high pressure liquid chromatography (HPLC) scheme was developed for

SARA-fractionation of crude oils, and an oscillating pendant drop tensiometer was used for

characterisation of interfacial rheological properties. For emulsion stability a cell for

determining the stability as a function of applied electric fields was used. In addition, near

infrared spectroscopy (NIR) was used throughout the work both for chemical and physical

characterisation of crude oils and model systems.

High-pressure NIR was used to study the aggregation of asphaltenes by pressure depletion. A

new technique for detection of asphaltene aggregation onset pressures based on NIR

combined with PCA was developed. It was also found that asphaltene aggregation is

reversible, but highly time-dependent. Finally, the size of asphaltene aggregates in model

systems was estimated by fitting NIR spectra to Rayleigh scattering theory.

IV

List of Papers

I. Narve Aske, Harald Kallevik, Johan Sjöblom.

Determination of Saturate, Aromatic, Resin and Asphaltenic (SARA)

Components in Crude Oils by means of Infrared and Near Infrared

Spectroscopy.

Energy & Fuels, 15 (5) 1304-1312, 2001.

II. Narve Aske, Robert Orr, Johan Sjöblom.

Interfacial Properties of Water-Crude Oil Systems using the Oscillating Pendant

Drop. Correlations to Asphaltene Solubility by Near Infrared Spectroscopy.

Submitted, Colloid and Polymer Science 2002.

III. Narve Aske, Harald Kallevik, Johan Sjöblom.

Water-in-Crude Oil Emulsion Stability Studied by Critical Electric Field

Measurements. Correlation to Physico-Chemical Parameters and Near Infrared

Spectroscopy.

In press, Journal of Petroleum Science and Engineering.

IV. Narve Aske, Harald Kallevik, Einar Eng Johnsen, Johan Sjöblom.

Asphaltene Aggregation from Live Crude Oils and Model Systems Studied by

High Pressure NIR Spectroscopy.

Accepted, Energy & Fuels 2002.

V. Narve Aske, Harald Kallevik, Johan Sjöblom.

Asphaltene Aggregate Size by Near Infrared Spectroscopy.

Submitted, Applied Spectroscopy 2002.

V

Additional publications

VI. Narve Aske, Robert Orr, Johan Sjöblom.

Dilatational Elasticity Moduli of Water-Crude Oil Interfaces using the Oscillating

Pendant Drop.

Accepted, Journal of Dispersion Science and Technology 2002.

VII. Johan Sjöblom, Harald Kallevik, Narve Aske, Inge Harald Auflem, Trond Erik Havre,

Øystein Sæther, Robert Orr.

Recent Development in the Understanding of the Stability and Destabilization of

Water-in-Crude Oil Emulsions.

Presented at: "The 3rd International Conference on Petroleum Phase Behavior

and Fouling", New Orleans, USA, March 10-14, 2002.

VIII. Johan Sjöblom, Narve Aske, Inge Harald Auflem, Øystein Brandal, Trond Erik

Havre, Øystein Sæther, Arild Westvik, Einar Eng Johnsen, Harald Kallevik.

Our Current Understanding of Water-in-Crude Oil Emulsions. Recent

Characterization Techniques and High Pressure Performance.

Advances in Colloid and Interface Science, Special Issue: A Collection of Invited

Papers in Honour of Professor J. Th. G. Overbeek on the Occasion of his 90th

Birthday, In press, 2002.

VI

Contents

Preface ...................................................................................................................... I Acknowledgements..................................................................................................... II Abstract................................................................................................................... III List of Papers............................................................................................................ IV 1 Introduction........................................................................................................1 2 Crude Oil Properties.............................................................................................2

2.1 Chemical composition..................................................................................2

2.2 Structure...................................................................................................5

2.3 Emulsions..................................................................................................8

3 Crude Oil Characterisation ..................................................................................11 3.1 SARA Separation ......................................................................................11

3.2 Interfacial Rheology ..................................................................................14

3.3 Near Infrared Spectroscopy........................................................................18

3.4 Critical Electric Field ..................................................................................21

4 Multivariate Data Analysis...................................................................................23 4.1 Principal Component Analysis (PCA) ............................................................23

4.2 Partial Least Squares Regression (PLS) ........................................................24

5 Main Results .....................................................................................................26 Paper I ............................................................................................................26

Paper II ...........................................................................................................29

Paper III ..........................................................................................................33

Paper IV...........................................................................................................37

Paper V............................................................................................................42

6 Concluding Remarks ..........................................................................................46 7 References .......................................................................................................48

1

1 Introduction

Subsea and down-hole processing of well fluids will be a central challenge on the

Norwegian Continental Shelf in the years to come. This type of processing presumes

more efficient separation of water / gas / oil, together with more cost-effective transport

of well fluids over long distances. More efficient separation requires more knowledge of

the mechanisms governing the separation process, and in this respect the indigenous

crude oil surfactants are known to play a major role. However, at the present our

knowledge of how these components influence separation processes is lacking in several

respects. Of special importance is knowledge of separation mechanisms at high pressures

and temperatures.

The Flucha II programme, a direct continuation of the first Flucha programme, aims to

increase the basic knowledge related to these issues. Central topics are physico-chemical

characterisation of crude oils and their indigenous surface-active components, like

asphaltenes, resins and naphthenic acids. The chemical composition and physical

characteristics of the crude oils are linked to emulsification and foamability at relevant

pressures and temperatures. The programme also aims at mapping the performance of

chemical additives acting as demulsifiers / inhibitors at operational conditions.

My contribution has been the build-up of a data matrix of over 20 crude oils, including

both North Sea and African production fields. Experimental techniques have been

developed to include characterising data such as SARA-fractions, interfacial rheology and

emulsion stability. The aggregation behaviour of asphaltenes at elevated pressures and

temperatures has also been studied. Near infrared spectroscopy in combination with

multivariate analysis has been used as an efficient tool throughout most of the work.

Crude Oil Properties

2

2 Crude Oil Properties

2.1 Chemical composition

Even though crude oils are a continuum of tens of thousands of different hydrocarbon

molecules, the proportions of the elements in crude oils vary over fairly narrow limits.

Nevertheless, a wide variation in properties is found from the lightest crude oils to the

highly asphaltenic crudes. The carbon content normally is in the range 83-87%, and the

hydrogen content varies between 10 and 14%. In addition, varying small amounts of

nitrogen, oxygen, sulfur and metals (Ni and V) are found in crude oils [1].

Due to the complex composition of crude oils, characterisation by the individual

molecular types is not possible, and elemental analysis is unattractive because it gives

only limited information about the constitution of petroleum due to the constancy of

elemental composition. Instead, hydrocarbon group type analysis is commonly employed

[2-9]. Knowledge of the distribution of major structural classes of hydrocarbons in crude

oils is needed in various fields in the petroleum industry. Examples are studies related to

reservoir evaluation, migration and maturity, degradation processes, processing, and

environmental effects [10].

The SARA-separation is an example of such group type analysis, separating the crude oils

in four main chemical classes based on differences in solubility and polarity. The four

SARA-fractions are the saturates (S), aromatics (A), resins (R), and the asphaltenes (A).

Instead of molecules or atoms, certain structures are here considered the components of

the crude oil, and the SARA-separation can be seen to give information somewhat

between that obtained by elemental analysis and analysis for individual molecules [1].

Figure 2-1 demonstrates the SARA-separation scheme developed for Paper I of this

thesis [11]. The chemical composition of crude oils will in the following be discussed on

the basis of the SARA-fractions. More information on the SARA-separation technique is

given in Chapter 3.

Crude Oil Properties

3

Crude oil

Asphaltenes

Maltenes

Resins Aromatics Saturates

n-hexane

precipitate solution

silica

trichloro-methane

n-hexane n-hexane

Figure 2-1: SARA-separation scheme.

Saturates: The saturates (aliphatics) are non-polar hydrocarbons, without double bonds,

but including straight-chain and branched alkanes, as well as cycloalkanes (naphtenes).

Cycloalkanes contain one or more rings, which may have several alkyl side chains. The

proportion of saturates in a crude oil normally decreases with increasing molecular

weight fractions, thus the saturates generally are the lightest fraction of the crude oil.

Wax is a sub-class of the saturates, consisting primarily of straight-chain alkanes, mainly

ranging from C20 to C30. Wax precipitates as a particulate solid at low temperatures, and

is known to effect emulsion stability properties of crude oil systems [12-14].

Aromatics: The term aromatics refer to benzene and its structural derivates. Aromatics

are common to all petroleum, and by far the majority of the aromatics contain alkyl

chains and cycloalkane rings, along with additional aromatic rings. Aromatics are often

classified as mono-, di-, and tri-aromatics depending on the number of aromatic rings

present in the molecule. Polar, higher molecular weight aromatics may fall in the resin or

asphaltene fraction.

Resins: This fraction is comprised of polar molecules often containing heteroatoms such

as nitrogen, oxygen or sulphur. The resin fraction is operationally defined, and one

common definition of resins is as the fraction soluble in light alkanes such as pentane and

heptane, but insoluble in liquid propane [1, 15, 16]. Since the resins are defined as a

solubility class, overlap both to the aromatic and the asphaltene fraction is expected.

Despite the fact that the resin fraction is very important with regard to crude oil

Crude Oil Properties

4

properties, little work has been reported on the characteristics of the resins, compared to

for instance the asphaltenes. However, some general characteristics may be identified.

Resins have a higher H/C ratio than asphaltenes, 1.2-1.7 compared to 0.9-1.2 for the

asphaltenes [16]. Resins are structural similar to asphaltenes, but smaller in molecular

weight (< 1000 g/mole). Naphthenic acids are commonly regarded as a part of the resin

fraction.

Asphaltenes: The asphaltene fraction, like the resins, is defined as a solubility class,

namely the fraction of the crude oil precipitating in light alkanes like pentane, hexane or

heptane. This precipitate is soluble in aromatic solvents like toluene and benzene. The

asphaltene fraction contains the largest percentage of heteroatoms (O, S, N) and

organometallic constituents (Ni, V, Fe) in the crude oil. The structure of the asphaltenes

has been the subject of several investigations, but is now believed to consist of polycyclic

aromatic clusters, substituted with varying alkyl side chains [15]. Figure 2-2 shows a

hypothetical asphaltene monomer molecule. The molecular weight of asphaltene

molecules has been difficult to measure due to the asphaltenes tendency to self-

aggregate, but molecular weights in the range 500-2000 g/mole are believed to be

reasonable [17-21]. Asphaltene monomer molecular size is in the range 12-24 Å [1, 18].

Figure 2-2: Hypothetical asphaltene molecule [22].

It is important to keep in mind that knowledge about the chemical composition of crude

oils, gained from for instance a SARA-analysis, cannot fully explain the crude oil

Crude Oil Properties

5

behaviour with regard to emulsion stability, asphaltene deposition etc. Equally important

is information of the structure of the crude oil, which is a result of interactions between

the continuum of chemical constituents in the oil. The interactions between the heavy

end molecules, the asphaltenes and resins, play the most significant role in this sense.

2.2 Structure

Asphaltenes are believed to be suspended as a microcolloid in the crude oil, consisting of

particles of about 3 nm [15]. Each particle consists of one or more aromatic sheets of

asphaltene monomers, with adsorbed resins acting as surfactants to stabilise the colloidal

suspension. The molecules are believed to be held together with π-bonds, hydrogen

bonds, and electron donor-acceptor bonds [20]. Under unfavourable solvent conditions

resins desorb from the asphaltenes, leading to an increase in asphaltene aggregate size,

and eventually precipitation of large asphaltene aggregates. This asphaltene aggregation

model is the so-called steric stabilisation model developed by Leontaritis and Mansoori

[23-25], based on the earlier work by Nellensteyn and others [26-28]. On the basis of

this model, resins are necessary to suspend the asphaltene aggregate, thus the

aggregate is an asphaltene-resin complex. Figure 2-3 shows a model of an asphaltene

aggregate stabilised by resin molecules.

Figure 2-3: Resin solvated asphaltene aggregate [29].

An alternative model of asphaltene stabilisation exists, referred to as the thermodynamic

model, and first reported by Hirscberg et al. [30]. In this approach the resins are not

considered explicitly, but are treated as an integrated part of the solvent medium. This

view implies that the asphaltene monomers and aggregates are in thermodynamic

equilibrium, solvated by the surrounding medium. Thus, the critical distinction between

Crude Oil Properties

6

the thermodynamic and steric stabilisation models lies in whether the asphaltene colloids

are solvated or suspended in the hydrocarbon media [15]. The main advantage of using

the thermodynamic approach when modelling asphaltene aggregation is that it utilizes

conventional thermodynamic methods for phase equilibra, like equations of state.

However, since the resins are not considered explicitly it does not sufficiently take into

account the resin-asphaltene interaction, which is based on experimental observations

[24]. Different thermodynamic asphaltene aggregation models have been reviewed by

Andersen and Speight [31], with the conclusion that they are lacking in several respects

and are not quantitatively correct.

The pronounced tendency of asphaltenes to self-aggregate is one of their most

characteristic traits, and responsible for a large proportion of the problems encountered

during crude oil processing and refining. Aggregation and precipitation of asphaltenes

may cause a large variety of problems, from formation damage, equipment plugging,

catalyst deactivation etc. In addition, asphaltene aggregation has a great influence on

emulsion stability of crude oil-water systems, as will be discussed in section 2.3. The

parameters that govern aggregation and precipitation of asphaltenes are both solvent

conditions, pressure and temperature; their effects have been studied in detail by

numerous authors [32-47].

The solubility of the heaviest crude oil fraction, the asphaltenes, depends on a delicate

balance between this fraction and the lighter fractions of the crude oil. Any unfavourable

disturbance in this balance may induce asphaltene aggregation. For instance, the

addition of light, paraffinic components to an asphaltene containing solution will lower

the solubility power with respect to the asphaltenes. As already stated, resin molecules

will react to the addition by desorbing from the asphaltenes in an attempt to re-establish

thermodynamic equilibrium, thus increasing the probability of asphaltene self-

aggregation [45]. Figure 2-4 illustrates increasing asphaltene aggregation due to

increasing amounts of straight-chain aliphatics relative to aromatics in the solution.

Mixing of crude oils from different sources during production or refining may cause such

aggregation effects, and the consequences can be severe asphaltene deposition on all

types of processing facilities [24].

Crude Oil Properties

7

Figure 2-4: Formation of larger asphaltene aggregates under unfavourable solvent conditions [29].

Asphaltenes are also known to aggregate by pressure depletion alone [30, 45, 48]. By

decreasing the pressure, the relative volume fraction of the light components within the

crude oil increases. This causes an increase in the solubility parameter difference

between the crude oil and the asphaltenes, reaching a maximum at the bubble point

pressure. Below the bubble point asphaltenes are more soluble again due to evaporation

of light crude oil components. The relative change in asphaltene solubility has been

shown to be highest for light crude oils that are undersaturated with gas, and which

usually contain only a small amount of asphaltenes. This means, somewhat surprisingly,

that heavy crudes will usually present fewer problems with asphaltene aggregation and

precipitation, despite their higher asphaltene content. Of course, heavy crude oils

generally possess higher resin amounts, which can explain some of this behaviour.

Temperature has a less pronounced effect on aggregation than crude oil composition and

pressure, but an increase in the temperature generally affects the aggregation of

asphaltenes by decreasing the solvating power of the crude oil [49]. However, some

controversy regarding the temperature effect exists in the literature. Some authors state

that the asphaltene aggregate size decreases with increasing temperature [50], while

others state that the precipitation of asphaltenes increases with temperature [1] .

The reversibility of the asphaltene aggregation is also a subject of some controversy.

Hirscberg et al. [30] assumed that the aggregation was reversible, but probably very

slow. Joshi et al. [51] found the precipitation from a live crude oil to be reversible in the

matter of minutes, except for a subtle irreversibility observed for the first

depressurisation of the crude oil. They also discussed the different behaviour of

Asphaltene aggregate

Aliphatic solvent

Aromatic solvent

Crude Oil Properties

8

asphaltenes precipitated from crude oils with excess n-alkanes and by asphaltenes

contained in the original crude oil. Hammami et al. [45] also found that the aggregation

was generally reversible, but that the kinetics of the redissolution varied significantly

depending on the physical state of the system. Peramanu et al. [52] reported differences

in the reversibility of solvent- and temperature-induced aggregation.

In Papers IV [53] and V [54] of this thesis near infrared spectroscopy is used to study

asphaltene aggregation behaviour both at ambient and at high-pressure conditions.

2.3 Emulsions

The concept of emulsions has been defined by IUPAC (1972) as [55]:

“An emulsion is a dispersion of droplets of one liquid in another one with which it is

incompletely miscible (..). In emulsions the droplets often exceed the usual limits for

colloids in size.”

For the petroleum industry the usual emulsions encountered are water droplets dispersed

in the oil phase (W/O), although the reverse situation is also possible. In addition to the

usual emulsion types, multiple emulsions of for instance oil droplets dispersed in water

droplets that are in turn dispersed in a continuous oil phase (O/W/O) can occur. Water

droplets in crude oil emulsions might be up to 100 micrometers in diameter, which is

large compared to the common definition of the upper limit of colloidal size (1 µm) [56].

The emulsion formation is a result of the co-production of water from the oil reservoir.

During processing, pressure gradients over chokes and valves introduce sufficiently high

mechanical energy input (shear forces) to disperse water as droplets in the oil phase

[57].

All emulsions, perhaps with the exception of microemulsions, are thermodynamically

unstable. However, the destabilisation may take considerable time, and a stable emulsion

is unable to resolve itself in a defined time period without some form of mechanical or

chemical treatment [56]. Water-in-crude oil emulsion destabilisation basically involves

three steps, namely flocculation, followed by sedimentation of water droplets due to

density differences, and finally coalescence of the individual water droplets, as illustrated

in Figure 2-5. Large droplet sizes, high density difference between the oil and the

aqueous phase, and low continuous phase (oil) viscosity causes high sedimentation rates.

Flocculation is the aggregation of two or several droplets, touching only at certain points,

Crude Oil Properties

9

and with virtually no change in total surface area. Coalescence is the process of droplets

fusing together and forming larger and larger droplets, until the oil and water is

separated into two discrete phases.

Flocculation Sedimentation Coalescence

Figure 2-5: Emulsion separation by flocculation, sedimentation, and coalescence.

In crude oil emulsions, emulsifying agents are present at the oil-water interface,

hindering this coalescence process. Such agents include scale and clay particles, added

chemicals or indigenous crude oil components like asphaltenes, resins, waxes and

naphthenic acids [57]. The mechanism by which indigenous crude oil components

stabilise a droplet interface is by producing a physical barrier for droplet-droplet

coalescence. The lighter resins act as individual monomers, in a similar manner to

traditional surfactants. The resins have a tendency to be more interfacially active than

the asphaltenes, thus be the first to reach and cover a fresh water-oil interface. This

lowers the interfacial tension, but is not regarded as a sufficient step to stabilise against

coalescence [57]. The main stabilising effect is believed to be caused by interaction with

the asphaltenes. This effect is very dependent on the aggregation state of the

asphaltenes; monomeric asphaltenes do not stabilise interfaces to the same extent as

nanosized asphaltene aggregates accumulating at the interface.

Kilpatrick and coworkers [20, 37, 38] postulate that the primary mechanism of

asphaltene stabilisation of water-in-oil emulsions is through the formation of a viscous,

crosslinked, three-dimensional network of asphaltene aggregates with high mechanical

strength. The importance of interactions between asphaltenes and resins in the

stabilisation of crude oil emulsions has also been pointed out by others [58-65]. Figure

2-6 illustrates the accumulation of asphaltene aggregates at an oil-water interface.

Kilpatrick and Spiecker [20, 29] used a biconical bob rheometer to probe the strength of

such asphaltenic films at the interface. We have in Paper II [66] employed an oscillating

pendant drop tensiometer for measuring the interfacial rheological properties of crude

Crude Oil Properties

10

oil-water systems. in Paper III [67] these results have been correlated to emulsion

stability.

Figure 2-6: Accumulation of asphaltene aggregates at the oil-water interface [29].

Crude Oil Characterisation

11

3 Crude Oil Characterisation

Crude oils from different sources exhibit a wide range of physical and chemical

properties. To predict the behaviour of any crude oil with regard to for instance emulsion

stability, knowledge of these properties is of utmost importance. Such properties include

characterising parameters like density, viscosity, interfacial tension, molecular weight

etc.

Predicting emulsion stability behaviour from characterising data is a difficult task due to

the fact that emulsion stability is the result of a complex interplay between the different

crude oil constituents. For instance, the amount of the interfacially active fractions, resins

and asphaltenes, are known to be important for emulsion stability, but equally important

is knowledge of their interaction behaviour.

In the following sections some of the main characterising techniques used throughout

this thesis are described. This includes methods for determining both the chemical

composition of crude oils and the interfacial properties of crude oil-water systems. In

addition, the application of near infrared spectroscopy (NIR) with regard to crude oil

characterisation is described. The use of NIR has been found to be especially useful since

the technique can be applied for both chemical and physical characterisation of crude oil

systems.

3.1 SARA Separation

3.1.1 Background

Traditionally chromatographic techniques have been extensively used for hydrocarbon

group type determinations like for instance the SARA-separation. Standard ASTM

procedures for SARA separation are available [68, 69]. However, these techniques, which

are based on liquid chromatography (LC), are lengthy, tedious, and laborious as well as

expensive to run on routine basis [7]. High performance liquid chromatography (HPLC)

has been demonstrated to be a very efficient alternative to the standard ASTM

procedures for SARA separation [4-7, 9].

No HPLC method is applicable to all types of hydrocarbon samples, therefore specific

methods for each type of sample are used [3]. For crude oils, deasphalting of the sample

is usually the first step. This is done by a standard asphaltene precipitation by alkanes

Crude Oil Characterisation

12

like pentane, hexane or heptane. The remaining SAR fractions are then separated by

HPLC using silica or bonded phase columns, and an alkane as the mobile phase. The

complexity of the material in a crude oil, and the lack of an aromatic/resin selective

column makes it impossible to draw a clear distinction between aromatics and resins.

Therefore, a method specific definition of aromatics and resins is usually employed.

Separation of saturates and aromatics, with backflushing of the resins, have been

obtained on amino columns, cyano columns and aminocyano columns. However, two

coupled columns such as cyano/aminocyano or silica/cyano are often employed. In this

case the purpose of the first column is to retain the resins, while the second retains the

aromatics. The saturate fraction has no retention on such columns, and is therefore

eluted directly through the columns. The resins and aromatics have to be eluted from

their respective columns with appropriate solvents.

3.1.2 Experimental

A SARA separation system was developed to characterise crude oils of interest. The main

components of the system are an HPLC-pump, two 7.8 x 300 mm µBondapak NH2-

columns in series, and an ultraviolet (UV) and a refractive index (RI) detector. The

system is shown schematically in Figure 3-1. The separation scheme was presented in

Chapter 2.1.

Figure 3-1: HPLC-system

Crude Oil Characterisation

13

The first step of the procedure is to remove the asphaltenes by n-hexane precipitation.

The rest of the oil is then diluted in n-hexane and injected onto the system through the

injection valve ahead of the separation columns. Hexane is also used as mobile phase.

The saturates, having no retention on the column material, elutes first and are detected

on the RI-detector. This fraction is followed by the aromatics which are detected both on

the UV- and the RI-detector. Both fractions are collected in separate vials after the

detectors. The resins must be eluted with a more polar mobile phase. This is

accomplished by reversing the flow through the columns by a backflush valve, and by

using trichloromethane as mobile phase. The solvent is evaporated from all three

collected fractions, and by including the asphaltene yield the SARA distribution may be

calculated.

Figure 3-2 illustrates typical detector response for a SARA fractionation. The example

shows a case where only one column is in use. The reason for employing two columns in

series was to achieve a better separation of the saturate and aromatic fraction. Using

only one column, one single RI-detector peak is detected, indicating overlapping between

the saturate and aromatic fraction. This problem was solved when introducing a second

column. The RI-detector response is seen to fall off rapidly, while the UV-detector

response of the aromatics continues to over 20 minutes. This is due to the much higher

sensitivity of the UV-detector. The B at 40 minutes indicates the start of

trichloromethane backflushing, and the resin fraction is seen to elute immediately after

this.

Saturates

AromaticsB

ResinsSaturates

Aromatics

Figure 3-2: Detector response during SARA separation.

Crude Oil Characterisation

14

The total yield of the fractions for crude oils is good, typically ranging from 92-100%.

Some light condensates display somewhat lower yields. The main reason for the material

loss is the solvent evaporation procedure, where nitrogen is blowed over the samples.

Radke et al. [8] studied the effect of evaporation using the same solvent evaporation

procedure applied in the present method. They showed that the loss was primarily due to

evaporation of saturates, and to some extent, the aromatics. The reported SARA-values

of the low-yield samples in this study are corrected to 100 % by adjusting the saturate

and aromatic values. Hence, the evaporation loss from the resin fraction is considered to

be negligible.

Further experimental details may be found in Paper I [11].

3.2 Interfacial Rheology

3.2.1 Background

The interfacial rheology of surfactant solutions influences the stability of many

technologically important systems such as foams, froths and emulsions [70]. The

mechanisms by which resins and asphaltenes stabilise crude oil emulsions by forming a

protective layer at the interface have already been discussed. Here the principles for the

measuring of the elastic and viscous properties of such a layer will be presented.

Generally, when an interface with adsorbed interfacially active molecules is stretched,

interfacial tension gradients are generated. The tension gradients will oppose the

stretching and try to restore the uniform interfacial tension state, i.e. the interface will

behave elastically. This is the so-called Gibbs-Marangoni effect. This is illustrated in

Figure 3-3 where a water drop in oil is being stretched either by shear forces or drop

collisions.

Crude Oil Characterisation

15

High interfacial tension

Low interfacial tensionSurfactants move to reduce the gradient in interfacial tension

Surfactants

Water

Oil

Shear forces or collisions thin the interfacial film

Figure 3-3: Gibbs-Marangoni effect at oil-water interface.

The main function of interfacially active molecules is not the interfacial tension lowering

they produce, but that their presence can lead to such gradients in interfacial tension

able to resist tangential stresses. In practice, emulsion droplets being stretched can

resist coalescence due to the elastic membrane, providing the droplet interfaces with a

self-healing mechanism [71]. Physical cross-linking of asphaltenic aggregates will also

contribute to this resistance to coalescence.

Interfacial rheological properties can be measured through the so-called interfacial

dilatational modulus. This property gives a measure of resistance to the creation of

interfacial tension gradients, and the rate at which such gradients disappear after the

deformation. The interfacial dilatational modulus, ε, is defined as the increase in

interfacial tension for a unit of relative increase in surface area [72]

Addlnγε = (1)

where γ is the interfacial tension and A is the interfacial area. For a small deformation of

the area, the change in interfacial tension can be written as a sum of an elastic and a

viscous contribution

dtAdAviscouselastic

dd

lnln ηεγ +∆=+=∆ (2)

where εd and ηd is the interfacial dilatational elasticity and viscosity, respectively. The two

contributions can be measured separately by subjecting the interface to small, periodic

oscillations at a given frequency. In an oscillatory experiment the interfacial area is

varied with time, t, according to

Crude Oil Characterisation

16

)exp(ln tiA ω∝∆ (3)

where ω is the frequency of the oscillations. Based on the above equations the interfacial

dilatational modulus can be written as a complex number

dd iωηεε += (4)

where the first term is equal to the elastic contribution, and the second term is

proportional to the viscous contribution.

The interfacial dilatational modulus is a complex function of both oscillation frequency

and concentration of interfacially active components in the bulk phase. The viscous part

of the interfacial dilatational modulus will represent a combination of internal relaxation

processes, and relaxation due to diffusion of matter between the interface and bulk

phases.

3.2.2 Experimental

Numerous techniques have been employed for the determination of interfacial rheological

properties, including canal surface viscometers, knife-edge and disc surface viscometers,

including the biconical bob viscometer [29, 59, 73-75]. These techniques measure the

interfacial shear rheological properties. We have measured the interfacial dilatational

rheological properties by using an oscillating pendant drop tensiometer. The system is

illustrated schematically in Figure 3-4. It operates by oscillating an oil drop in an aqueous

phase at constant frequency. Images are continuously taken of the drop profile, and by

knowing the densities of the two phases, the interfacial tension is calculated by use of

the Laplace equation [76]. The technique is known as axisymmetric drop shape analysis

(ADSA).

Crude Oil Characterisation

17

1

2

3

4

5

6 7

8

9

Figure 3-4: The pendant drop tensiometer consisting of an optical bench (1) with an halogen lamp

as light source (2), a cuvette containing the oil drop in the water phase (3), a CCD camera (7)

attached to a telecentric lens (6), a syringe (4), and a DC motor (5). The drop profile (9) is analysed

on the personal computer (8).

Since both the changes in interfacial area and interfacial tension are known, the absolute

value of the complex dilatational modulus, |ε|, can be calculated according to Eq. 5

)/( AA ∆∆= γε (5)

In addition, the phase angle, φ, between the changes in interfacial tension and interfacial

area is determined. The elastic and viscous contributions to the complex modulus may

then be distinguished by introducing this angle, so that

φεε cos=d (6a)

φεωη sin=d (6b)

For emulsion systems the interfacial concentration of surfactants is low due to the large

interfacial area created by the formation of many small droplets. Therefore the oil

systems under study was diluted with some solvent to better simulate the interfacial

concentrations encountered in real systems. The oil drop was oscillated with a frequency

of 0.1 Hz in all our studies.

Further experimental details may be found in Paper II [66].

Crude Oil Characterisation

18

3.3 Near Infrared Spectroscopy

Over the last 30 years near infrared spectroscopy has been increasingly used as an

analytical tool, particularly by the food and agricultural industries, but also by the textile

and polymer industries in addition to the petroleum industry [77]. The increasing

popularity is due to the four principal advantages of the method, namely speed,

simplicity, multiciplicity of analysis from a single spectrum and the non-consumption of

the samples. The main disadvantages of NIR spectroscopy have always been the

insensitivity to minor constituents and the broad absorption bands [78]. The latter

drawback is compensated by the rapid development of advanced and user-friendly

software for multivariate analysis.

The near infrared spectroscopic region lies between the visible and the mid infrared

regions of the electromagnetic spectrum. It is defined by ASTM as the spectral region

spanning the wavelength range 780-2526 nm. In mid infrared spectroscopy diatomic

molecules will vibrate as a harmonic oscillator, and the energy, Ev, will be given by [79]

021 υhcvEv

+= (7)

where h is Planck’s constant, v is the vibrational quantum number, ν0 is the frequency of

vibration and c is the speed of light. However, vibrations in polyatomic molecules involve

complex movement of their constituent atoms. In practice, such molecular vibrations

tend to be anharmonic, i.e. vibrations about the equilibrium position are non-symmetric.

This anharmonicity introduces overtones and combinations of the fundamental vibration

bands in the mid infrared region. These absorption bands can be found in the near

infrared region. The energy levels for anharmonic vibration may be written as

ev xhcvhcvE 0

2

0 21

21 υυ

+−

+= (8)

where xe is the anharmonicity constant. The intensity of the overtone and combination

bands is markedly lower than for the fundamental bands. When dealing with organic

compounds, as for crude oils, the most prominent NIR bands are those related to O-H,

C-H and N-H groups. Main absorption bands of NIR spectra are given in

Table 3-1.

Crude Oil Characterisation

19

Table 3-1: Near infrared absorption bands.

Absorption band Wavelength region [nm]

O - H First overtone 1400 - 1450

O - H Combinations 1900 - 1975

C – H Second overtone 1125 - 1225

C – H Combinations first overtone 1350 – 1450

C – H First overtone 1625 – 1775

C – H Combinations 1950 - 2450

One of the main advantages of near infrared spectroscopy when working with colloidal

systems like crude oils is the ability to also gain information on the physical state of the

system. In addition to absorption, near infrared spectra will display a baseline elevation

due to light scattering by aggregates or particles in solution. For small particles relative

to the wavelength (r/λ ≤ 0.05), Lord Rayleigh derived an expression for the total energy

scattered by a particle. The basic premise for Rayleigh scattering is that the particle is so

small that the electromagnetic field it experiences is uniform over the particle. By also

assuming that the particles were slightly lossy and dielectric, Rayleigh deducted an

expression for the scattering cross section of a particle [80]

2

2

2

4

657

21

32

+−

=nnr

sc λπσ (9)

where r is the particle radius, λ the wavelength of the incident light and n is the ratio of

the discrete phase to the continuous phase index of refraction. In the absence of multiple

scattering, and in the Rayleigh limit (r/λ ≤ 0.05), fewer, but larger spheres are much

more efficient scatterers than the same mass of smaller particles.

Within the Rayleigh limit the light extinction can be considered a sum of the absorbance

and scattering contribution, represented by the following particle cross-sections [81]

abssctot σσσ += (10)

where σtot, σsc and σabs are the total, scattering and absorption cross-sections,

respectively.

Crude Oil Characterisation

20

The light extinction depends exponentially on the number of particles and the total cross-

section

)exp(0

totNII σ−= (11)

where I and I0 are the intensities of the transmitted and incident light respectively, and N

is the number of particles. The relation between optical density (OD), light intensity,

number of particles and particle cross section is then given by

totNIIOD σ434.0log 0 =

= (12)

The measured optical density is thus a combination of absorption and scattering

contributions. The effect of multiple scattering is not accounted for in this equation.

Absence of multiple scattering implies that radiation scattered by a single particle

proceeds directly to the detector without any further scattering encounters. In case of

multiple scattering, the scattering and absorption cannot be treated separately.

Conditions for single scattering can usually be attained by working with dilute systems

and with small volumes.

Figure 3-5 demonstrates the effect of particle size on NIR spectra. During a sol-gel

preparation of silica particles, NIR spectra were taken at intervals. The increasing optical

density due to increasing particle size is clearly seen.

Crude Oil Characterisation

21

Wavelength [nm]

Opt

ical

Den

sity

Figure 3-5: NIR spectra of silica particles formed by sol-gel process.

Several authors have used NIR spectra for particle size determinations [82-85]. NIR

spectroscopy has also found widespread use in the determination of both physical and

chemical properties of crude oils and related materials [77, 86-91]. Throughout this

thesis the ability of NIR spectroscopy to gain both chemical and physical information on

crude oils and model systems will be demonstrated. In Papers IV [53] and V [54] near

infrared spectroscopy was used specifically for the detection of asphaltene aggregation

onset pressures, and aggregate size determinations.

3.4 Critical Electric Field

Demulsification by electrocoalescence is common in the petroleum industry. Water is

separated from the emulsion by applying an electric field of 1-10 kV/cm to cause

flocculation and coalescence of water droplets in the continuous oil phase [92]. The

electric field strength needed to cause such coalescence can be used as a measure of the

emulsion stability. In low electric fields water droplets surrounded by a rigid interfacial

film will attain a chain-like configuration. When increasing the electric field the droplets

will bridge the gap between the electrodes. Ultimately, an irreversible rupture of the

interfacial films between the droplets will increase the conductivity through the emulsion

sample [63, 93]. This critical electric field, Ecritical, may then be defined as a measure of

the emulsion stability. Figure 3-6 demonstrates the effect on the water droplets when

increasing the electric field over an emulsion.

Crude Oil Characterisation

22

0

0.5

1

1.5

2

2.5

0 0.1 0.2 0.3 0.4 0.5 0.6 0.7

Electric field [kV/cm]

Cur

rent

[mA

]Water cut 30%

Figure 3-6: Emulsion droplets in an increasing electric field.

In paper III [67] a newly developed electric field cell was used for the determination of

emulsion stability. The method is similar to the one employed by Kallevik et al. [94]. The

cell is depicted schematically in Figure 3-7. It consists of a Teflon plate with a 10 mm

diameter hole in the centre, with a brass plate on each side. The distance between the

plates is 0.5 mm, and the upper brass plate has two small holes for sample injection. The

brass plates are connected to a power supply, which can increase the applied voltage in

user-defined steps. The system is held together with isolating plexiglass plates. The

power supply delivers a maximum of 100 V DC, corresponding to a maximum electric

field of 2.0 kV/cm. Emulsions are injected into the cell volume, and the electric field over

the emulsion sample is increased until Ecritical is reached, as seen by a sudden increase in

conductivity.

<100 V

Plexiglass

Brass (3.0 mm)

Teflon (0.5 mm)

50 mm

Sample volume

Figure 3-7: Electric field cell for emulsion stability measurements.

Multivariate Data Analysis

23

4 Multivariate Data Analysis

Many of the scientific problems encountered are multivariate in nature. For instance, the

phenomenon of emulsion stability depends on the complex interplay of a number of

variables. Multivariate analysis is a tool for extracting the essential information related to

some properties of interest from a large data set. The use of multivariate analysis on

chemical problems is often termed chemometrics, and has been defined by Wold [95] as

“How to get chemically relevant information out of measured chemical data, and how to

represent and display this information”. Several review articles on chemometrics have

been published [96, 97].

4.1 Principal Component Analysis (PCA)

The main purpose of PCA is to extract the essential information contained in a large data

set of m samples, each characterised by n variables (m x n data matrix, often termed X).

PCA decomposes the data by finding combinations of variables that best describe the

main trends in the data set. The idea is that these main trends are more or less directly

related to the main phenomena that describe the data set [98]. Because the variables in

a large data set usually co-vary to a large extent, the effective dimension of the data set

can be reduced from n to fewer dimensions that are combinations of the original

variables. These combined variables are called principal components (PC) because they

are particular relevant for the data set. The principal components can be seen as a

projection of the original data set in n dimensions onto a new co-ordinate system of the

principal components. Each sample will have a value along these new axes, and the value

of the sample along any given PC is termed the score for the sample. The scores describe

how the samples relate to each other. Every PC is a linear combination of n coefficients

based on the original n variables. These coefficients for each PC are called the loadings

for the principal component. Loadings thus constitute a bridge between the original

variable space and the PC space.

Mathematically, PCA is based on an eigenvector decomposition of the covariance matrix

of the variables in the data set. Given a data matrix X with m rows of samples and n

columns of variables, the covariance matrix of X is defined as

1)cov(

T

−=m

XXX (13)

Multivariate Data Analysis

24

The result of the PCA procedure is a decomposition of the data matrix, X, into principal

components of score and loading vectors

mnkkiimn ×× +++++= EptptptptX TTT22

T11 ... (14)

Here ti is the score vector, pi is the loading vector and E is the residual matrix. The

direction of the first principal component (t1, p1) is the line in the variable space that best

describes the variation in the data matrix X. The direction of the second principal

component is given by the straight line that best describes the variation not described by

the first principal component, and so on. This implies that PC2 is orthogonal on PC1.

Thus, the original data set can be adequately described using a few orthogonal principal

components instead of the original variables, with no significant loss of information. The

result is that the original data set of n variables is decomposed to a structured part (the

principal components) and a residual part. The residual part can be considered noise that

contains little information of interest.

One of the most powerful tools offered by the principal component analysis is the score

plot. A score plot is any two pairs of score vectors plotted against each other. The score

plots can be viewed as particularly useful two-dimensional windows into the PC-space,

where relations between the samples are easily observed. Often over 90% of the

information contained in the original n-dimensional data set can be found in a plot of the

two first principal components. Thus PCA performs a dual purpose: a transformation into

a more relevant co-ordinate system (the PC-space), and a dimensionality reduction. The

number of principal components needed to describe the system must be evaluated from

case to case, and depends on which principal components contain the information of

interest.

4.2 Partial Least Squares Regression (PLS)

Regression between a large data set (X matrix) and a response (y vector) is often

impossible by ordinary least squares methods due to strongly correlated and redundant

information in the data set. This problem is especially prominent for spectral data sets.

One solution to this is to decompose the X matrix by PCA as shown in Equation 13, and

perform the regression between the resulting score vectors and the response. Such PCA

decomposition followed by regression is called principal component regression (PCR). The

PLS regression is an alternative multivariate regression method. In PCA and PCR

decomposition the scores and loadings are the vectors that best describe the variance of

Multivariate Data Analysis

25

the X matrix. In PLS decomposition the scores and loadings are the vectors that have the

highest covariance with the response vector y. The PLS decomposition is followed by a

regression between these score vectors and the response.

Due to the risk of overfitting the regression model, i.e. modelling noise, the optimum

number of principal components to be used must be determined. One way of doing this is

the cross validation technique. Cross validation checks a model by repeatedly taking out

different sub-sets of calibration samples from the model estimation, and instead using

them as temporary, local sets of secret test samples. If the model parameter estimates

are stable against these repeated perturbations, this indicates that the model is reliable

[99]. In the simplest case each subset contains only one sample, which is called full cross

validation. Alternatively the test set method may be used for validation. Here a

completely new data set is used exclusively for validation purposes. This method requires

that enough samples are available both for regression and validation.

Main Results

26

5 Main Results

This section summarises the main findings from the five papers of this thesis. Paper I

described the characterisation of 18 crude oils and condensates by the HPLC SARA-

fractionation procedure. It was then shown that the SARA values could be predicted by

multivariate analysis from infrared and near infrared spectroscopy. Paper II used the

oscillating pendant drop tensiometer to characterise the interfacial rheology of the same

crude oils as used in Paper I. Paper III first extended the characterisation work on the

samples by introducing more data, including emulsion stability properties. Then the crude

oil characterisation work done in Papers I and II was summarised by investigating which

crude oil parameters determined the emulsion stability. In Papers IV and V the attention

was turned to the aggregation of asphaltenes. In Paper IV the aggregation onset as a

function of pressure and temperature was studied by high-pressure near infrared

spectroscopy. In Paper V the size of asphaltene aggregates was determined by Rayleigh

scattering theory of near infrared light.

Paper I

The intention of Paper I was to investigate the possibility of predicting SARA-values from

infrared and near infrared spectra of crude oils by multivariate regression. A set of 18

crude oils and condensates were characterised by IR and NIR, and by the SARA-

procedure. Table 5-1 shows the SARA-distribution for the samples along with the origin

and density. The samples are seen to reflect a broad range in SARA-properties. For

instance, the value of saturates range from 24.4 wt% for sample 18 to 82.7 wt% for

sample 4. Sample 18 is a highly asphaltenic crude oil, while sample 4 is a light

condensate.

Main Results

27

Table 5-1: SARA-distribution and density.

SARA fractionation results

Crude oil

no.

Origin Saturates

(wt%)

Aromatics

(wt%)

Resins

(wt%)

Asphaltenes

(wt%)

Density

(g/cm3)

1 West Africa 47.9 36.5 15.2 0.4 0.914

2 North Sea 48.0 37.5 14.2 0.3 0.916

3 West Africa 41.2 36.4 20.4 2.1 0.916

4 North Sea 82.7 13.4 3.9 0.0 0.839

5 North Sea 62.7 23.6 12.2 1.5 0.844

6 North Sea 35.3 36.8 24.5 3.5 0.945

7 North Sea 41.8 38.8 18.7 0.6 0.914

8 North Sea 50.9 34.6 14.0 0.5 0.885

9 West Africa 40.6 32.1 20.6 6.6 0.888

10 North Sea 79.8 16.5 3.6 0.1 0.796

11 West Africa 57.3 27.9 13.5 1.3 0.873

12 North Sea 60.6 30.0 9.2 0.2 0.857

13 West Africa 42.4 36.1 20.5 1.0 0.921

14 North Sea 65.0 30.7 4.3 0.0 0.796

15 North Sea 50.3 31.4 17.5 0.7 0.898

16 North Sea 55.4 28.3 12.9 3.4 0.840

17 West Africa 54.5 28.8 14.9 1.8 0.873

18 France 24.4 43.4 19.9 12.4 0.939

A principal component analysis was first performed on both the IR- and the NIR-spectra.

This revealed that the IR-spectra of samples 15 and 17 were outliers, and these two

samples were therefore left out of the IR-calibration models. A total of four models based

on the IR-spectra were built, one for each of the four SARA-parameters. The same was

done for the NIR-spectra, producing a total of 8 prediction models. In order to obtain

robust prediction models from the spectroscopic data, a broad calibration range was

required. The sample set contained samples ranging from light condensates to heavy

crude oils, and should be well suited for calibration purposes. One drawback, however,

was the asphaltene distribution. Most samples were low in asphaltene content, and only a

few had high asphaltene contents.

Partial least squares regression (PLS), using the spectroscopic data as X-data, was used

for the modelling, and full cross validation was utilised to validate the models. For the IR-

spectra the original spectra were used, while the NIR-spectra were first-order

differentiated before modelling since this produced better models. Figure 5-1 shows

Main Results

28

example spectra used in the modelling, while Figure 5-2 shows the measured vs.

predicted plot for one of the calibration models.

0

0.2

0.4

0.6

0.8

1.0

1750 1671 1591 1512 1433 1354 1275 1196 1117 1038 959. 880. 801. 722.

-0.02

-0.01

0

0.01

0.02

1000 1200 1400 1600 1800 2000 2200

413

18

Wavenumber [cm-1]

Abs

orba

nce

13

18

Wavelength [nm]

IR NIR

0

0.2

0.4

0.6

0.8

1.0

1750 1671 1591 1512 1433 1354 1275 1196 1117 1038 959. 880. 801. 722.

-0.02

-0.01

0

0.01

0.02

1000 1200 1400 1600 1800 2000 2200

0

0.2

0.4

0.6

0.8

1.0

1750 1671 1591 1512 1433 1354 1275 1196 1117 1038 959. 880. 801. 722.

-0.02

-0.01

0

0.01

0.02

1000 1200 1400 1600 1800 2000 2200

413

18

Wavenumber [cm-1]

Abs

orba

nce

13

18

Wavelength [nm]

IR NIR

413

18

Wavenumber [cm-1]

Abs

orba

nce

13

18

Wavelength [nm]

413

18

Wavenumber [cm-1]

Abs

orba

nce

413

18

413

18

Wavenumber [cm-1]

Abs

orba

nce

13

18

Wavelength [nm]

13

18

13

18

Wavelength [nm]

IR NIR

Figure 5-1: IR-spectra and first-order differentiated NIR-spectra.

13

73 2

1 1516

17

12

14

10

4

18

9

8

11

5

Figure 5-2: PLS prediction model for saturates from NIR spectra.

Main Results

29

In Table 5-2 the predicted uncertainty in the eight models are compared to the

experimental uncertainty of the HPLC-procedure. RMSEP (Root Mean Square Error of

Prediction) is a measure of the average prediction error that can be expected, expressed

in the same units as the original response values (wt%). The main conclusion is that the

models based on infrared and near infrared spectroscopy have the same levels of

uncertainty as the original HPLC-procedure. One exception is the asphaltene models, and

this is probably due to the unfavourable sample set with regard to asphaltene content.

The SARA-values can thus be predicted in a fast and simple manner from spectroscopic

techniques as an alternative to the traditional and more tedious HPLC-procedure.

Table 5-2: Uncertainty in HPLC procedure compared to IR and NIR PLS predictions.

Experimental (wt%) RMSEP, IR (wt%) RMSEP, NIR (wt%)

Saturates 2.2 2.5 2.8

Aromatics 2.5 2.2 2.4

Resins 1.4 1.4 1.4

Asphaltenes 0.2 1.3 1.0

Paper II

This paper investigated the interfacial rheology of water-crude oil systems. An oscillating

pendant drop tensiometer was utilised to characterise 21 crude oils and condensates in

contact with a common aqueous phase. 18 of the samples were the same as those

characterised in Paper I. All crude oils and condensates of this study were diluted in a

heptane/toluene solvent before measuring. Both the effect of oil concentration and

solvent composition was investigated. For the concentration study all 21 samples were

diluted with a 50/50 vol% heptane/toluene solution (heptol50) at three oil

concentrations, namely 0.002, 0.01 and 0.02 ml oil/ml solvent. For the solvent

composition study, two crude oils were diluted in heptane/toluene solutions containing 0,

50, 90, 95 and 100 vol% heptane (heptol(0)-heptol(100)).

In another paper on the oscillating pendant drop tensiometer the interfacial dilatational

elasticity modulus of the samples was discussed [100]. This modulus incorporates both

an elastic and a viscous contribution. Since the viscous component was found to be

insignificant for the majority of the samples, only the interfacial elasticity was discussed

in detail in Paper II. In this paper the interfacial rheology data was in addition

supplemented by near infrared spectroscopy data in order to offer a better explanation to

the effects of varying oil concentration and solvent composition.

Main Results

30

Figure 5-3 shows the measured interfacial elasticity of the samples. Two distinct groups

of samples could be separated, the ten samples of decreasing elasticity from the

intermediate to the highest oil concentration (left) and the ten samples of increasing

elasticity in the same interval (right). The last sample, crude oil 13, was not included in

these plots due to difficulties measuring the interfacial elasticity at the lowest oil

concentration.

2

6

10

14

18

22

26

30

34

-3 -2.8 -2.6 -2.4 -2.2 -2 -1.8 -1.6 -1.4log (oil concentration)

Inte

rfac

ial e

last

icity

[mN

/m] 1

23561115192021

-2

0

2

4

6

8

10

12

14

16

18

-3 -2.8 -2.6 -2.4 -2.2 -2 -1.8 -1.6 -1.4log (oil concentration)

Inte

rfac

ial e

last

icity

[mN

/m] 4

789101214161718

Figure 5-3: Interfacial elasticity plotted against oil concentration.

It was observed that the concentration dependence could not be explained by the SARA-

determined amounts of interfacially active fractions in the crude oils, resins and

asphaltenes. Some kind of crude oil component interactions was the most likely

explanation. To find an explanation to the differences in oil concentration dependence on

the elasticity, the crude oils were characterised by NIR spectroscopy. In Figure 5-4 the

optical density at 1600 nm from the NIR spectra of the crude oil solutions is plotted

against the relation (Resins/Asphaltenes) as determined from the SARA-analysis. Since

resins are believed to disperse asphaltene aggregates, small values of this relation should

indicate larger asphaltene aggregates. This was confirmed from the figure where low R/A

values generally produced high values of optical density. High values of optical density

implied larger aggregates in the solution. It was also seen that at low oil concentrations

the aggregate size was small, and more or less the same for all crude oils, while

especially at the highest oil concentration very high values of optical density was found

for the low R/A crude oils.

Main Results

31

0.070

0.075

0.080

0.085

0.090

0.095

0.100

0.105

0.110

0 5 10 15 20 25 30 35 40 45 50(wt% resins)/(wt% asphaltenes)

OD 1

600

nm 0.002 ml/ml0.01 ml/ml0.02 ml/ml

281411291017181563197201121

High OD samples Low OD samples

Figure 5-4: Optical density at 1600 nm of the samples as a function of the R/A number.

It was further noted that most of the samples that displayed decreasing elasticity at high

oil concentrations (left hand side of Figure 5-3) were the same samples displaying high

optical density at high oil concentrations. The right hand side samples of Figure 5-3

corresponded to the low optical density samples (small aggregates). The only exceptions

to this correlation were samples 1, 2 and 7. In addition, samples 4 and 16 were not

included in the R/A plot due to lack of asphaltenes, but they naturally belong to the high

R/A group. Sample 5 was not characterised by NIR due to lack of sample material.

The main conclusion from this part was that the decreasing values of interfacial elasticity

at high oil concentrations were caused by the formation of large, apparently weaker

interfacially active, asphaltene aggregates. To further verify this the solutions of one of

the two crude oils studied at several solvent compositions were also characterised by NIR

spectroscopy, and compared with the values of interfacial elasticity. Figure 5-5 shows the

elasticity values and corresponding optical density as a function of crude oil concentration

and solvent composition.

Main Results

32

0

0.01

0.02

0.03

0.04

0.05

0.06

-3 -2.8 -2.6 -2.4 -2.2 -2 -1.8 -1.6log (oil concentration)

net O

D 16

00 n

m

8

10

12

14

16

18

20

22

24

-3 -2.8 -2.6 -2.4 -2.2 -2 -1.8 -1.6Log (oil concentration)

lnte

rfac

ial e

last

icity

[mN

/m]

heptol(0)heptol(50)heptol(95)heptol(100)

Figure 5-5: Interfacial elasticity and optical density of crude oil 15.

In poor solvents (high heptane content) the interfacial elasticity decreased with

increasing oil concentration. In good solvents the interfacial elasticity generally increased

with oil concentration. From the optical density results the formation of larger aggregates

is seen to be much more dominating in poorer solvents. Again, the lowering of the

interfacial elasticity appeared to be caused by the formation of larger asphaltene

aggregates. At the lowest oil concentration, the highest elasticity values were obtained in

the poorest crude oil surfactant solvents. In such solvents the interfacial activity of resins

and asphaltenes is high, and more or less no aggregation was taking place, as seen from

the optical density values. At higher oil concentrations this effect of interfacial activity

seemed to be opposed by the formation of asphaltene aggregates.

Paper II demonstrated that measuring interfacial rheological properties of crude oil

systems is highly dependent both on the oil concentration and what solvent is used for

dilution. The effect of high interfacial activity in aliphatic solvents is quickly opposed by

asphaltene aggregation when increasing the oil concentration. When comparing the

interfacial rheological properties of different crude oils, both these effects have to be

taken into consideration. The findings of this study are consistent with the asphaltene

aggregation model proposed by Kilpatrick and coworkers [20, 37, 38]. They found that

precipitated asphaltenes had a lower ability to form elastic interfaces. This was attributed

to lower interfacial activity of such precipitated aggregates and possibly high amounts of

defects in films of precipitated material.

Main Results

33

Paper III

In Paper III the characterisation work presented in Papers I and II was taken advantage

of. The critical electric field cell was developed to measure the emulsion stability of the

same 21 crude oils and condensates used in the earlier papers. By multivariate analysis

the emulsion stability was then correlated to the physico-chemical properties of the

samples. In addition to SARA-data and interfacial elasticity, properties like density,

interfacial tension, molecular weight, total acid number (TAN) and viscosity were added

to the data matrix. The aim of this study was to gain insight into which parameters

govern the emulsion stabilising properties of the crude oils. In addition, NIR spectra of

the crude oils were used as input data.

Table 5-3 shows the data matrix, including Ecritical values for water cuts of 20% and 30%.

t1 and t2 are the first and second score vector from a principal component analysis of the

NIR spectra. The elasticity values used were the values for the lowest oil concentration,

0.002 ml oil/ml solvent (see Paper II).

Table 5-3: Data matrix on crude oil and condensates.

Origin WC20

[kV/cm]

WC30

[kV/cm]

S

[wt%]

A

[wt%]

R

[wt%]

Asph.

[wt%]

(S+Asph)/

(R+A) R/(R+Asph)

1 West Africa 0.58 0.47 47.9 36.5 15.2 0.4 0.93 0.97

2 North Sea 0.87 0.61 48.0 37.5 14.2 0.3 0.93 0.98

3 West Africa 2.00 0.68 41.2 36.4 20.4 2.1 0.76 0.91

4 North Sea 0.00 0.00 82.7 13.4 3.9 0.0 4.78 1.00

5 North Sea 1.00 0.64 62.7 23.6 12.2 1.5 1.79 0.89

6 North Sea 0.91 1.03 45.5 37.1 16.0 1.4 0.88 0.92

7 North Sea 2.00 1.50 35.3 36.8 24.5 3.5 0.63 0.88

8 North Sea 0.55 0.45 56.0 29.6 14.1 0.3 1.29 0.98

9 North Sea 0.84 0.59 41.8 38.8 18.7 0.6 0.74 0.97

10 North Sea 0.53 0.33 50.9 34.6 14.0 0.5 1.06 0.97

11 West Africa 2.00 1.85 40.6 32.1 20.6 6.6 0.90 0.76

12 North Sea 0.00 0.00 79.8 16.5 3.6 0.1 3.98 0.97

13 West Africa 0.61 0.45 57.3 27.9 13.5 1.3 1.42 0.91

14 North Sea 0.59 0.08 60.6 30.0 9.2 0.2 1.55 0.98

15 West Africa 0.85 0.40 42.4 36.1 20.5 1.0 0.77 0.95

16 North Sea 0.00 0.00 65.0 30.7 4.3 0.0 1.86 1.00

17 North Sea 0.47 0.42 44.1 41.6 13.8 0.5 0.81 0.97

18 North Sea 0.93 0.43 50.3 31.4 17.5 0.7 1.04 0.96

19 North Sea 2.00 2.00 54.5 28.8 14.9 1.8 1.29 0.89

20 West Africa 0.91 0.72 55.4 28.3 12.9 3.4 1.43 0.79

21 France 2.00 1.70 24.4 43.4 19.9 12.4 0.58 0.62

Main Results

34

Table 5-3, continued

Origin t1 t2 OD,

1600 nm

Elasticity

[mN/m]

Density

[g/cm3]

IFT

[mN/m]

Mw

[g/mole]

Visc.,25°C

[kg/m s]

TAN

[mg KOH]

1 West Africa -5.45 0.11 0.168 11.5 0.914 20.5 234 18.7 1.10

2 North Sea -3.16 0.26 0.240 7.3 0.916 24.8 279 57.4 3.10

3 West Africa 8.91 0.16 0.659 7.8 0.916 26.4 310 143.0 1.50

4 North Sea -7.42 0.17 0.107 -1.1 0.839 37.1 166 1.8 0.69

5 North Sea -3.62 -0.43 0.236 16.5 0.844 12.8 201 5.4 0.18

6 North Sea -0.39 0.52 0.332 3.7 0.862 31.9 244 14.5 0.02

7 North Sea 5.99 0.81 0.544 11.7 0.945 27.4 333 386.6 2.30

8 North Sea -5.02 -0.10 0.185 6.7 0.850 24.7 216 6.6 0.17

9 North Sea -3.53 0.28 0.229 8.4 0.914 11.8 284 51.0 3.10

10 North Sea -4.54 0.12 0.197 5.0 0.885 22.8 234 11.6 2.70

11 West Africa 17.50 -0.49 0.991 10.6 0.888 29.0 260 27.8 0.49

12 North Sea -5.84 -0.60 0.165 -1.4 0.796 34.2 157 1.7 0.01

13 West Africa -2.87 -0.05 0.257 - 0.873 24.6 235 17.7 0.50

14 North Sea -5.11 -0.39 0.186 0.2 0.857 22.9 227 10.5 0.04

15 West Africa 1.33 -0.02 0.395 9.2 0.921 16.2 295 105.8 3.60

16 North Sea -7.36 0.18 0.107 -0.6 0.796 27.6 142 1.2 0.02

17 North Sea -4.94 -0.37 0.192 8.1 0.847 19.9 223 11.7 0.15

18 North Sea -4.24 0.14 0.206 14.8 0.898 19.5 249 19.1 1.20

19 North Sea 9.54 0.11 0.684 24.9 0.840 27.4 298 63.1 0.36

20 West Africa 4.91 -0.41 0.525 5.6 0.873 19.0 248 15.3 0.44

21 France 15.31 0.00 0.893 12.9 0.939 13.4 303 278.9 0.20

A PLS regression model was built using the average Ecritical values at water cuts of 20%

and 30% as response. Due to a skewed distribution the Ecritical values were logarithmically

transformed before modelling. This excluded the condensates from the model since these

samples did not form stable emulsions (zero Ecritical values). Figure 5-6 shows the

regression coefficients and the predicted vs. measured plot for the resulting model. The

R2 value of the model is 0.93. From the regression coefficients the amount of asphaltenes

and interfacial elasticity are seen to be main contributors to emulsion stability. In

addition, the NIR spectra (through the scores t1) seem to contain much information with

regard to emulsion stability properties. High values of aromatics and the relation

Resins/(Resins+Asphaltenes) seem to be the two main variables decreasing emulsion

stability.

Main Results

35

-1.2

-1

-0.8

-0.6

-0.4

-0.2

0

0.2

0.4

0.6

0.8

-1.2 -1 -0.8 -0.6 -0.4 -0.2 0 0.2 0.4 0.6 0.8

14

10 178

1

13 1518

92

5

206

3

117 21

19

Measured (ln Ecritical)

Pred

icte

d (ln

E criti

cal)

SaturatesA

romatics

Resins

Asphaltenes

t1, NIR

t2, NIR

(S+Asf)/R

+A)

R/(R

+Asf)

Elasticity

Density

IFT

MW

TAN

Viscosity

Reg

. Coe

ff. (

ln E

critc

al)

Figure 5-6: Regression coefficients and predicted vs. measured plot for the emulsion stability model.

Based on the full model, a reduced model was built, containing only a few of the most

important variables. Five parameters from the full model were kept, the four SARA-

parameters and the interfacial elasticity. All four SARA-parameters were kept since they

are determined in the same experiment. This model produced a R2 value of 0.82. This

means that data from only two experimental procedures, the SARA-separation and the

interfacial elasticity determination, are able to give a reasonable estimate of the emulsion

stability properties of a crude oil. Earlier attempts to correlate the emulsion stability to

SARA-data and some of the other variables in the data matrix were not successful,

underlining the importance of knowing the interfacial properties of crude oils when

discussing emulsion stabilisation/destabilisation.

The NIR spectra appeared to contain information on the emulsion stability properties of

the crude oils due to the high value of the t1 regression coefficient. t1, the scores of PC1,

was closely correlated to the baseline elevation caused by increasing aggregate size in

the crude oils. Based on this, a third PLS model with NIR spectra of the crude oils as X-

data, was constructed. Wavelengths from 1300-2200 nm were used to predict the same

Ecritical values as in the other two models. Figure 5-7 shows example NIR spectra and the

predicted vs. measured plot of the resulting model. The model produced a R2 value as

high as 0.95, confirming that near infrared spectroscopy is a very good source of

information for crude oil properties. In Paper I it was demonstrated that SARA-

parameters could be predicted from NIR spectra, and we showed in this paper (through

t1) that NIR spectra contain information on the aggregation state of the asphaltenes in

Main Results

36

the crude oils. These are all factors contributing to emulsion stability, and explains some

of the good predictive power of near infrared spectroscopy.

-1.2

-1

-0.8

-0.6

-0.4

-0.2

0

0.2

0.4

0.6

0.8

-1.2 -1 -0.8 -0.6 -0.4 -0.2 0 0.2 0.4 0.6 0.8

14 17

10

81

13

15

189 2

20

6

319

11721

Pred

icte

d (ln

E criti

cal)

Measured (ln Ecritical)

0

0.5

1.0

1.5

2.0

1300 1400 1500 1600 1700 1800 1900 2000 2100 2200

1

15

7

21

Opt

ical

den

sity

Wavelength (nm)

0

0.5

1.0

1.5

2.0

1300 1400 1500 1600 1700 1800 1900 2000 2100 2200

1

15

7

21

Opt

ical

den

sity

Wavelength (nm)

1

15

7

21

Opt

ical

den

sity

Wavelength (nm)

Figure 5-7: Example NIR spectra and predicted vs. measured plot for the NIR model.

One of the problems associated with the prediction of emulsion stability from single

parameters like SARA, elasticity, viscosity etc. is that detecting the effect of constituents

of very low concentration in the crude oil may be difficult. However, such constituents

may have a dramatic effect on the emulsion stability properties. One example is chemical

demulsifiers added at ppm-levels during crude oil production. A SARA-determination is

not able to distinguish between a crude oil sample with and without such additives. An

indirect analytical procedure like near infrared spectroscopy on the other hand, can be

able to detect such additives by measuring the effect they have on the crude oil. Figure

5-8 shows two NIR spectra of the same North Sea crude oil, the only difference being the

addition of 10-15 ppm of demulsifier. This crude oil was not included in the sample set

published in Papers I-III. It is seen that this addition has an adverse effect on the NIR

spectra, a difference that a SARA-analysis would not have revealed. The differences in

the spectra are due to smaller asphaltene aggregates in the sample with added

demulsifiers. The emulsion stability of the crude oil with no added demulsifier (as

measured by Ecritical) was twice as high as for the sample with addition. The interfacial

elasticity of the two samples was also measured, and it was found that the sample

without added demulsifier displayed higher values of interfacial elasticity than the treated

sample (15.3 mN/m vs. 11.8 mN/m). This again demonstrates the close correlation

between asphaltene aggregation, interfacial elasticity and emulsion stability.

Main Results

37

0.2

0.4

0.6

0.8

1.0

1.2

1.4

1000 1200 1400 1600 1800 2000 2200

No demulsifier added

Demulsifier added

Wavelength [nm]

Opt

ical

Den

sity

Figure 5-8: Effect on NIR spectra of crude oil with addition of demulsifier.

Paper IV

In Papers II and III the importance of asphaltene aggregation state was discussed both

in relation to interfacial rheology and emulsion stability. In addition, near infrared

spectroscopy was shown to contain information on this aggregation state through the

baseline elevation caused by light scattering. In Paper IV the asphaltene aggregation

behaviour was studied in more detail by use of a high-pressure NIR system shown in

Figure 5-9. The system combined the accurate pressure and temperature control offered

by a PVT rig, in which a high-pressure transmittance NIR cell was connected. Specially

designed software enabled direct principal component analysis (PCA) of the spectra in

order to easily detect asphaltene aggregation.

Main Results

38

x x

x x x x

x

Air cabinet

Personal computer

NIR spectrometer

Fibre optics

NIRcell

Cell 1

Cell 2

V1

V2

V3x x

x x x x

x

Air cabinet

Personal computer

NIR spectrometer

Fibre optics

NIRcell

Cell 1

Cell 2x

xx x

x x x

Air cabinet

Personal computer

NIR spectrometer

Fibre optics

NIRcell

Cell 1

Cell 2

V1

V2

V3

Figure 5-9: High-pressure near infrared spectroscopy setup.

Both a recombined crude oil and model systems of pentane, toluene and asphaltene were

studied. The model systems were constructed such as to be at the verge of asphaltene

precipitation. Two systems of 1.2 wt% asphaltene in 35 wt% and 40 wt% pentane-in-

toluene solvents were made. The crude oil contained 0.8 wt% asphaltenes, had a

reservoir bubble point pressure of 155 bar and was known to be susceptible to

asphaltene precipitation by pressure depletion. The systems were pressurised to 100 or

300 bar and charged to the high pressure rig at temperatures from 100°C to 150°C. The

systems were then depressurised in steps, and the resulting NIR spectra at each

pressure level were analysed by the PCA routine. The system was allowed about 20

minutes to equilibrate at each pressure level. It was found that at the asphaltene

aggregation onset pressure a distinct shift in the PCA score plots could be seen. Lowering

the pressure even further, the bubble point was detected by NIR spectra displaying low

absorbance due to gas evolution. In Figure 5-10 the procedure of asphaltene aggregation

onset detection (for the crude oil) from the NIR spectra, via the principal component

analysis, to the impact on the phase envelope is demonstrated. Starting from 300 bar,

first a decrease in optical density with pressure depletion due to the compressibility of

the crude oil was seen. As soon as aggregates started to grow the optical density

increased due to increased light scattering. Although this was difficult to see directly from

the spectra, it was clearly seen from the score plot to occur around 180 bar. In the phase

envelope for the crude oil the pressure depletion path is indicated along with the

detected asphaltene aggregation onset pressure at 180 bar. By performing the same

type of experiments at several temperatures the full asphaltene aggregation onset

envelope can be determined.

Main Results

39

0.4

0.6

0.8

1.0

1.2

1.4

1300 1400 1500 1600 17 00 1800 1900 2000 2100 2200

Wavelength [nm]

Opt

ical

Den

sity

300 bar

160 bar

155 bar (bubble point)0.4

0.6

0.8

1.0

1.2

1.4

1300 1400 1500 1600 17 00 1800 1900 2000 2100 2200

Wavelength [nm]

Opt

ical

Den

sity

300 bar

160 bar

155 bar (bubble point)

Wavelength [nm]

Opt

ical

Den

sity

300 bar

160 bar

155 bar (bubble point)

-0.08

-0.04

0

0.04

0.08

0.12

-0.2 -0.1 0 0.1 0.2 0.3 0.4 0.5 0.6

Scores PC1

Scor

es P

C2

300

280

260

240

220

200

190

180170

165

160

150 (liquid phase)

-0.08

-0.04

0

0.04

0.08

0.12

-0.2 -0.1 0 0.1 0.2 0.3 0.4 0.5 0.6

Scores PC1

Scor

es P

C2

300

280

260

240

220

200

190

180170

165

160

150 (liquid phase)

0

40

80

120

160

200

240

280

320

-50 50 150 250 350 450 550Temperature [C]

Pres

sure

[Bar

]

o Asphaltene aggregation onset

Critical point

0

40

80

120

160

200

240

280

320

-50 50 150 250 350 450 550Temperature [C]

Pres

sure

[Bar

]

o Asphaltene aggregation onset

Critical point

o Asphaltene aggregation onset

Critical point

PCA

To phase envelope

Figure 5-10: Detection of asphaltene aggregation onset pressure from high-pressure NIR

spectroscopy and PCA.

Main Results

40

Performing the same type of experiments on the model systems (containing only

asphaltenes in addition to solvents) produced the same type of results, thus confirming

that the shift in the score plot indeed was caused by asphaltene aggregation.

Depressurisation of a model system without asphaltenes showed no shift in the score

plot. Table 5-4 summarises the experimental conditions, detected onset pressures and

bubble points for the systems studied. The bubble point pressures for the low

temperature model systems were too low to be detected. As expected, the onset

pressure for the 40 wt% pentane solvent was higher than for the 35 wt% pentane

system. In addition, the onset pressure at 150°C was found to be higher than that at

100°C.

Table 5-4: Bubblepoint and asphaltene aggregation onset pressures.

System Temperature

[°C]

Bubblepoint

[bar]

Onset pressure

[bar]

Crude oil 125 155 180

35 wt% pentane 100 <2.5 20

35 wt% pentane 150 10 30

40 wt% pentane 100 <2.5 40

40 wt% pentane, no asphaltene 150 14 -

The reversibility of the asphaltene aggregation was also studied for the crude oil system

and the 35 wt% pentane system at 150°C. This was done by repressurising the systems

stepwise from the bubble point to 100 bar and 300 bar for the model system and the

crude oil respectively. When the original pressure was reached, the systems were left to

stand and NIR spectra were recorded in intervals until no further spectral change was

observed. In Figure 5-11 the optical density at 1600 nm for both the depressurisation

and the repressurisation for the two systems are shown.

Main Results

41

0.39

0.40

0.40

0.41

0.41

0.42

0.42

140160180200220240260280300320

Pressure [bar]

Opt

ical

Den

sity

@ 1

600

nm

DepressurisationRepressurisation

300

30072h

0.39

0.40

0.40

0.41

0.41

0.42

0.42

140160180200220240260280300320

Pressure [bar]

Opt

ical

Den

sity

@ 1

600

nm

DepressurisationRepressurisation

300

30072h

300

30072h

0.48

0.50

0.52

0.54

0.56

0.58

0.60

0.62

0.64

020406080100120

Pressure [bar]

Opt

ical

Den

sity

@ 1

600

nm

DepressurisationRepressurisation

100

100

23h

0.48

0.50

0.52

0.54

0.56

0.58

0.60

0.62

0.64

020406080100120

Pressure [bar]

Opt

ical

Den

sity

@ 1

600

nm

DepressurisationRepressurisation

100

100

23h

100

100

23h

Figure 5-11: Asphaltene aggregation and reversibility of crude oil and model system.

For the crude oil (upper plot) considerable redissolution of the aggregation was seen

when increasing the pressure from 150 to 170 bar, as seen by the decrease in optical

density at 1600 nm. However, no significant redissolution was then observed until the

crude oil was left at the original pressure of 300 bar. After 72 hours the aggregate size

had practically returned to its original state. For the model system on the other hand, the

Main Results

42

aggregates seemed to redissolve steadily during repressurising, but the redissolution

came to a stop after approximately 23 hours of equilibration at the original pressure of

100 bar. Thus, while the asphaltene aggregation of the crude oil was more or less

completely reversible, the model system asphaltene aggregation appeared to be only

partial reversible. The asphaltenes of the model system were stripped for the resin

fraction when they were being prepared. The lack of dispersing resins during the

repressurisation was believed to explain the different behaviour of the crude oil and the

model system. The possibility of full reversibility for the model system by allowing even

more time for equilibration could however not be ruled out.

Using NIR spectroscopy in combination with multivariate analytical techniques like

principal component analysis was shown to be a very efficient tool in studying asphaltene

aggregation behaviour. Very small spectral changes are detected in a score plot, enabling

an accurate determination of the onset pressure. In addition, bubble points are easily

detected from the spectral features.

Paper V

In several of the earlier papers of this thesis the ability of near infrared spectroscopy to

detect particle formation in crude oils has been emphasised. However, no quantitative

measure of the size of the particles could be given. In this last paper the possibility of

getting a measure of the asphaltene aggregate size was explored. The idea was to exploit

the equations for Rayleigh scattering by small, dielectric spheres.

In this paper the NIR measurements were performed at ambient conditions with a

transflectance probe with path length of 0.5 mm. The measured optical density from the

NIR spectra depended both on the particle radius, r, and the number of particles, N,

contained in the cell volume, as seen from the Rayleigh scattering expression

+−

==2

2

2

4

6

2184.13056434.0434.0

nnrNNOD sc λ

σ (15)

This equation follows from Equations 9-12 in Chapter 3. To use Equation 15 to predict

particle size, r, from the optical density, a measure of the number of particles contained

in the cell volume had to be found. For this purpose dilute aqueous solutions of

monodisperse polystyrene particles of known particle size were used. Since the radius of

the polystyrene particles was known, the experimental optical density curve of the

Main Results

43

particles could be fit to Equation 15 by only varying N, the number of particles. The

number of particles was determined indirectly by adjusting the cell volume in Equation 16

when fitting experimental optical density curves to Equation 15

10034

%3

,

×==

partpart

partsolvcell

part

totpart

r

wtVMM

Nρπ

ρ (16)

where Mpart, tot and Mpart is the total mass of particles and the mass of a single particle,

Vcell is the volume containing the particles, ρsolv and ρpart is the density of the solvent and

particles, wt%part is the weight percent of particles in solution, and rpart is the radius of

the particles.

The cell volume, a measure of the volume exposed to the NIR light, was found to be

approximately 0.31 cm3. To verify this value, the size of another set of polystyrene

particles was predicted by only varying the radius in Equation 15. The fit of Equation 15

to the experimental optical density curve gave a radius of 70.5 nm, which was considered

close enough to the known radius of 65 nm. It is important to note that the experimental

optical density curves used were net scattering curves, with the optical density of pure

water subtracted. This isolated the scattering contribution, assuming that the absorption

contribution of the particles was negligible. Figure 5-12 shows the fit of the experimental

and the calculated optical density curves for the volume calibration (115 nm radius) and

the verifying of the volume (65 nm radius).

0.018

0.022

0.026

0.030

0.034

0.038

1100 1140 1180 1220 1260 1300

Wavelength [nm]

Opt

ical

Den

sity

Rayleigh (115 nm, adjusted)Experimental (115 nm)

0.004

0.005

0.006

0.007

0.008

0.009

1100 1140 1180 1220 1260 1300Wavelength [nm]

Opt

ical

Den

sity

Rayleigh (70.5 nm)Experimental (65 nm)

Figure 5-12: Cell volume estimation by monodisperse polystyrene particles.

Main Results

44

For the determination of asphaltene aggregate size, pentane-precipitated asphaltenes

were first dissolved in toluene (0.19 wt%). To induce asphaltene aggregation this

solution was titrated with pentane, and NIR spectra were recorded after each pentane

addition. The optical density was seen to first decrease due to the pentane dilution,

before increasing due to light scattering by asphaltene aggregates forming at around 40

wt% added pentane. To predict the aggregate size the scattering contribution had again

to be isolated. To remove the solvent absorption the optical density of reference solutions

with the same amount of added pentane to toluene, but without asphaltenes, was

subtracted from all spectra. This isolated the asphaltene contribution (scattering and

absorption). The asphaltene absorption contribution was removed by assuming that

asphaltenes in pure toluene displayed no scattering. Therefore the first spectrum, of

asphaltenes in pure toluene, was subtracted from all spectra. The subtraction of this

spectrum had to be volume corrected since the asphaltene concentration continuously

decreased with pentane addition. Figure 5-13 shows the optical density at a single

wavelength, 1300 nm, for the pentane titrated asphaltene solutions. The absorption +

scattering curve is the net asphaltene contribution to the optical density. For the

scattering curve the asphaltene absorption in pure toluene is subtracted, leaving only the

scattering contribution. This curve is corrected for the dilution by pentane addition in the

corrected scattering curve. The first sign of aggregation is seen at around 40 wt%

pentane. These corrected scattering NIR spectra were used for the subsequent aggregate

size estimations.

-0.01

0.00

0.01

0.02

0.03

0.04

0.05

0 10 20 30 40 50 60weightpercent pentane

Opt

ical

Den

sity

absorption + scatteringscatteringcorrected scattering

Figure 5-13: Optical density at 1300 nm for the pentane titrated asphaltene-in-toluene solutions.

Main Results

45

With the light scattering from the asphaltene aggregates isolated, the optical density in

the region 1200-1600 nm was used to fit the experimental NIR curves to the Rayleigh

theory in Equation 15. The minimum aggregate size detected was for the 41.4 wt%

added pentane solution, which gave an aggregate radius of 32 nm. For aggregates larger

than approximately 100 nm in radius (52.3 wt% added pentane) the Rayleigh theory was

found to no longer be valid. The fit to the Rayleigh theory for these two systems is shown

in Figure 5-14.

0.006

0.010

0.014

0.018

0.022

0.026

1200 1300 1400 1500 1600Wavelength [nm]

Opt

ical

Den

sity

Rayleigh (100 nm)Experimental (52.3 wt% pentane)

0.0000

0.0002

0.0004

0.0006

0.0008

0.0010

0.0012

1200 1300 1400 1500 1600Wavelength [nm]

Opt

ical

Den

sity

Rayleigh (32 nm)Experimental (41.4 wt% pentane)

Figure 5-14: Estimation of aggregate radius for two solutions of asphaltenes in pentane and

toluene.

46

6 Concluding Remarks

The stabilisation/destabilisation of water-in-crude oil emulsions is a complex field that

have great implications on petroleum processing. Despite the huge economical and

environmental consequences caused by the failure to properly resolve such emulsions,

the mechanisms and factors controlling the process is still far from fully understood. This

work has tried to contribute to this understanding by correlating the physico-chemical

properties of very different crude oils to their emulsion stability properties. In addition,

asphaltene aggregation both at ambient and high-pressure conditions has been studied.

Techniques have been developed in order to determine parameters thought to influence

emulsion stability. This includes the build-up of a SARA-separation scheme by HPLC, and

application of the oscillating pendant drop technique on crude oils for interfacial rheology

measurements. In addition to establishing a data matrix of characterising parameters,

the use of indirect characterising by spectroscopic techniques has been utilised. This has

resulted in the observation that crude oil SARA-data can be determined both from

infrared and near infrared spectra in a fast and simple manner compared to the more

tedious traditional HPLC-method (Paper I). The oscillating pendant drop technique was

found to be well suited for measurements on crude oil systems, and the elasticity of a

crude oil-water interface was found to not be directly correlated to any of the SARA-

fractions of the crude oils (Paper II). This emphasised the fact that interfacial rheological

properties must be viewed as a result of complex interactions between the indigenous

crude oil components. This was confirmed from near infrared spectroscopy data, which

suggested that asphaltene aggregation had a great influence on the interfacial rheology.

A new cell for measuring emulsion stability, based on the application of electric fields,

was used as an alternative to traditional bottle tests. The technique was demonstrated to

give very reproducible data that was easily obtained (Paper III). The characterising data

on the 21 crude oils collected through Papers I and II was added to a data matrix that

already contained parameters like viscosity, density, molecular weight etc. The data was

then correlated to the obtained emulsion stability data. It was found that amount of

asphaltenes, interfacial elasticity and asphaltene aggregation was the parameters

contributing most to high stability. Near infrared spectroscopy was also found to produce

good estimates of emulsion stability. This was attributed to the fact that NIR contain both

chemical information (SARA, Paper I) and information on the asphaltene aggregation

state. Both these factors were shown to have a great influence on the emulsion stability.

47

As described, the level of asphaltene aggregation was found to influence both interfacial

rheology and emulsion stability (Papers II and III). Asphaltene aggregation, and

subsequent deposition, is also known to cause other problems like plugging of reservoirs

and processing equipment. In this respect the onset pressure of asphaltene aggregation

is an important parameter. In Paper IV the combination of a high-pressure NIR cell and

multivariate analysis was demonstrated to be a very efficient tool in the early detection

of this aggregation onset pressure. The system was also used to demonstrate the full

reversibility of crude oil asphaltene aggregation by repressurisation, although the kinetics

of the process was slow.

In the discussions of asphaltene aggregation level (Papers II-IV) no quantitative measure

of the size of the aggregates was given. In Paper V the near infrared spectral data was

coupled to the Rayleigh theory of light scattering to produce a measure of this size. It

was found that aggregate sizes down to about 30 nm in radius could be detected by NIR.

This further confirmed the usefulness of near infrared spectroscopy when working with

colloidal systems like crude oils. Throughout the work of this thesis NIR has been shown

to contain both chemical (SARA) and physical information (size of aggregates). By also

utilising the benefits of multivariate analysis tools like PCA and PLS, the data contained in

the NIR spectra has been extracted in a simple and efficient way.

48

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