Evaluation and Improvement of Screening Methods Applied to

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Evaluation and improvement of screening methods applied to asphaltene precipitation Verônica Jesus Pereira, Luisa Larroudé Olivieri Setaro, Gloria Meyberg Nunes Costa, and Sílvio Alexandre Beisl Vieira de Melo Energy Fuels, Just Accepted Manuscript • DOI: 10.1021/acs.energyfuels.6b02348 • Publication Date (Web): 15 Dec 2016 Downloaded from http://pubs.acs.org on December 20, 2016

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Energy & Fuels

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Evaluation and improvement of screening methods applied to asphaltene precipitation

3

Verônica J. Pereira, Luisa L.O. Setaro, Gloria M.N. Costa, Silvio A.B. Vieira de Melo*

4 5 6 7 8

Programa de Engenharia Industrial, Escola Politécnica, Universidade Federal da Bahia, Rua Aristides Novis, 2, 6º andar, Federação, Salvador, Bahia, CEP 40210-630, Brasil; tel. +55 71 32389802, fax: + 55 71 32839800, e-mail: [email protected].

9

Abstract

1

* To whom correspondence should be addressed.

10 11

This study aims to evaluate the performance of the four most known screening methods

12

used to predict the risk of asphaltene precipitation in crude oil employing a large

13

database from literature. The selection of these methods was based on the amount of

14

property data required for their application. Most methods reported in literature use

15

SARA analysis as the property to monitor the stability of the oil with respect to

16

asphaltene precipitation. Other methods require temperature, pressure and oil density

17

data to indicate the risk of asphaltene precipitation. Results showed inconsistency for

18

two of the four screening methods selected and improvements were proposed and

19

successfully validated.

20 21

Keywords: asphaltene precipitation, SARA analysis, oil stability, screening methods

22 23

1.Introduction

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During the production of oil and gas, changes in temperature, pressure and

25

composition of the reservoir fluids can occur and result in the destabilization of the

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fluid mixture leading to precipitation of asphaltene1. This is an undesirable

27

phenomenon because changes the permeability and wettability of the reservoir,

28

causes damage to the wellbore and can clog the well and surface facilities2. When it

29

occurs, there is a remarkable reduction of oil and gas production. Also, operations

30

to remove or prevent formation and deposition of solids are quite expensive and

31

usually include chemical and/or mechanical treatments as well as asphaltene

32

inhibitors.Therefore, it is important to investigate the risk of asphaltene precipitation

33

prior to implementing any production process such as a gas injection scheme.

34

ACS Paragon Plus Environment

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Although progress has been reached, the real mechanism of asphaltene

2

agglomeration, flocculation and precipitation has not been completely understood

3

yet3. Controversies about the asphaltene destabilization mechanism remain mainly

4

on two aspects: on one hand asphaltene is considered as colloidal suspensions while

5

on the other hand it is described as a liquid-liquid mixture4.

6 7

Asphaltenes are large molecules constituted primarily of carbon and hydrogen, with

8

small percentage of sulfur, nitrogen, oxygen, vanadium and nickel atoms per

9

molecule. The structure is composed of carbon and hydrogen rings, mainly

10

aromatic groups, where heteroatoms can be part of the ring structure or the links

11

connecting the rings 5.

12 13

There are several measurement techniques to investigate various aspects of

14

asphaltene precipitation: gravimetric technique, light scattering technique, acoustic

15

resonance technique, near-infrared spectroscopy, high pressure microscope2. The

16

experimental investigation requires a large number of experiments at reservoir

17

conditions of pressure and temperature and is often unfeasible and costly. There are

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also a variety of approaches to modeling asphaltene precipitation that can be divided

19

into five main groups2: polymer solubility models, equation of state models,

20

colloidal

21

thermodynamic models. Parameters estimation for these models needs experimental

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data that are usually very costly and time consuming to measure. Besides this fact,

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models to predict asphaltene precipitation are quite complicated and need many

24

properties. In this context, a simple and fast method to predict the risk of asphaltene

25

precipitation using few property data is much desirable. The screening test is a

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preliminary analysis that indicates the cases that would require further experimental

27

investigation.. As the characteristics of the oil vary according to the geographical

28

position of the well and the rock type, there is still not an agreement in literature

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about the most appropriate method to predict the crude oil stability. Therefore, the

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preliminary screening techniques to evaluate asphaltene stability mostly employs

31

data from the SARA analysis.

models,

thermodynamic

micellization

models

and

molecular

32 33

Crude oils are a complex mixture of thousands of components and for this reason

34

the complete characterization is not feasible. The choice of composition details is


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related to the application needed. A simple analysis is to consider the mixture into

2

fractions to characterize the oil by dividing the it into Saturate, Aromatic, Resin and

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Asphaltene fractions (SARA)6. The saturate fraction consists of nonpolar saturated

4

hydrocarbons including linear, branched, and cyclic saturated hydrocarbons, and

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the aromatics compounds contain one or more aromatic rings that are more

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polarizable7. The resins and asphaltene fractions have polar substituents and the

7

difference between resins and asphaltene is defined in terms of their solubility in

8

certain solvents. Resins are miscible with n-alkanes like pentane and n-heptane. On

9

the other hand, asphaltene is the fraction that is insoluble in n-alkanes like n-

10

heptane, but soluble in aromatic solvents like toluene2,7. Asphaltene consists of the

11

heaviest and most polar components in crude oil and its characteristics change for

12

each oil8. The risk of asphaltene precipitation in the oil is related to the percentage

13

of each of the SARA fractions. The relationship between SARA fractions and

14

asphaltene precipitation has been the subject of numerous investigations. Based on

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the assumption that the stabilization of asphaltene is supported by resins adsorbed

16

on their surface, Leontaritis and Manssori9 recommend using the ratio of resins to

17

asphaltene as an indicator of asphaltene stability. Fan et al.10 observed that the

18

amount of each of the SARA fractions in a crude oil is associated with the

19

asphaltene stability in that oil. Alkafeef. et al.11 observed that the rupture of the

20

balance of attraction forces between the adsorbed resin molecules and asphaltene

21

particles are the mean reason of the flocculation (destabilization) of colloidal

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asphaltene in the oil. Jamaluddin et al.12 suggested a method based on the asphaltene

23

to resin weight ratio to determine at what ratio asphaltene precipitation might occur.

24

In this methods, the risk of asphaltene precipitation increases as the resin content

25

decreases. According to the theories based on the colloidal nature of crude oils, Yen

26

et al.13 defined the colloidal instability index (CII). This is another screening

27

criterion expressed as the ratio of the sum of asphaltenes and saturates to the sum of

28

aromatics and resins, and it can be used to identify crude oil systems with

29

precipitation problems. Stankiewicz et al.14 proposed another method that relates the

30

risk of asphaltene precipitation with the ratio of saturates/aromatics to

31

asphaltene/resin.

32 33

De Boer et al.15 proposed a graphical method based on the solubility concept to

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detect the thermodynamic conditions at which the onset of asphaltene precipitation


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occurs. The plot can be used as a preliminary screening tool to identify the tendency

2

of the oil to asphaltene precipitation. In this case, it is also interesting to know the

3

composition of the oil, especially of the lighter components as they may affect

4

strongly on asphaltene precipitation. De Boer’s method also requires calculation of

5

in situ oil density.

6 7

As the experience has shown there are a significant number of asphaltene problems

8

for which prediction based on the solubility criteria in the de Boer’s plot is not

9

observed16. Improvement required for more accurate asphaltene prediction was

10

proposed by Wang and Creek16 .Their method relates the solubility parameter to the

11

square root of the molar volume of precipitating agents for a series of n-paraffins. It

12

provides a linear relationship between these two parameters and two areas on the

13

plot: a stable and an unstable one. However, it requires experimental data not easily

14

found in literature such as PVT and compositional data and stock-tank oil titration

15

experiments16. On the other hand, Shokrlu et al.17 modified the De Boer’s method

16

by specifying separate plots for each reservoir. The solubility parameter is

17

determined by using measures of the refractive index. Colloidal instability index,

18

asphaltene to resin ratio and asphaltene trend instability techniques were used to

19

confirm the accuracy of the predictions.

20 21

Although there are screening methods in literature to predict the risk of asphaltene

22

precipitation in crude oil, an evaluation based on a large number of oil database has

23

not been performed yet. Overall, in each approach, the conclusions are reached

24

using a limited number of oils.

25 26

The objective of this study is to evaluate and compare the screening methods of

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asphaltene precipitation mostly used in the oil industry employing the most

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extensively property database available in literature. For this purpose, experimental

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property data of 221 oils from different regions around the world are used. The

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performances of three methods proposed by Jamaluddin et al.12, Yen et al.13 and

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Stankiewicz et al.14, which use SARA analysis, were evaluated and compared. The

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method proposed by De Boer et al.15, which needs the oil density and the difference

33

between the bubble point and the reservoir pressure as input data, was evaluated

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and compared to Jamaluddin’s method.


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2. Screening methods

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In this section the four methods selected to screen the risk of asphaltene precipitation

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are briefly introduced. First, the three methods based on SARA analysis are described

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followed by the last one, which does not use SARA data: the De Boer’s method.

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Jamaluddin’s method12 evaluates the stability of reservoir fluid relative to the possibity

7

of asphaltene precipitation based on the weight ratio of resins and the weight ratio of

8

asphaltenes (SARA data) . Zendehboudi et al.2 proposed a graphic method based on

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Jamaluddin’s experimental data as shown in Figure 1Figure 1. The straight line divides

10

the stability of the oil in two areas: above the line oil is supposed to be unstable and

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under the line it is considered stable.

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Jamaluddin et al.12 found a relation between the asphaltene content and pressure, and

14

another one between the resin to asphaltene ratio and pressure. These relations allow

15

stating that the asphaltene fraction and the resin to asphaltene ratio behave in different

16

ways, i.e., asphaltene precipitation increases as the resin to asphaltene ratio decreases.

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Thus, a high content of asphaltene indicates that the oil is unstable if the asphaltene to

18

resin ratio is higher than 0.3514. As the criterion for stability is the asphaltene to resin

19

ratio below 0.35, the stable area is narrow.

20

21 22 23

Figure 1. Plot for predicting asphaltene precipitation based on Jamalluddin et al.’s experimental data2 .


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Yen et al.13 proposed a method based on the colloidal instability index (CII), which

3

considers oil as a colloidal system consisting of saturated, aromatics, resins and

4

asphaltenes pseudo components. CII expresses the stability of asphaltenes in terms of

5

these pseudo components and is defined as the ratio of the sum of the asphaltenes and

6

their (saturated) flocculants to the sum of their peptizers (resins and aromatics) in a

7

crude oil, as defined by Equation (1). CII above 0.9 means a propensity to asphaltene

8

aggregation and below 0.7 a tendency to solubilize the asphaltene18. If CII is lower

9

than 0.7, the oil is stable; if it is higher than 0.9, the oil is very unstable and if it is

10

between 0.7 and 0.9 the oil is supposed to present moderate instability. It is noteworthy

11

that these limits were determined from experimental data selected by the authors. There

12

is no theoretical basis in literature on how these limits were established. CII considers

13

each of SARA fractions as a pseudo component of the oil and that they sum up as

14

colloidal system to form the crude oil. Figure 2 shows the plot obtained from

15

Zendehboudi et al.2. There are two curves that limit three regions: above the upper line,

16

asphaltene precipitation is likely to occur; in the area between the lines, there are slight

17

concerns on asphaltene precipitation; and under the lower line, there is no concern on

18

asphaltene precipitation. This method uses SARA data as parameters: the total amount

19

of asphaltenes and saturates to the total amount of aromatics and resins ratio.

20 CII =

Saturates + Asphaltene s Aromatics + Resins

(1)

21 22

If the oil is considered to be a colloidal system, the resins form a micelle around the

23

asphaltene particles. The aromatic fraction links these micelles and the saturate fraction,

24

where the micelles are easier scattered6. The higher the sum of aromatics and resins are,

25

the lower the CII is and as a consequence the oil is considered stable due to the

26

solubility of asphaltenes and resins in aromatics and saturates.

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Figure 2. Yen’s method13 to predict asphaltene precipitation.

3 4

Stankiewicz et al.14 developed a method that also uses SARA data as parameters to

5

create a screening model based on a saturates to aromatics ratio and an asphaltenes to

6

resins ratio. Figure 3 shows a curve that delimits two regions: a stable one above the

7

curve and an unstable one below it. This curve was plotted through analogy with the

8

curve shown on Bahrami et al.14. It considers resins as peptizing agents in oil.

9

Therefore, if the value of asphaltene to resins ratio is higher than 0.35, the crude oil is

10

supposed to be unstable14.

11


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1 2

Figure 3. Stankiewicz’s method14 to predict asphaltene precipitation.

3 4

De Boer’s method considers that the oil density at in-situ conditions can be correlated

5

with the difference between the initial pressure and the bubble point pressure. The graph

6

from De Boer et al.15 is shown in Figure 4, in which there are two lines that separate

7

three different regions, analogue to Yen’s method. The differences between both

8

methods are in the variables employed. This method relies on the Flory-Huggins

9

approach and the Hirschberg’s thermodynamic model for asphaltene solubility. For a

10

constant oil density at the reservoir conditions as the difference between the reservoir

11

pressure and the saturation pressure increases, more unstable is the oil. Considering

12

hydrocarbons with the same molecular weight, aromatics present higher densities than

13

aliphatics. Thus, as asphaltenes are soluble in aromatics, the higher the density, the

14

more stable is the oil.

15 16


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Figure 4. De Boer’s15 criterion for predicting asphaltene precipitation.

3 4

Table 1 shows a nomenclature in order to easily identify all the methods evaluated in

5

the present work. Methods M1 to M5 have already been described in this section and

6

Figures 1 to 4 help to understand their application. Methods M6 and M7 are explained

7

in the next section.

8 9

Table 1. Identification of the methods. Methods M1 M2 M3 M4 M5 M6 M7

References Jamaluddin et al.12 Yen et al. 13 - Graphic method Yen et al. 13 - CII method Stankiewicz et al. 14 De Boer et al.15 Modified Jamaluddin Modified De Boer

10 11 12 13 14


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1

3. Results and Discussion

2

3.1 Influence of SARA analysis technique on the prediction of screening methods

3

for asphaltene precipitation

4 5

SARA fraction data can be determined by several analytical techniques but for this

6

reason the results can diverge a lot. These techniques have been modified over time and

7

there is no longer a standard one. Therefore, the employment of SARA analysis results

8

from these techniques can lead to errors if the users do not distinguish between them. A

9

survey of the analytical techniques used to obtain SARA data from the references used

10

in this study was performed and it is presented in Table 2. It is noted that several data

11

sources in literature do not provide information about which SARA analysis technique

12

was employed. On the other hand, we realize that there is a great variety of techniques

13

used between different data sources.

14 15

Table 2. Analytical techniques for SARA fractions determination given by the

16

references used in this study. Reference [19] [20] [21] [22] [23] [14] [24] [25] [12] [26] [27] [28] [29] [30] [31] [32] [33] [34] [35] [36] [16] [ 2, 13 ]

Method ----TLC-FID / SFC/ ASTMD6560 NFT60-115 ADE ASTM D4124 ASTM D4124-97 --ASTM D-3279 --HPLC / ASTMD3279-97 ----HPLC --ASTM D2007-11 --ASTM D3279 / ASTM D2700 ------IP-143

17


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Experimental SARA data obtained by different analytical technique for the same oil are

2

not easily found in literature. Fan et al.

3

techniques on the results of SARA data. In order to validate the comparative study of

4

the screening methods to asphaltene precipitation based on SARA analysis, we decided

5

to use experimental data available in literature for the oils and the three analytical

6

techniques suggested by Fan et al. 10. The results are summarized in Table 3.

10

evaluated the influence of three analytical

7 8

Table 3. Evaluation of the stability of oils available in literature10 from different SARA

9

analysis techniques using methods M1, M2 and M4.

Oil

A-95

C-LH-99

C-R-00

SQ-95

S-Ven-39

Tensleep-99

Method

Saturates (%wt)

Aromatics (%wt)

Resins (%wt)

Asphaltenes (%wt)

M1

M2

M4

ASTM D2007

46.2

19.7

18.6

8.8

unstable

unstable

unstable

HPLC

51.0

20.5

19.7

8.8

unstable

unstable

unstable

TLC-FID

13.8

12.4

13.4

7.1

unstable

unstable

unstable

ASTM D2007

38.8

23.6

23.9

3.4

stable

unstable

unstable

HPLC

49.4

21.5

25.6

3.4

stable

unstable

unstable

TLC-FID

12.5

15.3

13.5

4.4

unstable

stable

unstable

ASTM D2007

68.7

17.4

9.9

1.6

stable

unstable

unstable

HPLC

70.6

16.4

11.4

1.6

stable

unstable

unstable

TLC-FID

38.0

12.1

9.2

3.2

unstable

unstable

unstable

ASTM D2007

47.0

19.4

14.7

2.6

stable

unstable

unstable

HPLC

65.2

18.3

13.9

2.6

stable

unstable

unstable

TLC-FID

17.3

10.2

9.1

1.9

stable

unstable

unstable

ASTM D2007

45.6

27.8

14.2

6.1

unstable

unstable

unstable

HPLC

51.1

28.3

14.5

6.1

unstable

unstable

unstable

TLC-FID

17.2

10.5

14.5

10.4

unstable

unstable

unstable

ASTM D2007

59.0

22.9

11.7

3.2

stable

unstable

unstable

HPLC

64.0

19.8

12.9

3.2

stable

unstable

unstable

TLC-FID

26.5

18.8

8.1

6.9

unstable

unstable

unstable

10


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Jamaluddin’s method (M1) predicted the A-95 and S-Ven-39 oils as unstable and the

2

SQ-95 as stable independently of the technique used for SARA analysis. For the

3

remaining oils, there are differences in the result depending on the technique used. The

4

oils C-LH-99, C-R-00 and Tensleep-99 were predicted to be stable for the data obtained

5

with the ASTM and HPLC techniques, while data with the TLC-FID technique

6

predicted the instability of these oils.

7 8

Yen’s graphical method (M2) predicted asphaltene precipitation for most oils,

9

independent of the SARA analysis technique. Only the C-LH-99 oil with data obtained

10

from the TLC-FID technique was predicted as stable by M2 method. The results

11

obtained for the Stankiewicz’s method (M4) indicate the possibility of asphaltenes

12

precipitation for all oils regardless of the SARA analysis technique used.

13 14

Although only three techniques were used to determine the SARA fractions of these oils

15

presented in Table 3, the Stankiewicz’s method provided 100 % of agreement among

16

the predicted results for the risk of asphaltene precipitation for the oils evaluated

17

regarding the 3 SARA analytical techniques. The Jamaluddin’s and Yen’s methods

18

provided an average of 83.3 % and 94.4 % of agreement, respectively, among the

19

predicted results for asphaltene precipitation with the different SARA analysis

20

techniques. These results reveal that Jamaluddin’s method is more sensitive to the

21

analytical technique used to determine the SARA fractions.

22 23

3.2 Evaluation of screening methods to predict asphaltene precipitation

24

The methods M1 to M5 described in the previous section were developed considering

25

the behavior of few oils from different geographic areas available in literature. Thus, for

26

a broad evaluation of these methods, only oils with the following information were

27

selected: SARA analysis for both Yen’s and Stankiewicz’s method; resins and

28

asphaltenes data from SARA analysis for Jamaluddin’s method and bubblepoint

29

pressure, reservoir pressure (or upper onset pressure, UOP) and oil density in-situ for

30

De Boer’s method.

31 32

It is worth to remark that Yen’s, Stankiewicz’s and Jamaluddin’s methods are

33

exclusively based on SARA analysis, which is easily found. For this reason, they are

34

used only for preliminary and rough evaluations. As it is well known asphaltene


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precipitation strongly depends on the oil composition as well as the reservoir pressure

2

and temperature. When the temperature is fixed, pressure and composition are key

3

factors to asphaltene precipitation. In this case, De Boer’s method is more complete

4

than the others because also takes in account the pressure and oil composition. On the

5

other hand, experimental determination of the oil density at reservoir conditions is not

6

an easy task. Therefore, all these aspects should be considered for a good evaluation of

7

the capacity of this kind of methods for detecting asphaltene precipitation.

8 9

Table 4 shows a general framework of oils database used in this study. For a more

10

comprehensive assessment of the input data of these methods, details of the SARA

11

analysis are provided in the Appendix. The methods tested with sufficient data are

12

indicated by “X”. The results are shown in Figures 5 to 8.

13 14

In Table 4, only 35 out of 172 oils showed the same results for the prediction of stability

15

for all five methods, giving a total agreement in prediction of 24.1 %. For this

16

evaluation it would be important to know if the oils, for which SARA analysis is

17

available, show a tendency to asphaltene precipitation. However, this information is not

18

reported for most oils. Thus, we considered as the best methods those ones that provide

19

the same stability for the most number of oils.

20 21

Table 4 indicates that Jamaluddin’s12 method (M1) was applied to 145 oils and Figure

22

5 exhibits the respective results. The straight line in Figure 5 was plotted following the

23

graphic proposed by Zendehboudi et al.2. This method states that oils with high

24

asphaltene content and low proportion of resin to asphaltene are unstable. Results

25

revealed that most of the oils are stable (68.27 %), which correspond to oils with low

26

asphaltene content and low weight percentage of resins. This shows that method M1 is

27

not accurate if there is low asphaltene content and low resin content because even at this

28

condition the resin to asphaltene ratio can be high. This is expected for this method

29

because when the resin to asphaltene ration is high, the asphaltene content is supposed

30

to be low and therefore stable. This figure also shows that oils with low resins content

31

but high asphaltene content are supposed to be unstable, such as those by Carbognani et

32

al.20 and Pakoso et al.32. However, the literature reports that serious problems of

33

asphaltene precipitation can occur in reservoir with low content of asphaltene while


Energy & Fuels

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Page 14 of 31

1

reservoir with high content of asphaltene may not lead to any severe production

2

problems6,23.

3 4 5

Table 4. Total number of oils used in each method and the number of oils that matched stability for all applied methods. Methods Reference

Number of oils

M1

M2

M3

M4

M5

Number of oils with the same result for stability prediction

[20]

5

X

X

X

X

---

5

[34]

5

X

X

X

X

---

4

[32]

10

X

X

X

X

---

5

[36] [23]

62 29

X X

X ---

X ---

X ---

--X

10 5

[12]

2

X

X

X

X

X

0

[35]

3

X

X

X

X

---

0

[2]

2

X

X

X

X

---

1

--X X X X X X X X X X X 116

X ----------------------58

--0 1 1 0 1 0 0 1 1 0 0 35 24.1 %

[16] [14] [13] [30] [24] [33] [29] [21] [19] [31] [22] [27] Total

27 ------1 X X X 1 X X X 2 X X X 1 X X X 4 X X X 1 X X X 2 X X X 5 X X X 6 X X X 3 X X X 1 X X X 172 145 116 116 Percentage of oils with the same stability

6 7 8 9


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Energy & Fuels

1

2 3

Figure 5. Evaluation of Jamaluddin’s method (M1) using 145 oils.

4 5

Yen et al.’s13 proposed two methods, the graphic one (M2) shown in Figure 7 and the

6

CII Method (M3) calculated by Equation (1). Since the graphic method is based on the

7

same theory that supports Equation (1), it is expected to provide the same results as the

8

CII method. In Figure 7, both curves are roughly the CII parameter equalized to the

9

limits 0.7 and 0.9. If the oil shows CII higher than 0.9 it is considered unstable and if

10

CII is lower than 0.7 the oil is considered to be stable. Between these values, the oil

11

exhibits moderate instability, as can be calculated by Equations (2) and (3). In these

12

equations, the right side represents the y-axis and the left size (the sum of aromatics and

13

resins) represents the x-axis. As shown in Table 4, we employed 116 oils in both

14

methods M2 and M3 and only for two oils the same results were not observed. That

15

gives an agreement in prediction of about 98 %. The difference of 2% between these

16

two methods is related to the fact that the lines in Figure 6 were not be obtained with

17

angular coefficients exactly like the limits of 0.7 and 0.9 set for CII. 0.9(Aromatics + Resins) = (Saturates + Asphaltenes)

0.7(Aromatics + Resins) = (Saturates + Asphaltenes)


(2)

(3)

Energy & Fuels

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1

As mentioned before, the colloidal instability index reckons the oil as a colloidal

2

mixture where each of the SARA fractions is considered a pseudo component. As seen

3

in Figure 6, most of the oils tested are on the unstable region and only a few oils are on

4

the stable region or on the slight problems region. The oils considered unstable by this

5

method presented a higher sum of asphaltenes and saturated fractions than the total sum

6

of aromatics and resins fractions. Aromatics are good solvents for asphaltenes and the

7

saturate fraction is able to disperse the micelles formed by asphaltenes and resins.

8

Consequently, the asphaltenes fraction is considered stable by this method if the sum

9

plotted on the x-axis is higher than the sum plotted on the y-axis.

10

As observed in Figure 6, the data have a tendency of linear dispersion and this is related

11

to the restriction that the sum of weight composition is 100%.

12

13 14

Figure 6. Evaluation of Yen’s method (M2) using 116 oils.

15 16

Stankiewicz’s14 method (M4) was evaluated for 116 oils as shown in Table 4. Figure 7

17

illustrates the results for this method and shows that most of the oils are on the unstable

18

zone and so few are on the stable area, similarly to Yen’s method. This method is based

19

on the theory that consider resins are peptizing agents in crude mixtures so the higher

20

the asphaltenes to resins ratio is, the more stable is the oil14. Therefore, the ratio

21

expressed on the x-axis is supposed to be good at predicting asphaltenes precipitation.

22


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Energy & Fuels

1

Although Stankiewicz’s method (M4) provides results closer to those by Yen’s

2

methods (M2 and M3), M4 predictions for some oils were the same as those ones found

3

by Jamaluddin’s method (the opposite of Yen’s prediction). For example, De Oliveira

4

et al.’s21 oils were classified as stable by both Stankiewicz’s14 and Jamaluddin’s12

5

methods while both Yen’s13 methods predicted them as unstable. The same happens to

6

four out of six Plasencia et al.’s31 oils.

7

8 9

Figure 7. Evaluation of Stankiewicz’s method (M4) using 116 oils.

10 11

Comparison of Yen’s and Stankiewicz’s methods show that for some oils both methods

12

showed the same stability predictions, but Jamaluddin’s one presented an opposite

13

stability prediction, as indicated by the low percentage of oils with the same result for

14

stability prediction in Table 4. In order to evaluate the agreement between Yen’s

15

methods (both CII and graphic ones) and Stankiewicz’s method the percentage of oils

16

with the same stability prediction was calculate for both methods, excluding

17

Jamaluddin’s method. The percentage of oils with the same stability prediction for both

18

of Yen’s13 method and Stankiewicz’s14 was 62.1 %. Since the agreement on prediction

19

between Yen’s13 method and Stankiewicz’s14 method (62.1 %) was higher than the

20

agreement considering Jamaluddin’s12 method (24.1 %), this one was not identified as

21

enough accurate for predicting asphaltene precipitation. The low percentage of


Energy & Fuels

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1

agreement considering Jamaluddin’s method can be associated with the sensibility of

2

this method to SARA data.

3 4

Evaluation of De Boer’s method was difficult because oil density data at reservoir

5

conditions are not available for most oils. In order to overcome this hurdle, most of the

6

density values used in this method were calculated by using the software SPECS

7

(Separation and Phase Equilibrium Calculations - Technical University of Denmark).

8

The oil density was determined by a flash calculation at the reservoir pressure or at a

9

pressure value above the UOP if the reservoir pressure was not available. To calculate

10

the density using this software, input data of oil composition until C7+ fraction,

11

molecular weight and density of the C7+ fraction are required in addition to reservoir

12

pressure and temperature. This need for oil composition data and reservoir conditions

13

limited the evaluation of the De Boer’s method to a few oils as shown in Table 4.

14 15

De Boer’s method states that light crudes with high bubble pressures and large

16

difference between reservoir and bubble pressures are more susceptible to have

17

precipitation problems15. For hydrocarbons with the same molecular weight, paraffins

18

have lower density values than aromatics. Once asphaltenes are soluble in aromatics and

19

insoluble in paraffins, one can state that for high density oils values asphaltenes are

20

stable while for low density ones they are most likely unstable.

21 22

Figure 8 shows the results for the evaluation of De Boer’s method (M5) using 59 oils. In

23

this figure, some points for Fahim’s oils give the same results. As it can be seen in

24

Table 4, experimental data of only three sources could be employed in this method:

25

Wang et al, Fahim and Jamaluddin et al. Wang et al.’s16 oils are well distributed in the

26

three regions of the plot while Fahim’s23 oils are mostly dispersed between the unstable

27

area and the slight stability problems area. This occurs because oils with higher

28

differences between the reservoir pressure and the bubble point pressure are more likely

29

unstable while oils with higher densities are more likely stable. If the difference in

30

pressure and density are not too high nor too low, the oil is supposed to present mild

31

stability problems.

32 33

As the reservoir pressures for Fahim’s23 oils are not available the density values were

34

calculated by SPECS using a pressure above the UOP. Figure 8 shows that Fahim’s23


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Energy & Fuels

1

oils were dispersed through all of the unstable and the slightly unstable areas on De

2

Boer’s plot. Most of the oils were considered unstable (63.33 %) and 36.67 % were on

3

the slight stability problems area. For Wang et al.’s16 oils density and reservoir pressure

4

data are available in literature. For Jamaluddin et al.’s12 oils the reservoir pressure and

5

composition were provided and used to calculate the density values by SPECS. In all

6

cases, there is no oil stability data.

7 8

Figure 8. Evaluation of De Boer’s method (M5) using 59 oils.

9 10

Once it was so difficult to find the necessary data to test De Boer’s method, it was

11

compared only to Jamaluddin’s method. 30 oils from Fahim and 2 oils from Jamaluddin

12

were used in this comparison. Among these 32 oils compared, only 15.63 % showed the

13

same stability. This low percentage was due to the mild stability problems region on De

14

Boer’s plot because Jamaluddin’s plot does not exhibit a similar region. On De Boer’s

15

plot, 22 of the tested oils are situated on the slight problems region. Thus, calculation

16

excluding these oils led to an agreement of 50 % between both methods. Furthermore,

17

these methods are based on different types of data. De Boer’s method takes into account

18

the pressure and density at reservoir conditions while Jamaluddin’s method takes into

19

account the asphaltene and resin ratio.

20 21

3.3 Modified Jamaluddin’s method

22

In order to improve the ability of Jamaluddin’s12 method to predict asphaltene

23

precipitation, a new modification of this method is proposed in the present work.


Energy & Fuels

1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60

1

Jamaluddin’s12 method was chosen to be improved instead of Yen’s13 or

2

Stankiewicz’s14 ones because it needs as input data only asphaltene and resin weight

3

fractions, which allows to evaluate the stability for most oils with less data than the

4

other methods.

5 6

The Jamaluddin’s method is based on the behavior of single oil. In order to represent

7

the behavior of various oils there have been several attempts to improve Jamaluddin’s

8

method. The main change was basically on the reduction of angular coefficient of the

9

line plotted. By comparison with Yen’s and Stankiewicz methods, it is observed that the

10

major issue regarding Jamaluddin’s method is that most of the oils are considered

11

stable. That is why the percentage of oils with the same stability was low. Thus, new

12

attempts were performed to decrease the number of stable oils by decreasing the angular

13

coefficient of the line, i.e., providing more unstable oils. From previous evaluations,

14

there is a possibility of oil stability, i.e., the region where asphaltene precipitation dose

15

not occurs. Thus, changing the angular coefficient of the line can alter the area of the

16

regions of stability and instability aiming at a larger number of oils.

17

Graphically, the oils are distributed in the same way as given by Jamaluddin’s method,

18

shown in Figure 5. Evaluation of Jamaluddin’s method (M1) using 145 oils.Figure 5,

19

because the variables are the same (resins and asphaltenes fractions). However, the area

20

under the line is narrower and this allows classifying more oils as unstable.

21 22

Method M6 was proposed to improve the prediction of asphaltene precipitation based

23

on the method M1. Several attempts were made and the best one is shown in Figure 9,

24

the straight line was plotted by keeping constant the x-axis values of method M1 and

25

dividing the y-axis values by three. The same oils were used for both methods. M6

26

provides a prediction with 49 % of agreement when compared with Yen’s and

27

Stankiewicz’s methods.

28


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Energy & Fuels

1 2

Figure 9. Evaluation of method M6 using 145 oils.

3

In Figure 9 oils are supposed to be unstable above the line and stable below it. In this

4

case, the number of stable oils decreased to 33.79 %, which is the lowest value obtained

5

among several attempts. That is because the angular coefficient was the lowest found

6

among these attempts, and consequently the stable area was the narrowest.

7 8

The results obtained with method M6 were more satisfactory compared to the original

9

Jamaluddin’s method, which gave respectively 49 % and 24.1 % of oils with the same

10

stability, almost double. Besides, in method M6 the number of stable oils was divided

11

approximately by half in comparison to method M1. Also, in method M6 the number of

12

stable oils decreased and the percentage of oils increased in agreement with Yen and

13

Stankiewicz methods.

14 15

Validation of method M6 was performed employing 14 oils that had not been

16

previously used in this assessment. For all these oils it was known that asphaltene

17

precipitates. Thus, it was possible to independently evaluate the performance of these

18

methods. Methods M1 to M4 were also tested using 14 oils with asphaltene

19

precipitation data available. The number of oils for each reference is shown in Table 5.

20 21 22 23


Energy & Fuels

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1

Table 5. Number of oils with asphaltene precipitation data. Reference

Number of oils

[26]

2

[25]

4

[37]

1

[38]

2

[39]

2

[40]

2

[41]

1

Asphaltene precipitation information "Asphaltenes were stable in the reservoir fluid at the reservoir temperature"26. Experimental data of asphaltene precipitation are shown in Table 3 of the reference paper. Figure 2 of the reference paper shows asphaltene precipitation evidence at high pressure. “The results in Table 4 show that noticeable precipitation occurs for samples B and C under reservoir conditions”38. Percentage of precipitated asphaltene is provided in Figure 2 of the reference paper. As shown in Table 1 and Figure 1 of the reference paper the oil is unstable since the reservoir pressure is less than UOP. "The precipitation in the blank experiment (0 mol % CO2) is 0.22 wt % of the original oil."41

2 3

Table 6 shows the results of prediction for each method. Capital letters “S” and “U”

4

refer the oils were predicted as stable and unstable, respectively, and the symbol “?”

5

indicates that the oil was predicted mildly unstable. The most reliable method is the

6

modified Jamaluddin’s one (method M6) with 92.9 % of correct prediction for unstable

7

oils, followed by Jamaluddin (M1) and Stankiewicz’s (M4) both with accuracy of 71.4

8

% and Yen’s graphic method (M2) with 64.3 %.

9 10

The oil B of Negahban et al.26 was considered stable by Jamaluddin’s method (M1) and

11

modified Jamaluddin’s method (M6) because the content of asphaltene is low and resin

12

content is high. The new method proposed in the present study provided the best

13

prediction. Therefore, the goal to develop a method more reliable than Jamaluddin’s one

14

was achieved. Another point to be noted is that the original Jamaluddin’s method (M1)

15

provided the same percentage of prediction provided by Stankiewicz’s methods.

16 17 18 19


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Energy & Fuels

1

Table 6. Prediction results for oils with asphaltene precipitation data. References

[26]

[25] [37] [38] [39] [40] [41] 2 3

Oils M1 S S S U U U U U U U S S

M2 U U U U U U ? U ? U U U

Methods M3 U U U U ? U ? U ? U U U

M4 U U S U S U U U U U U U

A B 1 2 3 4 Bangestan B C A B A South S ? ? U America NA U U U U S- stable oil ; U- unstable oil ; ? – mildly unstable.

M6 U S U U U U U U U U U U U U

4

3.4 Modified De Boer’s method

5

To overcome some hurdles on finding data in literature to evaluate De Boer’s method,

6

modifications of this method were proposed and tested. Keeping the y-axis as originally

7

determined by De Boer’s method, the x-axis was changed according to data easier to

8

found in literature: fraction of resins to fraction of asphaltenes ratio, obtained from

9

SARA analysis. As said before it is important to remark that SARA analysis provides

10

information on oil instability and is relatively easy to perform. In this sense, these

11

modifications seeks to introduce a new variable that can affect a lot the oil instability.

12 13

The graph of Figure 10 was plotted using oils with asphaltene precipitation data

14

available in literature and oils with UOP data, as shown in Table 7. The oils with

15

asphaltene precipitation data indicated that asphaltene precipitates. So, a pressure above

16

UOP should be used to assure that oils do not precipitate. The limit curves obtained by

17

fitting the data are located between these oils, separating the data points in three

18

regions: above the upper line the oils are stable; below the lower line they are unstable;

19

and in between the lines, they are midly unstable. This occurs because asphaltenes in

20

Fahim’s oils do not precipitate as the pressure is above their UOP. Also there is

21

information in literature that asphaltene precipitates in the other studied oils.


Energy & Fuels

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1

Table 7. Number of oils with asphaltene precipitation data used on modified De Boer’s

2

method (M7). Reference

Number of oils

[14]

1

[23]

30

[28]

1

[38]

2

[40]

1

Asphaltene precipitation information As shown in Tables 3 and 7 of the reference paper the oil is unstable once the reservoir pressure is less than UOP. UOP was the reference pressure to evaluate the oil stability. As shown in Table 3 and Figure of the reference paper the oil is stable at reservoir pressure. Figure 2 of the reference paper shows asphaltene precipitation evidence at high pressure. As shown in Table 1 and Figure 1 of the reference paper the oil is unstable once the reservoir pressure is less than UOP.

3 4

Once the plot shown in Figure 10 was built with oils that have asphaltene stability data

5

available, the prediction is 100 % correct. Therefore, one can conclude that the results

6

show a good agreement with the data. The evaluation of different screening methods

7

with many crude oils is not found in literature though it is very important for the oil

8

industry.

9

10 11

Figure 10. Evaluation of method M7 using 35 oils.

12


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Energy & Fuels

14 Conclusions 2 3

In this work four screening methods to predict asphaltene precipitation were compared

4

and other two new methods were proposed by using 172 oils without guarantee of

5

asphaltene precipitation and 49 oils for which the asphaltene precipitation conditions are

6

known. The four methods initially evaluated showed a small number of oils that have

7

the same stability behavior (24.1 %). In order to improve this number, Yen’s methods

8

were compared only with Stankiewicz’s method and a much higher percentage was

9

obtained (62.1 %). Yen’s methods (M2 and M3) showed a percentage of agreement

10

extremely high (98.3 %), although it was expected to be 100% because the graphic

11

method was based on the CII parameter. However, this difference is associated with the

12

approximate fit of the angular coefficients of lines to the limits of CII in M2 method.

13 14

The percentage of agreement between methods M1, M2, M3 and M4 for prediction of

15

asphaltene precipitation was low. To enhance the performance of Jamaluddin’s method,

16

a modification on this method was proposed providing a more accurate method. For

17

instance, the results of prediction for oils with asphaltene precipitation data available

18

using the M6 method provided 92.9 % of agreement while prediction with original

19

Jamaluddin’s method (M1) provided 71.4 %. This method can also be compared using

20

the percentages obtained with both Yen’s and Stankiewicz’s methods, which were 49.0

21

% for M6.

22 23

De Boer’s method was also improved because it was hard to find the required input data

24

in literature. A new method was conceived using the initial pressure and bubble point

25

pressure, and resins to asphaltenes ratio given by SARA analysis rather than oil density

26

at reservoir conditions. This new method (M7) provided a different way to evaluate the

27

stability of the oil and it allows predicting the oil stability to an initial pressure condition

28

without needing oil density as input data. Based on the modified De Boer’s method,

29

when the pressure is below the UOP or the difference between the initial pressure and

30

the bubble pressure is small the oil is considered unstable with risk of asphaltene

31

precipitation. Therefore, the Jamaluddin’s modified method M6 is considered the most

32

suitable because it showed 92.9 % of correct prediction of asphaltene precipitation with

33

the oils that had asphaltene precipitation data from different geographical regions.


Energy & Fuels

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1

Probably one of the most important issues to address when looking for a good method

2

to predict asphaltene precipitation is that input data often come from SARA analysis. It

3

should be useful to employ methods that require different kind of input data. However,

4

it is very hard to find input data other than those from SARA analysis to be used in this

5

type of methods. On the other hand, one of the sources of errors is the use of SARA

6

data obtained by different techniques. For this reason, the use of screening methods to

7

asphaltene precipitation has a risk of error and they should be used carefully as a

8

preliminary step before a more complex study in a wellbore.

9 10 11 12

The authors acknowledge the support by ANP – Agência Nacional de Petróleo, Gás

13

Natural e Biocombustíveis and by Petrogal Brasil S.A., related to the grant from R&D

14

investment rule.

Acknowledgment

15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39

References (1) Verdier, S.; Carrier, H.; Andersen, S. I.; Daridon, J. Energy & fuels 2006, 20 (21), 1584–1590. (2) Zendehboudi, S.; Shafiei, A.; Bahadori, A.; James, L. A.; Elkamel, A.; Lohi, A. Chem. Eng. Res. Des. 2014, 92 (5), 857–875. (3) Chamkalani, A.; Mohammadi, A. H.; Eslamimanesh, A.; Gharagheizi, F.; Richon, D. Chem. Eng. Sci. 2012, 81, 202–208. (4) Hoepfner, M. P.; Limsakoune, V.; Chuenmeechao, V.; Maqbool, T.; Scott Fogler, H. Energy and Fuels 2013, 27 (2), 725–735. (5) Akbarzadeh, K.; Hammani, A.; Zhang, D.; Alleson, S.; Creek, J.; Kabir, S.; Jamaluddin, A. J.; Marshall, A. G.; Rodgers, R. P.; Mullins, O. C.; Solbakken, T. Oilf. Rev. 2007, No. September 2016, 22–43. (6) Ashoori, S.; Sharifi, M.; Masoumi, M.; Mohammad Salehi, M. Egypt. J. Pet. 2016, 0–4. (7) Fan, T.; Buckley, J. S. Energy and Fuels 2002, 16 (6), 1571–1575. (8) Forte, E.; Taylor, S. E. Adv. Colloid Interface Sci. 2015, 217, 1–12. (9) Leontaritis, K. J.; Mansoori, G. A. Spe 1987, 149–158. (10) Fan, T.; Wang, J.; Buckley, J. S. Improv. Oil Recover. Symp. 2002, 17. SPE – 75228. (11) AlKafeef, S.; Al-Medhadi, F.; Al-Shammari, A. Soc. Pet. Eng. 2005, 20 (2), 5–8. (12) Jamaluddin, a. K. M.; Creek, J.; Kabir, C. S.; McFadden, J. D.; D’Cruz, D.; Manakalathil, J.; Joshi, N.; Ross, B. J. Can. Pet. Technol. 2002, 41, 44–52.


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Appendix - SARA data used on this work Figures A.1 to A.4 show the SARA data used in this work and the references are listed in Table A.1. Oils numbered 1 to 8 in Figure A.1 have high asphaltene fractions while oils numbered

23

17 to 29 show very high saturates fraction and very low asphaltene content. Oils

24

numbered 9 to 16 have most of its content made of aromatics. In Figure A.2, all oils are

25

mostly constituted of saturates, most of them have resins as the second highest content

26

and the asphaltenes fraction is low for most of the oils. In Figure A.3, all of the oils

27

present the saturates fraction as the highest content and asphaltene fractions as the

28

lowest one. Oils numbered 1 to 23 in Figure A.4 show the saturates fraction as the

29

highest, except for oil number 3, which has more asphaltenes than saturates. The resins

30

content is the highest for oil number 27 in Figure A.4.

31 32


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Figure A.1. 29 SARA data used on this work.

4 5 6



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Table A.1. Number of the oil in Figures A.1 to A.4 and its references. Reference [20] [32] [12] [2] [14] [35] [34] [36] [36] [13] [30] [24] [33] [29] [21] [31] [19] [22] [27]

Figure A.1 A.1 A.1 A.1 A.1 A.1 A.1 A.2 A.3 A.4 A.4 A.4 A.4 A.4 A.4 A.4 A.4 A.4 A.4

3 4


Number 1 to 5 6 to 16 17 and 18 19 and 20 21 22 to 24 25 to 29 1 to 31 1 to 31 1 2 and 3 4 5 to 8 9 10 and 11 12 to 17 18 to 23 23 to 26 27

Evaluation and Improvement of Screening Methods Applied to

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