articulo mezcla de hidrocarburos 1

ngineering 57 (2007) 166–180www.elsevier.com/locate/petrol

Journal of Petroleum Science and E

Classification and characterisation of crudeoils based on distillation properties

Peter Behrenbruch ⁎, Thivanka Dedigama

Australian School of Petroleum, University of Adelaide, Australia 5005

Accepted 31 October 2005

Abstract

True boiling point (TBP) distillation is a widely used batch distillation process for the characterisation of crude oils, traditionallymainly for marketing and refining purposes. The TBP curve is obtained by plotting the cumulative mass or volume distillationfraction with increasing temperature. The shape of these curves is dependent on the volatility of components in a given crude oil.As such, these curves give a “footprint” of the composition of crude oils.

A newmethod of characterising crude oils based on the shape of TBP distillation curves is proposed. A gamma distribution is used tocharacterise the TBP distillation curve, and the parameters of the fitted distribution are used as characterisation parameters. The proposedmethod is found to describe experimental data verywell with just two parameters, and as such offers a very practical approach in terms ofclassifying crude oils. Ranges of values for the characterisation parameters for different types of crude oil are identified for a large set ofTBP data. The characterisation parameters can be correlated with a number of crude oil properties. They can also be used to predict thepetroleum fractions that can be produced from a given crude oil and hence correlated to the value of that crude.

As an alternative, it is shown how crude oil cut fractions may be classified with the aid of a ternary diagram, and the linkbetween this approach and the characterisation parameters introduced above is demonstrated.© 2006 Elsevier B.V. All rights reserved.

Keywords: True boiling point distillation; Crude oil; Characterisation; Classification; Gamma distribution; Ternary diagram

1. Introduction

Petroleum has an organic origin based on theaccumulation of plant and animal matter and the actionof heat and pressure over a long period of time on thisbiological material. It is a complex mixture of hydro-carbons that naturally occurs in reservoir rocks.Petroleum encompasses liquid hydrocarbons referred

⁎ Corresponding author. Tel.: +61 8 8303 8020; fax: +61 8 8303 8011.E-mail address: [email protected]

(P. Behrenbruch).

0920-4105/$ - see front matter © 2006 Elsevier B.V. All rights reserved.doi:10.1016/j.petrol.2005.10.016

to as crude oil, natural gas and solid hydrocarbons suchas tars. The chemical constituents of petroleum andtechniques available to separate and analyse the variouscompounds are discussed in Waples (1985). Majorgroups of compounds found in petroleum are saturatedhydrocarbons, including straight chained, branched andcyclic hydrocarb ons, simple aromatic hydrocarbons,small sulphur bearing compounds, resins and very largearomatic asphaltene compounds.

The varied composition of petroleum has led to theexistence of a number of characterisation systems. Eachof these characterisation systems is important for deci-sion making at some stage of exploration, production

mailto:[email protected]

http://dx.doi.org/10.1016/j.petrol.2005.10.016

Table 1List of TBP distillation data (source: Chevron, 2005)

Country Location Crude oil Dateofassay

Whole crude properties

Gravity(°API)

Specificgravity

Characterizationfactor (K factor)

Sulfur(wt.%)

Pour point(°C)

1 Angola Africa Kuito 2000 19.2 0.9390 11.6 0.68 −322 Angola Africa Cabinda 1996 32.8 0.8610 12.3 0.13 163 Angola Africa Nemba 2001 39.3 0.8282 12.2 0.19 −44 Angola Africa Palanca 2001 38.4 0.8329 12.1 0.17 105 Chad Africa Doba Blend 2002 20.5 0.9308 12.0 0.16 −46 Congo Africa Kitina 2002 37.7 0.8365 12.2 0.09 77 Congo Africa N'kossa 2002 46.8 0.7936 12.4 0.03 −218 Nigeria Africa Bonny Light 2000 35.8 0.8459 11.9 0.14 −189 Nigeria Africa Escravos 2000 34.4 0.8528 11.7 0.15 710 Nigeria Africa Pennington 1995 35.0 0.8497 11.7 0.08 1011 Australia Australia Barrow Island 1987 38.0 0.8346 11.7 0.05 −5412 Australia Australia Cossack 1998 48.1 0.7877 12.0 0.04 −1813 Australia Australia NWS condensate 1997 61.2 0.7344 12.3 0.01 −5114 Australia Australia Thevenard Island 2001 41.3 0.8189 NA 0.02 NA15 Indonesia SE Asia Duri 1994 20.8 0.9293 12.0 0.20 1016 Indonesia SE Asia Minas 1997 35.3 0.8482 12.6 0.09 3817 PNG SE Asia Kutubu Light 1999 45.1 0.8014 12.2 0.04 −118 China South China Sea Nanhai Light 2000 40.1 0.8246 12.4 0.06 3219 Thailand South China Sea Benchamas 2002 43.0 0.8108 12.7 0.04 3220 Thailand South China Sea Tantawan 2000 43.3 0.8094 12.5 0.05 1621 Canada Canada Hibernia 2000 35.9 0.8454 11.6 0.34 1322 Chile Sth America Rincon 1999 36.0 0.8447 11.8 0.37 −423 Argentina Sth America Medanito 1994 35.2 0.8486 12.2 0.41 −124 Venezuela Sth America Hamaca 2003 26.0 0.8987 12.1 1.55 −40

167P. Behrenbruch, T. Dedigama / Journal of Petroleum Science and Engineering 57 (2007) 166–180

and refining of crude oil. These systems can be broadlygrouped as follows.

(a) Geochemical parameters(b) Whole crude (live oil) properties(c) Assay properties

Fig. 1. TBP distillation curves for a selection of samples listed i

Geologists and geochemists use geological para-meters to identify and characterise crude oil, for crudeoil— source rock correlation and to measure the degreeof evolution (Tissot and Welte, 1984). A range ofgeochemical parameters is available for the character-isation of crude oil. Live crude properties are of interest

n Table 1. Numbers correspond to Sample No in Table 1.

Table 2Detailed TBP distillation data for Griff in crude oil (source: BHP Billiton, 2005)

Cumulativevolumepercentage

Temperature(°F)

Temperature(°C)

Midvolumepercentage

Specificgravity

APIgravity

Cumulativeweightpercentage

Cumulativeweightfraction yact

TemperaturedifferenceΔT (°C)

Weightfractiondifference Δyact

Weightfractiondensity

4.0 66.2 19 2.0 0.5738 115.1 3.1 0.031 19 0.031 0.001611.0 140 60 7.5 0.6351 91.3 9.0 0.090 41 0.059 0.001414.2 158 70 12.6 0.6715 79.2 11.8 0.118 10 0.028 0.002816.5 176 80 15.4 0.6888 73.9 13.9 0.139 10 0.021 0.002120.0 194 90 18.3 0.7002 70.6 17.1 0.171 10 0.032 0.003226.3 212 100 23.2 0.7129 67.0 23.0 0.230 10 0.059 0.005930.3 230 110 28.3 0.7230 64.2 26.9 0.269 10 0.039 0.003936.0 248 120 33.2 0.7271 63.1 32.4 0.324 10 0.055 0.005540.9 266 130 38.5 0.7325 61.7 37.1 0.371 10 0.047 0.004744.2 284 140 42.6 0.7390 60.0 40.3 0.403 10 0.032 0.003249.3 302 150 46.8 0.7434 58.8 45.3 0.453 10 0.050 0.005052.8 320 160 51.1 0.7523 56.6 48.8 0.488 10 0.035 0.003554.6 329 165 53.7 0.7567 55.5 50.6 0.506 5 0.018 0.003656.8 338 170 55.7 0.7590 54.9 52.8 0.528 5 0.022 0.004460.2 356 180 58.5 0.7645 53.6 56.2 0.562 10 0.034 0.003463.6 374 190 61.9 0.7714 51.9 59.7 0.597 10 0.035 0.003567.3 392 200 65.4 0.7866 48.4 63.5 0.635 10 0.038 0.003870.9 410 210 69.1 0.7935 46.8 67.3 0.673 10 0.038 0.003874.5 428 220 72.7 0.7995 45.5 71.1 0.711 10 0.038 0.003875.7 446 230 75.1 0.8050 44.3 72.4 0.724 10 0.013 0.001376.9 464 240 76.3 0.8127 42.6 73.7 0.737 10 0.013 0.001379.6 482 250 78.3 0.8204 41.0 76.6 0.766 10 0.029 0.002981.7 500 260 80.6 0.8272 39.6 78.9 0.789 10 0.023 0.002383.2 518 270 82.4 0.8339 38.2 80.6 0.806 10 0.017 0.001784.8 536 280 84 0.8380 37.4 82.4 0.824 10 0.018 0.001886.6 554 290 85.7 0.8419 36.6 84.4 0.844 10 0.020 0.002088.5 572 300 87.5 0.8465 35.7 86.6 0.866 10 0.022 0.002289.5 590 310 89 0.8513 34.7 87.7 0.877 10 0.011 0.001190.4 608 320 89.9 0.8556 33.9 88.8 0.888 10 0.011 0.001191.4 626 330 90.9 0.8595 33.1 89.9 0.899 10 0.011 0.001192.6 644 340 92 0.8635 32.4 91.3 0.913 10 0.014 0.001493.5 662 350 93.1 0.8706 31.0 92.3 0.923 10 0.010 0.001094.4 680 360 94 0.8818 29.0 93.4 0.934 10 0.011 0.001195.3 734 390 94.9 0.8790 29.5 94.5 0.945 30 0.011 0.000496.2 752 400 95.8 0.8794 29.4 95.6 0.956 10 0.011 0.001196.8 770 410 96.5 0.8797 29.4 96.3 0.963 10 0.007 0.000797.7 806 430 97.3 0.8831 28.7 97.3 0.973 20 0.010 0.000598.4 842 450 98.1 0.8928 27.0 98.1 0.981 20 0.008 0.000499.0 896 480 98.7 0.9085 24.3 98.8 0.988 30 0.007 0.000299.3 932 500 99.2 0.9273 21.1 99.1 0.991 20 0.003 0.000299.6 986 530 99.5 0.9536 16.9 99.5 0.995 30 0.004 0.0001100.0 986.0+ 530.0+ 99.8 0.9702 14.3 100.0 1.000 0.005

1.000

168 P. Behrenbruch, T. Dedigama / Journal of Petroleum Science and Engineering 57 (2007) 166–180

to reservoir engineers, who use these properties tounderstand fluid flow in the reservoir and to predictchanges in reservoir conditions with time, duringproduction. Crude oil assay properties are of most in-terest to petroleum refiners and crude oil marketers,who need to know the quantities of distillate fractionsthat a particular crude will produce during distillation.They also need to know the physical and chemicalproperties of the various fractions. Finally, the compo-sition of crude oil is closely linked to its price.

TBP distillation is one of the most common experi-mental procedures employed in ascertaining assay proper-ties of a crude oil. Many researchers have investigated theuse of TBP distillation data as a basis for characterisation ofcrude oils. Miquel et al. (1992) recognised that TBP distil-lation data were the most commonly available informationregarding the volatile behaviour of hydrocarbon mixtures.They proposed a characterisation system based on opti-mally selecting pseudo-fractions from the TBP distillationcurve. Haynes andMatthews (1991) proposed a continuous

Fig. 2. TBP distillation curves for Griffin crude oil.


approach to use TBP distillation data for heavy end char-acterisation of live crude oils in vapour–liquid equilibriumcalculations. Specific cut fractions (part of the TBP range),used in the generation of petroleum products directly influ-ence pricing of crude oils. The other major use of these datais in deciding refinery processes needed to refine a givencrude oil. TBP distillation can also be used as a method toisolate a specified fraction from a crude oil for testing.

The method for performing a TBP distillation exper-iment is described in ASTMD 2892 (American Society ofTesting and Materials, 1999a) and ASTM D 5236(American Society of Testing and Materials, 1999b) andthe procedure can be used to analyse hydrocarbonmixtures,including crude oils, condensates and petroleum fractions.However, the method cannot be used to analyse liquefiedpetroleum gasses, very light naphtha fractions and fractionswith initial boiling points (IBP) greater than 400 °C. TheTBP experiment is performed by distilling a sample ofcrude oil or petroleum fraction in a standardised fraction-ating column that is subject to specified operatingconditions. Distillation is carried out at atmospheric pres-sure from the IBP to about 210 °C vapour temperature, and

at reduced pressure beyond this point. Distillation under apartial vacuum avoids cracking of the more complexcomponents at elevated temperatures. Samples of distillateare collected at specified temperature cut points. Mass anddensity of each fraction are measured, and the distillationyield by mass is calculated. Volumetric yield can beestimated with mass and density data. Vapour temperaturesmeasured at reduced pressure are translated toAtmosphericEquivalent Temperature (AET). Distillation can usually becontinued up to an AET of approximately 400 °C. Finalresults of such experiments are TBP curves in mass and/orvolume yield versus boiling temperature expressed inAET.

The shape of a TPB curve is dependent on the type andquantities of hydrocarbon compounds that make up themixture being analysed. As such, this curve uniquely des-cribes a given crude oil in terms of its chemical makeup. Asan indicator of a crude oil's compositional makeup, a TBPdistillation curve has the added advantage that it is exten-sively performed for marketing purposes on all crude oils.These data are also easily accessed, as crude oil marketingcompanies publish these data in the public domain. SomeTBP distillation data and other assay properties publishedon the Internet by Chevron (2005) are listed in Table 1. Thelist covers a range of crude oil types from gas condensatesto heavier oils, containing samples from across the world,and constitutes one of two major data sets used in thecurrent study. Fig. 1 shows the TBP curve shapes for aselection of crude oils from this set of data. Three markercrude oils (Brent, West Texas Intermediate (WTI) andTapis) are also shown for comparison. The followingdetailed assay reports forAustralian crude oils published onthe BHP Billiton web site (BHP Billiton, 2005) have alsobeen used in the analysis.

• Griffin Crude Oil• Gippsland Crude Oil• Cossack Crude Oil• Laminaria Crude Oil• North West Shelf Condensate

Typical detailed TBP distillation data for Griffin crudeoil are shown in Table 2 as an example. The correspondingTBPdistillation (cumulativeweight fraction distilled versustemperature) curve is shown in Fig. 2(a). This curve can beconverted in to a weight fraction density curve, as shown inFig. 2(b). The weight fraction density can be calculated asfollows.

Weight fraction density of the ith fraction ¼ Dyact=DT

where,

Δyact =yact,I−yact,i−1ΔT =TI−Ti−1


yact,I Cumulative weight fraction of the ith
fraction
Ti Boiling temperature of ith fraction

The scatter seen on the experimental differentialcurve could be attributed to two factors. The first is dueto the lumping of compounds with close boiling points.The other is due to fluctuations in conditions during theexperiment. The variation is highlighted in the weightfraction density curve, as this is a differential form of thecumulative weight fraction distribution, which is some-what smoother by comparison.

A mathematical characterisation of TBP distillationcurves would be useful for a number of applications.These include physical property predictions, heavy endcharacterisation for EOS modelling and crude oilvaluation. In addition, the comparison of shapes ofdifferent TBP curves would allow for the identificationof groupings of oils based on composition.

The following sections detail a method of mathe-matically characterising a TBP distillation curve usingthe two-parameter gamma distribution. The fittingmethodology used is described and results for a rangeof crude oil types have been presented, demonstratingthe flexibility of the proposed methodology. Acorrelation between the fitting parameters and API

Fig. 3. Probability density functions and cumulative probability distribu

gravity is also shown. A new classification systembased on temperature cut fractions from TBPdistillation data, using a ternary diagram is introducedas an alternative to the PNA (Paraffins, Naphthenesand Aromatics) ternary diagram (Tissot and Welte,1984).

2. TBP curve characterisation

2.1. Gamma distributions

The gamma distribution has been widely used as amathematical function to characterise hydrocarbonmixtures. The main focus of this work has been todescribe the molecular weight distribution with theobjective of predicting phase behaviour of hydrocarbonmixtures. Whitson (1983) proposed a continuous ap-proach for modelling the molecular weight distributionof a hydrocarbon mixture using the three-parametergamma distribution for Equation of State (EOS) model-ling. Cotterman et al. (1985) also used the three-parame-ter gamma distribution to describe the molecular weightdistribution of hydrocarbon mixtures for process designapplications.

The probability distribution function (PDF) for agamma distribution can be described using the

tions for the gamma distribution with varying values of α and β.

Table 3Gamma distribution fitting for Griffin crude oil TBP distillation data

Experimental Predicted

TemperatureT (°C)

Cumulative weightpercentage

Cumulative weightfraction yact

Weight fractiondensity

Cumulative weightpercentage ypred

Weight fractiondensity

(yact−ypred)2

(yact−yact)2

19 3.1 0.031 0.0016 0.005 0.0007 0.00070 0.3901760 9.0 0.090 0.0014 0.081 0.0030 0.00008 0.3199470 11.8 0.118 0.0028 0.114 0.0035 0.00002 0.2890580 13.9 0.139 0.0021 0.150 0.0038 0.00012 0.2669190 17.1 0.171 0.0032 0.189 0.0041 0.00034 0.23487100 23.0 0.230 0.0059 0.231 0.0042 0.00000 0.18116110 26.9 0.269 0.0039 0.274 0.0043 0.00002 0.14949120 32.4 0.324 0.0055 0.317 0.0044 0.00004 0.10998130 37.1 0.371 0.0047 0.361 0.0043 0.00010 0.08102140 40.3 0.403 0.0032 0.404 0.0043 0.00000 0.06382150 45.3 0.453 0.0050 0.446 0.0042 0.00004 0.04106160 48.8 0.488 0.0035 0.487 0.0040 0.00000 0.02810165 50.6 0.506 0.0036 0.507 0.0039 0.00000 0.02239170 52.8 0.528 0.0044 0.527 0.0038 0.00000 0.01629180 56.2 0.562 0.0034 0.564 0.0037 0.00000 0.00877190 59.7 0.597 0.0035 0.600 0.0035 0.00001 0.00344200 63.5 0.635 0.0038 0.633 0.0033 0.00000 0.00043210 67.3 0.673 0.0038 0.665 0.0031 0.00006 0.00030220 71.1 0.711 0.0038 0.695 0.0029 0.00027 0.00307230 72.4 0.724 0.0013 0.722 0.0027 0.00000 0.00467240 73.7 0.737 0.0013 0.748 0.0025 0.00011 0.00662250 76.6 0.766 0.0029 0.771 0.0023 0.00003 0.01218260 78.9 0.789 0.0023 0.793 0.0021 0.00002 0.01779270 80.6 0.806 0.0017 0.813 0.0019 0.00005 0.02261280 82.4 0.824 0.0018 0.831 0.0018 0.00005 0.02835290 84.4 0.844 0.0020 0.848 0.0016 0.00002 0.03548300 86.6 0.866 0.0022 0.863 0.0015 0.00001 0.04425310 87.7 0.877 0.0011 0.877 0.0013 0.00000 0.04900320 88.8 0.888 0.0011 0.890 0.0012 0.00000 0.05399330 89.9 0.899 0.0011 0.901 0.0011 0.00001 0.05923340 91.3 0.913 0.0014 0.912 0.0010 0.00000 0.06624350 92.3 0.923 0.0010 0.921 0.0009 0.00000 0.07148360 93.4 0.934 0.0011 0.930 0.0008 0.00002 0.07749390 94.5 0.945 0.0004 0.950 0.0006 0.00003 0.08373400 95.6 0.956 0.0011 0.956 0.0005 0.00000 0.09022410 96.3 0.963 0.0007 0.961 0.0005 0.00001 0.09447430 97.3 0.973 0.0005 0.969 0.0004 0.00002 0.10072450 98.1 0.981 0.0004 0.976 0.0003 0.00003 0.10586480 98.8 0.988 0.0002 0.983 0.0002 0.00002 0.11047500 99.1 0.991 0.0002 0.987 0.0002 0.00002 0.11247530 99.5 0.995 0.0001 0.991 0.0001 0.00002 0.11517530+ 100.0 1.000

0.00228 3.57276


following two-parameter form (Hastings and Peacock,1975).

p xð Þ ¼ xa−1ex=b

baCðaÞ ð1Þ

where,

x Variable (0≤xb∞)α Shape parameter (αN0)

β Scale parameter (βN0)Γ(α) The gamma function with parameter α, given

by,

CðaÞ ¼Z l

0e−uua−1du ð2Þ

The cumulative probability function is given by,

PðXV xÞ ¼Z x

0pðxÞdx ð3Þ


Probability density functions and cumulative proba-bility distributions for different values of parameters αand β are shown in Fig. 3, indicating the diversity ofshapes that can be accommodated. From the definition ofthe two-parameter gamma distribution, the mean, stan-dard deviation andmode of the distribution are as follows.

Mean αβStandard deviation β

ffiffiffia

pMode β(α−1)

2.2. Methodology

The two parameters (α and β) of the gamma dis-tribution described previously are varied to obtain the bestfit with experimental data. The data in Table 2 for thedetailed assay of Griffin crude oil are used as an example.The predefined function GAMMADIST in MS Excel isused to generate the cumulative weight fraction data andweight fraction density data. The goodness of fit ismeasured using the coefficient of determination (R2) andthe Root Mean Square Error (RMSE). The Solver plug-in

Fig. 4. Gamma distribution fitted TBP distillation curves for Griffin crudeoil.

Fig. 5. Gamma distribution fitted TBP distillation curves for Cossackcrude oil.

from MS Excel is used to tune α and β to the optimumvalues while minimising RMSE.

The application of the methodology described abovefor the Griffin crude oil (Table 2 and Fig. 2(a) and (b)) isshown in Table 3. The fitted values ofα and β are 2.90 and63.48 respectively, and the cumulative weight fractionand weight fraction density curve are shown in Fig. 4(a)and (b), respectively. The RMSE and R2 are calculatedusing Eqs. (4) and (5), respectively.

RMSE ¼

ffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiXni¼1

ðyact−ypredÞ2

n

vuuut ð4Þ

R2 ¼ 1−

Xni¼1

ðyact−ypredÞ2

Pni¼1

ðyact−PyactÞ2ð5Þ

where,

yact Actual (experimental) valueypred Predicted value from model


y act Mean of actual valuesn Number of points

For the above example,

RMSE ¼ffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffi0:00228

41

r¼ 0:0075

R2 ¼ 1−0:002283:57276

¼ 0:9994

These values, together with a visual comparison of thefitted curve to the actual data, indicate that a good fit hasbeen achieved. RMSE was selected as the parameter foroptimisation (i.e., minimisation of error) using the Solveras it showed a better convergence when compared to theresults obtained by using R2 (i.e., aiming for an R2 valueof 1.0). This fitting is independent of the initial valuesused for α and β. The fitted curve shows a good visualagreement with experimental data points and R2 values of0.99 or better in all cases.

Fig. 6. Gamma distribution fitted TBP distillation curves for Gippslandcrude oil.

Fig. 7. Gamma distribution fitted TBP distillation curves for LaminariaCrude Oil.

2.3. Results

The same methodology described above has been ap-plied to other detailed samples. Fitted distributionsobtained for four other detailed TBP distillation curvesare shown in Figs. 5 6 7 and 8. As can be seen from thesegraphs, the methodology described has the ability to fitexperimental TPB curves verywell for a range of oil types.

The partial TBP distillation curves listed in Table 1consist of weight fraction data collected at eight tem-perature cut points ranging from 80 °C to 570 °C. Otherbasic assay properties such as API gravity, sulphur contentand UOPK factor are also available. The UOPK factor (orWatson characterisation factor) is defined as follows(Nelson, 1958)

K ¼ffiffiffiffiffiTB3

pS

ð6Þ

where,TB Average molal boiling temperature in degrees

RankineS Specific gravity at 60 °F

Table 4UOP K factor values for paraffin, naphthene and intermediate basecrude oils (from Nelson, 1958)

UOP K factor Crude oil base

12.9–12.2 Paraffinic base12.2–11.5 Intermediate base11.5–10.5 Naphthene base


The value of the K factor allows the characterisa-tion of the base of a crude oil as paraffin base,naphthene base or intermediate base. The ranges of Kfactor values corresponding to each base are listed inTable 4. The same methodology described above fordetailed assay results was used to fit a gammadistribution to each of these curves. The results areshown in Table 5. As with the detailed assay results,good fitting can be observed in all cases. Theseresults also demonstrate the flexibility of the newmethodology. As mentioned previously and shown inFig. 1, the 24 samples in Table 1 cover a wide rangeof crude oil types. Represented are oils with APIgravity ranging from 19.2 (for Kuito crude oil, aheavy oil from Angola) to 61.2 (for North West ShelfCondensate, a gas condensate). In addition, the listcontains crude oils with a wide distribution of totalsulphur content (0.0 to 1.55 wt.%), UOP K factor(11.6 to 12.7) and pour point (−54 °C to 38 °C). Forthis diverse group of oils, the methodology is able tofit gamma distributions with the coefficient of

Fig. 8. Gamma distribution fitted TBP distillation curves for NorthWest Shelf Condensate.

determination (R2) being better than 0.99 in eachcase. A visual comparison of the fitted and actualcurves confirms the excellent fit.

An attempt was made to correlate the gamma dis-tribution characterisation parameters (α and β) withAPI gravity. Various functional forms were testedagainst the available data. The form that gave thebest approximation to the experimental data was asfollows.

APIGravity ¼ aðbða−1Þ þ bÞc ð7Þ

where,

α and β Gamma distribution characterisation
parameters
a, b and c Constants

The functional form of Eq. (7) was selected as theterm β(α−1) corresponds to the modal temperature ofthe fitted distribution. As such the expectation is thatthere will be a relationship between the modal tem-perature and API gravity. The constants a, b and cwere tuned to get the best possible fit to theexperimental data by using the Solver plug-in fromMS Excel. The results for prediction of API gravityusing Eq. (7) are compared to experimental values forall 24 samples in Fig. 9. Values for constants a, b andc, and the coefficient of determination (R2) and RMSEfor these data are shown on the graph. Although thereis reasonable agreement between experimental andpredicted values, the degree of scatter on the graph ishigh, with indication of a secondary trend. This aspectwas further investigated in order to determine thereasons for this scatter, in terms of other availablecrude oil properties. The scatter observed on Fig. 9appeared to be related to the UOP K factor andoutlying points have been annotated on that figure.

As evident from Fig. 9, samples with a UOP Kfactor greater than 12.2 cluster as a separate trend,distinct from the main trend. This group correspondsto crude oils having a paraffin base. Therefore, an

Table 5Gamma distribution fitting results for the samples listed in Table 1

Crude oil Whole crude properties Gamma distributionfitting parameters

Gravity (°API) Characterization factor (K factor) Sulfur (wt.%) Pour point (°C) Alpha Beta R2

1 Kuito 19.2 11.6 0.68 −32 4.21 109.9 1.0002 Cabinda 32.8 12.3 0.13 16 2.42 182.2 0.9963 Nemba 39.3 12.2 0.19 −4 2.22 147.2 0.9974 Palanca 38.4 12.1 0.17 10 2.54 136.6 0.9965 Doba Blend 20.5 12.0 0.16 −4 6.09 88.7 0.9996 Kitina 37.7 12.2 0.09 7 2.18 170.1 0.9977 N'kossa 46.8 12.4 0.03 −21 1.95 139.3 0.9978 Bonny Light 35.8 11.9 0.14 −18 3.30 93.4 0.9949 Escravos 34.4 11.7 0.15 7 3.20 100.5 0.99610 Pennington 35.0 11.7 0.08 10 4.56 66.0 0.99811 Barrow Island 38.0 11.7 0.05 −54 3.67 69.0 0.99912 Cossack 48.1 12.0 0.04 −18 2.23 103.7 0.99813 NWS condensate 61.2 12.3 0.01 −51 2.42 54.7 1.00014 Thevenard Island 41.3 NA 0.02 NA 4.87 50.6 0.99815 Duri 20.8 12.0 0.20 10 4.07 142.0 0.99816 Minas 35.3 12.6 0.09 38 3.32 131.6 0.99817 Kutubu Light 45.1 12.2 0.04 −1 2.15 112.9 0.99518 Nanhai Light 40.1 12.4 0.06 32 3.54 97.9 0.99419 Benchamas 43.0 12.7 0.04 32 2.43 144.2 0.99420 Tantawan 43.3 12.5 0.05 16 2.81 111.7 0.99921 Hibernia 35.9 11.6 0.34 13 2.37 154.4 0.99822 Rincon 36.0 11.8 0.37 −4 2.77 128.0 1.00023 Medanito 35.2 12.2 0.41 −1 2.49 146.0 0.99924 Hamaca 26.0 12.1 1.55 −40 3.04 139.2 0.996


attempt was made to predict the API gravity of crudeoils with a paraffin base (K factor greater than 12.2)using the functional form of Eq. (7) and tuning the

Fig. 9. Predicted versus experimental values for API gravity using Eq.(7) for the 24 samples listed in Table 1. The UOP K factor for theoutlying samples has been annotated.

constants a, b and c to match the experimental data.Results from this API gravity prediction for paraffinbase crude oils are compared to experimental values

Fig. 10. Predicted versus experimental values for API gravity using Eq.(7) for paraffin base crude oils (UOP K factorN12.2).

Fig. 11. Predicted versus experimental values for API gravity using Eq.(7) for non-paraffin base crude oils (UOP K factorb12.2). The UOP Kfactor for the outlying samples has been annotated.


in Fig. 10. It can be seen that there is very goodagreement between the predicted and experimentalvalues. An attempt was then made to predict API

Fig. 12. Paraffins, naphthenes and aromatics (PNA) composition class

gravity for the non-paraffin base crude oils (K factorless than 12.2) using the same functional form andtuning a, b and c to get the best match with expe-rimental data. Results are shown in Fig. 11, indicatingthat, with the exception of three scattered point, thereis also very good agreement between the predictedand experimental API gravities.

3. Cut fraction characterisation

3.1. Methodology

One possible framework for classification of crudeoils, based on assay properties is given in Tissot andWelte (1984). They propose a classification based onrelative quantities of paraffins, naphthenes andaromatics (PNA) contained in a particular crude oil,displayed on a ternary diagram. They have appliedthis technique to a large number of samples andshow how this ternary diagram can be used for oil–oil and oil–source correlations. An example of theirclassification technique is shown in Fig. 12. Onedraw back of this PNA framework is that data forplotting the ternary diagram are not readily available.

ification ternary diagram (from Tissot and Welte, 1984, p. 440).

Fig. 13. Cut fraction temperature definitions used by three companies based on data published on their respective web pages.


An assay report would normally only contain thePNA break down for the lighter fraction (usuallyboiling at less than 200 °C) of the crude oil. As such,it is necessary to perform a separate analysis topopulate this type of ternary plot. The latter isperhaps one reason that this terminology appears tobe used relatively infrequent.

For the abovementioned reasons, a new ternaryplot based on assay properties is proposed, utilisingthree boiling temperature cut fractions from TBPdistillation. It should be noted that boiling temper-ature ranges for petroleum fractions vary regionallyand also among companies. Definitions used bythree companies are shown in Fig. 13, based on datapublished on the respective company web sites. Thisfigure indicates that, although companies use slightlydifferent definitions for individual cut fractions,there is some agreement between them. Three cutfractions were selected to correspond to distinctrefinery products. The cuts fractions were alsopicked to have a good distribution of points on the

Table 6Boiling temperature cut fractions selected for plotting the proposed cutfraction ternary diagram

Cut Fraction No. Temperature range Main distillation products

1 IBP to 200 °C Naphtha2 200 °C to 350 °C Kerosene and Gas Oil3 350 °C+ Atmospheric Residue

resulting ternary plot for various types of crude oilsavailable, ranging from gas condensates to heavyoils. Table 6 shows the three cut fractions that wereselected for the purpose of drawing the new ternaryplot.

Weight fractions are read by interpolating cumu-lative weight fractions distilled at 200 °C and 350 °Cfrom the distillation curve. The three weight

Fig. 14. Composition of temperature cut fractions (modified afterHunt, 1996, p. 45).

Fig. 15. Cut fraction ternary diagram showing the 24 samples listed in Table 1.


fractions, adding up to 1.0, are then calculated asfollows.

IBP to 200 °C fraction W200

200 °C to 350 °C fraction W350−W200

350 °C+fraction 1−W350

where,

W200 Cumulative weight fraction distilled at
200 °C in
W350 Cumulative weight fraction distilled at
350 °C
Fig. 16. Relationship between gamma distribution characte

Fig. 14 shows the link between cut fractionsselected above and chemical composition for a naphtheniccrude oil (Hunt, 1996). It can be seen that there is somedegree of correspondence between the selected fractionsand composition elements. The IBP to 200 °C fraction isricher in normal and iso-paraffin, while the 350 °C+fraction is richer in heavy aromatics and nitrogen, sulphurand oxygen (NSO) compounds.

3.2. Results

The proposed cut fraction ternary diagram, with thesamples from Table 1 plotted, is shown in Fig. 15. It can

risation parameters and cut fraction ternary diagram.

Fig. 17. Constant API gravity lines for paraffin base crude oils (UOP K factorN12.2).


be seen from the plot that there is a good distribution ofpoints on this ternary diagram, considering the variedfluid types plotted.

The location of a crude oil on the proposed ternarydiagram is governed by the relative quantities of eachfraction produced, being a function of the shape of theTBP distillation curve. As such, there is a unique rela-tionship between the two-parameter gamma distributioncharacterisation system described previously and thisternary diagram. Fig. 16 shows constant α and constantβ lines superimposed on the ternary plot. It can be seenthat a given pair of α and β lines have only one

Fig. 18. Constant API gravity lines for non-para

intersection. A given TPB curve shape (as defined by itsα and β values) will be uniquely located on the ternaryplot.

It has also been possible to identify the distributionof API gravity on the new cut fraction ternarydiagram. As described previously, a good correlationhas been obtained between the gamma distributioncharacterisation parameters (α and β) and API gravityfor paraffin and non-paraffin base crude oils. Thisrelationship, based on the functional form of Eq. (6),was used to calculate constant API gravity lines on theternary diagram.

ffin base crude oils (UOP K factorb12.2).


Eq. (6) can be rearranged as follows.

b ¼ ðG=aÞ1=c−bn o

=ða−1Þ ð8Þ

where,

α and β gamma distribution characterisation
parameters
G API gravitya, b and c Constants

This formulation allows the β value corresponding toa given α value to be calculated for a specified APIgravity. As shown previously, a given set of α and βvalues results is a unique point on the ternary diagram.As such, a constant API gravity locus on the ternarydiagram can be established by varying α and calculatingβ, using Eq. (8). Fig. 17 shows constant API gravitylines for the paraffin base crude oils from Table 1 (asdefined by having a UOP K factorN12.2). It can be seenthat the data points match well with the predictedconstant API gravity curves. Constant API gravity linesfor non-paraffin base crude oils (as defined by having aUOP K factorb12.2) are shown in Fig. 18, indicatinggood agreement between the data points and predictedcurves. A comparison of the two sets of constant APIgravity curves is shown in Figs. 17 and 18, indicatingthat the two sets are significantly different. It can beconcluded that the distribution of crude oils according toAPI gravity on the cut fraction ternary diagram isdependent on their base.

4. Summary and conclusions

A system for the characterisation of crude oils,based on TBP distillation curve data using a two-parameter form of the gamma distribution from sta-tistics, has been described. The new methodology hasbeen tested against a broad selection of crude oiltypes and is able to fit the cumulative TBP distillationcurve with a high degree of correlation in all cases.The gamma distribution characterisation parameters(α and β) have been related to API gravity using asingle functional form, having different constantsdepending on the base of the crude oil as defined bythe UOP K factor.

A cut fraction ternary diagram for crude oils has alsobeen introduced as an alternative to the traditional PNA

diagram. It is possible to display a wide range of crude oiltypes on the cut fraction ternary diagram. This ternarydiagram has the advantage that the data required topopulate it is easily available from crude oil assay reports.The distribution of crude oils according to theirAPI gravityon the cut fraction ternary diagramhas also been described.It has been shown that this distribution is a function of thebase of a crude oil as defined by the UOP K factor. Therelationship developed suggests that API gravity is afunction of the modal boiling temperature of a crude oil.

Acknowledgments

The authors wish to gratefully acknowledge thesupport of the sponsors: BHP Billiton, Chevron, Santosand Woodside Energy, for their financial support, fielddata and permission to publish the results presented.

References

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http://globaloil.bhpbilliton.com

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http://www.chevron.com/crudemarketing/

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