Method for predicting chemical or physical properties of crude oils

A method for predicting the properties of crude oils or their boiling fractions which comprises selecting a chemical or perceptual or physical or performance property or groups of properties of the crude oil or its boiling fractions and creating a training set from reference samples which contain characteristic molecular species present in the crude oil or its boiling fractions. The reference samples are subjected to GC/MS analysis wherein the often collinear data generated is treated by multivariate correlation methods. The training set produces coefficients which are multiplied by the matrix generated from a GC/MS analysis of an unknown sample to produce a predicted value of the chemical, performance, perceptual or physical property or groups of properties selected.

BACKGROUND OF THE INVENTION 
1. Field of the Invention 
This invention relates to a method for rapidly predicting the physical and 
chemical properties of a crude oil and/or its boiling fractions using a 
combination of gas chromatography and mass spectrometry. 
2. Description of the Related Art 
Traditional wet chemistry methods for obtaining physical and chemical 
properties which define crude oils quality are very time consuming. Crude 
oils are commonly subjected to distillation and the resultant distillation 
fractions subjected to numerous analytical and physical test. Crude oils 
typically contain many thousands of different chemical compounds and 
therefore only bulk properties for classes of compounds are usually 
measured, e.g., viscosity, pour point, API gravity and the like. 
Gas chromatography has been used to predict physical and performance 
properties of hydrocarbon mixtures boiling in the gasoline range. Crawford 
and Hellmuth, Fuel, 1990, 69, 443-447, describe the use of gas 
chromatography and principal components regression analysis to predict the 
octane values for gasolines blended from different refinery streams. 
Japanese laid-open patent application JP 03-100463 relates to a method of 
estimating the cetane number for fuel oils by separating an oil sample 
into its components using gas chromatograpy, measuring the signal strength 
of ion intensities at characteristic masses in the mass spectrum, and 
correlating these ion intensities to cetane number using multiple 
regression analysis. 
Combined gas chromatography/mass spectrometry (GS/MS) analysis has been 
done on crude oils. U.S. Pat. No. 5,119,315 discloses a method for 
aligning sample data such as a mass chromatogram with reference data from 
a known substance. Williams et al, 12th European Assoc. Organic Geochem., 
Organic Geochem. Int. Mtg. (Germany 09/16-20/85); Organic Geochemistry 
1986, Vol. 10 (1-3) 451-461, discusses the biodegradation of crude oils as 
measured by GC/MS analysis. 
It would be desirable to have a method for rapidly predicting properties of 
crude oils and/or their boiling fractions using gas chromatography/mass 
spectrometry which method involves analyzing collinear data. 
SUMMARY OF THE INVENTION 
This invention relates to a method for predicting physical, performance, 
perceptual and/or chemical properties of a crude oil which comprises: 
(a) selecting at least one property of the crude oil or its boiling 
fractions; 
(b) selecting reference samples, said reference samples containing 
characteristic compound types present in the crude oil or its boiling 
fractions and which have known values of the property or properties 
selected in step (a); 
(c) producing a training set by the steps of: 
(1) injecting each reference sample into a gas chromatograph which is 
interfaced to a mass spectrometer thereby causing at least a partial 
separation of the hydrocarbon mixture into constituent chemical components 
and recording retention times of the partially separated components; 
(2) introducing the constituent chemical components of each reference 
sample into the mass spectrometer, under dynamic flow conditions; 
(3) obtaining for each reference sample a series of time resolved mass 
chromatograms; 
(4) calibrating the retention times to convert them to atmospheric 
equivalent boiling points; 
(5) selecting a series of atmospheric boiling point fractions; 
(6) selecting within each boiling point fraction a series of molecular 
and/or fragment ions, said ions being representative of characteristic 
compounds or compound classes expected within the boiling point fraction; 
(7) (i) recording the total amount of mass spectral ion intensity of each 
characteristic compound or compound group selected in step c(6), and 
optionally (ii) multiplying total amounts of mass spectral ion intensities 
of each characteristic compound or compound group from (7)(i) by weighting 
factors to produce either weight or volume percent data; 
(8) forming the data from steps c(6) and either of c(7)(i) or c(7)(ii) into 
a X-block matrix; 
(9) forming the property data selected in (a) for reference samples 
selected in (b) into a Y-block matrix; 
(10) analyzing the data from steps c(8) and c(9) by multivariate 
correlation techniques including Partial Least Squares, Principal 
Component Regression, or Ridge Regression to produce a series of 
coefficients; 
(d) subjecting a crude oil or its boiling fractions to steps c(1) and c(3) 
in the same manner as the reference samples to produce a series of time 
resolved mass chromatograms; 
(e) repeating steps c(4) to c(8) for each mass chromatogram from step (d); 
(f) multiplying the matrix from step (e) by the coefficients from step 
c(10) to produce a predicted value of the property or properties for the 
crude oil or its boiling fractions. 
The Gas Chromatography/Mass Spectrometry (GC/MS) method described above can 
be used to predict a wide range of chemical and physical properties 
(including performance and perceptual properties) of crude oils such as 
chemical composition and concentration data on specific components, 
distillation properties, viscosity, pour point, cloud point, octane 
number, API gravity, and the like in a short time period.

DETAILED DESCRIPTION OF THE INVENTION 
Crude oils contain many thousands of different individual chemical 
compounds including organic, metallo-organic and inorganic compounds. A 
complete analysis of crude oil components would be extremely difficult 
even with modern instrumental techniques. In order to predict bulk 
properties of a crude oil or its boiling fractions, one must obtain 
information on key chemical components within different classes of the 
constituents of crude oils. The more chemical components identified, the 
better the prediction. However, these additional components greatly 
increase the data that must be quantitatively treated. 
The present method for predicting chemical and physical properties for 
crude oils involves quantitative identification of components using a 
combination of retention times from a GC analysis coupled with target 
fragment and/or molecular ions produced by the MS. The MS information is 
compared with a set of known properties from reference samples which form 
a training set. By mathematically comparing the experimental data against 
that of the training set, one may predict the desired properties of the 
unknown mixture. 
GC/MS utilizes a gas chromatograph interfaced with a mass spectrometer. 
While a chromatographic method such as supercritical fluid chromatography, 
liquid chromatography or size exclusion chromatography may be used to 
separate the mixture into components or mixtures of components, capillary 
gas chromatography is the preferred means for interfacing with a mass 
spectrometer. Both GC and MS utilize computer software for instrument 
control, data acquisition and data reduction. 
The sample mixture to be analyzed is first injected into a GC where the 
mixture components are separated as a function of retention time and 
boiling point. Only partial chromatographic resolution of mixture 
components is necessary. The GC oven temperature control is usually 
programmed for samples with a wide boiling range. Components may also be 
identified by a detector such as a flame ionization detector, thermal 
conductivity detector, atomic emission detector or electron capture 
detector. 
The separated or partially separated components are then transferred to the 
mass spectrometer under dynamic flow conditions. Since a GC operates under 
atmospheric pressure and a MS under vacuum conditions (about 10.sup.-3 
kPa), the instrument interface requires a coupling device such as a 
molecular separator (e.g., jet, membrane, etc.), open split coupler or 
capillary direct interface to efficiently transfer sample while minimizing 
carrier gas effects. 
Depending on the nature of the sample, the mixture may be introduced 
directly into a MS using a direct insertion probe without a prior GC 
separation step. Other thermal separation techniques not involving a GC 
may be used to introduce the sample into the mass spectrometer. 
In the MS, sample molecules are bombarded with high energy electrons 
thereby creating molecular ions which fragment in a pattern characteristic 
of the molecular species involved. A continuous series of mass spectra are 
obtained over a scan range of 10 or more daltons to at least 800 daltons. 
The mass spectral data may also be acquired under selected ion monitoring 
(SIM) mode. In the selected ion mode, care must be taken to select ions 
representative of the components of interest and to operate under 
repeatable conditions. A variety of MS instruments may be used including 
low resolution, high resolution, MS/MS (hybrid, triple quadrupole, etc.), 
ion cylotron resonance and time of flight. Any ionization technique may be 
used, such as electron ionization, chemical ionization, multiphoton 
ionization, field desorption, field ionization, etc., provided that the 
technique provides either molecular or fragment ions which are suitable 
for use in the analysis procedure. 
The results of sample analysis are a series of l mass spectra. The mass 
spectra are divided into n time intervals where n is an integer from 1 to 
l. At least one diagnostic ion is chosen from each of m time intervals 
where m is an integer from 1 to n. The term "diagnostic ion" refers to an 
ion which is representative a compound, a chemical class or a physical 
property correlated thereto. Regardless of whether mass spectra are 
obtained in the scan or selected ion monitoring mode, it is important that 
the mass spectra be obtained under repeatable conditions. 
If the mass spectral data are acquired in the scan mode, the mass range 
covered during the mass spectrometer acquisition should be sufficient to 
provide acquisition of all of the ions which could be used as diagnostic 
ions during mathematical treatment of each mass spectral scan. If the mass 
spectral data are acquired in the selected ion monitoring mode, then care 
must be taken that the ions selected for monitoring are suitable for use 
in measuring the components of interest. 
The sample mass spectral data are then compared to mass spectral data from 
a series of reference samples with known physical or chemical properties. 
In order to compare reference mass spectral data with sample mass spectral 
data, it is desirable to convert sample retention time data to atmospheric 
equivalent temperature data and also reference sample data to help ensure 
the integrity of the comparison. There are commercially available computer 
program available for such data alignment, for example, Hewlett-Packard 
GC/MS Software G1034C version C.01.05. 
The reference mass spectral data, and associated properties data, are 
arranged in matrix form for mathematical treatment as described below. In 
the case of chemical composition information, one matrix is formed of 
reference sample ion intensities at given masses and the other matrix 
contains known ion intensities for molecular fragment ions of known 
components. The training set for chemical composition data is thus made up 
of mass spectral data for different components characteristic of 
components expected to be found in the sample mixtures. Similar training 
sets can be formed for other chemical or perceptual or performance or 
physical properties of interest. These training sets form one block or 
matrix of data (Y-block or properties matrix). The actual sample mass 
spectral data (which may have been temperature aligned) form the other 
block (X-block) or matrix of data. These two matrices are subjected to 
mathematical treatment known as Partial Least Squares (PLS), or Principal 
Component Regression (PCR), or Ridge Regression (RR) to obtain a 
mathematically describable relationship between the properties data and 
mass spectral data, known as a model. Coefficients provided by this model 
are mathematically combined with the suitably treated mass spectral data 
from samples with unknown desired properties to: 
a) predict desired properties, 
b) assess the suitability of the model for such predictions, and 
c) diagnose the stability and general correctness of the process that 
yielded the mass spectral data. 
PLS/PCR/RR are described in the literature, e.g., Wold S., A. Ruhe, H. 
Wold, and W. J. Dunn, "The Collinearity Problem in Linear Regression. The 
Partial Least Squares (PLS) Approach to Generalized Inverses", SIAM J. 
Sci. Stat. Comput., 1984 5(3), 735-743, or Geladi P., and B. R. Kowalki, 
"Partial Least Squares Regression: A Tutorial", Anal. Chim. Acta, 1986, 
185, 1-17, or Hokuldsson A., "PLS Regression Methods", J. Chemometrics, 
1988, 2, 211-228, or in many other articles in journals such as the 
Journal of Chemometrics or Intelligent Laboratory Systems; Frank, I. and 
J. Friedman, "A Statistical View of Some Chemometrics Regression Tools", 
Technometrics, 1993, Vol. 35, No. 2; Jackson, J. E., "A User's Guide To 
Principal Components", Wiley-Interscience, New York, 1991; Montgomery, D. 
C. and E. A. Peck, "Introduction To Linear Regression Analysis", 
Wiley-Interscience, New York, 1990; and Martens, H., and T. Naes, 
"Multi-variable Calibration", Wiley-Interscience, New York, 1989. 
When dealing with a complex mixture, it is necessary to select appropriate 
masses or groups of masses at specific retention times for a particular 
compound or classes of compounds. This may be accomplished by either 
Hydrocarbon Compound Type Analysis or Chemist's Rules. For crude oils, it 
is preferred to use Hydrocarbon Compound Type Analysis. However, Chemist's 
Rules may be used and the selection of such masses is the basis for a set 
a rules which then forms the data for the training set. There are no set 
procedures for such a selection process. The researcher must select 
appropriate masses for 
These coefficients are then multiplied by the data matrix for the sample. 
The result is a prediction of the desired property or properties. The 
method of the invention is further illustrated by the following examples. 
EXAMPLE 1 
The method for predicting the physical or chemical properties for a range 
of boiling fractions of crude oils is demonstrated in this example using 
API gravity of crude oil fractions as the specific property for purposes 
of illustration. The method is generally applicable to a range of other 
physical properties as well as chemical, perceptual or performance 
properties of such mixtures, such as saturates and aromatics content, 
smoke point, pour point, viscosity, etc. 
The initial consideration is to establish a set of standard GC/MS operating 
parameters so that the GC/MS analytical data used for predicting 
properties are obtained under consistent operating conditions. The GC/MS 
instrument used in this example is a Hewlett-Packard 5970 Mass Selective 
Detector interfaced to a Hewlett-Packard 5890 Series II Gas Chromatograph. 
The GC/MS operating conditions are summarized in Table 1. 
______________________________________ 
GC Conditions 
______________________________________ 
Column Fused silica capillary column 
such as J&W DB 1 HT: 15 m .times. 
0.25 mm, 0.1 micron film 
thickness 
Temperature Program 
Initial Temperature (.degree.C.) 
-40 
Initial Time (minutes) 
0 
Program Rate (.degree.C./minute) 
10 
Final Temperature (.degree.C.) 
380 
Final Time (minutes) 
18 
Carrier Gas Helium 
Injection Volume .mu.L 
0.5 
Split Ratio 5:1 
Column Head Press, psi 
Approx. 2 
Interface Temperature (.degree.C.) 
300 
Mass Spectrometer Conditions 
Ionization Mode Electron Ionization, 70 eV 
nominal 
Mass Range Scanned (daltons) 
10-800 
scan/sec 1.56 
______________________________________ 
A Gerstel injector was used to introduce the sample without discrimination 
and simultaneously maintain vacuum-tight seals throughout the system 
during the analysis. The injector was programmed at a fast, controllable 
rate (12.degree. C./sec) from -150.degree. C. to 400.degree. C. A dilute 
solution (about 2%) of sample in CS.sub.2 was introduced with an 
autosampler. 
In order to predict properties of an unknown hydrocarbon mixture, it is 
first necessary to select reference samples having known values of the 
property or properties to form a model training set. In this example, a 
suite of 46 crude oils were used, covering a broad range of API gravity as 
shown in Table 2. 
TABLE 2 
______________________________________ 
COUNTRY API GRAVITY 
______________________________________ 
U.S.A. - 1 29.00 
Nigeria - 1 32.10 
Saudi Arabia - 1 
32.50 
Saudi Arabia - 2 
27.30 
Saudi Arabia - 3 
32.50 
Saudi Arabia - 4 
30.40 
Abu Dhabi - 1 43.50 
Venezuela 22.00 
Chad - 1 21.80 
Nigeria - 2 43.10 
Angola 32.70 
Australia 39.70 
Dubai 31.90 
U.S.A. - 1 29.00 
Nigeria - 1 32.10 
Saudi Arabia - 1 
32.50 
Saudi Arabia - 2 
27.30 
Saudi Arabia - 3 
32.50 
Saudi Arabia - 4 
30.40 
Abu Dhabi - 1 43.50 
Venezuela 22.00 
Chad - 1 21.80 
Nigeria - 2 43.10 
Angola 32.70 
Australia 39.70 
Dubai 31.90 
Denmark 33.20 
U.K. 23.10 
Nigeria - 3 36.50 
Nigeria - 4 29.10 
U.S.A. - 2 29.60 
U.S.A. - 3 33.80 
Kuwait - 1 31.90 
U.S.A. - 4 21.90 
Egypt 30.00 
Norway 33.50 
U.S.A. - 5 19.00 
U.S.A. - 6 19.40 
Iran 34.20 
Chad - 2 17.20 
Kuwait - 2 31.90 
Cameroon 21.10 
Yemen - 1 44.70 
Yemen - 2 47.80 
Yemen - 3 31.00 
Chad - 3 24.60 
Abu Dhabi - 2 40.40 
Abu Dhabi - 3 39.50 
Nigeria - 5 27.90 
Mexico 38.50 
Oman 34.20 
U.S.A. - 7 45.00 
Gabon 33.70 
Russia - 1 28.10 
Algeria 43.30 
Russia - 2 32.60 
Syria 36.20 
U.S.A. - 8 18.00 
Malaysia 47.00 
______________________________________ 
A data treatment method should be selected prior to obtaining raw GC/MS 
data. Two types of data treatments which may be used are Chemist's Rules 
and Hydrocarbon Compound Type Analysis as described, for example, in 
Robinson, C. J., "Low Resolution Determination of Aromatics and Saturates 
in Petroleum Fractions", Analytical Chemistry, 43(11), 1425-1434 (1971). 
The data treatment procedures involve two separate sections: (1) a 
calibration section to convert the retention time axis to the boiling 
point axis; and (2) the actual Hydrocarbon Compound Type Analysis or the 
Chemist's Rules which are based on a selected series of masses and 
correspond to prominent compounds or compound types expected for the type 
of hydrocarbon mixture under investigation. These compounds or compound 
types are selected on the basis that they have prominent molecular and/or 
fragment ions unique to that compound or molecular series. A portion of a 
set of the Chemist's Rules are shown in Table 3. 
TABLE 3 
__________________________________________________________________________ 
RETENTION TIME.sup.(d) 
RULES.sup.(a) 
COMPOUND.sup.(b) 
Mass.sup.(c) START END 
__________________________________________________________________________ 
1 Paraffins 
43 
57 
71 
85 
99 
113 
0.400 3.162 
2 Cycloparaffins 
41 
55 
69 
83 
97 
111 
0.400 3.162 
3 Toluene 91 
92 0.400 2.860 
4 C.sub.n H.sub.2n-8 
117 
131 
145 
159 
163 
177 
2.851 3.162 
5 C.sub.n H.sub.2n-6 
91 
105 
119 
133 
147 
161 
2.851 3.162 
6 C.sub.n H.sub.2n-12 
141 
155 
169 
183 
197 
211 
2.851 3.162 
7 C.sub.n H.sub.2n-10 
115 
129 
143 
157 
171 
185 
2.851 3.162 
8 C.sub.n H.sub.2n-18 
178 
191 
205 
219 
233 
247 
2.851 3.162 
9 n-C8 43 
57 
71 
85 
99 
114 
3.162 3.200 
10 i-Paraffins 
43 
57 
71 
85 
99 
113 
3.200 4.100 
11 Cycloparaffins 
41 
55 
69 
83 
97 
111 
3.200 4.100 
12 C.sub.n H.sub.2n-8 
117 
131 
145 
159 
163 
177 
3.200 4.100 
13 C.sub.n H.sub.2n-6 
91 
105 
119 
133 
147 
161 
3.200 4.100 
14 C.sub.n H.sub.2n-12 
141 
155 
169 
183 
197 
211 
3.200 4.100 
15 C.sub.n H.sub.2n-10 
115 
129 
143 
157 
171 
185 
3.200 4.100 
16 C.sub.n H.sub.2n-18 
178 
191 
205 
219 
233 
247 
3.200 4.100 
.cndot. 
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137 n-C24 43 
57 
71 
85 
99 
338 
10.912 
10.964 
138 i-Paraffins 
43 
57 
71 
85 
99 
113 
10.964 
14.800 
139 Cycloparaffins 
41 
55 
69 
83 
97 
111 
10.964 
14.800 
140 C.sub.n H.sub.2n-8 
117 
131 
145 
159 
163 
177 
10.964 
14.800 
141 C.sub.n H.sub.2n-6 
91 
105 
119 
133 
147 
161 
10.964 
14.800 
142 C.sub.n H.sub.2n-12 
141 
155 
169 
183 
197 
211 
10.964 
14.800 
143 C.sub.n H.sub.2n-10 
115 
129 
143 
157 
171 
185 
10.964 
14.800 
144 C.sub.n H.sub.2n-18 
178 
191 
205 
219 
233 
247 
10.964 
14.800 
__________________________________________________________________________ 
.sup.(a) Rule number, integer index 
.sup.(b) Compound or group of compounds rule applies to 
cycloparaffins 
alkylated 1 ring cycloparaffins 
C.sub.n H.sub.2n-6 
alkylated benzenes 
C.sub.n H.sub.2n-8 
alkylated indanes 
C.sub.n H.sub.2n-10 
alkylated indenes 
C.sub.n H.sub.2n-12 
alkylated naphthalenes 
C.sub.n H.sub.2n-18 
alkylated phenanthrenes/anthracenes 
.sup.(c) Masses used in Rule up to n may be specified, where n is an 
integer which is equal to the 
number of masses scanned during the time interval (d) either in full 
scan mode or selected ion 
monitoring mode!. 
.sup.(d) Retention time for both starting and ending expected retention 
times based on historical averages 
in minutes. 
A calibration table based on standard curve-fitting mathematical procedures 
is used to establish relationships between the measured retention times 
and the known boiling points of a standard mixture of n-alkanes covering 
the carbon number range: C.sub.5 to higher than C.sub.60. Table 4 displays 
a typical calibration table containing the retention times of the 
n-alkanes and their known boiling points. A similar calibration can be 
performed using the inherent information of the hydrocarbon compounds 
identified by their mass spectra in the mass chromatogram of the sample 
and their known boiling points. In that manner, all mass chromatographic 
information obtained in the retention time axis is converted to the 
boiling point axis. Hydrocarbon Compound Type Analysis or Chemist's Rules 
procedures are applied to pre-selected boiling point intervals or 
fractions. These boiling point intervals are specified by the user. It is 
also possible to use a combination of Hydrocarbon Type Analysis and 
Chemist's Rules wherein Hydrocarbon Type Analysis is applied to the 
boiling point intervals and Chemist's Rules are applied within one or more 
of the intervals. This type of treatment provides more detailed 
information concerning the specific interval or intervals. The retention 
time to boiling point calibration accounts for slight shifts in retention 
times which may result from column degradation, column head pressure 
fluctuations, changes in column carrier gas linear velocity, or minor 
fluctuations in the GC column oven temperatures or other causes. 
Hydrocarbon Compound Type Analysis is preferred as this method reduces the 
amount of data to be treated. Since one is usually concerned with bulk 
properties of crude oils, compound types within selected boiling intervals 
provide sufficient data to permit prediction of these properties. 
TABLE 4 
______________________________________ 
Carbon Retention Boiling 
Number Time (min) 
Point (C) 
______________________________________ 
5 1.415 36 
6 3.029 69 
7 4.497 98 
8 6.281 126 
9 8.05 151 
10 9.677 174 
11 11.21 196 
12 12.6 216 
14 15.19 254 
15 16.359 271 
16 17.511 287 
17 302 
18 19.564 316 
20 21.443 344 
22 369 
24 24.792 391 
26 26.262 412 
28 27.684 431 
30 28.933 449 
32 30.197 466 
34 31.320 481 
36 32.458 496 
38 33.454 509 
40 34.498 522 
42 35.415 534 
44 36.381 545 
46 37.283 556 
48 38.249 567 
50 39.247 576 
52. 40.245 585 
54 41.737 594 
56 43.211 601 
58 44.431 608 
60 46.120 615 
______________________________________ 
Once the conversion of the retention time axis to the boiling point axis is 
accomplished, the Hydrocarbon Compound Type Analysis or Chemist's Rules 
are applied to the raw mass spectrometric data. Typical user-specified 
boiling point intervals with their corresponding end points are shown on 
Table 5. Mass spectrometric information is derived for these intervals 
using Hydrocarbon Compound Type Analysis or Chemist's Rules. Typical 
information obtained with the Hydrocarbon Compound Type Analysis for a 
crude oil is shown in Table 5. 
TABLE 5 
__________________________________________________________________________ 
End Boiling pt. (deg C.) 
85 88 100 
125 
150 
175 
205 
220 
235 
265 
295 
Retention time (min) 
3.8 
3.85 
4.4 
5.85 
7.7 
9.55 
11.55 
13.15 
14.25 
15.85 
18 
Weight Percent 
2.33 
0.27 
2.71 
5.12 
4.79 
4.98 
5.4 
2.77 
2.73 
5.1 
5.15 
__________________________________________________________________________ 
Paraffins 0.5 
0.06 
2.34 
3.46 
2.97 
3.06 
3.08 
1.83 
1.58 
2.33 
2.2 
1-ring cycloparaffins 
1.7 
0.2 
0.35 
1.07 
0.97 
0.84 
0.95 
0.38 
0.46 
0.85 
0.97 
2-ring cycloparaffins 
0 0 0 0 0.06 
0.15 
0.28 
0.19 
0.19 
0.38 
0.36 
3-ring cycloparaffins 
0 0 0 0 0.01 
0 0 0.02 
0.04 
0.24 
0.25 
Alkylbenzenes 
0 0 0 0.58 
0.67 
0.91 
1.05 
0.23 
0.22 
0.4 
0.33 
Naphthenebenzenes 
0 0 0 0 0 0 0.01 
0.09 
0.13 
0.3 
0.22 
Dinaphthenebenzenes 
0 0 0 0 0 0 0 0 0.01 
0.06 
0.15 
Naphthalenes 0 0 0 0 0 0.01 
0.03 
0.01 
0.09 
0.29 
0.4 
Acenaphthene/Dibenzofurans 
0 0 0 0 0 0 0 0 0 0 0.04 
Fluorenes 0.01 
0 0 0 0 0 0 0 0 0.02 
0.03 
Phenanthrenes 
0 0 0 0 0 0 0 0 0 0 0 
Naphthenephenanthrenes 
0.1 
0.01 
0.01 
0 0 0 0 0 0 0 0 
Pyrenes 0 0 0 0 0.01 
0 0 0 0 0 0 
Chrysenes 0.01 
0 0 0 0 0 0 0 0 0 0 
Perylenes 0 0 0 0 0 0 0 0 0 0 0 
Dibenzanthracenes 
0 0 0 0 0 0 0 0 0 0 0 
Benzothiophenes 
0 0 0 0 0 0 0 0.01 
0.02 
0.13 
0.19 
Dibenzothiophenes 
0 0 0 0 0.02 
0 0 0 0 0 0 
Naphthobenzothiophenes 
0 0 0 0 0 0 0 0 0 0 0 
Cn.H2n-36/Cn.H2n-26.S 
0 0 0 0 0.08 
0 0 0 0 0 0 
Cn.H2n-38/Cn.H2n-28.S 
0 0 0 0 0 0 0 0 0 0 0 
Cn.H2n-26 0 0 0 0 0 0 0 0 0 0 0 
Cn.H2n-42/Cn.H2n-32.S 
0 0 0 0 0 0 0 0 0 0 0 
Cn.H2n-44/Cn.H2n-34.S 
0.01 
0 0 0 0 0 0 0 0 0 0 
Cn.H2n-32 0 0 0 0 0 0 0 0 0 0 0.01 
Total Saturates 
2.2 
0.28 
2.89 
4.54 
4.01 
4.05 
4.31 
2.42 
2.26 
3.9 
3.78 
Monoaromatics 
0 0 0 0.56 
0.67 
0.81 
1.06 
0.32 
0.36 
0.76 
0.69 
Diaromatics 0.01 
0 0 0 0 0.02 
0.03 
0.01 
0.09 
0.31 
0.47 
Triaromatics 0.1 
0.01 
0.01 
0 0 0 0 0 0 0 0 
Tetraaromatics 
0.01 
0 0 0 0.01 
0 0 0 0 0 0 
Pentaaromatics 
0 0 0 0 0 0 0 0 0 0 0 
Thiophenoaromatics 
0 0 0 0 0.02 
0 0 0.01 
0.02 
0.13 
0.19 
Unidentified Aromatics 
0.01 
0 0.01 
0 0.08 
0 0 0 0 0 0.01 
Total Aromatics 
0.13 
0.02 
0.02 
0.58 
0.79 
0.94 
1.09 
0.35 
0.47 
1.2 
1.37 
__________________________________________________________________________ 
End Boiling pt. (deg C.) 
320 
345 
400 427 
455 
483 
510 537 
565 
593 
Retention time (min) 
19.95 
21.7 
24.65 
27.75 
29.9 
32.1 
34.3 
36.6 
39.05 
41.65 
Weight Percent 
3.84 
3.92 
8.34 
3.94 
3.88 
3.62 
3.36 
3.38 
3.19 
3.08 
__________________________________________________________________________ 
Paraffins 1.57 
1.16 
2.12 
0.71 
0.49 
0.33 
0.15 
0.01 
0 0 
1-ring cycloparaffins 
0.73 
0.73 
1.44 
0.84 
0.82 
0.55 
0.49 
0.45 
0.35 
0.08 
2-ring cycloparaffins 
0.26 
0.33 
0.61 
0.29 
0.35 
0.27 
0.3 0.11 
0.13 
0.07 
3-ring cycloparaffins 
0.18 
0.21 
0.47 
0.29 
0.41 
0.47 
0.53 
0.81 
0.53 
0.18 
Alkylbenzenes 
0.25 
0.29 
0.49 
0.19 
0.18 
0.14 
0.14 
0.13 
0.09 
0 
Naphthenebenzenes 
0.12 
0.15 
0.33 
0.16 
0.17 
0.21 
0.14 
0.14 
0.07 
0 
Dinaphthenebenzenes 
0.13 
0.15 
0.3 0.18 
0.16 
0.19 
0.21 
0.16 
0.15 
0.09 
Naphthalenes 0.18 
0.29 
0.19 
0.1 
0.12 
0.11 
0.07 
0.05 
0.03 
0 
Acenaphthene/Dibenzofurans 
0.09 
0.14 
0.3 0.18 
0.15 
0.14 
0.15 
0.16 
0.17 
0.19 
Fluorenes 0.06 
0.13 
0.39 
0.2 
0.18 
0.19 
0.2 0.33 
0.64 
1.45 
Phenanthrenes 
0.02 
0.11 
0.23 
0.08 
0.09 
0.08 
0.04 
0.01 
0 0 
Naphthenephenanthrenes 
0 0.03 
0.17 
0.13 
0.14 
0.15 
0.04 
0.01 
0 0 
Pyrenes 0.01 
0.04 
0.2 0.18 
0.11 
0.08 
0.08 
0.06 
0.02 
0 
Chrysenes 0 0.01 
0.07 
0.05 
0.1 
0.05 
0.04 
0.06 
0.09 
0.14 
Perylenes 0 0 0 0.04 
0.03 
0.07 
0.06 
0.08 
0.05 
0.05 
Dibenzanthracenes 
0 0 0.01 
0.01 
0.01 
0.02 
0.03 
0.04 
0.02 
0 
Benzothiophenes 
0.14 
0.15 
0.35 
0,17 
0.18 
0.17 
0.17 
0.17 
0.13 
0 
Dibenzothiophenes 
0.1 
0 0.64 
0.15 
0.12 
0.1 
0.13 
0.12 
0.1 
0.07 
Naphthobenzothiophenes 
0 0 0.04 
0.13 
0.13 
0.1 
0.1 0.07 
0.05 
0.01 
Cn.H2n-36/Cn.H2n-26.S 
0 0 0 0 0.07 
0.05 
0 0 0 0 
Cn.H2n-38/Cn.H2n-28.S 
0 0 0 0 0 0.04 
0 0.02 
0.02 
0 
Cn.H2n-26 0 0 0 0.09 
0.1 
0.09 
0.18 
0.23 
0.38 
0.73 
Cn.H2n-42/Cn.H2n-32.S 
0 0 0 0 0 0 0.03 
0.08 
0.04 
0 
Cn.H2n-44/Cn.H2n-34.S 
0 0 0 0 0 0 0 0.01 
0.01 
0 
Cn.H2n-32 0 0 0 0 0 0.03 
0.08 
0.11 
0.12 
0.03 
Total Saturates 
2.74 
2.44 
4.84 
1.93 
1.87 
1.82 
1.47 
1.38 
1.01 
0.33 
Monoaromatics 
0.5 
0.58 
1.11 
0.53 
0.51 
0.54 
0.49 
0.43 
0.32 
0.09 
Diaromatics 0.33 
0.55 
0.88 
0.46 
0.44 
0.44 
0.42 
0.54 
0.84 
1.64 
Triaromatics 0.02 
0.14 
0.4 0.22 
0.24 
0.23 
0.07 
0.02 
0 0 
Tetraaromatics 
0.01 
0.05 
0.27 
0.22 
0.21 
0.11 
0.13 
0.12 
0.11 
0.14 
Pentaaromatics 
0 0 0.01 
0.05 
0.04 
0.09 
0.09 
0.1 
0.06 
0.05 
Thiophenoaromatics 
0.24 
0.15 
1.03 
0.45 
0.41 
0.37 
0.4 0.35 
0.28 
0.08 
Unidentified Aromatics 
0 0 0 0.09 
0.17 
0.21 
0.29 
0.43 
0.57 
0.76 
Total Aromatics 
1.1 
1.48 
3.7 2.01 
2.01 
2 1.89 
2 2.18 
2.78 
__________________________________________________________________________ 
The analysis summarized in Table 5 is done for each reference sample. The 
results from these respective analyses form a training set which is 
subjected to mathematical treatment. The goal is to develop a model that 
can be used to predict the unknown properties of future samples using 
their mass spectral data only. The mathematical treatments are described 
by multivariate correlation techniques such as Projection to Latent 
Structures (PLS) or otherwise known as Partial Least Squares (PLS), 
Principal Component Regression (PCR), and Ridge Regression (RR). These 
techniques are superior to ordinary multiple linear regression in their 
ability to treat collinearity amongst variables in the X-block or GC/MS 
data matrix (and Y-block or properties matrix for PLS), and in their 
ability to handle the quantity of data generated by the analysis of crude 
oils. Ordinary Multiple Linear Regression cannot be used to treat 
collinear variables. 
PLS/PCR/RR are numerical analysis techniques for detecting and formulating 
a mathematical structure (model) within a data set comprising observations 
associated with multiple objects. Each object has associated with it 
observations for multiple variables, the latter being common to all 
objects. These multiple variables are assigned into two categories, known 
as X-block and Y-block. Observations associated with all variables in the 
X-block are realized from a common process (GC/MS data in this case). 
Observations associated with variables in the Y-block (known properties in 
this case) are realized from processes that may be different for each 
variable. The data set used to construct this mathematical model is 
referred to as the model calibration data set. 
The common use of PLS/PCR/RR is to apply the model developed from the 
calibration data set to X-block observations realized for new objects (not 
in the calibration data set) to predict values for the corresponding 
variables in the Y-block for these new objects, without having to execute 
the Y-block processes used in the calibration data set. Using diagnostics 
that are simultaneously generated by the PLS/PCR/RR model, assessment of 
whether the new objects can be adequately described by the model, and 
whether the model is used in an extrapolation mode versus interpolation 
mode can be performed. 
PLS/PCR addresses the collinearity features in the X-block and Y-block, by 
suitably reducing the dimensionality in both X- and Y-blocks (for PLS), 
and X-block only (for PCR) to form the model. Collinearity is a term 
referring to the existence of relationships between variables within the 
block itself. In the PLS modeling algorithm a number of independent 
dimensions in the X- and Y-blocks are identified by forming 
pseudo-variables known as principal components or latent vectors through 
different sets of linear combinations of original variables in each block. 
Each set of such combinations constitutes an independent dimension. It 
comprises a set of coefficients that each value associated with each 
variable in the block is to be weighted by to arrive at the new value for 
this dimension. The values for the new, reduced dimensions in the Y-block 
are regressed onto their counterparts in the new, reduced dimensions of 
the X-block to arrive at the most parsimonious dimension size (number of 
latent vectors) and their associated weights, with the final goal of one 
linear equation generated to permit prediction of Y-block variables using 
X-block variables. The number of dimensions used to construct the model is 
determined through optimization of a criterion known as PRESS (Prediction 
Error Sum of Squares), cumulated by a Cross Validation (CV) technique 
using the training data set, and, following the general model parsimony 
principle. 
For PCR, the number of independent dimensions in the X-block are first 
selected and identified in a similar fashion as in PLS by forming 
principal components. Then, for each variable in the Y-block, a model is 
obtained by performing ordinary multiple linear regression using the 
Principal Components as "Prediction Variables". 
For Ridge Regression, the collinearity problem is dealt with in a different 
manner than PLS/PCR. Here a diagonal matrix known as the Lambda Matrix is 
added to the Covariance Matrix of the X-block with the net effect of 
stabilizing the numerical computation needed to obtain the model 
coefficients. The selection of Lambda values is through optimization of 
PRESS criterion using cross validation of the training set. 
Thus, the Chemist's Rules or Hydrocarbon Types data for the various 
reference samples derived from GC/MS analysis form the X-block variables. 
PLS/PCR/RR treatment may require preliminary reorganization of the X-block 
data, such as transposition and removal of redundant data and constants or 
mathematical transformations. The Y-block variables are the property (or 
properties) to be predicted, and may also require mathematical 
transformations such as logarithmic or geometric, as well as 
reorganization. The data blocks may be represented by: 
X-Block Matrix 
Molecular Types Analysis (n samples.times.20 columns) 
##EQU1## 
Y-Block Matrix 
Measured Property or Properties (n samples)! 
##EQU2## 
The Y-block may be a single observation per set of Hydrocarbon Type 
Compound Type analysis as shown above, or it may be a n.times.m matrix of 
observations, where there are m different properties to be predicted. 
The results of the PLS/PCR/RR treatment of the training set data are a 
series of coefficients. Compound type data from an unknown sample (or 
samples) are then treated in the same way as the X-block matrix in the 
training set, and the coefficients applied to generate the prediction of 
the desired property or properties. Table 6 illustrates the quality of 
predicted API gravity for each sample in the training set. The data are 
presented in sets of three, one set for each of the crudes listed in Table 
2: the first pair of columns represents the value for API gravity for the 
257.degree.-302.degree. F. boiling range, the second pair 
428.degree.-455.degree. F., and the third pair for a residuum beyond 
1049.degree. F. 
TABLE 6 
______________________________________ 
Pre- Pre- Pre- 
Measured* 
dicted Measured* dicted 
Measured* 
dicted 
257-302.degree. F. 
428-455.degree. F. 
1049+.degree. F. 
______________________________________ 
50.87 51.53 52.90 53.43 57.08 56.80 
58.22 57.77 54.55 56.90 56.90 56.17 
53.80 53.33 53.90 55.40 41.88 45.64 
51.73 52.09 55.33 53.69 51.85 53.15 
54.71 54.95 50.28 52.10 47.72 47.44 
50.72 50.57 48.69 47.93 54.08 54.25 
52.70 53.98 55.90 56.12 50.80 52.70 
54.43 54.92 50.97 53.11 52.18 52.52 
53.34 52.99 53.82 54.39 53.17 47.04 
55.90 56.72 49.63 50.46 52.26 52.25 
52.50 51.99 56.87 55.08 53.17 48.09 
54.46 54.73 55.29 55.40 49.91 49.42 
54.69 54.13 57.87 56.04 53.44 53.13 
51.10 52.73 55.70 52.70 56.47 54.80 
54.19 54.70 54.48 55.56 51.76 52.70 
54.51 54.42 36.38 36.96 39.11 39.08 
42.76 43.38 43.36 42.81 42.21 42.37 
41.93 41.94 42.93 42.84 38.53 39.59 
31.77 30.78 39.95 39.90 41.14 40.54 
40.44 39.46 40.54 39.90 34.58 36.95 
31.48 31.09 36.65 37.18 33.79 32.96 
38.55 37.69 39.10 40.04 43.47 42.45 
35.09 37.68 40.44 40.58 37.96 38.92 
37.09 36.43 37.18 38.12 42.18 41.30 
28.68 29.30 43.47 42.82 33.37 33.77 
40.58 40.48 40.54 39.76 40.65 40.38 
32.53 32.83 43.35 43.36 41.64 42.88 
32.85 32.72 40.83 40.77 43.37 42.52 
42.93 44.04 43.00 42.23 40.05 37.68 
44.57 43.12 39.39 39.41 41.39 42.12 
36.70 36.53 44.11 44.98 6.20 7.44 
6.20 7.69 7.50 6.26 3.50 3.48 
6.40 4.48 5.50 5.65 10.50 12.63 
4.20 6.66 17.30 18.70 10.20 11.36 
13.40 15.22 5.70 7.69 4.10 4.37 
12.90 9.53 13.60 14.11 12.40 10.52 
10.20 10.26 10.00 9.76 12.40 12.19 
5.20 5.06 4.74 4.46 5.70 6.52 
12.30 12.12 1.89 2.38 1.83 0.59 
6.00 9.41 14.90 13.91 5.20 6.81 
5.60 4.46 11.30 12.21 11.35 10.81 
7.60 9.27 20.70 19.37 13.50 11.34 
11.20 9.81 5.70 6.22 6.50 7.45 
12.70 11.20 19.60 18.31 19.70 19.99 
5.20 4.46 16.00 12.25 6.00 6.45 
11.80 11.18 2.50 2.05 14.60 15.44 
______________________________________ 
*measured by ASTM D 28792 
EXAMPLE 2 
The procedure of Example 1 was repeated for predicting pour point for crude 
oil fractions. Table 7 illustrates the quality of the predicted pour point 
for each sample in the training set. The data represent the value for pour 
point for the 428.degree.-455.degree. F. boiling range. 
TABLE 7 
______________________________________ 
PREDICTED VS. MEASURED POUR (.degree.F.) 
MEASURED* PREDICTED 
______________________________________ 
-44 -55 
-49 -59 
-41 -39 
-33 -30 
-31 -32 
-37 -30 
-32 -36 
-63 -76 
-112 -121 
-39 -41 
-32 -48 
-62 -53 
-38 -49 
-72 -62 
-162 -168 
-47 -54 
-82 -84 
-70 -86 
-64 -60 
-35 -40 
-85 -62 
-39 -36 
-55 -60 
-56 -58 
-46 -33 
-41 -41 
-134 -140 
-34 -32 
-134 -124 
-39 -44 
-32 -35 
-57 -49 
-158 -125 
-35 -35 
-29 -28 
-94 -90 
-36 -33 
-48 -38 
-34 -39 
-34 -36 
-48 -62 
-46 -33 
-42 -48 
-43 -37 
-53 -61 
-20 -20 
______________________________________ 
*measured by ASTM D 97-87 
EXAMPLE 3 
The procedure of Example 1 was repeated for predicting cloud points, freeze 
points, refractive indices and vol % true boiling point yield for the 
crude oils of Example 1. FIG. 1 is a plot of predicted vs. observed cloud 
point. FIG. 2 is a plot of predicted vs. observed freeze points. FIG. 3 is 
a plot of predicted vs. observed refractive indices. FIG. 4 is a plot of 
predicted vs. observed cumulative volume percent true boiling point 
yields. 
EXAMPLE 4 
The plot of FIG. 4 contains numerous observations of predicted vs. observed 
vol % true boiling yields. This example shows the comparison between the 
predicted vs. observed cumulative vol % true boiling point yields for 
three of the individual crudes contributing to FIG. 4. The results for 
these three individual crudes are shown in FIGS. 5-7. In these figures, 
"+" is predicted and "o" is observed.