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Determination of residue quality and yields from 1H Determination of residue quality and yields from 1H NMR spectra of crude oils and chemometrics for
process optimization
ERTC, November 2017
Many different crudes processed to takeadvantage of market opportunities
Knowledge of composition and quality atthe refinery gate is needed
Motivations
| 2ERTC ‘17
Request for reliable and readily available qualitydata is increasing ….
….laboratory methods (e.g. EN, ASTM, IP)elaborate, expensive and time consuming
Increase availability of analytical data on CDU residues
Reduce analysis time (TBP, Potstill and lab work)
Objective
| 3ERTC ‘17
Develop a viable solution to estimate crude oilresidue properties accurately and in a timelyfashion.
Spectroscopy
Mathematics to
correlate the
9 8 7 6 5 4 3 2 1 0
TMS
Chemical Shift (ppm)
e.g IR/NMR Spectra
Data Analysis
Background
| 4ERTC ‘17
Analytical data
(e.g. density, sulfur
content, yields, etc.)
correlate the
data Data Analysis
(Chemometrics)
Available for decades but with history of alternate fortune
Nuclear Magnetic Resonance (NMR)
Spectroscopic technique particularly suited toanayze crude oils and crude oil residues
| 5ERTC ‘17
- Requires minimal or no sample preparation- It is suited for opaque and dense samples- It is very rapid
-1012345678910
Crude Oil database
Crude oil samples were
provided by the Sarlux
refinery (Sarroch, Italy)
| 6ERTC ‘17
500+ Crudes; 70+ Crude Types
The database was built in Sartecwith the crude assay dataacquired over the last 20 years
9 8 7 6 5 4 3 2 1 0
Chemical Shift (ppm)
Crude Oil/ residue 1H-NMR
Database
+
Models
State of the Art
| 7ERTC ‘17
Aromatici Totali (%p)
3
9
15
21
3 9 15 21
Misutato
Pre
det
to
CAL
VAL
H (%p)
10
11
12
13
14
15
10 11 12 13 14 15
Misutato
Pre
de
tto
CAL
VAL
Densità (Kg/m3)
800
820
840
860
880
900
920
940
960
800 820 840 860 880 900 920 940 960
Misutato
Pre
de
tto
CAL
VAL
KUOP
11.4
11.8
12.2
12.6
13
11.4 11.8 12.2 12.6 13
Misutato
Pre
de
tto
CAL
VAL
C (%p)
83
84
85
86
87
88
83 84 85 86 87 88
Misutato
Pre
det
to
CAL
VAL
Models
The New Method
9 8 7 6 5 4 3 2 1 0
Chemical Shift (ppm)
Crude Oil 1H-NMR
Additional Info(i.e. one or more
measured crude oil properties)
It departs from previous applications:
| 8ERTC ‘17
It departs from previous applications:
−Exploits the correlation between the quality of thecrude and its cuts
−Combines spectroscopic and quality data
−Enhances prediction accuracy
Patent EP3141897A1
9 8 7 6 5 4 3 2 1 0
Chemical Shift (ppm)
Crude Oil 1H-NMR
Spectrum
Database
+
Models
AdditionalInfo+
The New Method
| 9ERTC ‘17
Aromatici Totali (%p)
3
9
15
21
3 9 15 21
Misutato
Pre
det
to
CAL
VAL
H (%p)
10
11
12
13
14
15
10 11 12 13 14 15
Misutato
Pre
de
tto
CAL
VAL
Densità (Kg/m3)
800
820
840
860
880
900
920
940
960
800 820 840 860 880 900 920 940 960
Misutato
Pre
de
tto
CAL
VAL
KUOP
11.4
11.8
12.2
12.6
13
11.4 11.8 12.2 12.6 13
Misutato
Pre
de
tto
CAL
VAL
C (%p)
83
84
85
86
87
88
83 84 85 86 87 88
Misutato
Pre
det
to
CAL
VAL
Models
The New Method
In the present embodiment it can be used to estimateresidue properties from :
• Crude Spectrum + 1 or more Measured Crude Property
• Crude Spectrum + 1 or more Estimated Crude Property
| 10
• Crude spectrum + 1 or more Measured/ Estimated ARProperty
ERTC ‘17
9 8 7 6 5 4 3 2 1 0
Chemical Shift (ppm)
Crude Oil 1H-NMRAdditional Info
(i.e. one measuredcrude oil property)
P
The New Method
| 11ERTC ‘17
PLS
Atmospheric ResidueYieldsVR (%p)
DensityVR (Kg/m3)
Metals (ppm)
SulfurVR (%p)
AsphalthenesVR (%p)
TANVR (mgKOH/g)
Vacuum ResidueYieldsVR (%p)
DensityVR (Kg/m3)
Viscosity @140 VR (cSt)
SulfurVR (%p)
AsphalthenesVR (%p)
MCRTVR (%p)
Results
-1
0
1
2
3
4
5
6
7
0 1 2 3 4 5 6 7
Pre
dic
ted
Y (
37
0+
Zo
lfo
%p
)
Reference Y (370+ Zolfo %p)
Predicted vs. Reference
val
cal
-1
0
1
2
3
4
5
6
7
0 1 2 3 4 5 6 7
Pre
dic
ted
Y (
37
0+
Zo
lfo
%p
)
Reference Y (370+ Zolfo %p)
Predicted vs. Reference
val
cal
Sulfur 370 + Sulfur 370 + New Method
| 12ERTC ‘17
Reference Y (370+ Zolfo %p) Reference Y (370+ Zolfo %p)
Asphalthenes 370+
-1
1
3
5
7
9
11
13
15
0 2 4 6 8 10 12 14 16
Pre
dic
ted
Y (
37
0+
Asf
alt
en
i %
p)
Reference Y (370+ Asfalteni %p)
Predicted vs. Reference
val
cal
-1
1
3
5
7
9
11
13
15
17
0 2 4 6 8 10 12 14 16
Pre
dic
ted
Y (
37
0+
Asf
alt
en
i %
p)
Reference Y (370+ Asfalteni %p)
Predicted vs. Reference
val
cal
Asphalthenes 370+ New Method
ResultsTAN 370+
-0.5
0
0.5
1
1.5
2
2.5
0 0.5 1 1.5 2 2.5 3
Pre
dic
ted
Y (
37
0+
TA
N m
gK
OH/g
)
Predicted vs. Reference
val
cal
-1
-0.5
0
0.5
1
1.5
2
2.5
3
0 0.5 1 1.5 2 2.5 3
Pre
dic
ted
Y (
37
0+
TA
N m
gK
OH/g
)
Predicted vs. Reference
val
cal
TAN 370+ New Method
| 13ERTC ‘17
Reference Y (370+ TAN mgKOH/g) Reference Y (370+ TAN mgKOH/g)
Sulfur 540+
-1
0
1
2
3
4
5
6
7
8
0 1 2 3 4 5 6 7 8
Pre
dic
ted
Y (
54
0+
Zo
lfo
%p
)
Reference Y (540+ Zolfo %p)
Predicted vs. Reference
val
cal
-1
0
1
2
3
4
5
6
7
8
0 1 2 3 4 5 6 7 8
Pre
dic
ted
Y (
54
0+
Zo
lfo
%p
)
Reference Y (540+ Zolfo %p)
Predicted vs. Reference
val
cal
Sulfur 540+ New Method
ResultsVBN 540+
34
39
44
49
54
59
34 39 44 49 54 59
Pre
dic
ted
Y (
54
0+
VB
N @
50
°C)
Reference Y (540+ VBN @50°C)
Predicted vs. Reference
val
cal
34
39
44
49
54
59
34 39 44 49 54 59
Pre
dic
ted
Y (
54
0+
VB
N @
50
°C)
Reference Y (540+ VBN @50°C)
Predicted vs. Reference
val
cal
VBN 540+ New Method
| 14ERTC ‘17
MCRT 540+
0
5
10
15
20
25
30
35
40
0 5 10 15 20 25 30 35
Pre
dic
ted
Y (
54
0+
MC
RT
%p
)
Reference Y (540+ MCRT %p)
Predicted vs. Reference
val
cal
0
5
10
15
20
25
30
35
0 5 10 15 20 25 30 35
Pre
dic
ted
Y (
54
0+
MC
RT
%p
)
Reference Y (540+ MCRT %p)
Predicted vs. Reference
val
cal
MCRT 540+ New Method
Further development
• Expand number of estimated properties
• Test during operations
• Transferred onto a 60 MHz permanent magnet
| 15
• Transferred onto a 60 MHz permanent magnet
instrument
ERTC ‘17
Poll Question 1
In which area this method would bring more benefit?
• Planning and scheduling
| 16ERTC ‘17
• Planning and scheduling
• Operations / technology
• Inspection
• Trading
THANK YOU !!
| 17
UNIVERSITA' DEGLI STUDI di CAGLIARIDipartimento di Scienze Chimiche e
Geologiche
ERTC ‘17
