Hydrocarbon Processing - February 2022 - 26

Digital Technologies
TABLE 1. Comparison of the K model vs. diff erent compatibility tests: Unstable (U),
stable (S) or metastable (M)
Crude
oils
AH
AEL
AL
CII
U
U
M
Ratawi M
SB
RG
U
S
Murban M
RJ
ML
Erha
MH
U
M
U
M
Antan M
Mascila M
Kuwaity M
AM
NB
M
M
CSI
U
U
S
U
U
U
S
U
S
S
S
S
S
M
S
S
SI QQA SP
U
S
M
U
S
U
S
S
S
S
S
S
S
U
M
S
U
M
U
U
S
U
M
U
M
S
S
M
U
U
U
M
U
S
S
U
S
U
S
U
S
S
S
S
S
U
U
S
SCP
U
S
S
U
S
U
S
S
S
S
S
S
S
S
S
S
Spot
test
U
S
S
U
S
U
S
U
S
S
U
S
U
S
S
U
Composite
result
U
S
S
U
S
U
S
U
S
S
S
S
S
M
S
S
K model
results
U
S
S
U
S
U
S
U
S
S
S
S
U
S
S
U
Refi nery
experience
U
S
S
-
S
-
S
-
S
S
S
S
S
S
S
-
the ratios of the physical parameters of
known crude oils, and by the composite
compatibility measures determined from
multiple compatibility test results of the
known crude oils. The model's equation
is provided in Eq. 1. This effectively correlates
characteristics of asphaltene molecules
and their behavior. The content of
aliphatic carbon attached to the aromatic
core of asphaltene in a heavy crude oil is
the primary deciding factor for determining
crude blend compatibility when blending
with light crude oil. Higher aliphatic
carbon attached to the aromatic core of asphaltene
in heavy crude imparts instability
when blending with light crude oils.
K = k1 × (1/C) + k2 × (C/A) +
k3 × (C/S) + k4 × (log(S)/C) +
k5 × (A/S) + k6 × (S/V) +
k7 × (V/A) + k8 × (V/C)
(1)
where k1-k8 are the coefficients of regression.
When K ≥ 0, the crude oils are compatible;
when K < 0, they are incompatible.
The proprietary prediction softwarea
accurately predicts the composite results
of all comprehensive laboratory test
methods within a few minutes. The prediction
software enables refiners to predict
compatibility of multiple crude oils
(up to 10) within a short amount of time.
The parameters for blend optimization
are blend compatibility, blend viscosity,24
blend
pour,25
blend acidity,19
sulfur, blend nitrogen19
yield.19
blend
and total distillate
The optimization module also
considers crude oils/tank storage availability
(FIG. 2).
FIG. 2. Optimization module in the prediction model softwarea
other components present in the oil system.
The typical physical model has been
depicted in FIG. 1. These experiments can
take a long time to conduct-1 wk per
blend of two crude oils at a time-and
are, therefore, tedious.
Proprietary prediction model softwarea
.
A novel tool for predicting the
26 FEBRUARY 2022 | HydrocarbonProcessing.com
.
compatibility of crude oil blends and
blend optimization for increasing heavy
crude oil processing has been developed.
This model uses the four physical parameters
of crude oils-sulfur, carbon
residue, API and kinematic viscosity-as
input for blending optimization.14-23
The model is developed by coefficients
obtained via regression analysis between
Validations. Seventy different crude oils
have been used for the development of the
model prediction software, while 16 neat
crude oils and 14 crude oil blends have
been used for validation (TABLES 1-2).
The K value was determined based on
the compatibility model in Eq. 1 for 16
crude oils. The results of the compatibility
parameter K were compared with the
compatibility results based on saturate,
aromatic, resin and asphaltene (SARA)
analysis and spot tests.
It is known that any single test method
is inadequate to make accurate decisions
regarding the compatibility of crude oils
and blends. In this case, individual and
composite results of all known laboratory
test methods have been considered
to validate the K model. In addition, some
of the 16 crude oils were also processed
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