25th International Meeting on Organic Geochemistry IMOG 2011

P-342
Chemometric analysis of crude oil composition and fluid
properties
Andreas Linge Tomren 1 , Tanja Barth 1 , Kjetil Folgerø 1,2 , Johan Carlson 1,3
1 University of Bergen, Bergen, Norway, 2 Christian Michelsen Research, Bergen, Norway, 3 Luleå University
of Technology, Luleå, Sweden (corresponding author:andreas.tomren@kj.uib.no)
Background: Multiphase flow meters provide online
monitoring of the flow rates of oil, water and gas in
pipelines for the oil industry. Multiphase meters can
help optimize the petroleum production, increasing
the oil recovery and lowering the investments and
operational costs [1][2]. Due to the complex composition
of petroleum, the accuracy of the multiphase
meters may still be enhanced, which can be done by
improved understanding of the relationship between
chemical variability and physical principles that
determine fluid properties and behaviour. Multivariate
analysis of different data types describing the
chemical composition of crude oils relative to their
electrical, physical, chemical and magnetic properties
have therefore been carried out. The aim of the
present work is to identify correlations between the
crude oil properties and composition, and to see if it is
possible to build quantitative calibration models
describing the influence of oil composition on the
measured signals used for online monitoring.
Results: Principal Component Analysis (PCA) [3]
performed on Infrared (IR) spectra of a set of crude
and model oils show that three groupings occur,
separating biodegraded oils, nonbiodegraded oils and
condensates. PCA performed on Gas Chromatography
(GC) data of the same set of crude and model
oils show similar groupings, indicating that it is
possible to separate the three types of oils.
Partial Least Squares [4] calibration models for
prediction of different properties based on GC and IR
have been built. Some models give good results, for
example for density, permittivity and the biodegradation
level. This indicates that the models can
predict some unknown properties of new oils based
on GC and IR data with reasonably good results.
Predicted (e_st)
2.80
2.60
Predicted vs Measured for e_st
2.40
2.20
29
4
10 3
12
1
7
5
6
2.00
8
2.00 2.20 2.40 2.60 2.80
Measured (e_st)
Figure 1: Plot of predicted vs measured value for
prediction of static permittivity based on GC data. Red
numbers indicate objects used to build the model,
blue numbers indicate objects that were used to
validate the predictive quality of the model. A
maximum of 5% error was obtained
More complex relationships have also been explored
with the aim of quality control of the multiphase meter
settings. At present, the signals used to quantify the
phases are calibrated at the initial startup of each
measuring unit, and changes in oil composition over
the production lifetime may require recalibration for
accurate measurement. Models that relate the
signals to oil composition based on widely available
chemical crude oil characterisation techniques are
being developed and tested for practical applications.
[1]: Thorn, R.; Johansen, G.A.; Hammer, E. A. Threephase
flow measurement in the offshore oil industry:
Is there a place for process tomography. In
1st World Conference on Industry Process Tomography,
Buxton, Greater Manchester, U.K., 1999.
[2]: Falcone, G.; Hewitt, G.F.; Alimonti, C.; Harrison,
B. Multiphase flow metering: Current trends and
future developments. J. Pet. Technol. 2002, 54, 77.
[3]: Wold, S.; Esbensen, K.; Geladi, P. Principal com
ponent analysis. Chemometrics and Intelligent Laboratory
Systems, 1987, 2, 37.
[4]: Hoskuldsson, A. A combined theory for PCA
and PLS. J. Chemometrics 1995. 9, 91.
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