Estimate of Saturation Pressures of Crude Oil by Using
Ensemble-Smoother-Assisted Equation of State
Guangfeng Liu
Zhaoqi Fan
Xiaoli Li
Daihong Gu
10.1021/acs.iecr.8b04812.s001
https://acs.figshare.com/articles/journal_contribution/Estimate_of_Saturation_Pressures_of_Crude_Oil_by_Using_Ensemble-Smoother-Assisted_Equation_of_State/7413335
The
equation of state (EOS) has been extensively used to evaluate
the saturation pressures of petroleum fluids. However, the accurate
determination of empirical parameters in the EOS is challenging and
time-consuming, especially when multiple measurements are involved
in the regression process. In this work, an ensemble smoother (ES)
-assisted EOS method has been proposed to compute the saturation pressure
by intelligently optimizing the to-be-tuned parameters. To be specific,
the to-be-tuned parameters for the Peng–Robinson EOS (PR EOS)
are integrated into a model input matrix and the measured saturation
pressures are collected into a model output matrix. The model input
matrix is then integrally and iteratively updated with respect to
the model output matrix by using the iterative ES algorithm. For convenience,
an in-house module is compiled to implement the ES-assisted EOS for
determining the saturation pressures of crude oils. Subsequently,
the experimentally measured saturation pressures of 45 mixtures of
heavy oil and solvents are used to validate the performance of the
in-house module. In addition, 130 measured saturation pressures of
worldwide light oil samples are collected to verify the applicability
of the developed ES-assisted EOS method. The in-house module is found
to be competent by not only matching 45 measured saturation pressures
with a better agreement than a commercial simulator but also providing
a quantitative means to analyze the uncertainties associated with
the estimated model parameters and the saturation pressure. Moreover,
the application of the ES-assisted EOS to 130 light oil samples distinctly
demonstrates that the new method greatly improves the accuracy and
reliability of the EOS regression. Consequently, the in-house module
representing the ES-assisted EOS is proven as an efficient and flexible
tool to determine the saturation pressure under various conditions
and implement uncertain analyses associated with the saturation pressure.
2018-11-20 00:00:00
module
model input matrix
ES-assisted EOS
PR
130 light oil samples
to-be-tuned parameters
ES-assisted EOS method
light oil samples
model output matrix
saturation pressure
saturation pressures
iterative ES algorithm