Asphaltene precipitation can promote a drastic reduction
in oil
production because of asphaltene precipitation and deposition damage.
Therefore, screening models to predict the risk of asphaltene damage
and equations of state (EoS) to predict the asphaltene onset pressure
(AOP) are useful to prevent production drops and optimize the management
of oil resources. Most asphaltene screening models have been focused
on the oil compositions (SARA analysis); however, these screening
models do not consider key variables for asphaltene stability such
as the temperature, pressure, well depth, gas–oil ratio, and
so on. As the EoS are typically based on experimental data, to fit
parameters needed to reproduce the experimental AOP, expensive laboratory
analyses are required for this objective. In this study, a classification
machine learning (CML) model based on support vector machines was
proposed to predict the asphaltene damage risk from the asphaltene
stability class index data and the in situ live crude oil densities.
In addition, a model based on linear regression (MLR) to predict AOP
from the reservoir pressure, saturation pressure, temperature, and
some in situ live crude oil compositions was proposed. In total, 24
crude oils were evaluated experimentally to propose the classification
model, and a perfect classification accuracy (100%) was obtained in
both cases. The CML results were compared to compositional screenings,
where classification accuracy was between 29 and 88%, the best accuracy
being obtained from the well-known de Boer plot. The MLR model was
obtained from data from 53 live crude oils, using hypothesis tests
to select the statistically representative characteristics regarding
AOP, and a determination coefficient of 0.77 was obtained. The proposed
integrated ensemble model contributes to predicting the potential
risk of damage due to asphaltene precipitation and estimating a pressure
range where these asphaltenes precipitate, allowing the necessary
preventive measures to be taken to avoid an oil production decline.
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