Water and salt associated with oil production can cause considerable operational problems.
Therefore, desalting/dehydration plants are often installed in crude oil production units to remove
water-soluble salts from an oil stream. The performance of the desalting/dehydration process
depends on various process parameters interacting with each other. These parameters include
concentration of demulsifying agents, heating, wash water, salt concentration, and rate of mixing
with wash water. In this study, the performance of the desalting/dehydration process was
evaluated by calculating the salinity and water cut efficiencies that are expected to depend on
the values of these five process parameters. The work concentrated on modeling and optimizing
the performance of the desalting/dehydration process system. It was an attempt to develop and
apply an artificial neural network (ANN) as a modeling technique for simulating and optimizing
the desalting/dehydration process system. ANNs were selected due to their potential for modeling
highly nonlinear relationships of the parameters involved in the desalting/dehydration process
system. The neural network model predictions were compared with the actual observations, and
the results were shown to be consistent. The prepared neural network model was then used to
optimize the performance of the process. A composite objective function for measuring the
performance of the desalting process was used in conjunction with the prepared ANN within an
optimization model. The outcome of this research will help in improving oil production operations
and therefore in lowering the cost per barrel produced.
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