A robust approach to predict distillate rate through steam distillation process for oil recovery

ABSTRACT

Added values to project economy from oil recovery process and difficulties of the oil recovery mechanism are the main differences between light oil and conventional oil reservoirs. One needs to gain distillate rate in the steam distillation technique in crude oil recovery; however, this needs particular experiments. In this research, the approach inspired a new intelligent solution named the least square support vector machine to monitor the distillate rate in the steam distillation method for oil recovery goals. The suggested low parameter model is applied to the gathered experimental data from prestigious attentions to train and develop and test the introduced approach. The predicted outcomes from the low parameter model were contrasted to the relevant measured data points and generated results of conventional and empirical correlations. A comparison between the gained solutions of our method and the alternatives proves that the proposed vector machine approach estimates the distillate rate in the steam distillation process for oil recovery more precisely in contrast with the previous and empirically applied models.

KEYWORDS:

• Distillate rate
• least square
• modeling
• oil recovery
• steam distillation
• support vector machine