A New Chaos Particle Swarm Optimization Combining the Chaotic Perturbation

Li, Mengxia; Liao, Ruiquan

doi:10.14257/ijsip.2015.8.4.04

Cited by 1 publication

(1 citation statement)

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“…In this paper, we use an improved particle swarm optimization [19], which is described in the next section, to determine the coefficients …”

Section: Cubic Function Fitting Model Of Aziz Residualmentioning

confidence: 99%

Improved Aziz Prediction Model of Pressure Gradient for Multiphase Flow in Wells

Li¹,

Liao²,

Luo³

et al. 2016

IJHT

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It is crucial for the completion parameter design and production performance detection interpretation to have an accurate pressure gradient. The Aziz prediction model of pressure gradient is a common calculation model in oil-gas field development. The laboratory experiment results of multiphase flow show that the average prediction relative error is 29.62% and the maximum relative error reaches 70.1%. By comparing the prediction residual of the Aziz model with the experiment condition parameters, as the volume flow rate of the liquid phase is constant, this paper considers that the prediction residual of the Aziz model is closely related to the gas liquid ratio and has no clear correlation with the water fraction of fluid phase. Based on unifying the orders of magnitude of the prediction residual and the gas liquid ratio, this paper uses a cubic function of the gas liquid ratio to fit the prediction residual of the model to obtain a new pressure prediction method which is called Aziz-I model. The results for ninety groups of experimental data show that the average prediction relative error of the Aziz-I model is reduced to 10.82%. Hence, the Aziz-I model improves the prediction accuracy of pressure gradient for multiphase flow.

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“…In this paper, we use an improved particle swarm optimization [19], which is described in the next section, to determine the coefficients …”

Section: Cubic Function Fitting Model Of Aziz Residualmentioning

confidence: 99%