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A PSO-ANFIS framework for prediction of density of bitumen diluted with solvents

机译:一种PSO-ANFIS框架,用于预测溶剂稀释的沥青密度的预测

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摘要

One of the important properties in petroleum engineering calculations in heavy oil reservoirs is the density of bitumen diluted with solvents. It is required in newly developed solvent based enhanced oil recovery methods. Hence, developing accurate models for prediction of this parameter is essential. To tackle this issue, this study presents an accurate model based on adaptive neuro-fuzzy inference system trained by particle swarm optimization (PSO-ANFIS) for estimation of density of bitumen diluted with solvents and hydrocarbon mixtures using experimental data from literature. The accuracy and reliability of results were evaluated by utilizing various statistical and graphical approaches and comparing the predictions of the developed model with literature models. The analysis showed that the PSO-ANFIS model is capable to predict the experimental data with acceptable error and high accuracy. The predictions of the PSO-ANFIS model were also better than the literature models.
机译:石油储存器中石油工程计算中的一个重要性质是用溶剂稀释的沥青密度。 新开发的基于溶剂的增强的采油方法需要。 因此,开发用于预测此参数的准确模型至关重要。 为了解决这个问题,本研究提出了一种基于粒子群优化(PSO-ANFIS)训练的自适应神经模糊推理系统的准确模型,用于使用来自文献的实验数据稀释的溶剂和烃混合物的沥青密度的估计。 通过利用各种统计和图形方法来评估结果的准确性和可靠性,并将开发模型与文献模型进行比较来评估。 分析表明,PSO-ANFIS模型能够以可接受的误差和高精度预测实验数据。 PSO-ANFIS模型的预测也比文献模型更好。

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