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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:稠油油藏石油工程计算中的一个重要特性是溶剂稀释沥青的密度。这是新开发的基于溶剂的强化采油方法所必需的。因此,开发预测该参数的精确模型至关重要。为了解决这个问题,本研究提出了一个基于粒子群优化(PSO-ANFIS)训练的自适应神经模糊推理系统的精确模型,用于使用文献中的实验数据估计溶剂和碳氢化合物混合物稀释的沥青密度。通过使用各种统计和图形方法,并将所开发模型的预测结果与文献模型进行比较,对结果的准确性和可靠性进行了评估。分析表明,PSO-ANFIS模型能够以可接受的误差和较高的精度预测实验数据。PSO-ANFIS模型的预测也优于文献模型。

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