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Applying FCM-ANFIS algorithm as a novel computational method for prediction of viscosity of bitumen and heavy alkane mixture

机译:应用FCM-ANFIS算法作为预测沥青和重质烷烃混合物粘度的新型计算方法

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摘要

Recently the studies expressed that the noticeable number of oil reservoirs in all over the world are heavy oil and bitumen reservoirs. So the importance of enhancement of oil recovery (EOR) processes for heavy oil and bitumen reservoirs is highlighted. The Dilution of the reservoir fluid by solvents such as tetradecane is one of well-known methods for these types of reservoirs which effects oil recovery by decreasing viscosity. In the present study, Fuzzy c-means (FCM) algorithm was coupled with Adaptive neuro-fuzzy inference system (ANFIS) to predict viscosity of bitumen and tetradecane in terms of temperature, pressure and weight percent of tetradecane. The coefficients of determination for training and testing steps were calculated such as 0.9914 and 0.9613. The comparison of results and experimental data expressed that FCM-ANFIS algorithm has great potential for estimation of viscosity of bitumen and tetradecane.
机译:最近,研究表明,世界各地的石油储层数量是重油和沥青水库。 因此,突出了石油和沥青水库的加油(EOR)过程的重要性。 通过诸如四烷的溶剂的储液流体的稀释是这些类型的储层的众所周知的方法之一,这些储层通过降低粘度来影响溢油。 在本研究中,模糊C-MATIOM(FCM)算法与适应性神经模糊推理系统(ANFIS)偶联,以在四癸烷的温度,压力和重量%方面预测沥青和四烷的粘度。 计算训练和测试步骤的测定系数如0.9914和0.9613。 结果和实验数据的比较表明,FCM-ANFIS算法具有诸如沥青和四烷粘度估计的潜力。

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