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New Viscosity Correlations for Saturated and Undersaturated Arabian Crude Oil

机译:饱和和濒危阿拉伯原油的新粘度相关性

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Newly developed correlations for undersaturated and saturated Arabian crude oil viscosities were developed and tested using two datasets of experimental measurements. The datasets cover 71 data points of measured undersaturated viscosity (μ_(undersaturated)), pressure (P), temperature (T), bubble point pressure (P_b), gas specific gravity (γ _g), crude oil API, viscosity at bubble point pressure (μ_b) and dead oil viscosity (μ_d) and 79 data points of saturated viscosity (μ_(saturated)), pressure (P), temperature (T), bubble point pressure (P_b), gas specific gravity (γ _g), crude oil API, viscosity at bubble point pressure (μ_b) and dead oil viscosity (μ_d), gas–oil ratio (GOR) and gas solubility (R_s). The viscosity models were developed utilizing 80% of the datasets using forward step-wise regression method. The selection of the independent variables was carried out using graphical alternating conditional expectation program (GRACE), a nonparametric regression method, which produce and generate plots for the optimal transformation of the dependent and independent variables. The program also performs lowand high-degree polynomial curve fit up to six-degree polynomial to create the desired model. The models’ accuracy was validated using the rest of the datasets, and their efficiency was tested against some commonly used correlations utilizing average absolute relative error, average relative error and cross plots. The developed models proved to be very efficient and they accurately predicted the experimental undersaturated and saturated crudes viscosities with average absolute relative errors of 1.79% and 5.89%, respectively.
机译:使用两种实验测量数据集开发和测试了对不饱和和饱和的阿拉伯原油粘度的新开发的相关性。数据集盖71测得的下饱和粘度的数据点(μ_(不饱和)),压力(P),温度(T),气泡点压力(P_B),气体特异性重力(γ_G),粗油API,泡点处的粘度压力(μ_b)和死油粘度(μ_d)和79个饱和粘度的数据点(μ_(饱和)),压力(p),温度(t),气泡点压(p_b),气体比重(γ_g),粗油API,泡沫点压(μ_b)和死油粘度(μ_d),气体油比(GOR)和气体溶解度(R_s)。使用前向步骤 - 明智的回归方法利用80%的数据集进行粘度模型。使用图形交替的条件期望程序(Grace),非参数回归方法进行独立变量的选择,其产生和生成诸如依赖性变量的最佳变换的图。该程序还执行低于六程度多项式的Lowand高度多项式曲线以创建所需的模型。使用其余数据集进行验证模型的准确性,并测试其利用平均绝对相对误差,平均相对误差和交叉图的一些常用相关性的效率。所开发的模型被证明是非常有效的,并且他们准确地预测了实验性缺乏和饱和的粘度,分别具有1.79%和5.89%的平均绝对相对误差。

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