Modeling viscosity of light and intermediate dead oil systems using advanced computational frameworks and artificial neural networks

Different Nonlinear Regression Techniques and Sensitivity Analysis as Tools to Optimize Oil Viscosity Modeling

“…Different regression techniques [3][4][5][6][7][8][9][10][11][12][13][14] and artificial intelligence [15,16] (machine learning, neural network) approaches have been applied to model petroleum characteristics. Nonlinear regression has been the most used approach for model parameter estimation [17]. Typically, it minimizes an objective function based on the sum of squares of errors between experimental and calculated values [17].…”

“…Nonlinear regression has been the most used approach for model parameter estimation [17]. Typically, it minimizes an objective function based on the sum of squares of errors between experimental and calculated values [17]. Usually, the models have various parameters to be determined, and sometimes multiple solutions of the objective function can be obtained.…”

“…Usually, the models have various parameters to be determined, and sometimes multiple solutions of the objective function can be obtained. The optimal solution depends mostly on the initial guess of parameters [17]. The appropriate parameter estimation has been reported to assure by application of sensitivity analysis on the calculated parameter values [17].…”

“…The optimal solution depends mostly on the initial guess of parameters [17]. The appropriate parameter estimation has been reported to assure by application of sensitivity analysis on the calculated parameter values [17]. The sensitivity analysis (SA) is the study of how the variation in the output of a model (numerical or otherwise) can be apportioned, qualitatively or quantitatively, to different sources of variation, and how the given model depends on the information fed into it [18].…”

“…Hernández et al [3], Hosseinifar, and Jamshidi [4], Samano et al [17], and Alcazar, and Ancheyta [21], after the application of nonlinear regression, employed sensitivity analysis to find the most appropriate values of the model parameters. This approach was also adopted in this work and extended not only to the model parameters but also to the given data.…”