Smart learning strategy for predicting viscoelastic surfactant (VES) viscosity in oil well matrix acidizing process using a rigorous mathematical approach

This piece of study attempts to accurately anticipate the apparent viscosity of the viscoelastic surfactant (VES) based self-diverting acids as a function of VES concentration, temperature, shear rate, and pH value. The focus not only is on generating computer-aided models but also on developing a straightforward and reliable explicit mathematical expression. Towards this end, Gene Expression Programming (GEP) is used to connect the aforementioned features to and the target. The GEP network is trained using a wide dataset adopted from open literature and leads to an empirical correlation for fulfilling the aim of this study. The performance of the proposed model is shown to be fair enough. The accuracy analysis indicates satisfactory Root Mean Square Error and R-squared values of 7.07 and 0.95, respectively. Additionally, the proposed GEP model is compared with literature published correlations and established itself as the superior approach for predicting the viscosity of VES-based acids. Accordingly, the GEP model can be potentially served as an efficient alternative to experimental measurements. Its obvious advantages are saving time, lowering the expenses, avoiding sophisticated experimental procedures, and accelerating the diverter design in stimulation operations. The Gene Expression Programming evolutionary algorithm is proposed for modeling the viscosity of Viscoelastic Surfactant-based self-diverting acids. The viscoelastic surfactant viscosity correlation presents high accuracy which is demonstrated through multiple analyses. The Gene Expression Programming algorithm is a reliable tool expediting the diverter design phase of each stimulation operation.

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