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

Abstract

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. Article Highlights 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.

Coalbed Methane Production Model Based on Random Forests Optimized by a Genetic Algorithm

“…of ARD (%) refer to minimum and maximum absolute relative deviation values, respectively. The error functions in Table 2 were calculated by the following equations: 54 …”

“…Error Analysis of the RF and GA−RF Algorithms The error functions in Table2were calculated by the following equations:54 …”

Residual Damage, Its Consequences, and Remedial Measures on Post Hydrofrac Well Productivity: Learnt Lessons

“…According to the studies conducted in the 1990s, where VES was applied in a relatively large number of wells, there was a need for reviving fractured wells by adopting remedial actions. In this aspect, Mahdaviara et al [59] noted that hydrocarbons could break VES fluids more efficiently when specific amounts of salts are removed from the brine. The authors concluded that it is not possible to achieve these two conditions in most cases as the wells might fail to produce sufficient hydrocarbons to break the VES solutions.…”