Sonatrach

About: Sonatrach is a company organization based out in Algiers, Algeria. It is known for research contribution in the topics: Hydraulic fracturing & Structural basin. The organization has 460 authors who have published 494 publications receiving 6339 citations. The organization is also known as: Sonatrach SPA.

Flow of viscoplastic fluids containing hybrid nanoparticles: Extended Buongiorno’s model

Abstract: This paper investigates numerically the flow of a temperature-dependent viscoplastic fluid containing hybrid Ag and MgO nanoparticles through a ventilated heated cavity. A new approach for modeling the hybrid nanoparticle migration is proposed. The proposed model, which is based on Buongiorno’s non-homogeneous model, takes into account the nanoparticles mass flux due to both Brownian and thermophoretic diffusion, for each solid constituent. This is done by adding a mass conservation equation for each nanoparticle type, considering no mass flux at the walls. The results reveal that the nanoparticle distribution is the same for both constituents due to the dominance of inertia and buoyancy forces over slip mechanisms.

Structure–property relationships of the thermal gelation of partially hydrolyzed polyacrylamide/polyethylenimine mixtures in a semidilute regime

Abstract: In this work, the structure–property relationships of the thermal gelation of partially hydrolyzed polyacrylamide (PHPA) and polyethylenimine (PEI) mixtures were investigated under realistic conditions of temperature (80 °C) and salinity (total dissolved solids = 3.4 g/l) of the Algerian reservoir (Tin Fouye Tabankort) prior to a conformance control application. The reactants were characterized with regard to their hydrolysis degree or branching degree using 13C-nuclear magnetic resonance, and viscosity–average molecular weights ($$ \bar{M}_{\text{v}} $$) were estimated using the Mark–Houwink equation and intrinsic viscosities measurements. The polymers had molecular weights that varied from 5 to 10 × 106 g/mol for PHPAs with initial hydrolysis degrees between 6 and 20 mol%, while the molecular weights of the PEI were between 2 and 67 × 104 g/mol with a constant branching degree of 57–59. Consequently, the effect of steady shear on the gelation time was investigated followed by the effect of reactant concentrations, the polymer and cross-linker molecular weights, the polymer’s hydrolysis degree, the temperature and the initial pH. All experiments were conducted in a semidilute concentration regime while maintaining practical initial gelant viscosities. As a result, the gelation time was found to decrease with reactant concentrations, molecular weights and temperature (Ea = 62 kJ/mol) and to increase with hydrolysis degree.

Predicting solubility of nitrous oxide in ionic liquids using machine learning techniques and gene expression programming

Abstract: Background - Nitrous oxide (N2O), as a potent greenhouse gas, is increasingly becoming a major multidisciplinary concern in recent years. Therefore, the removal of N2O using powerful green solvents such as ionic liquids (ILs) has turned into an attractive way to reduce the amount of N2O in the atmosphere. Methods -The aim of this study was to establish rigorous models that can predict the solubility of N2O in various ILs. To achieve this, three advanced soft-computing methods, viz. cascaded forward neural network (CFNN), radial basis function neural network (RBFNN), and gene expression programming (GEP) were trained and tested using comprehensive experimental measurements. Significant Findings - The obtained results demonstrated that the newly implemented models can predict the solubility of N2O in ILs with high accuracy. Besides, it was found that the CFNN model optimized using Levenberg-Marquardt (LM) algorithm was the best predictive paradigm (R2=0.9994 and RMSE=0.0047). Lastly, the Leverage technique was carried out, and the statistical validity of the newly implemented model was documented as more than 96% of data were located in the applicability realm of this paradigm.