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.

Synthesis of polypropylene-graft-maleic anhydride compatibilizer and evaluation of nylon 6/polypropylene blend properties

Abstract: Polymer blends based on polyolefins are of a great interest owing to their broad spectrum of properties and practical applications. However, because of poor compatibilities of components, most of these systems generally exhibit high interfacial tension, a low degree of dispersion and poor mechanical properties. It is generally accepted that polypropylene (PP) and nylon 6 (N6) are not compatible and that their blending results in poor materials. The compatibility can be improved by the addition of a compatibilizer, and in this study PP was functionalized by maleic anhydride (MAH) in the presence of an optimized amount of dicumyl peroxide (DCP). The reaction was carried out in the molten state using an internal mixer. Then, once the compatibilizer polypropylene-graft-maleic anhydride (PP-g-MAH) was prepared, it was added at various concentrations (2.5–10 wt%) to 30/70 glass fibre reinforced N6 (GFRN6) PP, and the mechanical properties were evaluated. It was found that the incorporation of the compatibilizer enhanced the tensile properties (tensile strength and modulus) as well as the Izod impact properties of the notched samples. This was attributed to better interfacial adhesion as evidenced by scanning electron microscopy (SEM). The optimum in these properties was achieved at a critical PP-g-MAH concentration. Copyright © 2005 Society of Chemical Industry

Experimental Investigation of Wettability Alteration of Oil-Wet Carbonates by a Non-ionic Surfactant

Abstract: Wettability alteration toward a more water-wet state was found to be a promising approach for oil recovery improvement in oil-wet and naturally fractured carbonate reservoirs. This approach has bee...

Integrating the LSSVM and RBFNN models with three optimization algorithms to predict the soil liquefaction potential

Abstract: Liquefaction has caused many catastrophes during earthquakes in the past. When an earthquake is occurring, saturated granular soils may be subjected to the liquefaction phenomenon that can result in significant hazards. Therefore, a valid and reliable prediction of soil liquefaction potential is of high importance, especially when designing civil engineering projects. This study developed the least squares support vector machine (LSSVM) and radial basis function neural network (RBFNN) in combination with the optimization algorithms, i.e., the grey wolves optimization (GWO), differential evolution (DE), and genetic algorithm (GA) to predict the soil liquefaction potential. Afterwards, statistical scores such as root mean square error were applied to evaluate the developed models. The computational results showed that the proposed RBFNN-GWO and LSSVM-GWO, with Coefficient of Determination (R2) = 1 and Root Mean Square Error (RMSE) = 0, produced better results than other models proposed previously in the literature for the prediction of the soil liquefaction potential. It is an efficient and effective alternative for the soil liquefaction potential prediction. Furthermore, the results of this study confirmed the effectiveness of the GWO algorithm in training the RBFNN and LSSVM models. According to sensitivity analysis results, the cyclic stress ratio was also found as the most effective parameter on the soil liquefaction in the studied case.

Antagonistic activity of Bacillus sp. obtained from an Algerian oilfield and chemical biocide THPS against sulfate-reducing bacteria consortium inducing corrosion in the oil industry

Abstract: The present study enlightens the role of the antagonistic potential of nonpathogenic strain B21 against sulfate-reducing bacteria (SRB) consortium. The inhibitor effects of strain B21 were compared with those of the chemical biocide tetrakishydroxymethylphosphonium sulfate (THPS), generally used in the petroleum industry. The biological inhibitor exhibited much better and effective performance. Growth of SRB in coculture with bacteria strain B21 antagonist exhibited decline in SRB growth, reduction in production of sulfides, with consumption of sulfate. The observed effect seems more important in comparison with the effect caused by the tested biocide (THPS). Strain B21, a dominant facultative aerobic species, has salt growth requirement always above 5% (w/v) salts with optimal concentration of 10–15%. Phylogenetic analysis based on partial 16S rRNA gene sequences showed that strain B21 is a member of the genus Bacillus, being most closely related to Bacillus qingdaonensis DQ115802 (94.0% sequence similarity), Bacillus aidingensis DQ504377 (94.0%), and Bacillus salarius AY667494 (92.2%). Comparative analysis of partial 16S rRNA gene sequence data plus physiological, biochemical, and phenotypic features of the novel isolate and related species of Bacillus indicated that strain B21 may represent a novel species within the genus Bacillus, named Bacillus sp. (EMBL, FR671419). The results of this study indicate the application potential of Bacillus strain B21 as a biocontrol agent to fight corrosion in the oil industry.

On the evaluation of solubility of hydrogen sulfide in ionic liquids using advanced committee machine intelligent systems

Abstract: Ionic Liquids (ILs) are increasingly emerging as new innovating green solvents with great importance from academic, industrial, and environmental perspectives. This surge of interest in considering ILs in various applications is owed to their attractive properties. Involvements in the gas sweetening and the reduction of the amounts of sour and acid gasses are among the most promising applications of ILs. In this study, new advanced committee machine intelligent systems (CMIS) were introduced for predicting the solubility of hydrogen sulfide (H2S) in various ILs. The implemented CMIS models were gained by linking robust data-driven techniques, namely multilayer perceptron (MLP) and cascaded forward neural network (CFNN) beneath rigorous schemes using group method of data handling (GMDH) and genetic programming (GP). The proposed paradigms were developed using an extensive database encompassing 1243 measurements of H2S solubility in 33 ILs. The performed comprehensive error investigation revealed that the newly implemented paradigms yielded very satisfactory prediction performance. Besides, it was found that CMIS-GP provided more accurate estimations of H2S solubility in ILs compared with both the other intelligent models and the best-prior paradigms. In this regard, the developed CMIS-GP exhibited overall average absolute relative deviation (AARD) and coefficient of determination (R2) values of 2.3767% and 0.9990, respectively. Lastly, the trend analyses demonstrated that the tendencies of CMIS-GP predictions were in excellent accordance with the real variations of H2S solubility in ILs with respect to pressure and temperature.