Browsing by Author "Benamara, Chahrazed"

Now showing 1 - 4 of 4

Inhibition of asphaltene flocculation in light crude oil: effect of pressure, temperature and inhibitor concentration
(Taylor and Francis Online, 2018) Gharbi, Kheira; Benyounes, Khaled; Benamara, Chahrazed
In this study, a light crude oil sample was taken from Hassi Messaoud field to characterize its physicochemical properties. The asphaltene flocculation onset was determined in the dead oil by Flocculation Titrimeter equipment. The petroleum resins have been extracted from the same crude oil and tested as an inhibitor of asphaltene flocculation then their efficiency has been studied at different conditions of pressure and temperature. The results point out that the extracted resins may have two different effects on the onset point depending upon the operator conditions and the concentration of the added resins to crude oil.
Modeling wax disappearance temperature using advanced intelligent frameworks
(American Chemical Society, 2019) Benamara, Chahrazed; Nait Amar, Menad; Gharbi, Kheira; Hamada, Boudjema
The deposition of wax is one of the most potential problems that disturbs the flow assurance during production processes of hydrocarbon fluids. In this study, wax disappearance temperature (WDT) that is recognized as a vital parameter in such circumstances is modeled using advanced machine learning techniques, namely, radial basis function neural network (RBFNN) coupled with genetic algorithm (GA) and artificial bee colony (ABC). Besides, an accurate and user-friendly correlation was established by implementing the group method of data handling. Results revealed the high reliability of the proposed hybrid models and the established correlation. Moreover, RBFNN coupled with ABC (RBFNN-ABC) was found to be the best paradigm with an overall average absolute relative error value of 0.5402% and a total coefficient of determination (R2) of 0.9706. Furthermore, the performance comparison showed that RBFNN-ABC and the established explicit correlation outperform the prior intelligent and thermodynamic models. Finally, by performing the outlier detection, the quality of the utilized database was assessed, the applicability realm of the best model was delineated, and only one point was found as doubtful
Modeling wax disappearance temperature using robust white-box machine learning
(Elsevier Ltd, 2024) Nait Amar, Menad; Zeraibi, Noureddine; Benamara, Chahrazed; Djema, Hakim; Saifi, Redha; Gareche, Mourad
Wax deposition is one of the major operational problems encountered in the upstream petroleum production system. The deposition of this undesirable scale can cause a variety of challenging problems. In order to avoid the latter, numerous parameters associated with the mechanism of wax deposition should be determined precisely. In this study, a new smart correlation was proposed for the accurate prediction of Wax disappearance temperature (WDT) using a robust explicit-based machine learning (ML) approach, namely gene expression programming (GEP). The correlation was developed using comprehensive experimental measurements. The obtained results revealed the promising degree of accuracy of the suggested GEP-based correlations. In this context, the newly-introduced correlations provided excellent statistical metrics (R2 = 0.9647 and AARD = 0.5963 %). Furthermore, performance of the developed correlation outperformed that of many existing approaches for predicting WDT. In addition, the trend analysis performed on the outcomes of the proposed GEP-based correlations divulged their physical validity and consistency. Lastly, the findings of this study provide a promising benefit, as the newly developed correlations can notably improve the adequate estimation of WDT, thus facilitating the simulation of wax deposition-related phenomena. In this context, the proposed correlations can supply the effective management of the production facilities and improvement of project economics since the provided correlation is a simple-to-use decision-making tool for production and chemical engineers engaged in the management of organic deposit-related issues.
Prediction of Wax Appearance Temperature Using Artificial Intelligent Techniques
(Springer, 2020) Benamara, Chahrazed; Gharb, Kheira; Nait Amar, Menad; Hamada, Boudjema
The paraffin particles can promote and be involved in the formation of deposits which can lead to plugging of oil production facilities. In this work, an experimental prediction of wax appearance temperature (WAT) has been performed on 59 Algerian crude oil samples using a pour point tester. In addition, a modeling investigation was done to create reliable WAT paradigms. To do so, gene expression programming and multilayers perceptron optimized with Levenberg–Marquardt algorithm (MLP-LMA) and Bayesian regularization algorithm were implemented. To generate these models, some parameters, namely density, viscosity, pour point, freezing point and wax content in crude oils, have been used as input parameters. The results reveal that the developed models provide satisfactory results. Furthermore, the comparison between these models in terms of accuracy indicates that MLP-LMA has the best performances with an overall average absolute relative error of 0.23% and a correlation coefficient of 0.9475.