Publications Scientifiques

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Author: search.filters.author.Nait Amar, Menad

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    Rigorous Explainable Artificial Intelligence Models for Predicting CO2-Brine Interfacial Tension: Implications for CO2 Sequestration in Saline Aquifers
    (American Chemical Society, 2025) Nait Amar, Menad; Youcefi, Mohamed Riad; Alqahtani, Fahd Mohamad; Djema, Hakim; Ghasemi, Mohammad
    Carbon capture and sequestration (CCS) is an attractive approach for reducing carbon dioxide (CO2) emissions, with saline aquifers offering promising sites for long-term sequestration. Interfacial tension (IFT) between CO2 and brine plays a crucial role in the trapping efficiency. This study develops explainable artificial intelligence (XAI) models to accurately predict the IFT in CO2–brine systems. Three advanced machine learning models, namely, Super Learner (SL), Elman Neural Network (ENN), and Power Law Ensemble Model, were implemented based on a data set comprising 2616 measurements. Among the established paradigms, SL achieved the highest accuracy (RMSE = 0.7813 and R2 = 0.9953) across diverse conditions. To enhance model transparency, Local Interpretable Model-agnostic Explanations and SHAP (SHapley Additive Explanations) interpretability techniques were employed, confirming strong alignment with experimental trends. Comparative analysis further demonstrated that the SL scheme surpasses existing literature models. Overall, this study highlights the effectiveness of XAI-based predictive modeling for accurately estimating the CO2–brine IFT under diverse operational conditions. Future implementation in real CCS projects can offer valuable insights into injection strategies, trapping mechanisms, and long-term formation stability
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    Rigorous Explainable Artificial Intelligence Models for Predicting CO2-Brine Interfacial Tension: Implications for CO2 Sequestration in Saline Aquifers
    (American Chemical Society, 2025) Nait Amar, Menad; Youcefi, Mohamed Riad; Alqahtani, Fahd Mohamad; Djema, Hakim; Ghasemi, Mohammad
    Carbon capture and sequestration (CCS) is an attractive approach for reducing carbon dioxide (CO2) emissions, with saline aquifers offering promising sites for long-term sequestration. Interfacial tension (IFT) between CO2 and brine plays a crucial role in the trapping efficiency. This study develops explainable artificial intelligence (XAI) models to accurately predict the IFT in CO2–brine systems. Three advanced machine learning models, namely, Super Learner (SL), Elman Neural Network (ENN), and Power Law Ensemble Model, were implemented based on a data set comprising 2616 measurements. Among the established paradigms, SL achieved the highest accuracy (RMSE = 0.7813 and R2 = 0.9953) across diverse conditions. To enhance model transparency, Local Interpretable Model-agnostic Explanations and SHAP (SHapley Additive Explanations) interpretability techniques were employed, confirming strong alignment with experimental trends. Comparative analysis further demonstrated that the SL scheme surpasses existing literature models. Overall, this study highlights the effectiveness of XAI-based predictive modeling for accurately estimating the CO2–brine IFT under diverse operational conditions. Future implementation in real CCS projects can offer valuable insights into injection strategies, trapping mechanisms, and long-term formation stability
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    Predicting Methane Hydrate Formation Temperature in the Presence of Diverse Brines Using Explainable Artificial Intelligence
    (American Chemical Society, 2025) Nait Amar, Menad; Zeraibi, Noureddine; Alqahtani, Fahd Mohamad; Djema, Hakim; Benamara, Chahrazed; Saifi, Redha; Gareche, Mourad; Ghasemi, Mohammad; Merzoug, Ahmed
    Thisstudy presents three advanced techniques, includingthe leastsquares support vector machine (LSSVM), categorical boosting (CatBoost),and cascaded forward neural network (CFNN), to model methane hydrateformation temperature (MHFT) across various brines under a wide pressurerange. Utilizing a comprehensive data set of nearly 1000 samples,the models underwent rigorous training and testing phases. Graphicalanalyses and statistical assessment confirmed the high accuracy ofthe implemented models, with the CFNN scheme outperforming the others,achieving a total root-mean-square error (RMSE) of 0.3569 and an R2 of 0.9977. Comparison with existing modelsfurther highlighted the CFNN model’s superior performance.Additionally, the Shapley Additive exPlanning (SHAP) method was employedto enhance the aspects related to predictions’ explainabilityby assessing the impact of different inputs on the outcomes. Lastly,the proposed model holds significant potential for advancing industrialand academic applications related to hydrate phenomena
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    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.
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    Toward robust models for predicting carbon dioxide absorption by nanofluids
    (John Wiley and Sons Inc, 2022) Nait Amar, Menad; Djema, Hakim; Belhaouari, Samir Brahim; Zeraibi, Noureddine; https://doi.org/10.1002/ghg.2166
    The application of nanofluids has received increased attention across a number of disciplines in recent years. Carbon dioxide (CO2) absorption by using nanofluids as the solvents for the capture of CO2 is among the attractive applications, which have recently gained high popularity in various industrial aspects. In this work, two robust explicit-based machine learning (ML) methods, namely group method of data handling (GMDH) and genetic programming (GP) were implemented for establishing accurate correlations that can estimate the absorption of CO2 by nanofluids. The correlations were developed using a comprehensive database that involved 230 experimental measurements. The obtained results revealed that the proposed ML-based correlations can predict the absorption of CO2 by nanofluids with high accuracy. Besides, it was found that the GP-based correlation yielded more precise predictions compared to the GMDH-based correlation. The GP-based correlation has an overall coefficient of determination of 0.9914 and an overall average absolute relative deviation of 3.732%. Lastly, the carried-out trend analysis confirmed the compatibility of the proposed GP-based correlation with the real physical tendency of CO2 absorption by nanofluids
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    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.
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    Optimization of WAG in real geological field using rigorous soft computing techniques and nature-inspired algorithms
    (Elsevier, 2021) Nait Amar, Menad; Jahanbani Ghahfarokhi, Ashkan; Ng, Cuthbert Shang Wui; Zeraibi, Noureddine
    To meet the ever-increasing global energy demands, it is more necessary than ever to ensure increments in the recovery factors (RF) associated with oil reservoirs. Owing to this challenge, enhanced oil recovery (EOR) techniques are increasingly gaining more significance as robust strategies for producing more oil volumes from mature reservoirs. Water alternating gas (WAG) injection is an EOR method intended at improving the microscopic and macroscopic displacement efficiencies. To handle and implement successfully this technique, it is of vital importance to optimize its operating parameters. This study targeted at implementing robust proxy paradigms for investigating the suitable design parameters of a WAG project applied to real field data from “Gullfaks” in the North Sea. The proxy models aimed at reducing significantly the rum-time related to the commercial simulators without scarifying the accuracy. To this end, machine learning (ML) approaches, including multi-layer perceptron (MLP) and radial basis function neural network (RBFNN) were implemented for estimating the needed parameters for the formulated optimization problem. To improve the reliability of these ML methods, they were evolved using optimization algorithms, namely Levenberg–Marquardt (LM) for MLP, and ant colony optimization (ACO) and grey wolf optimization (GWO) for RBFNN. The performance analysis of the proxy models revealed that MLP-LMA has better prediction ability than the other two proxy paradigms. In this context, the highest average absolute relative deviation noticed per runs by MLP-LMA was lower than 3.60%. Besides, the best-implemented proxy was coupled with ACO and GWO for resolving the studied WAG optimization problem. The findings revealed that the suggested proxies are cheap, accurate, and practical in emulating the performance of numerical reservoir model. In addition, the results demonstrated the effectiveness of ACO and GWO in optimizing the parameters of WAG process for the real field data used in this study
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    Robust smart schemes for modeling carbon dioxide uptake in metal - organic frameworks
    (Elsevier, 2021) Nait Amar, Menad; Ouaer, Hocine; Abdelfetah Ghriga, Mohammed
    The emission of greenhouse gases such as carbon dioxide (CO2) is considered the most acute issue of the 21st century around the globe. Due to this fact, significant efforts have been made to develop rigorous techniques for reducing the amount of CO2 in the atmosphere. Adsorption of CO2 in metal–organic frameworks (MOFs) is one of the efficient technologies for mitigating the high levels of emitted CO2. The main aim of this study is to examine the aptitudes of four advanced intelligent models, including multilayer perceptron (MLP) optimized with Levenberg-Marquardt (MLP-LMA) and Bayesian Regularization (MLP-BR), extreme learning machine (ELM), and genetic programming (GP) in predicting CO2 uptake in MOFs. A sufficiently widespread source of data was used from literature, including more than 500 measurements of CO2 uptake in13 MOFs with various pressures at two temperature values. The results showed that the implemented intelligent paradigms provide accurate estimations of CO2 uptake in MOFs. Besides, error analyses and comparison of the prediction performance revealed that the MLP-LMA model outperformed the other intelligent models and the prior paradigms in the literature. Moreover, the MLP-LMA model yielded an overall coefficient of determination (R2) of 0.9998 and average absolute relative deviation (AARD) of 0.9205%. Finally, the trend analysis confirmed the high integrity of the MLP-LMA model in prognosticating CO2 uptake in MOFs, and its predictions overlapped perfectly the measured values with changes in pressure and temperature
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    Predicting solubility of nitrous oxide in ionic liquids using machine learning techniques and gene expression programming
    (Elsevier, 2021) Nait Amar, Menad; Ghriga, MMohammed Abdelfetah; Ben Seghier, Mohamed El Amine; Ouaer, Hocine
    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. © 2021 Taiwan Institute of Chemical Engineers
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    Adaptive surrogate modeling with evolutionary algorithm for well placement optimization in fractured reservoirs
    (Elsevier, 2019) Redouane, Kheireddine; Zeraibi, Noureddine; Nait Amar, Menad
    Well placement optimization is a decisive task for the reliable design of field development plans. The use of optimization routines coupled to reservoir simulation models as an automatic tool is a popular practice, which could improve the decision-making process on well placement problems. However, despite the various automatic techniques developed, there is still a lack of robust computer-added optimization tool, which can solve the well placement problem with high accuracy in reasonable time while handling the technical constraints properly. In this paper, a hybrid intelligent system is proposed to deal with a real well placement problem with arbitrary well trajectories, complex model grids, and linear and nonlinear constraints. In this intelligent approach, a Genetic Algorithm (GA) combined with a hybrid constraint-handling strategy is applied in conjunction with a constrained space-filling sampling design, Gaussian Process (GP) surrogate model, and one proposed adaptive sampling routine. This self-adaptive framework allows to consecutively augment the quality of surrogate, enhance the accuracy of the process, and thus guide the optimization rapidly into the optimal solution. To demonstrate the efficiency of the developed method, a full-field reservoir case is considered. This case covers a real well placement project in a fractured unconventional reservoir of El Gassi, which is a mature field located in Hassi-Massoud, Algeria. The obtained results highlighted the effectiveness of the proposed approach for solving the real well placement problem with high accuracy in reasonable CPU-time. These auspicious features make it a reliable tool to be used on other real optimization projects