Publications Internationales

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Author: search.filters.author.Djema, HakimHas files: Yes

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    Predicting viscosity of CO2–CH4 binary mixtures using robust white-box machine learning frameworks: implication for carbon capture, utilization, and storage
    (Springer Science and Business Media, 2025) Alatefi, Saad; Youcefi, Mohamed Riad; Amar, Menad Nait; Djema, Hakim
    Carbon capture, utilization, and storage (CCUS) technologies, particularly those involving pure and impure carbon dioxide (CO2) injection for enhanced oil recovery (EOR), are vital for mitigating greenhouse gas emissions while optimizing energy production. The viscosity of carbon dioxide-methane (CO2–CH4) binary systems plays a critical role in determining flow behavior, injectivity, and storage efficiency in subsurface formations. However, direct experimental measurements of viscosity are often costly, time-consuming, and constrained by operational limitations. Furthermore, existing predictive correlations frequently exhibit limited accuracy across wide ranges of pressure, temperature, and composition, hindering their application in practical CCUS and EOR scenarios. This study introduces a white-box machine learning framework based on multi-gene genetic programming (MGGP) to predict the viscosity of CO2–CH4 mixtures with enhanced precision. A comprehensive dataset comprising 742 experimental measurements was utilized to construct explicit mathematical correlations as functions of pressure, temperature, and CO2 mole fraction. Extensive statistical analyses and graphical validations confirmed the high fidelity of the developed models. The MGGP-based schemes achieved a low total RMSE of 2.6343 and an excellent R2 of 0.9942, outperforming four previously established models. Trend analyses and Shapley additive explanations (SHAP) further reinforced the model’s reliability, highlighting the dominant influence of pressure, followed by CO2 mole fraction and temperature, on viscosity behavior. The proposed explicit and user-friendly correlations, combining accuracy with interpretability, provide valuable tools for industrial applications, particularly in the simulation, design, and optimization of CCUS and CO2-EOR projects under a wide range of operating conditions.
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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.