Publications Internationales

Permanent URI for this collectionhttps://dspace.univ-boumerdes.dz/handle/123456789/13

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Author: search.filters.author.Youcefi, Mohamed RiadHas files: No

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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 the viscosity of hydrogen – methane blends at high pressure for hydrogen transportation and geo-storage: Integration of robust white-box machine learning frameworks
    (Elsevier, 2025) Alatefi, Saad; Youcefi, Mohamed Riad; Amar, Menad Nait; Djema, Hakim
    The integration of hydrogen into underground storage systems is pivotal for large-scale energy management, often involving blends with methane to leverage existing infrastructure. Accurate viscosity prediction of hydrogen – methane blends under subsurface conditions is essential for optimizing flow assurance and operational safety. Accordingly, this study employs three data-driven models, namely Genetic Expression Programming (GEP), Group Method of Data Handling (GMDH), and Multi-Gene Genetic Programming (MGGP), to predict the viscosity of hydrogen – methane mixtures for transportation and underground storage applications. A comprehensive dataset of 313 experimentally measured values from the literature were utilized to develop and validate the established correlations. The MGGP paradigm emerged as the top performer, achieving a root mean square error (RMSE) of 0.4054 and an R2 value of 0.9940, outperforming both GEP and GMDH, as well as prior predictive models. The consistency of the dataset was confirmed using the Leverage approach, ensuring robust predictions. In addition, the Shapley Additive Explanations technique revealed key factors influencing the viscosity predictions, enhancing the interpretability of the best-performing correlation. Furthermore, comparative trend analysis demonstrated the MGGP correlation's superior accuracy and robustness across varying blend compositions and operational conditions. These findings offer a reliable and simple-to-use predictive correlation for engineers and researchers designing hydrogen transport and storage systems, supporting efficient energy storage and the transition to a low-carbon economy
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    A simplified integrated asset model for predicting liquid loading in gas wells with aquifer water influx
    (Elsevier, 2025) Belimane, Zakarya; Youcefi, Mohamed Riad
    This study presents a simplified Integrated Asset Model (IAM) specifically designed to address critical challenges in water management within hydrocarbon production systems, particularly the dynamic interaction between gas production and aquifer water influx. By focusing on the mechanisms that lead to liquid loading, often triggered by encroaching formation water, the model offers a novel approach to managing subsurface multiphase flow. The IAM integrates key components of inflow performance (IPR), tubing performance (TPR), aquifer and material balance equations within a pseudo-transient framework to simulate the well’s response to water-induced liquid accumulation. An advanced mechanistic multiphase wellbore model monitors important parameters such as liquid holdup, mixture density, flow regime transitions, and dimensionless Reynolds (Re) and Weber (We) numbers. The pseudo-transient nodal analysis iteratively updates these properties, allowing the model to capture the transient behavior in the presence of aquifer drive. The Firefly metaheuristic algorithm is employed to optimize system performance by identifying the equilibrium point at the bottomhole. The model reveals that slug flow at the bottomhole is a strong indicator of incipient liquid loading, thereby facilitating earlier detection and intervention. This approach enhances both the detection and prediction of liquid loading, improving water control strategies, gas lift planning, and production scheduling. Sensitivity analysis further shows that aquifer volume, compressibility, and productivity index (J) significantly promotes liquid accumulation. By accurately simulating the onset and behavior of liquid loading under aquifer support, this work contributes a valuable tool for proactive water management, optimized deliquification planning, and sustained well productivity in gas fields
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    Development of an expert-informed rig state classifier using naive bayes algorithm for invisible loss time measurement
    (Springer Nature, 2024) Youcefi, Mohamed Riad; Boukredera, Farouk Said; Ghalem, Khaled; Hadjadj, Ahmed; Ezenkwu, Chinedu Pascal
    The rig state plays a crucial role in recognizing the operations carried out by the drilling crew and quantifying Invisible Lost Time (ILT). This lost time, often challenging to assess and report manually in daily reports, results in delays to the scheduled timeline. In this paper, the Naive Bayes algorithm was used to establish a novel rig state. Training data, consisting of a large set of rules, was generated based on drilling experts’ recommendations. This dataset was then employed to build a Naive Bayes classifier capable of emulating the cognitive processes of skilled drilling engineers and accurately recognizing the actual drilling operation from surface data. The developed model was used to process high-frequency drilling data collected from three wells, aiming to derive the Key Performance Indicators (KPIs) related to each drilling crew’s efficiency and quantify the ILT during the drilling connections. The obtained results revealed that the established rig state excelled in automatically recognizing drilling operations, achieving a high success rate of 99.747%. The findings of this study offer valuable insights for drillers and rig supervisors, enabling real-time visual assessment of efficiency and prompt intervention to reduce ILT.
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    Drill string torsional vibrations modeling with dynamic drill pipe properties measurement and field validation
    (American Society of Mechanical Engineers (ASME), 2022) Boukredera, Farouk Said; Hadjadj, Ahmed; Youcefi, Mohamed Riad
    This paper aims to present the drill string torsional dynamics through a lumped parameter modeling using the basic physical notions with continuous measurement of drill pipe mechanical properties (inertia, damping, and stiffness). The model represents the mechanical properties as a variable for each drilled stand. A rock bit interactions model is employed in the system considering the kinetic friction as variable and depends on surface drilling parameters and the well length. Field data, including surface and downhole recorded velocities, are used to validate the model by comparing both velocities and to confirm the existence of drill string vibrations together with the simulation results (bit velocity)