Publications Scientifiques

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Author: search.filters.author.Youcefi, Mohamed Riad

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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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    AI-Driven Optimization of Drilling Performance Through Torque Management Using Machine Learning and Differential Evolution
    (Multidisciplinary Digital Publishing Institute (MDPI), 2025) Boukredera, Farouk Said; Hadjadj, Ahmed; Youcefi, Mohamed Riad; Ouadi, Habib
    The rate of penetration (ROP) is the key parameter to enhance drilling processes as it is inversely proportional to the overall cost of drilling operations. Maximizing the ROP without any limitation can induce drilling dysfunctions such as downhole vibrations. These vibrations are the main reason for bottom hole assembly (BHA) tool failure or excessive wear. This paper aims to maximize the ROP while managing the torque to keep the depth of cut within an acceptable range during the cutting process. To achieve this, machine learning algorithms are applied to build ROP and drilling torque models. Then, a metaheuristic algorithm is used to determine the optimal technical control parameters, the weight on bit (WOB) and revolutions per minute (RPM), that simultaneously enhance the ROP and mitigate excessive vibrations. This paper introduces a new methodology for mitigating drill string vibrations, improving the rate of penetration (ROP), minimizing BHA failures, and reducing drilling costs
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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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    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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    Enhancing Porosity Prediction in Reservoir Characterization through Ensemble Learning: A Comparative Study between Stacking, Bayesian Model Optimization, Boosting, and Random Forest
    (Slovnaft VURUP a.s, 2024) Youcefi, Mohamed Riad; Alshokri, Ayman Inamat; Boussebci, Walid; Ghalem, Khaled; Hadjadj, Asma
    Accurate estimation of porosity is a critical factor in reservoir characterization. This study aims to enhance porosity prediction through the implementation and comparison of various stacking ensemble learning strategies. A dataset comprising 273 points, which consists of well logs and core measurements, was collected from two wells for model development. Four base learners, including Support Vector Regression (SVR), Multi-Layer Perceptron (MLP), Random Forest Regression (RFR), and XGBoost, were trained on this dataset. These models were then integrated using multiple stacking ensemble techniques, such as weighted averaging, Bayesian model averaging, and RFR as a meta-learner. Meta-learners were trained on predictions from the base learners, generated through cross-validation on leave-out data. Performance evaluations of both base and meta learners were conducted on a separate testing dataset using statistical and graphical error analysis. Results indicate that all learners demonstrated robust performance, with weighted averaging outperforming other strategies on testing data. The stacking ensemble approach, particularly through weighted averaging, effectively improved base learner performance on testing data by leveraging individual model strengths and mitigating weaknesses. The findings of this study are valuable for geoscientists and reservoir engineers in achieving accurate reservoir characterization and facilitating exploration activities.
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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)
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    Real-Time prediction of plastic viscosity and apparent viscosity for Oil-Based drilling fluids using a committee machine with intelligent systems
    (Springer, 2022) Youcefi, Mohamed Riad; Hadjadj, Ahmed; Bentriou, Abdelak; Boukredera, Farouk Said
    he prediction of drilling mud rheological properties is a crucial topic with significant importance in analyzing frictional pressure loss and modeling the hole cleaning. Based on Marsh viscosity, mud density, and solid percent, this paper implements a committee machine intelligent system (CMIS) to predict apparent viscosity (AV) and plastic viscosity (PV) of oil-based mud. The established CMIS combines radial basis function neural network (RBFNN) and multilayer perceptron (MLP) via a quadratic model. Levenberg–Marquardt algorithm was applied to optimize the MLP, while differential evolution, genetic algorithm, artificial bee colony, and particle swarm optimization were used to optimize the RBFNN. A databank of 440 and 486 data points for AV and PV, respectively, gathered from various Algerian fields was considered to build the proposed models. Statistical and graphical assessment criteria were employed for investigating the performance of the proposed CMIS. The obtained results reveal that the developed CMIS models exhibit high performance in predicting AV and PV, with an overall average absolute relative deviation (AARD %) of 2.5485 and 4.1009 for AV and PV, respectively, and a coefficient of determination (R2) of 0.9806 and 0.9753 for AV and PV, respectively. A comparison of the CMIS-AV with Pitt's and Almahdawi's models demonstrates its higher prediction capability than these previously published correlations