Publications Scientifiques

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    Evaluating Storage Potential and Integrity of Depleted Reservoirs for CO₂ Injection
    (2025) Zakarya, Belimane; Youcefi, Mohamed Ryad; Benbrik, Abderrahmane; Hadjadj, Ahmed
    As global industrial activity grows, carbon dioxide emissions increase, intensifying greenhouse effect and climate change and demanding solutions beyond renewable energy. This study investigates CO₂ sequestration in subsurface formations as a promising mitigation strategy to support international climate goals and reduce carbon levels. Using CMG 2021 software, different trapping mechanisms, including structural, residual, and solubility trapping, were evaluated in detail to determine their individual and combined contributions to overall storage capacity. Results show that integrating all three mechanisms increases storage potential by 30% compared with structural trapping alone. In addition, geological uncertainty was addressed through Monte Carlo simulations. For that, multiple realizations were generated by varying key reservoir parameters such as porosity, permeability and hysteresis-related parameter. This probabilistic approach allows for a more robust assessment of storage capacity variability and enhances prediction confidence. Furthermore, caprock integrity was evaluated using a two-way geomechanical coupling approach with the Bendis model. The findings indicate that injection-induced pressure reduces effective stresses within the caprock, which may promote tensile failures and create potential leakage pathways. This integrated analysis demonstrates that coupling numerical simulation and probabilistic tools support safer, more effective CO₂ storage, which offers a viable long-term solution for global climate change mitigation efforts.
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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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    Numerical study of temperature and pressure effects of a yield-power law fluid flow on frictional pressure losses for laminar and turbulent regimes
    (Taylor and Francis, 2024) Messaoud, Nadia; Hadjadj, Ahmed; Ferroudji, Hicham
    The effective determination of pressure losses depends on accurate knowledge of the drilling fluid rheology. As the fluid circulates deeper around the wellbore, its rheological behavior undergoes significant alterations due to the variations in downhole conditions encountered. The present study investigates the effects of the rheological properties of Yield-power law fluid at various pressures and temperatures on annular pressure losses and velocity profiles. Simulations were performed using Computational Fluid Dynamics to examine the fluid flow in turbulent and laminar regimes. Comparison between numerical, experimental and slot approximation model results showed a good agreement. Results indicated that pressure losses have reduced in both regimes with increasing temperature, at a constant pressure. However the pressure has the opposite effect at a constant temperature. For a drilling fluid flow velocity of 1 m/s, the elevation of temperature from 25 °C to 90 °C, decreases the pressure drop gradient by (31% to 48%) at low and high- pressure conditions respectively. Whereas, the influence of increasing pressure on pressure losses is more apparent at 25 °C. Earlier transition from laminar to turbulent is observed with temperature rise. Therefore, the temperature effect on pressure losses in the turbulent region; is shown for different Generalized Reynolds numbers
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    Effect of drill pipe orbital motion on non-Newtonian fluid flow in an eccentric wellbore : a study with computational fluid dynamics
    (Springer, 2021) Ferroudji, Hicham; Hadjadj, Ahmed; Ofei, Titus Ntow; Gajbhiye, Rahul Narayanrao; Rahman, Mohammad Azizur; Qureshi, M. Fahed
    To ensure an effective drilling operation of an explored well, the associated hydraulics program should be established care- fully based on the correct prediction of a drilling fluid’s pressure drop and velocity field. For that, the impact of the drill string orbital motion should be considered by drilling engineers since it has an important influence on the flow of drilling fluid and cuttings transport process. In the present investigation, the finite volume method coupled with the sliding mesh approach is used to analyze the influence of the inner cylinder orbital motion on the flow of a power-law fluid (Ostwald-de Waele) in an annular geometry. The findings indicate that the orbital motion positively affects the homogeneity of the power- law axial velocity through the entire eccentric annulus; however, this impact diminishes as the diameter ratio increases. In addition, higher torque is induced when the orbital motion occurs, especially for high values of eccentricity and diameter ratio; nonetheless, a slight decrease in torque is recorded when the fluid velocity increases
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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
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    New model for standpipe pressure prediction while drilling using group method of data handling
    (Elsevier, 2021) Youcefi, Mohamed Riad; Hadjadj, Ahmed; Boukredera, Farouk Said
    The continuous evaluation of the measured Stand Pipe Pressure (SPP) against a modeled SPP value in real-time involves the automatic detection of undesirable drilling events such as drill string washouts and mud pump failures. Numerous theoretical and experimental studies have been established to calculate the friction pressure losses using different rheological models and based on an extension of pipe flow correlations to an annular geometry. However, it would not be feasible to employ these models for real-time applications since they are limited to some conditions and intervals of application and require input parameters that might not be available in real-time on each rig. In this study, we applied the Group Method of Data Handling (GMDH) to develop a trustworthy model that can predict the SPP in real-time as a function of mud flow, well depth, RPM and the Fan VG viscometer reading at 600 and 300 rpm. In order to accomplish the modeling task, 3351 data points were collected from two wells from Algerian fields. Graphical and statistical assessment criteria disclosed that the model predictions are in excellent agreement with the experimental data with a coefficient of determination of 0.9666 and an average percent relative error less than 2.401%. Furthermore, another data (1594 data points) from well-3 was employed to validate the developed correlation for SPP. The obtained results confirmed that the proposed GMDH-SPP model can be applied in real-time to estimate the SPP with high accuracy. Besides, it was found that the proposed GMDH correlation follows the physically expected trends with respect to the employed input parameters. Lastly, the findings of this study can help for the early detection of downhole problems such as drill string washout, pump failure, and bit balling
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    Numerical study of parameters affecting pressure drop of power-law fluid in horizontal annulus for laminar and turbulent flows
    (Springer, 2019) Ferroudji, Hicham; Hadjadj, Ahmed; Haddad, Ahmed; Ofei, Titus Ntow
    Efficient hydraulics program of oil and gas wells has a crucial role for the optimization of drilling process. In the present paper, a numerical study of power-law fluid flow through concentric (E = 0.0) and eccentric annulus (E = 0.3, E = 0.6 and E = 0.9) was performed for both laminar and turbulent flow regimes utilizing a finite volume method. The effects of inner pipe rotation, flow behavior index and diameter ratio on the pressure drop were studied; furthermore, the appearance and development of secondary flow as well as its impact on the pressure drop gradient were evaluated. Results indicated that the increment of the inner pipe rotation from 0 to 400 rpm is found to decrease pressure drop gradient for laminar flow in concentric annulus while a negligible effect is observed for turbulent flow. The beginning of secondary flow formation in the wide region part of the eccentric annulus (E = 0.6) induces an increase of 9% and a slight increase in pressure drop gradient for laminar and turbulent flow, respectively. On the other hand, the variation of the flow behavior index and diameter ratio from low to high values caused a dramatic increase in the pressure drop. Streamlines in the annulus showed that the secondary flow is mainly induced by eccentricity of the inner pipe where both high values of diameter ratio and low values of flow behavior index tend to prevent the secondary flow to appear
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    Predictionofnaturalgashydratesformationusingacombinationofthermodynamicandneuralnetworkmodeling
    (Elsevier, 2019) Rebai, Noura; Hadjadj, Ahmed; Benmounah, Abdelbaki; Abdallah, S.Berrouk; M.Boualleg, Salim
    During the treatment or transport of natural gas, the presence of water, even in very small quantities, can trigger hydrates formation that causes plugging of gas lines and cryogenic exchangers and even irreversible damages to expansion valves, turbo expanders and other key equipment. Hence, the need for a timely control and monitoring of gas hydrate formation conditions is crucial. This work presents a two-legged approach that combines thermodynamics and artificial neural network modeling to enhance the accuracy with which hydrates formation conditions are predicted particularly for gas mixture systems. For the latter, Van der Waals-Platteeuw thermodynamic model proves very inaccurate. To improve the accuracy of its predictions, an additional corrective term has been approximated using a trained network of artificial neurons. The validation of this approach using a database of 4660 data points shows a significant decrease in the overall relative error on the pressure from around 23.75%–3.15%. The approach can be extended for more complicated systems and for the prediction of other thermodynamics properties related to the formation of hydrates
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    The impact of orbital motion of drill pipe on pressure drop of non-newtonian fluids in eccentric annulus
    (Penerbit Akademia Baru, 2020) Ferroudji, Hicham; Hadjadj, Ahmed; Azizur Rahman, Mohammad; Hassan, Ibrahim; NtowOfei, Titus; Haddad, Ahmed
    For all drilling operation method used to explore a well, the hydraulics program design associated to the well must be carried out carefully. A wrong estimation of pressure drop of the drilling fluid in the annular space can induce several problems, like: stuck pipe, lost circulation and insufficient hole cleaning. ANSYS Fluent 18.2code based on the finite volume method (FVM) is employed to evaluate the orbital motion impact ofdrill pipe on frictional pressure drop of non-Newtonian fluids (Ostwald-de Waele and Herschel-Bulkley models) flowing in laminar and turbulent regimes where the inner cylinder (drill pipe) makes an orbital motion around the centre of the outer cylinder (casing) and pure rotation around its own axis. Moreover, impact of the eccentricity on frictional pressure drop is discussed. Numerical results exhibit that as the Reynolds number increases, effect of the orbital motion speed of the inner cylinder becomes more severe on frictional pressure drop of the Ostwald-de Waele fluid for laminar regime. However, after a certain speed, frictional pressure drop begins to decrease. In addition, increase of the eccentricity induces a decrease of frictional pressure drop of the Ostwald-de Waele fluid in which this effect is more pronounced when the inner cylinder makes orbital motion for both laminar and turbulent regimes
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    Rate of penetration modeling using hybridization extreme learning machine and whale optimization algorithm
    (Springer link, 2020) Youcefi, Mohamed Riad; Hadjadj, Ahmed; Bentriou, Abdelhak; Boukredera, Farouk Said
    Modeling the rate of penetration (ROP) plays a fundamental role in drilling optimization since the achievement of an optimum ROP can drastically reduce the overall cost of drilling activities. Evolved Extreme learning machine (ELM) with the evolutionary algorithms and multi-layer perceptron with Levenberg-Marquardt training algorithm (MLP-LMA) were proposed in this study to predict ROP. This paper focused mainly on two aspects. The first one was the investigation of the whale optimization algorithm (WOA) to optimize the weights and biases between input and hidden layers of ELM to enhance its prediction accuracy. The other was to adopt a prediction methodology that seeks to update the predictive model at each formation in order to reduce the dimension of input data and mitigate the effect of non real-time data such as the formation properties on the bit speed prediction. The prediction models were trained and tested using 3561 data points gathered from an Algerian field. The statistical and graphical evaluation criteria show that the ELM-WOA exhibited higher accuracy and generalization performance compared with the ELM-PSO and MLP-LMA. Furthermore, ELM-WOA was compared with two well-known ROP correlations in the literature, and the comparison results reveal that the proposed ELM-WOA model is superior to the pre-existing correlations. The findings of this study can help for the achievement of an optimum ROP and the reduction of the non-productive time. In addition, the outputs of this study can be used as an objective function during the real-time optimization of the drilling operation