Publications Scientifiques

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Author: search.filters.author.Hadjadj, AhmedSubject: search.filters.subject.Artificial neural network

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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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    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