Browsing by Author "Boukredera, Farouk Said"

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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
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.
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)
Etude du phénomène de vibration dans le train des tiges pour l’amélioration des performances de forages des puits pétroliers
(Universite de Boumerdes : Faculté des hydrocarbures et de la chimie, 2022) Boukredera, Farouk Said; Hadjadj, Ahmed(Directeur de thèse)
La réussite d’un forage des puits de pétrole et de gaz repose presque entièrement sur le bon mouvement du train des tiges. Ces derniers font constamment face à des phénomènes de vibrations de types axial (bit bounce), torsionel (stick slip) et latéral (whirl) causant de multiples problèmes tels que la dégradation de l’outil de forage, la réduction de la qualité des parois des puits et du ROP, ce qui va nuire considérablement à la performance de forage surtout en termes de temps non productif (NPT : Non Productive Time). La maitrise du phénomène de vibration ne peut être que bénéfique pour le travail et la durée de vie du train des tiges de forage. Généralement, les sociétés des services et les opérationnels de forage remédient à ces problèmes en optant par des pratiques de terrain quand c’est possible, tels que l’augmentation du RPM (Révolution Per Minute), la réduction du WOB (Weight On Bit) et du ROP (Rate Of Penetration). Dans le cadre de ce travail de thèse de doctorat, on s’intéresse à la modélisation du phénomène de vibration dans le but de prévenir des complications pratiques tels que les cassures, l’usure, et l’amélioration des performances du forage en entier. On procédera par une approche pratique, ensuite un calcul analytique puis on utilisera des méthodes numériques plus raffinées pour une meilleure approche de la problématique dont les données seront prises dans des situations réelles de forage pétrolier et gazier dans les différents champs pétroliers et gaziers Algériens
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
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
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